scholarly journals Anisotropic coarse-grained statistical potentials improve the ability to identify nativelike protein structures

2003 ◽  
Vol 118 (16) ◽  
pp. 7658 ◽  
Author(s):  
N.-V. Buchete ◽  
J. E. Straub ◽  
D. Thirumalai
Keyword(s):  
Protein Structures ◽  
Coarse Grained ◽  
Statistical Potentials

scholarly journals All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Majid Masso
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Binding Energies ◽  
Coarse Grained ◽  
Amber Force Field ◽  
Statistical Potentials ◽  
Knowledge Based ◽  
Native Proteins ◽  
Interacting Atoms ◽  
Atomic Coordinates

Recent advances in understanding protein folding have benefitted from coarse-grained representations of protein structures. Empirical energy functions derived from these techniques occasionally succeed in distinguishing native structures from their corresponding ensembles of nonnative folds or decoys which display varying degrees of structural dissimilarity to the native proteins. Here we utilized atomic coordinates of single protein chains, comprising a large diverse training set, to develop and evaluate twelve all-atom four-body statistical potentials obtained by exploring alternative values for a pair of inherent parameters. Delaunay tessellation was performed on the atomic coordinates of each protein to objectively identify all quadruplets of interacting atoms, and atomic potentials were generated via statistical analysis of the data and implementation of the inverted Boltzmann principle. Our potentials were evaluated using benchmarking datasets from Decoys-‘R’-Us, and comparisons were made with twelve other physics- and knowledge-based potentials. Ranking 3rd, our best potential tied CHARMM19 and surpassed AMBER force field potentials. We illustrate how a generalized version of our potential can be used to empirically calculate binding energies for target-ligand complexes, using HIV-1 protease-inhibitor complexes for a practical application. The combined results suggest an accurate and efficient atomic four-body statistical potential for protein structure prediction and assessment.

scholarly journals Comparative roles of charge,π, and hydrophobic interactions in sequence-dependent phase separation of intrinsically disordered proteins

2020 ◽  
Vol 117 (46) ◽  
pp. 28795-28805
Author(s):  
Suman Das ◽  
Yi-Hsuan Lin ◽  
Robert M. Vernon ◽  
Julie D. Forman-Kay ◽  
Hue Sun Chan
Keyword(s):  
Phase Separation ◽  
Electrostatic Interactions ◽  
Intrinsically Disordered Proteins ◽  
Protein Structures ◽  
Coarse Grained ◽  
Disordered Proteins ◽  
Chain Model ◽  
Statistical Potentials ◽  
Intrinsically Disordered ◽  
Sequence Dependent

Endeavoring toward a transferable, predictive coarse-grained explicit-chain model for biomolecular condensates underlain by liquid–liquid phase separation (LLPS) of proteins, we conducted multiple-chain simulations of the N-terminal intrinsically disordered region (IDR) of DEAD-box helicase Ddx4, as a test case, to assess roles of electrostatic, hydrophobic, cation–π, and aromatic interactions in amino acid sequence-dependent LLPS. We evaluated three different residue–residue interaction schemes with a shared electrostatic potential. Neither a common hydrophobicity scheme nor one augmented with arginine/lysine-aromatic cation–π interactions consistently accounted for available experimental LLPS data on the wild-type, a charge-scrambled, a phenylalanine-to-alanine (FtoA), and an arginine-to-lysine (RtoK) mutant of Ddx4 IDR. In contrast, interactions based on contact statistics among folded globular protein structures reproduce the overall experimental trend, including that the RtoK mutant has a much diminished LLPS propensity. Consistency between simulation and experiment was also found for RtoK mutants of P-granule protein LAF-1, underscoring that, to a degree, important LLPS-driving π-related interactions are embodied in classical statistical potentials. Further elucidation is necessary, however, especially of phenylalanine’s role in condensate assembly because experiments on FtoA and tyrosine-to-phenylalanine mutants suggest that LLPS-driving phenylalanine interactions are significantly weaker than posited by common statistical potentials. Protein–protein electrostatic interactions are modulated by relative permittivity, which in general depends on aqueous protein concentration. Analytical theory suggests that this dependence entails enhanced interprotein interactions in the condensed phase but more favorable protein–solvent interactions in the dilute phase. The opposing trends lead to only a modest overall impact on LLPS.

Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction?

2014 ◽  
Vol 12 (05) ◽  
pp. 1450022 ◽  
Author(s):  
Hamed Tabatabaei Ghomi ◽  
Jared J. Thompson ◽  
Markus A. Lill
Keyword(s):  
Free Energy ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Coarse Grained ◽  
Scoring Functions ◽  
Statistical Potentials ◽  
Three Body ◽  
Multi Body

Distance-based statistical potentials have long been used to model condensed matter systems, e.g. as scoring functions in differentiating native-like protein structures from decoys. These scoring functions are based on the assumption that the total free energy of the protein can be calculated as the sum of pairwise free energy contributions derived from a statistical analysis of pair-distribution functions. However, this fundamental assumption has been challenged theoretically. In fact the free energy of a system with N particles is only exactly related to the N-body distribution function. Based on this argument coarse-grained multi-body statistical potentials have been developed to capture higher-order interactions. Having a coarse representation of the protein and using geometric contacts instead of pairwise interaction distances renders these models insufficient in modeling details of multi-body effects. In this study, we investigated if extending distance-dependent pairwise atomistic statistical potentials to corresponding interaction functions that are conditional on a third interacting body, defined as quasi-three-body statistical potentials, could model details of three-body interactions. We also tested if this approach could improve the predictive capabilities of statistical scoring functions for protein structure prediction. We analyzed the statistical dependency between two simultaneous pairwise interactions and showed that there is surprisingly little if any dependency of a third interacting site on pairwise atomistic statistical potentials. Also the protein structure prediction performance of these quasi-three-body potentials is comparable with their corresponding two-body counterparts. The scoring functions developed in this study showed better or comparable performances compared to some widely used scoring functions for protein structure prediction.

scholarly journals Distributions of experimental protein structures on coarse-grained free energy landscapes

2015 ◽  
Vol 143 (24) ◽  
pp. 243153 ◽  
Author(s):  
Kannan Sankar ◽  
Jie Liu ◽  
Yuan Wang ◽  
Robert L. Jernigan
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Coarse Grained ◽  
Energy Landscapes ◽  
Free Energy Landscapes

scholarly journals A Generic Force Field for Simulating Native Protein Structures Using Dissipative Particle Dynamics

Soft Matter ◽  
2021 ◽  
Author(s):  
Rakesh K Vaiwala ◽  
Ganapathy Ayappa
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Molecular Dynamics Simulations ◽  
Dissipative Particle Dynamics ◽  
Protein Structures ◽  
Particle Dynamics ◽  
Coarse Grained ◽  
Native Protein ◽  
Dynamics Simulations

A coarse-grained force field for molecular dynamics simulations of native structures of proteins in a dissipative particle dynamics (DPD) framework is developed. The parameters for bonded interactions are derived by...

scholarly journals ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

2019 ◽  
Vol 47 (W1) ◽  
pp. W471-W476 ◽  
Author(s):  
Rasim Murat Aydınkal ◽  
Onur Serçinoğlu ◽  
Pemra Ozbek
Keyword(s):  
Protein Structure ◽  
Normal Mode Analysis ◽  
Shortest Paths ◽  
Protein Structures ◽  
Interaction Strength ◽  
Biological Information ◽  
Coarse Grained ◽  
Mode Analysis ◽  
Residue Interaction ◽  
Natural Variant

AbstractProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

KORP: knowledge-based 6D potential for fast protein and loop modeling

2019 ◽  
Vol 35 (17) ◽  
pp. 3013-3019 ◽  
Author(s):  
José Ramón López-Blanco ◽  
Pablo Chacón
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Joint Probability ◽  
Protein Modeling ◽  
Supplementary Information ◽  
Joint Probability Distribution ◽  
Loop Modeling ◽  
Statistical Potentials ◽  
Knowledge Based ◽  
Backbone Atoms

Abstract Motivation Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue–residue interactions from known protein structures using a simple backbone-based representation. Results We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Using a minimalist representation of only three backbone atoms per residue, KORP utilizes a six-dimensional joint probability distribution to outperform state-of-the-art statistical potentials for native structure recognition and best model selection in recent critical assessment of protein structure prediction and loop-modeling benchmarks. Compared with the existing methods, our side-chain independent potential has a lower complexity and better efficiency. The superior accuracy and robustness of KORP represent a promising advance for protein modeling and refinement applications that require a fast but highly discriminative energy function. Availability and implementation http://chaconlab.org/modeling/korp. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Protocols for fast simulations of protein structure flexibility using CABS-flex and SURPASS

2019 ◽  
Author(s):  
Aleksandra Badaczewska-Dawid ◽  
Andrzej Kolinski ◽  
Sebastian Kmiecik
Keyword(s):  
Protein Function ◽  
Md Simulation ◽  
Protein Structures ◽  
Md Simulations ◽  
System Size ◽  
Coarse Grained ◽  
Simulation Tools ◽  
Speed Up ◽  
Structure Flexibility ◽  
Computational Resources

SummaryConformational flexibility of protein structures can play an important role in protein function. The flexibility is often studied using computational methods, since experimental characterization can be difficult. Depending on protein system size; computational tools may require large computational resources or significant simplifications in the modeled systems to speed-up calculations. In this work, we present the protocols for efficient simulations of flexibility of folded protein structures that use coarse-grained simulation tools of different resolutions: medium, represented by CABS-flex, and low, represented by SUPRASS. We test the protocols using a set of 140 globular proteins and compare the results with structure fluctuations observed in MD simulations, ENM modeling and NMR ensembles. As demonstrated, CABS-flex predictions show high correlation to experimental and MD simulation data, while SURPASS is less accurate but promising in terms of future developments.

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