微EnvPPI:微环境意识优化使得可通用的蛋白质-蛋白质相互作用预测成为可能
Kun Yang1, Yifan Chen2, Yanshi Wei1
1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China.
Journal of chemical information and modeling
|October 24, 2025
概括
通过考虑残留微环境,MicroEnvPPI改善了蛋白质与蛋白质相互作用的预测. 这一框架提高了确定细胞功能网络和治疗点的准确性和通用性.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对细胞功能和药物发现至关重要.
- 像AlphaFold这样的现有模型可能错过了残留级微环境细节,限制了PPI预测准确度.
- 了解这些微环境是推动PPI推断的关键.
研究的目的:
- 引入MicroEnvPPI,这是一个用于增强蛋白质-蛋白质相互作用预测的新型框架.
- 通过关注残留微环境,提高PPI预测的准确性和通用性.
- 利用结构和上下文信息进行全面的微环境表征.
主要方法:
- 将残留物的物理化学特征和ESM-2嵌入物与AlphaFold预测的结构整合起来.
- 采用辅助任务,包括图形对比学习和掩盖,以优化微环境表示.
- 在MicroEnvPPI框架内共同培训全球PPI预测和微环境优化任务.
主要成果:
- 在PPI预测中,MicroEnvPPI表现出更好的准确性和通用性.
- 该模型在具有挑战性的数据分割 (DFS,BFS) 上表现强,表明对新型相互作用的强烈概括.
- 对残留微环境的全面描述提高了预测能力.
结论:
- MicroEnvPPI提供了一种强大的方法来推进蛋白质-蛋白质相互作用网络分析.
- 该框架对微环境的重点显著改善了PPI预测.
- 这项工作对理解细胞网络和识别新的治疗点有意义.
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