蛋白质结构预测和设计用于高吞吐量计算
Vinay Saji Mathew1,2, Gretta D Kellogg2,3, William Km Lai4,5
1Department of Industrial Engineering, Pennsylvania State University, University Park, PA 16802, USA.
bioRxiv : the preprint server for biology
|August 8, 2025
概括
现在可用于蛋白质结构预测工具的容器化解决方案,如AlphaFold3和RFdiffusion,使先进的分子生物学可访问. 这些工具提高了计算效率,加速了合成生物学和治疗学的科学发现.
科学领域:
- 分子生物学分子生物学
- 结构生物学 结构生物学
- 计算生物学 计算生物学
背景情况:
- 机器学习和结构生物学最近的进步导致了强大的蛋白质结构预测和设计工具.
- 像AlphaFold3,Chai-1,Boltz-2和RFdiffusion这样的工具可以在原子分辨率下建模蛋白质结构和复合体.
- 这些工具有可能彻底改变合成生物学和治疗开发.
研究的目的:
- 解决采用先进蛋白质结构预测和设计算法的障碍.
- 开发用于AlphaFold3,Chai-1,Boltz-2和RF扩散的容器化解决方案.
- 为研究人员提高这些工具的可访问性和计算效率.
主要方法:
- 开发了针对AlphaFold3,Chai-1,Boltz-2和RFdiffusion的容器化解决方案,并对各种架构 (x86,ARM) 进行了优化.
- 创建了OmniFold,这是一个优化的包装平台,可以同时执行AlphaFold3,Chai-1和Boltz-2的GPU,并有效地利用它们.
- 提供预编译的容器和定义文件作为开源在Sylabs和GitHub.
主要成果:
- 成功容器化关键蛋白质结构预测和设计工具.
- 针对各种计算架构的优化容器化解决方案.
- 开发了OmniFold平台,以提高多个预测工具的性能和可用性.
- 将容器化解决方案公开提供,以促进更广泛的采用.
结论:
- 容器化解决方案和OmniFold平台显著提高了先进的蛋白质结构预测和设计工具的可访问性和可用性.
- 这些发展降低了计算障碍,促进了可重现性,加速了分子生物学中的科学发现.
- 开源的可用性促进了合成生物学和治疗研究的更广泛的采用和创新.
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