通过绑定站点描述器全面检测和表征人类可用药物的口袋
Arnau Comajuncosa-Creus1, Guillem Jorba1, Xavier Barril2,3
1Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
Nature communications
|September 10, 2024
概括
PocketVec使用反向虚拟选来识别可用药物的蛋白质口袋. 这种方法揭示了结合点之间的新奇相似之处,有助于药物发现和化学探针开发.
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.
- 药物发现 药物发现
背景情况:
- 鉴定药物可用口袋对于基于目标的药物发现至关重要.
- 目前用于导出口袋描述符的现有方法在适用性和范围上面临限制.
研究的目的:
- 介绍PocketVec,一种用于生成可使用药物的口袋描述器的新方法.
- 系统地识别和描述人类蛋白质组中的可用药物口袋.
- 发现超越传统结构或基于序列的方法的新口袋相似之处.
主要方法:
- PocketVec通过对类分子的反向虚拟选生成口袋描述符.
- 利用实验确定的蛋白质结构和AlphaFold2模型进行全蛋白质组的口袋识别.
- 在超过12亿对对口袋比较中进行了大规模的相似性搜索.
主要成果:
- 在克服关键限制的同时,PocketVec表现出与领先的方法相匹配的性能.
- 在20,000个人类蛋白质域内确定了超过32,000个可药物结合的结合点.
- 发现了与常规方法无法检测到的显著可用药物的口袋相似性,包括在没有结晶抑制剂的蛋白质中.
结论:
- PocketVec提供了一种强大的方法,用于药用口袋的表征和分析.
- 整个蛋白质组的分析揭示了广泛的可药性口袋相似性,扩大了对可药性景观的理解.
- 这些发现有助于优先考虑开发化学探针,以探索新的治疗点.
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