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可准口袋的人类总体
Kristy A Carpenter1, Russ B Altman2,3,4,5
1Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
Journal of cheminformatics
|December 25, 2025
概括
我们创建了可定位口袋 (HOTPocket) 的人类总体数据集,包含在人类蛋白质组中超过240万个预测的连接体结合口袋. 一种新的评分方法,热口袋NN,有效地识别可使用药物的口袋.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 药物发现 药物发现
背景情况:
- 预测连接体结合口袋对于药物发现至关重要,但缺乏全面的人类蛋白质组范围的数据集.
- 现有的口袋寻找方法有局限性,在特定的用例中表现出色.
- 结合多种策略的合并方法为大规模,多样化的口袋预测提供了潜力.
研究的目的:
- 创建一个全面的,人类蛋白质组的数据集,预测的连接体结合口袋.
- 开发和验证一种新的口袋评分方法,用于识别可使用药物的口袋.
- 为研究界提供免费可用的资源.
主要方法:
- 在PDB和AlphaFold2结构上使用七种口袋寻找方法组装了可定位口袋 (HOTPocket) 人类全方位数据集.
- 应用了一种新的评分方法,热口袋NN,过和策划超过240万预测口袋.
- 与已知的口袋和基准数据集 (Astex Diverse Set,PoseBusters) 相比验证的热口袋NN.
主要成果:
- 该HOTPocket数据集包含了在人类蛋白质组中超过240万个预测口袋.
- 热口袋NNN成功地恢复了已知的连接体结合口袋,包括新的口袋.
- 在精度评估中,hotpocketNN在精度评估中超过了P2Rank和Fpocket等组成方法.
- 在KRAS和mu阿片类受体中确定了可药物的口袋.
结论:
- 热口袋数据集和热口袋NN方法代表了蛋白质组范围内的可药物口袋识别的重大进步.
- 热口袋NN在预测可吸毒口袋方面表现出卓越的性能和通用性.
- 这些自由可用的资源将通过提供全面的口袋信息来加速药物发现工作.
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