用大模型预测蛋白构象变化，仅靠一种状态就能发现新构象。
MD-LLM-1: A Large Language Model for Molecular Dynamics
- 用Mistral 7B微调成分子动力学大模型，学习蛋白运动规律。
- 只用一个构象训练，就能预测出其他未见的构象状态。
- 适合研究蛋白构象动态的生物物理与结构生物学工作者。
分子动力学（MD）是模拟分子系统的重要方法，但对许多生物相关的宏分子系统在时空尺度上仍计算昂贵。为探索深度学习解决此问题的潜力，我们提出分子动力学大语言模型（MD-LLM）框架，展示大模型如何学习蛋白动力学并发现训练中未见的状态。通过微调Mistral 7B得到首个实现版本MD-LLM-1，并应用于T4溶菌酶和Mad2蛋白系统，结果显示仅基于一个构象状态的训练即可预测其他构象状态。这表明MD-LLM-1能够学习蛋白构象景观的内在规律，尽管尚未显式建模其热力学与动力学特性。
原文摘要 · Abstract (English)
Molecular dynamics (MD) is a powerful approach for modelling molecular systems, but it remains computationally intensive on spatial and time scales of many macromolecular systems of biological interest. To explore the opportunities offered by deep learning to address this problem, we introduce a Molecular Dynamics Large Language Model (MD-LLM) framework to illustrate how LLMs can be leveraged to learn protein dynamics and discover states not seen in training. By applying MD-LLM-1, the first implementation of this approach, obtained by fine-tuning Mistral 7B, to the T4 lysozyme and Mad2 protein systems, we show that training on one conformational state enables the prediction of other conformational states. These results indicate that MD-LLM-1 can learn the principles for the exploration of the conformational landscapes of proteins, although it is not yet modeling explicitly their thermodynamics and kinetics.
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