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Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins.

ABSTRACT: With more than a third of the genome encoding for metal-containing biomolecules, the in silico prediction of how metal ions bind to proteins is crucial in chemistry, biology, and medicine. To date, algorithms for metal-binding site prediction are mainly based on sequence analysis. Those methods have reached enough quality to predict the correct region of the protein and the coordinating residues involved in metal-binding, but they do not provide three-dimensional (3D) models. On the contrary, the prediction of accurate 3D models for protein-metal adducts by structural bioinformatics and molecular modeling techniques is still a challenge. Here, we present an update of our multipurpose molecular modeling suite, GaudiMM, to locate metal-binding sites in proteins. The approach is benchmarked on 105 X-ray structures with resolution lower than 2.0 Å. Results predict the correct binding site of the metal in the biological scaffold for all the entries in the data set. Generated 3D models of the protein-metal coordination complexes reach root-mean-square deviation values under 1.0 Å between calculated and experimental structures. The whole process is purely based on finding poses that satisfy metal-derived geometrical rules without needing sequence or fine electronic inputs. Additional post-optimizations, including receptor flexibility, have been tested and suggest that more extensive searches, required when the host structures present a low level of pre-organization, are also possible. With this new update, GaudiMM is now able to look for metal-binding sites in biological scaffolds and clearly shows how explicitly considering the geometric particularities of the first coordination sphere of the metal in a docking process provides excellent results.

SUBMITTER: Sciortino G

PROVIDER: S-EPMC6648054 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins.

Sciortino Giuseppe G Garribba Eugenio E Rodríguez-Guerra Pedregal Jaime J Maréchal Jean-Didier JD

ACS omega 20190219 2

With more than a third of the genome encoding for metal-containing biomolecules, the in silico prediction of how metal ions bind to proteins is crucial in chemistry, biology, and medicine. To date, algorithms for metal-binding site prediction are mainly based on sequence analysis. Those methods have reached enough quality to predict the correct region of the protein and the coordinating residues involved in metal-binding, but they do not provide three-dimensional (3D) models. On the contrary, th ...[more]

PMID: 31459585

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Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins.

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Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins.

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