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In combination with its efficient confidence ranking, this unlocks elevated modeling capabilities for monomeric structures and foremost for protein assemblies. However, the approach struggles with GPU cost and data storage. Here we introduce MassiveFold, an optimized and customizable version of AlphaFold that runs predictions in parallel, reducing the computing time from several months to hours. MassiveFold is scalable and able to run on anything from a single computer to a large GPU infrastructure, where it can fully benefit from all the computing nodes.<\/jats:p>","DOI":"10.1038\/s43588-024-00714-4","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T05:02:18Z","timestamp":1731301338000},"page":"824-828","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["MassiveFold: unveiling AlphaFold\u2019s hidden potential with optimized and parallelized massive sampling"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5652-684X","authenticated-orcid":false,"given":"Nessim","family":"Raouraoua","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7868-034X","authenticated-orcid":false,"given":"Claudio","family":"Mirabello","sequence":"additional","affiliation":[]},{"given":"Thibaut","family":"V\u00e9ry","sequence":"additional","affiliation":[]},{"given":"Christophe","family":"Blanchet","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3772-8279","authenticated-orcid":false,"given":"Bj\u00f6rn","family":"Wallner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3957-9470","authenticated-orcid":false,"given":"Marc F.","family":"Lensink","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6807-6621","authenticated-orcid":false,"given":"Guillaume","family":"Brysbaert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"714_CR1","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583\u2013589 (2021).","journal-title":"Nature"},{"key":"714_CR2","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1002\/prot.26237","volume":"89","author":"A Kryshtafovych","year":"2021","unstructured":"Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)-Round XIV. Proteins 89, 1607\u20131617 (2021).","journal-title":"Proteins"},{"key":"714_CR3","doi-asserted-by":"publisher","first-page":"D439","DOI":"10.1093\/nar\/gkab1061","volume":"50","author":"M Varadi","year":"2022","unstructured":"Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439\u2013D444 (2022).","journal-title":"Nucleic Acids Res."},{"key":"714_CR4","doi-asserted-by":"publisher","unstructured":"Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https:\/\/doi.org\/10.1101\/2021.10.04.463034 (2021).","DOI":"10.1101\/2021.10.04.463034"},{"key":"714_CR5","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1002\/prot.26609","volume":"91","author":"MF Lensink","year":"2023","unstructured":"Lensink, M. F. et al. Impact of AlphaFold on structure prediction of protein complexes: the CASP15-CAPRI experiment. Proteins 91, 1658\u20131683 (2023).","journal-title":"Proteins"},{"key":"714_CR6","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1002\/prot.26617","volume":"91","author":"A Kryshtafovych","year":"2023","unstructured":"Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)-Round XV. Proteins 91, 1539\u20131549 (2023).","journal-title":"Proteins"},{"key":"714_CR7","doi-asserted-by":"publisher","first-page":"959160","DOI":"10.3389\/fbinf.2022.959160","volume":"2","author":"I Johansson-\u00c5khe","year":"2022","unstructured":"Johansson-\u00c5khe, I. & Wallner, B. Improving peptide-protein docking with AlphaFold-Multimer using forced sampling. Front. Bioinform. 2, 959160 (2022).","journal-title":"Front. Bioinform."},{"key":"714_CR8","doi-asserted-by":"publisher","first-page":"btad573","DOI":"10.1093\/bioinformatics\/btad573","volume":"39","author":"B Wallner","year":"2023","unstructured":"Wallner, B. AFsample: improving multimer prediction with AlphaFold using massive sampling. Bioinform. Oxf. Engl. 39, btad573 (2023).","journal-title":"Bioinform. Oxf. Engl."},{"key":"714_CR9","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1002\/prot.26562","volume":"91","author":"B Wallner","year":"2023","unstructured":"Wallner, B. Improved multimer prediction using massive sampling with AlphaFold in CASP15. Proteins 91, 1734\u20131746 (2023).","journal-title":"Proteins"},{"key":"714_CR10","doi-asserted-by":"publisher","first-page":"e4865","DOI":"10.1002\/pro.4865","volume":"33","author":"R Yin","year":"2024","unstructured":"Yin, R. & Pierce, B. G. Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy. Protein Sci. Publ. Protein Soc. 33, e4865 (2024).","journal-title":"Protein Sci. Publ. Protein Soc."},{"key":"714_CR11","doi-asserted-by":"publisher","first-page":"102645","DOI":"10.1016\/j.sbi.2023.102645","volume":"81","author":"D Sala","year":"2023","unstructured":"Sala, D., Engelberger, F., Mchaourab, H. S. & Meiler, J. Modeling conformational states of proteins with AlphaFold. Curr. Opin. Struct. Biol. 81, 102645 (2023).","journal-title":"Curr. Opin. Struct. 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