GASS-Metal: identifying metal-binding sites on protein structures using genetic algorithms. Issue 5 (21st May 2022)

• Record Type: Journal Article
• Authors: Paiva, Vinícius A; Mendonça, Murillo V; Silveira, Sabrina A; Ascher, David B; Pires, Douglas E V; Izidoro, Sandro C
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 5(2022)
• Publication Date: 2022-05-21
• Subjects: metalloprotein -- metal coordination site -- metal-binding site -- metal-binding site prediction -- metal ion -- genetic algorithms; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac178
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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 https://gassmetal.unifei.edu.br . The GASS-Metal source code is available at https://github.com/sandroizidoro/gassmetal-local .