All in one: a highly detailed rotamer library improves both accuracy and speed in the modelling of sidechains by dead-end elimination

Maeyer, Marc De; Desmet, Jan; Lasters, Ignace

doi:10.1016/s1359-0278(97)00006-0

Cited by 127 publications

(114 citation statements)

References 65 publications

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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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“…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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“…bone-independent library (Ponder and Richards 1987;Tuffery et al 1991;De Maeyer et al 1997;Lovell et al 2000) and a backbone-dependent library (Dunbrack and Karplus 1993). Both of them have been widely used for predicting side-chain conformations.…”

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confidence: 99%

“…Many strategies have been proposed to solve this problem: Monte Carlo searches (Holm and Sander 1992;Vasquez 1995), genetic algorithms (Tuffery et al 1991), neural networks (Hwang and Liao 1995), mean-field optimization (Koehl and Delarue 1994;Mendes et al 1999), dead-end elimination (DEE) method (Desmet et al 1992;De Maeyer et al 1997), and actual combinatorial searches (Dunbrack and Karplus 1993;Wilson et al 1993;Bower et al 1997). Although DEE is considered to be the most powerful algorithm, designed to identify global minimum energy conformations, its predictions are far from being 100% accurate even for the core residues (De Maeyer et al 1997;Looger and Hellinga 2001). Recently, Xiang and Honig (2001) obtained the greatest accuracy for core residues with an extensive library of 7560 rotamers.…”

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confidence: 99%

Side‐chain modeling with an optimized scoring function

Liang

Grishin²

2002

Protein Science

120

109

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Modeling side-chain conformations on a fixed protein backbone has a wide application in structure prediction and molecular design. Each effort in this field requires decisions about a rotamer set, scoring function, and search strategy. We have developed a new and simple scoring function, which operates on side-chain rotamers and consists of the following energy terms: contact surface, volume overlap, backbone dependency, electrostatic interactions, and desolvation energy. The weights of these energy terms were optimized to achieve the minimal average root mean square (rms) deviation between the lowest energy rotamer and real side-chain conformation on a training set of high-resolution protein structures. In the course of optimization, for every residue, its side chain was replaced by varying rotamers, whereas conformations for all other residues were kept as they appeared in the crystal structure. We obtained prediction accuracy of 90.4% for 1 , 78.3% for 1 + 2 , and 1.18 Å overall rms deviation. Furthermore, the derived scoring function combined with a Monte Carlo search algorithm was used to place all side chains onto a protein backbone simultaneously. The average prediction accuracy was 87.9% for 1 , 73.2% for 1 + 2 , and 1.34 Å rms deviation for 30 protein structures. Our approach was compared with available side-chain construction methods and showed improvement over the best among them: 4.4% for 1 , 4.7% for 1 + 2 , and 0.21 Å for rms deviation. We hypothesize that the scoring function instead of the search strategy is the main obstacle in side-chain modeling. Additionally, we show that a more detailed rotamer library is expected to increase 1 + 2 prediction accuracy but may have little effect on 1 prediction accuracy.

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“…First, rotamers with rotamer-backbone interaction energies greater than 20 kcal/mol or pairs with pairwise interaction energies greater than 50 kcal/mol were eliminated from consideration. 6,22 Then, simple Goldstein DEE singles elimination was applied until no further rotamers could be eliminated. 6,23 The input configuration for each trajectory was either the BMEC or the result of a short MC run.…”

Section: Methodsmentioning

confidence: 99%

Dramatic performance enhancements for the FASTER optimization algorithm

Allen

Mayo

2006

J Comput Chem

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FASTER is a combinatorial optimization algorithm useful for finding low-energy side-chain configurations in side-chain placement and protein design calculations. We present two simple enhancements to FASTER that together improve the computational efficiency of these calculations by as much as two orders of magnitude with no loss of accuracy. Our results highlight the importance of choosing appropriate initial configurations, and show that efficiency can be improved by stringently limiting the number of positions that are allowed to relax in response to a perturbation. The changes we describe improve the quality of solutions found for large-scale designs and allow them to be found in hours rather than days. The improved FASTER algorithm finds lowenergy solutions more efficiently than common optimization schemes based on the deadend elimination theorem and Monte Carlo. These advances have prompted investigations into new methods for force field parameterization and multiple state design. 23

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All in one: a highly detailed rotamer library improves both accuracy and speed in the modelling of sidechains by dead-end elimination

Cited by 127 publications

References 65 publications

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

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

Side‐chain modeling with an optimized scoring function

Dramatic performance enhancements for the FASTER optimization algorithm

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