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

submitted on 17.07.2020, 15:45 and posted on 20.07.2020, 05:34 by José-Emilio Sánchez-Aparicio, Laura Tiessler-Sala, Lorea Velasco-Carneros, Lorena Roldán-Martín, Giuseppe Sciortino, Jean-Didier Maréchal

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.


Spanish MINECO (grant CTQ2017-87889-P)

Generalitat de Catalunya (2017SGR1323)

Spanish Ministerio de Ciencia, Innovación y Universidades (grant FPU18/05895)

University of the Basque Country (predoctoral grant PIF17/22)

Generalitat de Catalunya (grant 2020FI_B2_01000)

Regione Autonoma della Sardegna (grant RASSR79857)


Email Address of Submitting Author


Universitat Autònoma de Barcelona



ORCID For Submitting Author


Declaration of Conflict of Interest

There are no conflicts

Version Notes

Version 1.0


Read the published paper

in Journal of Chemical Information and Modeling