ArticleScientific reports2018
Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Molecular Docking and In-Silico Analysis of Natural Biomolecules against Dengue, Ebola, Zika, SARS-CoV-2 Variants of Concern and Monkeypox Virus.International journal of molecular sciences · 2022Article
- A NovelBiology · 2021Article
- Refinement of protein-protein complexes in contact map space with metadynamics simulations.Proteins · 2019Article
- D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings.Journal of computer-aided molecular design · 2019Article
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Authors and funding
9 authors.
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Abstract
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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