Abstract

ColabFold 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.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com.

ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.

Details

Title
ColabFold: making protein folding accessible to all
Author
Mirdita, Milot 1   VIAFID ORCID Logo  ; Schütze, Konstantin 2   VIAFID ORCID Logo  ; Moriwaki, Yoshitaka 3   VIAFID ORCID Logo  ; Heo, Lim 4   VIAFID ORCID Logo  ; Ovchinnikov, Sergey 5   VIAFID ORCID Logo  ; Steinegger, Martin 6   VIAFID ORCID Logo 

1  Max Planck Institute for Multidisciplinary Sciences, Quantitative and Computational Biology, Göttingen, Germany 
2  Seoul National University, School of Biological Sciences, Seoul, South Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905) 
3  The University of Tokyo, Department of Biotechnology, Graduate School of Agricultural and Life Sciences, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2169 1048); The University of Tokyo, Collaborative Research Institute for Innovative Microbiology, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2169 1048) 
4  Michigan State University, Department of Biochemistry and Molecular Biology, East Lansing, USA (GRID:grid.17088.36) (ISNI:0000 0001 2195 6501) 
5  Harvard University, JHDSF Program, Cambridge, USA (GRID:grid.38142.3c) (ISNI:0000 0004 1936 754X); Harvard University, FAS Division of Science, Cambridge, USA (GRID:grid.38142.3c) (ISNI:0000 0004 1936 754X) 
6  Seoul National University, School of Biological Sciences, Seoul, South Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University, Artificial Intelligence Institute, Seoul, South Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University, Institute of Molecular Biology and Genetics, Seoul, South Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905) 
Pages
679-682
Publication year
2022
Publication date
Jun 2022
Publisher
Nature Publishing Group
ISSN
15487091
e-ISSN
15487105
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41592-022-01488-1
ProQuest document ID
2674579182
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.