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协价:可解释和歧视的集体变量揭示了人体细胞中依赖质的切换 - - 视网醇结合蛋白2

Myongin Oh¹, Changin Oh², Eshra Tabassum¹

¹Department of Chemistry, Faculty of Science, Memorial University of Newfoundland, 45 Arctic Ave, St. John's, Newfoundland A1C 5S7, Canada.

Journal of chemical theory and computation

|November 25, 2025

概括

新的机器学习管道Covalent从分子动力学数据中发现可解释的集体变量. 这种方法揭示了蛋白质关门开关和联体诱导的稳定机制,推进了生物分子模拟.

科学领域:

生物分子模拟的模拟.
机器学习用于结构生物学.
计算生物物理学的计算生物物理.

背景情况:

识别集体变量 (CV) 对于加强生物分子系统的采样和机制分析至关重要.
现有的方法往往难以找到既有歧视性又可解释性的简历.
这一挑战限制了从分子动力学 (MD) 模拟中获得的机械洞察力.

研究的目的:

介绍Covalent,一种新的监督机器学习管道,用于发现可解释的集体变量 (CV).
将Covalent应用于人类细胞视网醇结合蛋白II (CRBP2) 以了解其结构动态和连接体相互作用.
为了证明Covalent能够生成物理透明的CVs以进行机械解释.

主要方法:

协同使用监督机器学习方法,结合过器-包装器-替换功能道.
它使用了和线性差异分析 (GDHLDA) 和后 hoc子空间旋转的 Riemannian 优化的变体.
该管道在阿波,视网醇结合和2-劳洛伊尔甘油 (2-LaG) 结合状态下的CRBP2的MD轨迹上进行了测试.

主要成果:

协价识别了具有明确机械解释的线性CV,在类分离性方面表现优于主要组件分析.
学习的CV突出显示了关门开关,带诱导的Ser76稳定,以及CRBP2腔周围的重新排列.
分析显示了连接体特异性相互作用切换和内部空隙体积的减少,表明连接体结合时更紧密的包装.

结论:

协同提供了一种实用的方法,可以从MD数据中发现物理透明的简历.
鉴定到的CV提供了对蛋白质动力学和连接体结合的有价值的机制性见解.
这种方法成功地证明了CRBP2的全态状态不是预先组织的,而是需要联体诱导的稳定.

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