GASS-Metal: identifying metal-binding sites on protein structures using genetic algorithms.

Vinícius A Paiva; Murillo V Mendonça; Sabrina A Silveira; David B Ascher; Douglas E V Pires; Sandro C Izidoro
Metals are present in >30% of proteins found in nature and assist them to perform important biological functions, including storage, transport, signal transduction and enzymatic activity. Traditional and experimental techniques for metal-binding site prediction are usually costly and time-consuming, making computational tools that can assist in these predictions of significant importance. Here we present Genetic Active Site Search (GASS)-Metal, a new method for protein metal-binding site prediction. The method relies on a parallel genetic algorithm to find candidate metal-binding sites that are structurally similar to curated templates from M-CSA and MetalPDB. GASS-Metal was thoroughly validated using homologous proteins and conservative mutations of residues, showing a robust performance. The ability of GASS-Metal to identify metal-binding sites was also compared with state-of-the-art methods, outperforming similar methods and achieving an MCC of up to 0.57 and detecting up to 96.1% of the sites correctly. GASS-Metal is freely available at The GASS-Metal source code is available at
ISSN 1477-4054
Published 20 Sep 2022
Volume 23
Issue 5
DOI 10.1093/bib/bbac178
Type Journal Article | Research Support, Non-U.S. Gov't