Molecular dynamics is crucial for understanding molecular systems but its applicability is often limited by the vast timescales of rare events like protein folding. Enhanced sampling techniques overcome this by accelerating the simulation along key reaction pathways, which are defined by collective variables (CVs). However, identifying effective CVs that capture the slow, macroscopic dynamics of a system remains a major bottleneck. This work proposes a novel framework coined BioEmu-CV that learns these essential CVs automatically from BioEmu, a recently proposed foundation model for generating protein equilibrium samples. In particular, we re-purpose BioEmu to learn time-lagged generation conditioned on the learned CV, i.e., predict the distribution of molecular states after a certain amount of time. This training process promotes the CV to encode only the slow, long-term information while disregarding fast, random fluctuations. We validate our learned CV on fast-folding proteins with two key applications: (1) estimating free energy differences using on-the-fly probability enhanced sampling and (2) sampling transition paths with steered molecular dynamics. Our empirical study also serves as a new systematic and comprehensive benchmark for MLCVs on fast-folding proteins larger than Alanine Dipeptide.

翻译：分子动力学对于理解分子系统至关重要，但其应用常受限于罕见事件（如蛋白质折叠）的广阔时间尺度。增强采样技术通过沿关键反应路径加速模拟来克服这一限制，这些路径由集体变量定义。然而，识别能捕获系统缓慢宏观动力学的有效集体变量仍是一个主要瓶颈。本研究提出了一种名为BioEmu-CV的新框架，该框架能够从BioEmu自动学习这些关键集体变量，BioEmu是近期提出的一种用于生成蛋白质平衡样本的基础模型。具体而言，我们重新利用BioEmu来学习基于已学习集体变量的时滞生成，即预测经过特定时间后分子状态的分布。这一训练过程促使集体变量仅编码缓慢的长期信息，同时忽略快速随机波动。我们在快速折叠蛋白质上验证了所学习的集体变量，主要应用于两个方面：（1）利用即时概率增强采样估计自由能差异；（2）通过引导分子动力学采样过渡路径。我们的实证研究还为大于丙氨酸二肽的快速折叠蛋白质上的机器学习集体变量提供了一个新的系统化综合基准。