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September 19, 2025Nature Methods47 citationsOpen Access

GPU-accelerated homology search with MMseqs2

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FKFelix KallenbornACAlejandro ChacónCHChristian Hundt

Key Points

  • MMseqs2 delivers a 6× speed improvement for single-protein searches compared to traditional CPU methods.
  • Using eight GPUs, MMseqs2 outperforms alternatives by 2.4-fold for larger batch queries, improving cost-effectiveness.
  • The tool accelerates protein structure prediction with ColabFold by 31.8× compared to the standard AlphaFold2 approach.
  • Protein structure searches with Foldseek are enhanced by 4–27×, highlighting significant efficiency gains.

Abstract

Abstract Rapidly growing protein databases demand faster sensitive search tools. Here the graphics processing unit (GPU)-accelerated MMseqs2 delivers 6× faster single-protein searches than CPU methods on 2 × 64 cores, speeds previously requiring large protein batches. For larger query batches, it is the most cost-effective solution, outperforming the fastest alternative method by 2.4-fold with eight GPUs. It accelerates protein structure prediction with ColabFold 31.8× over the standard AlphaFold2 pipeline and protein structure search with Foldseek by 4–27×. MMseqs2-GPU is available under an open-source license at https://mmseqs.com/ .

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Cite This Study

Kallenborn et al. (2025) studied this question.

synapsesocial.com/papers/68d466af31b076d99fa651c0https://doi.org/10.1038/s41592-025-02819-8
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