将力量解释为深度学习梯度可以提高预测蛋白质结构的质量
Jonathan Edward King1, David Ryan Koes2
1Joint PhD Program in Computational Biology, Carnegie Mellon University-University of Pittsburgh, Pittsburgh, Pennsylvania.
Biophysical journal
|December 17, 2023
概括
深度学习蛋白质结构预测模型可以通过结合物理原理来改进. 用分子动力学力场进行训练可以提高下游应用的精度和结构质量.
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.
- 机器学习是机器学习.
背景情况:
- 像AlphaFold2这样的深度学习模型在蛋白质结构预测方面实现了高精度.
- 目前的预测可能缺乏像分子对接这样的任务所需的物理现实主义.
- 整合物理直觉可以提高预测的蛋白质结构的实用性.
研究的目的:
- 开发一种用于训练深度学习蛋白质结构预测模型的新方法.
- 为了提高预测蛋白质结构的准确性和物理现实性.
- 为了使预测结构在下游应用中直接使用.
主要方法:
- 提出了一个自定义的PyTorch损失函数,OpenMM-Loss,表示潜在能量.
- 集成OpenMM-Loss与SidechainNet软件包,用于所有原子蛋白质结构.
- 应用该方法来微调OpenFold模型.
主要成果:
- 精心调整的OpenFold产生了与原始预测相似的精度的蛋白质结构.
- 预测的结构呈现出较低的潜在能量,表明物理现实性得到了改善.
- 在微调预测中,MolProbity指标显示结构质量提高.
结论:
- 用分子动力学力场训练深度学习模型是有效的.
- 开放MM-Loss函数可以提高蛋白质结构预测的物理质量.
- 这种方法增强了深度学习模型在结构生物学应用中的实用性.
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