An Accurate Metalloprotein-Specific Scoring Function
and Molecular Docking Program Devised by a Dynamic Sampling and Iteration
Optimization Strategy
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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 MpSDock<sub>Zn</sub>, an extensive comparison was performed against six universal, popular
docking programs: Glide XP mode, Glide SP mode, Gold, AutoDock, AutoDock4<sub>Zn</sub>, 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<sub>Zn</sub> had a competitive ability to sample and identify native
binding poses with a higher success rate than the other six docking
programs