Extending the accuracy limits of prediction for side-chain conformations

Xiang, Zhaoyin

doi:10.1006/jmbi.2001.4985

Cited by 165 publications

(352 citation statements)

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“…In the same way, due to the absence of correct representation of the solvent, prediction of exposed residues is difficult to evaluate. The crystal could also constrain exposed residues and so most of prediction accuracy is given only for core residues [94]. This study highlights the interest of using other criteria to evaluate side-chain prediction methods.…”

Section: Discussionmentioning

confidence: 98%

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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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Section: Discussionmentioning

confidence: 98%

“…SCAP specificities lead to a four coordinate rotamer libraries [94]. The method used a CHARMM force field to perform a minimization.…”

Section: Introductionmentioning

confidence: 99%

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

Faure¹,

Bornot²,

Brevern³

2008

Biochimie

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“…The computational expense of such a detailed library was mitigated by prescreening the rotamers by using only hard sphere overlap as a criterion (using a cell list for computational efficiency), allowing many rotamers to be excluded before performing any energy evaluations. The method we use for the combinatorial optimization is also adapted from the method of Xiang and Honig, 31 which is similar in spirit to earlier work by Bruccoleri and Karplus. 14 In brief, all side-chains are initially built onto the fixed backbone in a random rotamer state, and then each side-chain in the protein is optimized one at a time, holding the others fixed.…”

Section: Side-chain Optimizationmentioning

confidence: 99%

A hierarchical approach to all‐atom protein loop prediction

et al. 2004

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The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone rootmean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 Å for 5 residue loops, 1.00/0.44 Å for 8 residue loops, and 2.47/1.83 Å for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 Å for 5 residue loops, 0.84 Å for 8 residue loops, and 1.63 Å for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.

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“…12,13 A third group of methods uses either distance geometry or optimization techniques to satisfy spatial restraints obtained from the sequence-template alignment. [14][15][16] There are also many methods that specialize in the modeling of loops [17][18][19] and side chains 20,21 within the restrained environment provided by the rest of the structure.…”

Section: Comparative Modeling and Threadingmentioning

confidence: 99%

Evolution and Physics in Comparative Protein Structure Modeling

et al. 2002

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From a physical perspective, the native structure of a protein is a consequence of physical forces acting on the protein and solvent atoms during the folding process. From a biological perspective, the native structure of proteins is a result of evolution over millions of years. Correspondingly, there are two types of protein structure prediction methods, de novo prediction and comparative modeling. We review comparative protein structure modeling and discuss the incorporation of physical considerations into the modeling process. A good starting point for achieving this aim is provided by comparative modeling by satisfaction of spatial restraints. Incorporation of physical considerations is illustrated by an inclusion of solvation effects into the modeling of loops.

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Extending the accuracy limits of prediction for side-chain conformations

Cited by 165 publications

References 0 publications

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

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

A hierarchical approach to all‐atom protein loop prediction

Evolution and Physics in Comparative Protein Structure Modeling

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