Thioamide substitutions of the peptide backbone have been shown to stabilize therapeutic and imaging peptides toward proteolysis. In order to rationally design thioamide modifications, we have developed a novel Rosetta custom score function to classify thioamide positional effects on proteolysis in substrates of serine and cysteine proteases. Peptides of interest were docked into proteases using the FlexPepDock application in Rosetta. Docked complexes were modified to contain thioamides parametrized through the creation of custom atom types in Rosetta based on ab intio simulations. Thioamide complexes were simulated, and the resultant structural complexes provided features for machine learning classification as the decomposed values of the Rosetta score function. An ensemble, majority voting model was developed to be a robust predictor of previously unpublished thioamide proteolysis holdout data. Theoretical control simulations with pseudo-atoms that modulate only one physical characteristic of the thioamide show differential effects on prediction accuracy by the optimized voting classification model. These pseudo-atom model simulations, as well as statistical analyses of the full thioamide simulations, implicate steric effects on peptide binding as being primarily responsible for thioamide positional effects on proteolytic resistance.

中文翻译：

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Rosetta 机器学习模型准确分类硫代酰胺对蛋白水解的位置影响。

肽骨架的硫代酰胺取代已被证明可以稳定治疗和成像肽对蛋白水解的影响。为了合理设计硫代酰胺修饰，我们开发了一种新的 Rosetta 自定义评分函数来分类硫代酰胺位置对丝氨酸和半胱氨酸蛋白酶底物蛋白水解的影响。使用 Rosetta 中的 FlexPepDock 应用程序将感兴趣的肽与蛋白酶对接。对接的配合物进行了修改，以包含通过在 Rosetta 中基于ab intio创建自定义原子类型来参数化的硫代酰胺模拟。模拟了硫代酰胺配合物，得到的结构配合物为机器学习分类提供了作为 Rosetta 评分函数的分解值的特征。开发了一个整体、多数投票模型，以作为以前未发表的硫代酰胺蛋白水解保留数据的稳健预测器。仅调节硫代酰胺的一种物理特性的假原子的理论控制模拟显示出优化投票分类模型对预测准确性的不同影响。这些假原子模型模拟，以及完整硫代酰胺模拟的统计分析，暗示对肽结合的空间效应主要负责硫代酰胺位置对蛋白水解抗性的影响。