Application of a Self-consistent Mean Field Theory to Predict Protein Side-chains Conformation and Estimate Their Conformational Entropy

Koehl, Patrice; Delarue, Marc

doi:10.1006/jmbi.1994.1366

Cited by 345 publications

(381 citation statements)

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“…Several representations of side-chain flexibility incorporating multiple higher-energy conformations have been used in prediction and design [20][21][22][23] . However, the predictions resulting from models commonly used in protein design simulations have not been directly compared to experimental data on the amplitude of side-chain motion.…”

Section: Introductionmentioning

confidence: 99%

A Simple Model of Backbone Flexibility Improves Modeling of Side-chain Conformational Variability

Friedland

Linares

Smith

et al. 2008

Journal of Molecular Biology

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The considerable flexibility of side-chains in folded proteins is important for protein stability and function, and may play a role in mediating pathways of energetic connectivity between allosteric sites. While sampling side-chain degrees of freedom has been an integral part of several successful computational protein design methods, the predictions of these approaches have not been directly compared to experimental measurements of side-chain motional amplitudes. In addition, protein design methods generally keep the backbone fixed, an approximation that may substantially limit the ability to accurately model side-chain flexibility. Here we describe a Monte Carlo approach to modeling side-chain conformational variability and validate our method against a large dataset of methyl relaxation order parameters derived from Nuclear Magnetic Resonance experiments (17 proteins and a total of 530 data points). We also evaluate a model of backbone flexibility based on Backrub motions, a type of conformational change frequently observed in ultra-high resolution Xray structures that accounts for correlated side-chain backbone movements. The fixed-backbone model performs reasonably well with an overall rmsd between computed and predicted side-chain order parameters of 0.26. Notably, including backbone flexibility leads to significant © 2008 Elsevier Ltd. All rights reserved. * Corresponding author: Kortemme@cgl.ucsf.edu. AUTHOR CONTRIBUTIONSGDF and TK conceived and designed the experiments. GDF and AJL performed the experiments. GDF, TK, and AJL analyzed the data and wrote the paper. CAS contributed reagents/materials/analysis tools. SUPPORTING INFORMATION AVAILABLEOne figure with population distributions of ubiquitin I13 and L15 chi 1 and chi 2. One table with detailed results from Model 1; one table with results from Models 2 and 3 for nonpolar methyl groups only; and one table with detailed parameter sensitivity results from Model 2.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access

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Section: Introductionmentioning

confidence: 99%

A Simple Model of Backbone Flexibility Improves Modeling of Side-chain Conformational Variability

Friedland

Linares

Smith

et al. 2008

Journal of Molecular Biology

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“…Side chain conformation prediction is also a difficult task [82,83]. Thus, different methods have been proposed to predict side chain conformations [84][85][86][87][88].…”

Section: Introductionmentioning

confidence: 99%

Protein contacts, inter-residue interactions and side-chain modelling

Faure¹,

Bornot²,

Brevern³

2008

Biochimie

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To cite this version:Guilhem Faure, Aurélie Bornot, Alexandre De Brevern. Protein contacts, inter-residue interactions and side-chain modelling.: protein contacts. Biochimie, Elsevier, 2008, 90 (4) AbstractThree-dimensional structures of proteins are the support of their biological functions.Their folds are stabilized by contacts between residues. Inner protein contacts are generally described through direct atomic contacts, i.e. interactions between side-chain atoms, while contact prediction methods mainly used inter-C distances. In this paper, we have analyzed the protein contacts on a recent high quality non-redundant databank using different criteria.First, we have studied the average number of contacts depending on the distance threshold to define a contact. Preferential contacts between types of amino acids have been highlighted.Detailed analyses have been done concerning the proximity of contacts in the sequence, the size of the proteins and fold classes. The strongest differences have been extracted, highlighting important residues.Then, we studied the influence of five different side-chain conformation prediction methods (SCWRL, IRECS, SCAP, SCATD and SSCOMP) on the distribution of contacts.The prediction rates of these different methods are quite similar. However, using a distance criterion between side-chains, the results are quite different, e.g. SCAP predicts 50% more contacts than observed, unlike other methods that predict fewer contacts than observed.Contacts deduced are quite distinct from one method to another with at most 75% contacts in common. Moreover, distributions of amino acid preferential contacts present unexpected behaviors distinct from previously observed in the X-ray structures, especially at the surface of proteins. For instance, the interactions involving Tryptophan greatly decrease.

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“…However, protein design is NP-hard (Kingsford et al, 2005), making algorithms that guarantee optimality expensive for larger designs where many residues are allowed to mutate simultaneously. Therefore, researchers have developed tractable approximations of the protein design problem to obtain provably good approximate solutions (Leach and Lemon, 1998;Roberts et al, 2012;Chen et al, 2009;Lilien et al, 2005;Georgiev and Donald, 2007;Donald, 2011, Smadbeck et al, 2014, or employed heuristic approaches to rapidly generate candidate solutions (Lee and Subbiah, 1991;Kuhlman and Baker, 2000;Jones, 1994;Desjarlais and Handel, 1995;Koehl and Delarue, 1994;Jiang et al, 2000;Donald, 2011). Heuristic sampling of sequences quickly generates locally optimal candidate sequences, whereas provable algorithms are guaranteed to return the global minimum energy conformation (GMEC).…”

Section: Introductionmentioning

confidence: 99%

BWM*: A Novel, Provable, Ensemble-Based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design

Jou

Jain

Georgiev

et al. 2015

Lecture Notes in Computer Science

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Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O(nw 2 q 3 2 w ) time and enumerates each additional conformation in merely O(n log q) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.

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Application of a Self-consistent Mean Field Theory to Predict Protein Side-chains Conformation and Estimate Their Conformational Entropy

Cited by 345 publications

References 0 publications

A Simple Model of Backbone Flexibility Improves Modeling of Side-chain Conformational Variability

A Simple Model of Backbone Flexibility Improves Modeling of Side-chain Conformational Variability

Protein contacts, inter-residue interactions and side-chain modelling

BWM*: A Novel, Provable, Ensemble-Based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design

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