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ColabFold: making protein folding accessible to all

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dc.contributor.authorMirdita, Milot-
dc.contributor.authorSchutze, Konstantin-
dc.contributor.authorMoriwaki, Yoshitaka-
dc.contributor.authorHeo, Lim-
dc.contributor.authorOvchinnikov, Sergey-
dc.contributor.authorSteinegger, Martin-
dc.date.accessioned2022-09-30T06:04:57Z-
dc.date.available2022-09-30T06:04:57Z-
dc.date.created2022-06-27-
dc.date.created2022-06-27-
dc.date.issued2022-06-
dc.identifier.citationNature Methods, Vol.19 No.6, pp.679-682-
dc.identifier.issn1548-7091-
dc.identifier.urihttps://hdl.handle.net/10371/185150-
dc.description.abstractColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.cpm/sokrypton/colabfold and its novel environmental databases are available at https://colab-fold.mmseqs.com.-
dc.language영어-
dc.publisherNature Publishing Group-
dc.titleColabFold: making protein folding accessible to all-
dc.typeArticle-
dc.identifier.doi10.1038/s41592-022-01488-1-
dc.citation.journaltitleNature Methods-
dc.identifier.wosid000802918900005-
dc.identifier.scopusid2-s2.0-85131047515-
dc.citation.endpage682-
dc.citation.number6-
dc.citation.startpage679-
dc.citation.volume19-
dc.description.isOpenAccessY-
dc.contributor.affiliatedAuthorSteinegger, Martin-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordPlusPREDICTION-
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Related Researcher

  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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