Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA.
Center for the Science and Engineering of Living Systems, Washington University in St. Louis, St. Louis, MO, USA.
Nat Commun. 2021 May 21;12(1):3023. doi: 10.1038/s41467-021-23246-1.
Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of β-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.
理解蛋白质生化特性（如活性和稳定性）的结构决定因素是生物学和医学的主要挑战。比较具有不同生化特性的蛋白质变体的计算机模拟是推动进展的一种越来越强大的手段。然而，成功往往取决于用于简化每个变体采用的复杂结构组合的降维算法。不幸的是，常见的算法依赖于关于哪些结构特征是重要的潜在误导性假设，例如强调较大的几何变化而不是较小的变化。在这里，我们提出了 DiffNets，这是一种自监督自动编码器，通过要求它们学习的低维表示足以预测蛋白质变体之间的生化差异，从而避免了这些假设，并自动识别相关特征。例如，DiffNets 自动识别了微妙的结构特征，这些特征可以预测β-内酰胺酶变体的相对稳定性和肌球蛋白同工型的工作比。DiffNets 也应该适用于理解其他扰动，例如配体结合。
