一个基于变压器的整体框架,用于预测蛋白质-蛋白质相互作用地点
Minjie Mou1, Ziqi Pan1, Zhimeng Zhou1
1College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
Research (Washington, D.C.)
|September 29, 2023
概括
确定蛋白质与蛋白质相互作用 (PPI) 位点对于药物发现至关重要. 一个新的整体框架,EnsemPPIS,使用序列信息准确预测PPI站点,优于现有方法.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 结构生物学是结构生物学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对细胞功能至关重要,是药物发现的关键目标.
- 目前用于识别PPI站点的计算方法往往缺乏准确性或适用性有限,严重依赖结构数据.
- 需要采用整合多样信息的方法,以准确,全蛋白质组的PPI部位预测.
研究的目的:
- 开发一个新的整体框架,EnsemPPIS,用于准确预测蛋白质-蛋白质相互作用地点.
- 通过集成基于序列的全球,本地和残留物相互作用特征来克服现有方法的局限性.
- 提供一个广泛适用的工具,用于全蛋白质组的PPI位点识别.
主要方法:
- 开发 EnsemPPIS,一个使用变压器和封闭卷积网络的整体框架.
- 使用变压器网络直接从蛋白质序列中提取残留相互作用.
- 综合全球和本地顺序特征,使用集体学习策略.
主要成果:
- 与现有方法相比,EnsemPPIS在多个PPI站点预测任务中表现出卓越的性能和更广泛的适用性.
- 模式分析证实了EnsemPPIS能够仅从序列信息中学习残留物相互作用的能力.
- 该方法有效地捕捉了全球/本地模式和复杂的残留物相互作用.
结论:
- EnsemPPIS提供了一个准确且广泛适用的解决方案,用于仅使用序列数据识别蛋白质-蛋白质相互作用位点.
- 该框架从序列信息中学习的能力在药物发现和蛋白质功能研究中推进了计算方法.
- EnsemPPIS为PPI位点的全蛋白质组分析提供了一个有价值的工具.
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