BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

Abstract

With a large amount of research dedicated to decoding how metallic species bind to protein, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal binding templates.These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parametrization from a statistical analysis.Then, the successful benchmark of BioMetAll on a set of more than 50 metal-binding X-Ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems which structures (either experimental or theoretical) are not optimal for metal binding sites. We report here its application on three different challenging cases i) the modulation of metal-binding sites during conformational transition in human serum albumin, ii) the identification of possible routes of metal migration in hemocyanins, and iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology, and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding. File list (2)download file view on ChemRxiv BioMetAll.pdf (20.92 MiB) download file view on ChemRxiv BioMetAll_SI.pdf (5.62 MiB)

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications

“…Identification of cavities followed by their validation against pre-defined geometric patterns of the protein backbone [46] N.A.…”

“…BioMetAll expands upon the concept incorporated as part of the GaudiMM suite (see the preceding paragraph) by making the assumption that the geometric patterns of the protein backbone permit the identification of preorganized MBSs [ 46 ]. The structural analysis of the conformation of the backbone, instead of the side chains, should make the predictions less dependent on the high quality of the structure and also on the metal-induced reorganization of the site, which often does not greatly affect the protein backbone [ 47 ].…”

Learning to Identify Physiological and Adventitious Metal-Binding Sites in the Three-Dimensional Structures of Proteins by Following the Hints of a Deep Neural Network

“…At the sequence level, the conservation pattern of Cys and His residues are crucial. Information about conservation of Cys and His, along with Asp and Glu, has been extensively used for the sequence-based prediction of the occurrence of metal-binding sites. − Intriguingly, information on Asp and Glu did not have a significant impact on the performance of the neural classifier, whereas the conservation of Asn played some role. The latter finding is difficult to rationalize: the only indication we have is that Asn is about 1.6 times more common in the second sphere of physiological sites than adventitious sites.…”

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

“…GaudiMM [ 150 ] adopted a multiobjective genetic algorithm to search metal-binding sites in biological scaffolds. BioMetAll focused on the conformation of the potential metal-binding site, associated with the geometric organization of the protein backbone [ 151 ]. It was also proved to have good performance on the applications including the modulation and mutation of the metal-binding residues.…”