一个工作流程来创建一个高质量的蛋白质-连接体结合数据集用于培训,验证和预测任务
Yingze Wang1, Kunyang Sun1, Jie Li1
1Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California Berkeley CA 94720 USA.
概括
用于预测蛋白质-连接体结合能量的评分函数 (SF) 可能由于常见数据集的结构问题而不准确. 本研究引入了HiQBind-WF和HiQBind,以提高药物发现的数据质量和可靠性.
科学领域:
- 计算化学是一种计算化学.
- 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:
- 评分函数 (SFs) 对于预测蛋白质-连接体结合能量至关重要.
- 高质量的结构和具有约束力的测试数据对于开发准确的SFs至关重要.
- 像PDBbind这样的现有数据集包含影响SF性能的结构文物.
研究的目的:
- 为了解决蛋白质-配体数据集中的结构文物.
- 为策划这些数据集开发一个半自动化工作流程 (HiQBind-WF).
- 创建一个高质量的,独立的数据集 (HiQBind) 用于SF开发.
主要方法:
- 开发了一系列用于半自动化策划工作流程的算法.
- 从BioLiP,绑定MOAD和绑定DB与PDB结构中集成的数据.
- 实现了HiQBind-WF以固定蛋白质-连接体复合体中的结构工件.
主要成果:
- 在广泛使用的蛋白质 - 连接物数据集中确定了常见的结构文物.
- 通过使用HiQBind-WF.成功策划了非共价蛋白质-配体数据集.
- 创建了HiQBind数据集,提高了SF培训和测试的数据质量.
结论:
- HiQBind-WF 工作流改善了蛋白质 - 连接体数据集的准确性和可靠性.
- HiQBind数据集为开发和验证SF提供了一个更强大的资源.
- HiQBind的开源性质促进了药物发现研究的透明度和可重复性.
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