Prop3D:一个灵活的,基于Python的机器学习平台,具有蛋白质结构性质和生物物理数据
Eli J Draizen1,2, John Readey3, Cameron Mura4,5
1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA. edraizen@gmail.com.
BMC bioinformatics
|January 4, 2024
概括
Prop3D是一个新的平台,用于创建和共享机器学习 (ML) 的特色蛋白质域数据集. 它提供了Prop3D-20sf数据集,帮助可重复的ML管道开发,同时防止数据泄露.
科学领域:
- 结构生物信息学 结构生物信息学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML),特别是深度学习,正在通过揭示生物分子数据中的复杂关系来推进结构生物信息学.
- 高质量,可访问的数据集对于ML至关重要,但其创建具有挑战性,特别是对于复杂的3D蛋白质结构.
- 可重复的培训和测试数据集对于在结构生物信息学中可靠的ML模型开发至关重要.
研究的目的:
- 介绍Prop3D,一个创建,共享和重复使用特色蛋白质域库的平台.
- 提供来自CATH的Prop3D-20sf数据集,用于机器学习应用.
- 促进在结构生物信息学中开发可靠和可重复的ML管道.
主要方法:
- 开发了Prop3D框架,用于使用高度可扩展数据服务 (HSDS) 创建系统和可重复的数据集.
- 特色蛋白质域具有不同的生物物理和进化性质,从原子细节到残留水平的保护.
- 在云和HPC资源上部署了Prop3D框架,用于可扩展的数据生成.
主要成果:
- 通过Prop3D,可以创建和共享具有丰富功能集的蛋白质域库.
- 现在可以使用Prop3D-20sf数据集,其中包含来自20个CATH家族的特色蛋白质域.
- 数据集可以通过公共HSDS实例访问,并通过Python包装器与流行的ML框架兼容.
结论:
- 通过HSDS,Prop3D提供了一个可定制的工作流,用于通过HSDS在云平台上生成可扩展的数据集.
- 该Prop3D-20sf数据集加速了ML管道的开发,并确保了可重复性.
- 在Prop3D-20sf中数据集的构建解决了来自蛋白质进化关系的数据泄露问题.
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