Variational autoencoder frameworks have demonstrated success in reducing complex nonlinear dynamics in molecular simulation to a single nonlinear embedding. In this work, we illustrate how this nonlinear latent embedding can be used as a collective variable for enhanced sampling and present a simple modification that allows us to rapidly perform sampling in multiple related systems. We first demonstrate our method is able to describe the effects of force field changes in capped alanine dipeptide after learning about a model using AMBER99. We further provide a simple extension to variational dynamics encoders that allows the model to be trained in a more efficient manner on larger systems by encoding the outputs of a linear transformation using time-structure based independent component analysis (tICA). Using this technique, we show how such a model trained for one protein, the WW domain, can efficiently be transferred to perform enhanced sampling on a related mutant protein, the GTT mutation. This method shows promise for its ability to rapidly sample related systems using a single transferable collective variable, enabling us to probe the effects of variation in increasingly large systems of biophysical interest.

中文翻译：

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可转移的神经网络，用于增强蛋白质动力学的采样

在将分子模拟中的复杂非线性动力学减少到单个非线性嵌入方面，变体自动编码器框架已证明是成功的。在这项工作中，我们说明了如何将这种非线性潜在嵌入用作增强采样的集合变量，并提出一种简单的修改方法，使我们能够在多个相关系统中快速执行采样。我们首先证明我们的方法在学习使用AMBER99的模型后能够描述加帽的丙氨酸二肽中力场变化的影响。我们进一步提供了对变分动力学编码器的简单扩展，它允许通过使用基于时间结构的独立分量分析（tICA）对线性变换的输出进行编码，从而在大型系统上以更有效的方式对模型进行训练。使用这项技术，我们展示了如何针对一种蛋白质（WW域）进行训练的模型可以有效地转移以对相关的突变蛋白质GTT突变进行增强的采样。这种方法显示了其使用AFP快速对相关系统进行采样的能力的希望。单个可转移的集体变量，这使我们能够探究日益变化的大型生物物理系统中变化的影响。