T. Adam Van Wart
- Courses5
- Reviews26
- School: Ave Maria University
- Campus:
- Department: Religion
- Email address: Join to see
- Phone: Join to see
-
Location:
5050 Ave Maria Blvd
Ave Maria, FL - 34142 - Dates at Ave Maria University: April 2018 - May 2018
- Office Hours: Join to see
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Professor Van Wart is really amazing. All professors should be like him. You really internalize the lecturers as he reviews over and over again the main material of the course in a way that is engaging, moving, and hilarious.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Prof. Wart is totally amazing! I definitely enjoyed his class.
N/A
Would take again: Yes
For Credit: Yes
0
0
Awesome
Professor Van War helps you learn and let's you know specifically what you need to know for the tests!
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Good
He is an incredible Professor. He cares about his students by seeking to share his knowledge in an easy way to understand. His class was engaging and mind-blowing.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Professor Wart is and overall he is great teacher.He really knows how to explain topics. He encourages students to come to his office hours as well as understand and be engaged. I personally was really interested and did well in his class.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Prof. Wart is a great professor and is really good at explaining things.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Mr. Wart is a great professor. He will make sure that you actually learn the material.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Prof. Van Wart really wants everyone to understand the lectures. You'll never leave the class without learning something,
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Good
The exams and quizzes covered the topics that were discussed in the lectures.
N/A
Would take again: Yes
For Credit: Yes
0
0
Awesome
Doctor Van Wart allows his students learn the material and also to bring their own knowledge of real life examples, which helps solidify any shakey topics through argumentation about the hot, modern, ethical topics.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Prof. Van Wart is a good professor. He explains the material well which makes it very easy to learn.
N/A
Would take again: Yes
For Credit: Yes
0
0
Awesome
Doctor Van Wart is one of the most amazing professors I ever had. He gave us all the information we need to know. He did not try to trick us or anything. He is a great guy that is always willing to offer help if we needed it.
N/A
Would take again: Yes
For Credit: Yes
0
0
Awesome
Awesome professor!! Would keep lectures interesting with open class discussions and always explained concepts in a really clear way.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Prof. Van Wart is one of my favorite professors ever. He's insightful and funny. Lectures were long but engaging. Topics can be complicated but he walks you through it in clear and intelligent ways.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Doctor Van Wart is great as a professor. He is open to questions as well as class discussions, plus his lectures are very clear. You will find the class easy and enjoyable as long as you read and take notes during lectures.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Prof. Van Wart is my favorite professor! He is always prepared and he gives interesting lectures. 10/10 would recommend taking him.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
I enjoyed Professor Van Wart's. He gave excellent lectures, he always holds your attention, uses good examples. He does a great job laying out material in a clear and organized manner. Not very difficult to pass, but fun and you still learn!
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Prof. Van Wart does an amazing job of discussing the material in an understandable way. He also gives personal examples and references.
N/A
Would take again: Yes
For Credit: Yes
0
0
Mandatory
Awesome
Prof. Van Wart is the best theology professor in this campus.
Biography
Ave Maria University - Religion
Experience
University of California, Irvine
Post Doctoral Scholar
Mobley Lab at UCI
Projects include computational modeling, free energy calculations, and educational outreach.Orange Coast College
Adjunct Professor
T. Adam worked at Orange Coast College as a Adjunct Professor
Mt. San Antonio College
Adjunct Professor
T. Adam worked at Mt. San Antonio College as a Adjunct Professor
San Diego Mesa College
Adjunct Professor
T. Adam worked at San Diego Mesa College as a Adjunct Professor
Santa Ana College
Adjunt Professor
T. Adam worked at Santa Ana College as a Adjunt Professor
Education
University of California, San Diego
Doctor of Philosophy (Ph.D.)
Chemistry
Biophysical ChemistryUniversity of California, Davis
B.S. Chemistry
Chemistry
Generic undergraduate degree with emphasis in Chemical EngineeringUniversity of California, Irvine
Chemical and Material Physics
Physical Chemistry
University of California, Irvine
Post Doctoral Scholar
Mobley Lab at UCI Projects include computational modeling, free energy calculations, and educational outreach.
Publications
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Solving evolution equations using interacting trajectory ensembles
Hogan, Patrick; Van Wart, Adam; Donoso, Arnaldo; Martens, Craig C.
In this paper, we describe a general approach to solving evolution equations for probability densities using interacting trajectory ensembles. Assuming the existence of a positive definite (probabilistic) description of the state of the system, we derive general equations of motion for the trajectories in the kinematic space (e.g., configuration or phase space). The vector field describing the time rate of change of the trajectory ensemble members depends, in general, on both external forces and on the probability density itself. The dependence of the equations of motion on the probability density lead to interactions between the ensemble members and a loss of their statistical independence. The formalism is illustrated by a number of numerical examples. For multidimensional systems, a gauge-like freedom exists in the choice of the underlying vector field, which leaves the evolution of the probability density invariant.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Computational approaches to mapping allosteric pathways.
Curr Opin Struct Biol.
Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Exploring Residue Component Contributions to Dynamical Network Models of Allostery
Journal of Chemical Theory and Computation
Allosteric regulation in biological systems is of considerable interest given the vast number of proteins that exhibit such behavior. Network models obtained from molecular dynamics simulations have been shown to be powerful tools for the analysis of allostery. In this work, different coarse-grain residue representations (nodes) are used together with a dynamical network model to investigate models of allosteric regulation. This model assumes that allosteric signals are dependent on positional correlations of protein substituents, as determined through molecular dynamics simulations, and uses correlated motion to generate a signaling weight between two given nodes. We examine four types of network models using different node representations in Cartesian coordinates: the (i) residue α-carbons, (ii) the side chain center of mass, (iii) the backbone center of mass, and the entire (iv) residue center of mass. All correlations are filtered by a dynamic contact map that defines the allowable interactions between nodes based on physical proximity. We apply the four models to imidazole glycerol phosphate synthase (IGPS), which provides a well-studied experimental framework in which allosteric communication is known to persist across disparate protein domains (e.g., a protein dimer interface). IGPS is modeled as a network of nodes and weighted edges. Optimal allosteric pathways are traced using the Floyd Warshall algorithm for weighted networks, and community analysis (a form of hierarchical clustering) is performed using the Girvan–Newman algorithm. Our results show that dynamical information encoded in the residue center of mass must be included in order to detect residues that are experimentally known to play a role in allosteric communication for IGPS. More broadly, this new method may be useful for predicting pathways of allosteric communication for any biomolecular system in atomic detail.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Application of Molecular-Dynamics Based Markov State Models to Functional Proteins
J. Chem. Theory Comput.
Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Weighted Implementation of Suboptimal Paths (WISP): An Optimized Algorithm and Tool for Dynamical Network Analysis
Journal of Chemical Theory and Computation
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
