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Date: | 2018 |
Abstract: | In this article, we present a new approach to expand the range of application of protein-ligand docking methods in the prediction of the interaction of coordination complexes (i. e. , metallodrugs, natural and artificial cofactors, etc. ) with proteins. To do so, we assume that, from a pure computational point of view, hydrogen bond functions could be an adequate model for the coordination bonds as both share directionality and polarity aspects. In this model, docking of metalloligands can be performed without using any geometrical constraints or energy restraints. The hard work consists in generating the convenient atom types and scoring functions. To test this approach, we applied our model to 39 high-quality X-ray structures with transition and main group metal complexes bound via a unique coordination bond to a protein. This concept was implemented in the protein-ligand docking program GOLD. The results are in very good agreement with the experimental structures: the percentage for which the RMSD of the simulated pose is smaller than the X-ray spectra resolution is 92. 3% and the mean value of RMSD is < 1. 0 Å. Such results also show the viability of the method to predict metal complexes-proteins interactions when the X-ray structure is not available. This work could be the first step for novel applicability of docking techniques in medicinal and bioinorganic chemistry and appears generalizable enough to be implemented in most protein-ligand docking programs nowadays available. |
Grants: | Ministerio de Economía y Competitividad CTQ2014-54071-P Agència de Gestió d'Ajuts Universitaris i de Recerca 2014/SGR-989 |
Note: | Altres ajuts: COST Action CM1306 |
Rights: | Tots els drets reservats. |
Language: | Anglès |
Document: | Article ; recerca ; Versió acceptada per publicar |
Subject: | Coordination chemistry and metal complexes ; Protein binding site prediction ; Protein-ligand docking |
Published in: | Journal of Computational Chemistry, Vol. 39, Issue 1 (January 2018) , p. 42-51, ISSN 1096-987X |
Postprint 27 p, 1.8 MB |