Hits:

Indexed by:期刊论文

Date of Publication:2015-04-01

Journal:JOURNAL OF CHEMICAL INFORMATION AND MODELING

Included Journals:SCIE、EI、PubMed、Scopus

Volume:55

Issue:4

Page Number:833-847

ISSN No.:1549-9596

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, miiltiobjective 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 metalloproteinspecific 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 MpSDock(Zn), an extensive comparison was performed against six universal, popular docking programs: Glide XP mbde, Glide SP mode, Gold, Auto Dock, AutoDock4(zn), 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, MpSDock(Zn) had a competitive ability to sample and identify native binding poses With a higher success rate than the other six docking programs.