Stability strengths and weaknesses in protein structures detected by statistical potentials: Application to bovine seminal ribonuclease

2015 ◽  
Vol 84 (1) ◽  
pp. 143-158 ◽  
Author(s):  
Marie De Laet ◽  
Dimitri Gilis ◽  
Marianne Rooman
Keyword(s):  
Protein Structures ◽  
Statistical Potentials ◽  
Bovine Seminal Ribonuclease

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 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 SPServer: split-statistical potentials for the analysis of protein structures and protein–protein interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Joaquim Aguirre-Plans ◽  
Alberto Meseguer ◽  
Ruben Molina-Fernandez ◽  
Manuel Alejandro Marín-López ◽  
Gaurav Jumde ◽  
...  
Keyword(s):  
Amino Acid ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Web Server ◽  
Protein Docking ◽  
Scoring Functions ◽  
Statistical Potentials ◽  
Protein Folds ◽  
Protein Interfaces

Abstract Background Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein–protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. Results Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. Conclusions While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. Server address https://sbi.upf.edu/spserver/.

scholarly journals A little walk from physical to biological complexity: protein folding and stability

2016 ◽  
Author(s):  
Fabrizio Pucci ◽  
Marianne Rooman
Keyword(s):  
Protein Folding ◽  
Amino Acid ◽  
Thermal Resistance ◽  
Protein Structures ◽  
Amino Acid Substitutions ◽  
Biological Complexity ◽  
Statistical Potentials ◽  
Bioinformatics Tools ◽  
The Stability ◽  
Boltzmann Law

As an example of topic where biology and physics meet, we present the issue of protein folding and stability, and the development of thermodynamics-based bioinformatics tools that predict the stability and thermal resistance of proteins and the change of these quantities upon amino acid substitutions. These methods are based on knowledge-driven statistical potentials, derived from experimental protein structures using the inverse Boltzmann law.

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.

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