Protein functional landscapes, dynamics, allostery: a tortuous path towards a universal theoretical framework

2010 ◽  
Vol 43 (3) ◽  
pp. 295-332 ◽  
Author(s):  
Pavel I. Zhuravlev ◽  
Garegin A. Papoian
Keyword(s):  
Free Energy ◽  
Common Ground ◽  
Normal Mode Analysis ◽  
Energy Landscape ◽  
Principal Component ◽  
Dynamics Simulation ◽  
Mode Analysis ◽  
General Description ◽  
Topographical Map ◽  
Energy Surfaces

AbstractEnergy landscape theories have provided a common ground for understanding the protein folding problem, which once seemed to be overwhelmingly complicated. At the same time, the native state was found to be an ensemble of interconverting states with frustration playing a more important role compared to the folding problem. The landscape of the folded protein – the native landscape – is glassier than the folding landscape; hence, a general description analogous to the folding theories is difficult to achieve. On the other hand, the native basin phase volume is much smaller, allowing a protein to fully sample its native energy landscape on the biological timescales. Current computational resources may also be used to perform this sampling for smaller proteins, to build a ‘topographical map’ of the native landscape that can be used for subsequent analysis. Several major approaches to representing this topographical map are highlighted in this review, including the construction of kinetic networks, hierarchical trees and free energy surfaces with subsequent structural and kinetic analyses. In this review, we extensively discuss the important question of choosing proper collective coordinates characterizing functional motions. In many cases, the substates on the native energy landscape, which represent different functional states, can be used to obtain variables that are well suited for building free energy surfaces and analyzing the protein's functional dynamics. Normal mode analysis can provide such variables in cases where functional motions are dictated by the molecule's architecture. Principal component analysis is a more expensive way of inferring the essential variables from the protein's motions, one that requires a long molecular dynamics simulation. Finally, the two popular models for the allosteric switching mechanism, ‘preexisting equilibrium’ and ‘induced fit’, are interpreted within the energy landscape paradigm as extreme points of a continuum of transition mechanisms. Some experimental evidence illustrating each of these two models, as well as intermediate mechanisms, is presented and discussed.

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

scholarly journals Approximating deformation fields for the analysis of continuous heterogeneity of biological macromolecules by 3D Zernike polynomials

IUCrJ ◽  
2021 ◽  
Vol 8 (6) ◽  
Author(s):  
David Herreros ◽  
Roy R. Lederman ◽  
James Krieger ◽  
Amaya Jiménez-Moreno ◽  
Marta Martínez ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Structural Characteristics ◽  
Normal Mode Analysis ◽  
Principal Component ◽  
Current Data ◽  
Mode Analysis ◽  
Biological Macromolecules ◽  
Mathematical Basis ◽  
Common Framework ◽  
Low Dimensional

Structural biology has evolved greatly due to the advances introduced in fields like electron microscopy. This image-capturing technique, combined with improved algorithms and current data processing software, allows the recovery of different conformational states of a macromolecule, opening new possibilities for the study of its flexibility and dynamic events. However, the ensemble analysis of these different conformations, and in particular their placement into a common variable space in which the differences and similarities can be easily recognized, is not an easy matter. To simplify the analysis of continuous heterogeneity data, this work proposes a new automatic algorithm that relies on a mathematical basis defined over the sphere to estimate the deformation fields describing conformational transitions among different structures. Thanks to the approximation of these deformation fields, it is possible to describe the forces acting on the molecules due to the presence of different motions. It is also possible to represent and compare several structures in a low-dimensional mapping, which summarizes the structural characteristics of different states. All these analyses are integrated into a common framework, providing the user with the ability to combine them seamlessly. In addition, this new approach is a significant step forward compared with principal component analysis and normal mode analysis of cryo-electron microscopy maps, avoiding the need to select components or modes and producing localized analysis.

scholarly journals Molecular Dynamic Simulation of 3-(5-Chloro-2, 4-dihydroxyphenyl)-pyrazole-4-carboxamide and HSP90 Molecular Chaperone Interaction

2011 ◽  
Vol 8 (4) ◽  
pp. 1566-1573
Author(s):  
Leila Baramakeh
Keyword(s):  
Free Energy ◽  
Molecular Chaperone ◽  
Trajectory Analysis ◽  
Stable Equilibrium ◽  
Energy Landscape ◽  
Heat Shock Protein 90 ◽  
Dynamics Simulation ◽  
Biophysical Processes ◽  
Soft Core Potential ◽  
Preferential Binding

The calculation of free energy differences of a system is of great importance as the rate and extent of many if not all chemical and biophysical processes are governed by the nature of underlying free energy landscape. In this study the preferential binding of 3-(5-chloro-2, 4-dihydroxyphenyl)–pyrazole-4-carboxamide (4BC) and Heat shock protein 90(Hsp90) molecular chaperone has been evaluated using molecular dynamics simulation. A soft core potential was used during the mutations to facilitate the creation and deletion of atoms. Trajectory analysis showed a stable equilibrium after energy minimization. Potential energy plot showed equilibrium around -69520 and -183859 kJ/mol for Hsp90 and Hsp90-4BC. Kinetic energy also was calculated for Hsp90 and Hsp90-4BC as 44500 and 65928.29 kJ/mol, respectively.

The type IA topoisomerase catalytic cycle: A normal mode analysis and molecular dynamics simulation

2008 ◽  
Vol 71 (4) ◽  
pp. 1984-1994 ◽  
Author(s):  
Bing Xiong ◽  
David L. Burk ◽  
Jianhua Shen ◽  
Xiaomin Luo ◽  
Hong Liu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Normal Mode ◽  
Normal Mode Analysis ◽  
Catalytic Cycle ◽  
Dynamics Simulation ◽  
Mode Analysis ◽  
Type Ia

scholarly journals Data-Driven Reaction Coordinate Discovery in Overdamped and non-Conservative Systems: Application to Optical Matter Structural Isomerization

2020 ◽  
Author(s):  
Shiqi Chen ◽  
Curtis Peterson ◽  
John Parker ◽  
Stuart A. Rice ◽  
Andrew Ferguson ◽  
...  
Keyword(s):  
Normal Mode Analysis ◽  
Principal Component ◽  
Reaction Coordinate ◽  
Data Driven ◽  
Mode Analysis ◽  
Reference Structure ◽  
Structural Transitions ◽  
Linear Discriminant ◽  
Conservative Systems ◽  
Structural Isomerization

Abstract Powerful technique as it is to study the collective fluctuations and structural transitions in under-damped conservative systems, normal mode analysis cannot be applied to the optical matter (OM) system, an overdamped system subject to non-conservative forces, which consists of (nano-)particle constituents in solution that can self-organize into ordered arrays bound by electrodynamic interactions. We propose a data-driven approach based on principal component analysis (PCA) of deviations from a reference structure to determine the soft collective modes of non-conservative overdamped systems, such as OM structures, and harmonic linear discriminant analysis (HLDA) of time trajectories to estimate the reaction coordinate for structural transitions, which is demonstrated for structural isomerization of a six-particle OM system. The reaction coordinate we discover is in accord with committor analysis and the identified mechanism is in agreement with experiment. The PCA-HLDA approach to data-driven discovery of reaction coordinates aids in the understanding of non-conservative and overdamped systems.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

2019 ◽  
Vol 21 (3) ◽  
pp. 815-835 ◽  
Author(s):  
Zhongjie Liang ◽  
Gennady M Verkhivker ◽  
Guang Hu
Keyword(s):  
Protein Dynamics ◽  
High Throughput ◽  
Molecular Mechanisms ◽  
Normal Mode Analysis ◽  
Allosteric Regulation ◽  
Genomic Medicine ◽  
Biological Data ◽  
Dynamics Simulation ◽  
Mode Analysis ◽  
Evolutionary Analysis

Abstract Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

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