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用GPU加速的同质性搜索使用MMseqs2

Felix Kallenborn¹, Alejandro Chacon², Christian Hundt²

¹Department of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.

|September 19, 2025

概括

图形处理单元 (GPU) 加速的MMseqs2显著加快了蛋白质数据库搜索和结构预测. 该工具为大规模的生物数据分析提供了具有成本效益的解决方案,提高了研究效率.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
结构生物学结构生物学

背景情况:

蛋白质数据库正在迅速扩大,需要更快,更敏感的搜索工具.
当前的计算方法难以跟上日益增长的生物数据量.

研究的目的:

介绍和评估图形处理器 (GPU) 加速的MMseqs2工具的性能.
为了证明MMseqs2-GPU在蛋白质序列和结构分析方面的效率增长.

主要方法:

对单蛋白搜索的基于CPU的方法进行MMseqs2-GPU的比较.
评估使用多个GPU进行大型查询批量搜索的MMseqs2-GPU.
评估MMseqs2-GPU对蛋白质结构预测 (ColabFold) 和结构搜索 (Foldseek) 管道的影响.

主要成果:

与CPU方法相比,MMseqs2-GPU实现了6倍更快的单蛋白搜索.
对于大批量,MMseqs2-GPU比使用八个GPU的领先替代品快2.4倍.
加速了 ColabFold 的 31.8 倍和 Foldseek 的 4-27 倍.

结论:

用GPU加速的MMseqs2为蛋白质数据库的搜索和分析提供了相当大的加速.
该工具提高了关键生物信息学任务的效率,例如结构预测和搜索.
MMseqs2-GPU为现代生物研究提供了具有成本效益和强大的解决方案.

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