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<p>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 <a href="https://mmseqs.com/">https://mmseqs.com/</a>.</p>

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GPU-accelerated homology search with MMseqs2

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月18日

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  • Felix Kallenborn,
  • Alejandro Chacon,
  • Christian Hundt,
  • Hassan Sirelkhatim,
  • Kieran Didi,
  • Sooyoung Cha,
  • Christian Dallago,
  • Milot Mirdita,
  • Bertil Schmidt,
  • Martin Steinegger

摘要

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/.