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MassiveFold: unveiling AlphaFold’s hidden potential with optimized and parallelized massive sampling

2024/04/30 by Nessim Raouraoua, Claudio Mirabello, Thibaut Véry +6 · 1 voice
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Biofuel production and bioconversion #Enzyme Structure and Function #Protein Structure and Dynamics

paper · pdf · doi:10.21203/rs.3.rs-4319486/v1

crossref issued 2024/04/30 · crossref published 2024/04/30 · openalex publication_date 2024/04/30 · crossref created 2024/04/30 · crossref deposited 2024/11/12 · openalex created_date 2025/10/10 · crossref indexed 2026/04/11 · openalex updated_date 2026/07/25

Abstract

Abstract Massive sampling in AlphaFold enables access to increased structural diversity; in combination with its powerful 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. MassiveFold removes these restraints as an optimized and customizable version of AlphaFold that runs predictions in parallel, offering the full benefit of enhanced sampling for protein structure and assembly modeling.

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