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

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Bibliographic Details
Title: GASS-Metal: identifying metal-binding sites on protein structures using genetic algorithms.
Authors: 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.
Source: Briefings in bioinformatics [Brief Bioinform] 2022 Sep 20; Vol. 23 (5).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
Database: MEDLINE Ultimate
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Description
ISSN:1477-4054
DOI:10.1093/bib/bbac178