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.

Unveiling VIVO2+ Binding Modes to Human Serum Albumins by an Integrated Spectroscopic–Computational Approach

“…Starting from the spectroscopic information, the interaction with V IV O 2+ and HSA d was examined with a multiscale computational approach: i) a complete model for the defatted albumin adding the last five N‐terminal missing amino acids to the X‐ray structure PDB 1AO6 of the free HSA d structure was generated; ii) the model was minimized and subsequently 100 ns of molecular dynamics (MD) were carried out to relax the new portion introduced (the RMSD plots of the trajectories are shown in Figures S13 and S14 in the Supporting Information); iii) the MD trajectory was clustered and the representative structures of each pull of snapshots probed for zones in which at least two histidine residues featured α‐ and β‐carbons within a range of distances from each queried grid point of 3.4–7.2 and 2.6–6.4 Å, respectively; iv) the models that satisfied these criteria were further evaluated with docking calculations; v) docking solutions were refined at full quantum mechanics (QM) level of theory and this step was followed by density functional theory (DFT) simulations of the EPR HFC constants A z , which were compared with the experimental ones.…”

“…The implemented clustering algorithm was used to generate ten clusters taking the RMSD between frames as the distance metric. The representative structure of the most populated cluster was used in the further structural analysis in which each structure was probed for the zones with at least two potential coordinating residues (Asp, Glu, Asn, Gln, and His) using multisite.py …”

Biospeciation of Potential Vanadium Drugs of Acetylacetonate in the Presence of Proteins

“…To identify potential binding sites, the protein space was first probed with multisite.py for zones where at least two potential coordinating residues (Asp, Glu, Asn, Gln, and His) featured an α-and a β-carbon within a range of distances from each queried grid point of 2.6-6.4 and 3.4-7.2 Å, respectively (Sciortino et al, 2019b). Both the protonation states at δ and ε nitrogens of His imidazole ring were taken into account during the simulations.…”

Chemoinformatic Resources for Organometallic Drug Discovery

“…In some instances, "simple" 2D descriptors can be enough to obtain a useful and predictive model. In other instances, more accurate descriptors are required to capture the molecular shape and 3D information for explaining and/or predicting biological activity or assessing metal-binding sites in proteins [17].…”