Biomacromolecules manifest dynamic conformational fluctuation and involve mutual interconversion among metastable states. A robust mapping of their conformational landscape often requires the low-dimensional projection of the conformational ensemble along optimized collective variables (CVs). However, the traditional choice for the CV is often limited by user-intuition and prior knowledge about the system, and this lacks a rigorous assessment of their optimality over other candidate CVs. To address this issue, we propose an approach in which we first choose the possible combinations of inter-residue Cα-distances within a given macromolecule as a set of input CVs. Subsequently, we derive a non-linear combination of latent space embedded CVs via auto-encoding the unbiased molecular dynamics simulation trajectories within the framework of the feed-forward neural network. We demonstrate the ability of the derived latent space variables in elucidating the conformational landscape in four hierarchically complex systems. The latent space CVs identify key metastable states of a bead-in-a-spring polymer. The combination of the adopted dimensional reduction technique with a Markov state model, built on the derived latent space, reveals multiple spatially and kinetically well-resolved metastable conformations for GB1 β-hairpin. A quantitative comparison based on the variational approach-based scoring of the auto-encoder-derived latent space CVs with the ones obtained via independent component analysis (principal component analysis or time-structured independent component analysis) confirms the optimality of the former. As a practical application, the auto-encoder-derived CVs were found to predict the reinforced folding of a Trp-cage mini-protein in aqueous osmolyte solution. Finally, the protocol was able to decipher the conformational heterogeneities involved in a complex metalloenzyme, namely, cytochrome P450.

中文翻译：

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用于发现生物大分子中亚稳态集合的深度自编码器框架

生物大分子表现出动态构象波动，涉及亚稳态之间的相互转换。其构象景观的稳健映射通常需要沿着优化的集体变量 (CV) 对构象集合进行低维投影。然而，CV 的传统选择通常受到用户直觉和系统先验知识的限制，并且缺乏对它们相对于其他候选 CV 的最优性的严格评估。为了解决这个问题，我们提出了一种方法，在该方法中，我们首先选择残基间 C α的可能组合- 给定大分子内的距离作为一组输入 CV。随后，我们通过在前馈神经网络的框架内自动编码无偏分子动力学模拟轨迹，推导出潜在空间嵌入 CV 的非线性组合。我们证明了派生的潜在空间变量在阐明四个层次复杂系统中的构象景观方面的能力。潜在空间 CV 确定了弹簧珠聚合物的关键亚稳态。采用的降维技术与马尔可夫状态模型的结合，建立在派生的潜在空间上，揭示了 GB1 β 的多个空间和动力学解析良好的亚稳态构象-簪。基于自编码器衍生的潜在空间 CV 的基于变分方法的评分与通过独立成分分析（主成分分析或时间结构独立成分分析）获得的 CV 的定量比较证实了前者的最优性。作为实际应用，发现自编码器衍生的 CV 可以预测 Trp 笼微型蛋白质在渗透剂水溶液中的增强折叠。最后，该协议能够破译复杂金属酶（即细胞色素 P450）中涉及的构象异质性。