BioMetAll v2.0: Introducing Scores, Metal Discrimination, and Side-Chain Descriptors for Predicting Metal-Binding Sites in Proteins.

Authors

Marechal, J. D., Fernandez Diaz, R., Pena Losada, R., Sanchez Aparicio, J. E., Gao, W., Alemany, M.

Abstract

Predicting the location of metal-binding sites in proteins is crucial for fundamental biological questions and biotechnological applications. Over the past decade, the rise in metal-bound protein structures in the Protein Data Bank, combined with advanced statistical models such as deep learning, has accelerated the development of metal-binding site prediction tools. Several approaches are now available, offering high-quality benchmarks and predictive performance. Our initial development in this area is BioMetAll, whose first version was based on backbone pre-organization. Here, we introduce its second version, featuring two major updates: 1) metal-specific scoring functions and 2) prediction using backbone geometry alone or in combination with first coordination sphere descriptors. Apart from demonstrating metal sensitivity and yielding better benchmarking results, this new version allows the assessment of the influence of considering the metal first coordination sphere versus backbone pre-organization on how metallic species bind to proteins.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 14 Jul 2026.

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