Focused grid‐based resampling for protein docking and mapping

Mamonov, Artem B.; Moghadasi, Mohammad; Mirzaei, Hanieh; Zarbafian, Shahrooz; Grove, Laurie E.; Bohnuud, Tanggis; Vakili, Pirooz; Paschalidis, Ioannis Ch.; Vajda, Sándor; Kozakov, Dima

doi:10.1002/jcc.24273

Cited by 6 publications

(11 citation statements)

References 73 publications

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“…This is not for the lack of trying: during the last two years we have developed and tested several refinement algorithms. In terms of computational efficiency, the most promising approach was local resampling of near‐native clusters on a denser grid . The method has been tested on 49 complexes using structures obtained by docking unbound protein pairs.…”

Section: Resultsmentioning

confidence: 99%

“…The method has been tested on 49 complexes using structures obtained by docking unbound protein pairs. The resampling increased the number of near‐native structures for 31 of the 49 complexes, in 19 cases by >50%, whereas the number of near‐native structures substantially decreased only in 5 cases . Since we generally had more near‐native structures than in the initial docking, we clustered these with a smaller clustering radius, and expected to find some cluster centers closer to the native structure.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of computational efficiency, the most promising approach was local resampling of near-native clusters on a denser grid. 37 The method has been tested on 49 complexes using structures obtained by docking unbound protein pairs. The resampling increased the number of nearnative structures for 31 of the 49 complexes, in 19 cases by >50%, whereas the number of near-native structures substantially decreased only in 5 cases.…”

Section: The Bad: Difficult Problems That Need Solutionmentioning

confidence: 99%

“…The resampling increased the number of nearnative structures for 31 of the 49 complexes, in 19 cases by >50%, whereas the number of near-native structures substantially decreased only in 5 cases. 37 Since we generally had more near-native structures than in the initial docking, we clustered these with a smaller clustering radius, and expected to find some cluster centers closer to the native structure. However, this was the case only for a few of the proteins studied, and generally the distances of clusters centers from the X-ray structure did not substantially change.…”

Section: The Bad: Difficult Problems That Need Solutionmentioning

confidence: 99%

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New additions to the ClusPro server motivated by CAPRI

et al. 2017

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The heavily used protein-protein docking server ClusPro performs three computational steps as follows: (1) rigid body docking, (2) RMSD based clustering of the 1000 lowest energy structures, and (3) the removal of steric clashes by energy minimization. In response to challenges encountered in recent CAPRI targets, we added three new options to ClusPro. These are (1) accounting for Small Angle X-ray Scattering (SAXS) data in docking; (2) considering pairwise interaction data as restraints; and (3) enabling discrimination between biological and crystallographic dimers. In addition, we have developed an extremely fast docking algorithm based on 5D rotational manifold FFT, and an algorithm for docking flexible peptides that include known sequence motifs. We feel that these developments will further improve the utility of ClusPro. However, CAPRI emphasized several shortcomings of the current server, including the problem of selecting the right energy parameters among the five options provided, and the problem of selecting the best models among the 10 generated for each parameter set. In addition, results convinced us that further development is needed for docking homology models. Finally we discuss the difficulties we have encountered when attempting to develop a refinement algorithm that would be computationally efficient enough for inclusion in a heavily used server.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: The Bad: Difficult Problems That Need Solutionmentioning

confidence: 99%

Section: The Bad: Difficult Problems That Need Solutionmentioning

confidence: 99%

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New additions to the ClusPro server motivated by CAPRI

et al. 2017

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“…A successful strategy is to use Monte Carlo -based sampling 4 . An alternative method that resamples around low-energy PIPER structures has also been proposed 5 . A host of methods seek to leverage the funnel-like shape of the energy function 6 – 8 .…”

Section: Introductionmentioning

confidence: 99%

Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes

Zarbafian

Moghadasi

Roshandelpoor

et al. 2018

Sci Rep

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We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth “permissive” subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.

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Protein–ligand docking using FFT based sampling: D3R case study

Padhorny

Hall

Mirzaei

et al. 2017

J Comput Aided Mol Des

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Fast Fourier transform (FFT) based approaches have been successful in application to modeling of relatively rigid protein-protein complexes. Recently, we have been able to adapt the FFT methodology to treatment of flexible protein-peptide interactions. Here, we report our latest attempt to expand the capabilities of the FFT approach to treatment of flexible protein-ligand interactions in application to the D3R PL-2016-1 challenge. Based on the D3R assessment, our FFT approach in conjunction with Monte Carlo minimization off-grid refinement was among the top performing methods in the challenge. The potential advantage of our method is its ability to globally sample the protein-ligand interaction landscape, which will be explored in further applications.

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Focused grid‐based resampling for protein docking and mapping

Cited by 6 publications

References 73 publications

New additions to the ClusPro server motivated by CAPRI

New additions to the ClusPro server motivated by CAPRI

Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes

Protein–ligand docking using FFT based sampling: D3R case study

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