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Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit

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阿尔法速:通过GPU加速的MSA构建实现高通量AlphaFold 3.

Benjamin C Perry, Jeonghyeon Kim, Philip A Romero

bioRxiv : the preprint server for biology

|February 27, 2026

概括

此摘要是机器生成的。

通过使用GPU加速搜索优化多个序列对齐 (MSA) 生成,AlphaFast显著加速生物分子建模. 这种框架大大减少了准确的蛋白质结构预测的运行时间和成本.

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科学领域:

计算生物学是一种计算生物学.
结构生物学是结构生物学.
生物信息学是一种生物信息学.

背景情况:

准确的生物分子建模对于生物研究至关重要.
AlphaFold 3 (AF3) 提供了高质量的预测,但由于缓慢的多重序列对齐 (MSA) 产生而受到阻碍.
由于MSA的CPU-bound性质限制了AF3.3的速度和可扩展性.

研究的目的:

开发一种更快的MSA生成方法来克服AlphaFold 3的局限性.
为了减少AlphaFold 3的工作流中的计算瓶.
为了实现快速和经济有效的蛋白质结构预测.

主要方法:

将GPU加速的MMseqs2序列搜索集成到一个名为AlphaFast的下降框架中.
使用单个GPU进行MSA构建和端到端运行时分析.
在多个GPU上部署,用于高吞吐量结构预测.

主要成果:

在MSA建设中实现了68.5倍的加速度.
在单个GPU上减少了22.8x的端到端运行时间.
启用了每次输入8秒的结构预测,使用四个GPU保持精度.
证明了成本效益高的无服务器部署,每输入0.035美元.

结论:

在AlphaFold 3中,AlphaFast有效地消除了MSA瓶.
该框架为生物分子建模提供了可观的加速和成本降低.
AlphaFast使精确的蛋白质结构预测更容易获得和更有效.