GPU-accelerated homology search with MMseqs2

2024/11/15 by Felix Kallenborn, Alejandro Chacón, Christian Hundt +7 · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Genomics and Phylogenetic Studies #Machine Learning in Bioinformatics

paper · pdf · doi:10.1101/2024.11.13.623350

openalex publication_date 2024/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Rapidly growing protein databases demand faster sensitive sequence similarity detection. We present GPU-accelerated search utilizing intra-query parallelization delivering 6x faster single-protein searches compared to state-of-the-art CPU methods on 2×64 cores—speeds previously requiring large protein batches. It is most cost effective, including in large-batches at 0.45x MMseqs2-CPU speed (8 GPUs delivering 2.4x). It accelerates ColabFold structure prediction 31.8x compared to AlphaFold2 and Foldseek search 4-27x. MMseqs2-GPU is open-source at mmseqs.com .

Citations

• Scoring function for automated assessment of protein structure template quality
• Sensitive protein alignments at tree-of-life scale using DIAMOND
• Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
• Highly accurate protein structure prediction with AlphaFold
• Accurate prediction of protein structures and interactions using a three-track neural network
• Critical assessment of methods of protein structure prediction (CASP)—Round XIV
• ColabFold: making protein folding accessible to all
• Fast and accurate protein structure search with Foldseek
• Accurate structure prediction of biomolecular interactions with AlphaFold 3

Cited by

• Assessing the validity of leucine zipper constructs predicted by AlphaFold

Discussions

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