MassiveFold: unveiling AlphaFold’s hidden potential with optimized and parallelized massive sampling
Year: 2024
Type: article
Abstract: Massive sampling in AlphaFold enables access to increased structural diversity. 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.
Source: Nature Computational Science
Institutions: Centre National de la Recherche Scientifique, Université de Lille, Unité de Glycobiologie Structurale et Fonctionnelle, Linköping University, Science for Life Laboratory, National Bioinformatics Infrastructure Sweden, Université Paris-Saclay, Institut du développement et des ressources en informatique scientifique, Inserm, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Institut Français de Bioinformatique
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,290
OpenAlex ID: W4404224798