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

Autor: Paiva VA; Department of Computer Science, Universidade Federal de Viçosa, Viçosa, Brazil., Mendonça MV; Institute of Technological Sciences, Campus Theodomiro Carneiro Santiago, Universidade Federal de Itajubá, Itabira, Brazil., Silveira SA; Department of Computer Science, Universidade Federal de Viçosa, Viçosa, Brazil., Ascher DB; School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland, Australia.; Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.; Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia., Pires DEV; Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.; School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia., Izidoro SC; Institute of Technological Sciences, Campus Theodomiro Carneiro Santiago, Universidade Federal de Itajubá, Itabira, Brazil.
Jazyk: angličtina
Zdroj: Briefings in bioinformatics [Brief Bioinform] 2022 Sep 20; Vol. 23 (5).
DOI: 10.1093/bib/bbac178
Abstrakt: 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.
(© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje