Literature DB >> 25746437

An accurate metalloprotein-specific scoring function and molecular docking program devised by a dynamic sampling and iteration optimization strategy.

Fang Bai1,2, Sha Liao3, Junfeng Gu1, Hualiang Jiang4, Xicheng Wang1, Honglin Li3.   

Abstract

Metalloproteins, particularly zinc metalloproteins, are promising therapeutic targets, and recent efforts have focused on the identification of potent and selective inhibitors of these proteins. However, the ability of current drug discovery and design technologies, such as molecular docking and molecular dynamics simulations, to probe metal-ligand interactions remains limited because of their complicated coordination geometries and rough treatment in current force fields. Herein we introduce a robust, multiobjective optimization algorithm-driven metalloprotein-specific docking program named MpSDock, which runs on a scheme similar to consensus scoring consisting of a force-field-based scoring function and a knowledge-based scoring function. For this purpose, in this study, an effective knowledge-based zinc metalloprotein-specific scoring function based on the inverse Boltzmann law was designed and optimized using a dynamic sampling and iteration optimization strategy. This optimization strategy can dynamically sample and regenerate decoy poses used in each iteration step of refining the scoring function, thus dramatically improving both the effectiveness of the exploration of the binding conformational space and the sensitivity of the ranking of the native binding poses. To validate the zinc metalloprotein-specific scoring function and its special built-in docking program, denoted MpSDockZn, an extensive comparison was performed against six universal, popular docking programs: Glide XP mode, Glide SP mode, Gold, AutoDock, AutoDock4Zn, and EADock DSS. The zinc metalloprotein-specific knowledge-based scoring function exhibited prominent performance in accurately describing the geometries and interactions of the coordination bonds between the zinc ions and chelating agents of the ligands. In addition, MpSDockZn had a competitive ability to sample and identify native binding poses with a higher success rate than the other six docking programs.

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Year:  2015        PMID: 25746437     DOI: 10.1021/ci500647f

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  1 in total

1.  Multi-Body Interactions in Molecular Docking Program Devised with Key Water Molecules in Protein Binding Sites.

Authors:  Wei Xiao; Disha Wang; Zihao Shen; Shiliang Li; Honglin Li
Journal:  Molecules       Date:  2018-09-11       Impact factor: 4.411

  1 in total

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