OPUS‐Rota: A fast and accurate method for side‐chain modeling

Lu, Mingyang; Dousis, Athanasios; Ma, Jianpeng

doi:10.1110/ps.035022.108

Cited by 55 publications

(95 citation statements)

References 51 publications

(101 reference statements)

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“…In this case, the present framework determines the transferable potentials that provide an optimal approximation to the many-body potential of mean force for each protein. Notably, by directly calculating the reference state and incorporating correlations between interactions in the CG model, the present work addresses two additional challenges for determining protein potentials: (i) identifying the correct reference state (12,19,70) and (ii) assigning appropriate weights for various contributions in previous KBPs (71)(72)(73).…”

Section: Discussionmentioning

confidence: 99%

Recovering physical potentials from a model protein databank

Mullinax

Noid

2010

Proc. Natl. Acad. Sci. U.S.A.

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Knowledge-based approaches frequently employ empirical relations to determine effective potentials for coarse-grained protein models directly from protein databank structures. Although these approaches have enjoyed considerable success and widespread popularity in computational protein science, their fundamental basis has been widely questioned. It is well established that conventional knowledge-based approaches do not correctly treat manybody correlations between amino acids. Moreover, the physical significance of potentials determined by using structural statistics from different proteins has remained obscure. In the present work, we address both of these concerns by introducing and demonstrating a theory for calculating transferable potentials directly from a databank of protein structures. This approach assumes that the databank structures correspond to representative configurations sampled from equilibrium solution ensembles for different proteins. Given this assumption, this physics-based theory exactly treats many-body structural correlations and directly determines the transferable potentials that provide a variationally optimized approximation to the free energy landscape for each protein. We illustrate this approach by first constructing a databank of protein structures using a model potential and then quantitatively recovering this potential from the structure databank. The proposed framework will clarify the assumptions and physical significance of knowledge-based potentials, allow for their systematic improvement, and provide new insight into many-body correlations and cooperativity in folded proteins.protein structure prediction | coarse-grained models | inverse problems | Yvon-Born-Green theory A ccurate potentials are essential for quantitative models of protein structure, dynamics, and function. Although atomistic force fields provide an accurate description of protein structure and fluctuations on nanosecond time scales (1), atomically detailed models remain prohibitively expensive for investigating processes that evolve on microsecond time scales or longer. In contrast, low-resolution coarse-grained (CG) models provide a highly efficient alternative for characterizing protein dynamics on time scales that are inaccessible to atomistic models (2). Indeed, since the seminal work of Levitt and Warshel (3), CG protein models have provided a powerful tool for protein structure prediction (4), for studying protein self-assembly (5), for characterizing folding dynamics (6-8), and for investigating functional fluctuations (9). Consequently, the development of transferable CG potentials that accurately model protein structure would represent a significant advance for many areas of computational protein science.Following the pioneering work of Tanaka and Scheraga (10), many investigators (11-15) have employed the structural correlations observed within the Protein Data Bank (PDB) (16) to determine effective "knowledge-based" statistical potentials (KBPs). In particular, quasi-chemical (17) and Boltzmann-...

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Section: Discussionmentioning

confidence: 99%

Recovering physical potentials from a model protein databank

Mullinax

Noid

2010

Proc. Natl. Acad. Sci. U.S.A.

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“…OPUS − Rota [16] uses a backbone-dependent rotamer library [7], and its energy function is described by:…”

Section: Algorithms For the Side-chain Packing Problemmentioning

confidence: 99%

“…Although many methods such as RASP [17], OSCARstar [14], SIDEPRO [18], SCMF-PDRL [8], OPUS-Rota [16], CIS-RR [3], and SCWRL4 [11] have been proposed to deal with the PSCPP, comparison works have been rather scarce [19,17]. Most of the methods present only a brief comparison analysis of their new proposed method against previously proposed ones on particular test instances.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Analysis of the Performance of SideChain Packing Algorithms

Brizuela

Corona

Lezcano

et al. 2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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This paper presents a brief description of the protein side chain packing problem (PSCPP) and a performance assessment, on this problem, of three state-of-the-art algorithms: SCWRL4, OPUS-Rota, and CIS-RR. In order to perform a fair comparison, the algorithms are evaluated on three data sets, two of them were previously proposed in the literature and a set of 723 protein structures proposed here. Experimental results show that the achieved accuracy when evaluating the side chain's first torsion angle (χ1) is of approximately 86% and around 69% for the first and the second torsion angles (χ1+2), for all methods. Although all the algorithms achieve similar accuracies, SCWRL4 requires on average, less computation effort than the others. We highlight relevant aspects that need to be considered in order to verify whether or not this 86% is a theoretical upper bound for the algorithms' performance as well as what might become a promising direction to follow in case an improvement is possible.

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“…This is clearly demonstrated by the considerable progress made through the developments of both exhaustive and heuristic techniques. Successful methods in this field include applications of the dead-end elimination theorem, [4][5][6][7] simulated annealing, 8,9 Monte Carlo search, 10 and graph theoretical approaches, [11][12][13] among others (for recent reviews, see Refs. 14 and 15).…”

Section: Introductionmentioning

confidence: 99%

“…Section III describes our implementation of the SCMF-PB formalism, with an emphasis on efficiency. The Results section compares the predictive powers of SCMF-PB with four other leading softwares for side-chain prediction, OPUS-ROTA, 9 SCAP, 10 TREEPACK, 38 and SCWRL4, 16 as they are tested on the DRESS database. 39 We conclude with a discussion on possible improvements and new applications of the SCMF-PB method.…”

Section: Introductionmentioning

confidence: 99%

Adapting Poisson-Boltzmann to the self-consistent mean field theory: Application to protein side-chain modeling

Koehl

Orland

Delarue

2011

The Journal of Chemical Physics

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We present an extension of the self-consistent mean field theory for protein side-chain modeling in which solvation effects are included based on the Poisson-Boltzmann (PB) theory. In this approach, the protein is represented with multiple copies of its side chains. Each copy is assigned a weight that is refined iteratively based on the mean field energy generated by the rest of the protein, until self-consistency is reached. At each cycle, the variational free energy of the multi-copy system is computed; this free energy includes the internal energy of the protein that accounts for vdW and electrostatics interactions and a solvation free energy term that is computed using the PB equation. The method converges in only a few cycles and takes only minutes of central processing unit time on a commodity personal computer. The predicted conformation of each residue is then set to be its copy with the highest weight after convergence. We have tested this method on a database of hundred highly refined NMR structures to circumvent the problems of crystal packing inherent to x-ray structures. The use of the PB-derived solvation free energy significantly improves prediction accuracy for surface side chains. For example, the prediction accuracies for χ 1 for surface cysteine, serine, and threonine residues improve from 68%, 35%, and 43% to 80%, 53%, and 57%, respectively. A comparison with other side-chain prediction algorithms demonstrates that our approach is consistently better in predicting the conformations of exposed side chains.

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OPUS‐Rota: A fast and accurate method for side‐chain modeling

Cited by 55 publications

References 51 publications

Recovering physical potentials from a model protein databank

Recovering physical potentials from a model protein databank

An Experimental Analysis of the Performance of SideChain Packing Algorithms

Adapting Poisson-Boltzmann to the self-consistent mean field theory: Application to protein side-chain modeling

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