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蛋白质的温度依赖结构组合的深度生成建模

Giacomo Janson¹, Alexander Jussupow¹, Michael Feig¹

¹Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.

bioRxiv : the preprint server for biology

|March 31, 2025

概括

深度学习模型现在可以高效地生成原子化蛋白质合集. 一个新的模型,aSAMt,捕捉了温度依赖的蛋白质动态,并将其推广到训练条件之外.

科学领域:

计算生物学计算生物学
结构生物学结构生物学
人工智能在生物化学中的应用

背景情况:

深度学习推进了蛋白质结构预测,但与结构合集和可变性作斗争.
分子动力学 (MD) 模拟生物分子动力学,但在计算上是密集的.
目前对合奏的深度学习模型缺乏原子化的细节和环境因素的整合.

研究的目的:

开发一种深度学习模型,用于生成原子化的蛋白质构成组合.
创建一个包含环境因素,特别是温度的可转移模型.
为了提高模拟蛋白质动态和热行为的效率和准确性.

主要方法:

引入了aSAM (原子结构自编码模型),这是一个在MD数据上训练的潜在扩散模型.
开发了aSAMt,aSAM的扩展,作为一个温度调节的可转移发电机.
在mdCATH数据集上训练模型,并根据长期MD模拟和实验数据进行验证.

主要成果:

aSAM有效地模拟了潜空间中的原子,改善了侧链和骨干扭矩角度采样.
aSAMt准确地捕捉了温度依赖的蛋白质组合特性,并将其推广到未见的温度.
高温训练改善了能源景观的深度发电机探索; aSAMt反映了实验热行为.

结论:

aSAM和aSAMt提供了一种计算效率高的方法来生成精确的原子化蛋白质组合.
温度调节增强了蛋白质动态深度学习模型的概括性和实用性.
这项工作代表了通用集体生成的重要一步,补充了基于物理的模拟.

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