CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable Machine Learning

Targeted covalent inhibition is a powerful therapeutic modality in the drug discoverer’s toolbox. Recent advances in covalent drug discovery, in particular, targeting cysteines, have led to significant breakthroughs for traditionally challenging targets such as mutant KRAS, which is implicated in diverse human cancers. However, identifying cysteines for targeted covalent inhibition is a difficult task, as experimental and in silico tools have shown limited accuracy. Using the recently released CovPDB and CovBinderInPDB databases, we have trained and tested interpretable machine learning (ML) models to identify cysteines that are liable to be covalently modified (i.e., “ligandable” cysteines). We explored myriad physicochemical features (pKa, solvent exposure, residue electrostatics, etc.) and protein–ligand pocket descriptors in our ML models. Our final logistic regression model achieved a median F1 score of 0.73 on held-out test sets. When tested on a small sample of holo proteins, our model also showed reasonable performance, accurately predicting the most ligandable cysteine in most cases. Taken together, these results indicate that we can accurately predict potential ligandable cysteines for targeted covalent drug discovery, privileging cysteines that are more likely to be selective rather than purely reactive. We release this tool to the scientific community as CovCysPredictor.靶向共价抑制是药物研发工具库中极具应用价值的治疗策略。近年来共价药物研发领域取得诸多进展，尤其是针对半胱氨酸的靶向策略，为传统上难以攻克的靶点（如与多种人类癌症密切相关的突变型KRAS）带来了重大突破。然而，筛选可用于靶向共价抑制的半胱氨酸仍是一项极具挑战的任务，现有实验与计算机辅助工具的预测精度均较为有限。依托最新发布的CovPDB与CovBinderInPDB数据库，我们训练并验证了可解释机器学习（Machine Learning, ML）模型，用于筛选易发生共价修饰的半胱氨酸（即“可配体化”半胱氨酸）。我们在模型中纳入了诸多理化特征（包括pKa值、溶剂可及性、残基静电势等）以及蛋白质-配体口袋描述符。我们最终构建的逻辑回归模型在留出测试集上取得了0.73的中位数F1值。当在少量全蛋白样本上进行测试时，我们的模型同样表现出优异的预测性能，在多数情况下可准确识别出最具可配体化潜力的半胱氨酸。综合来看，上述结果表明，我们的模型可精准预测用于靶向共价药物研发的潜在可配体化半胱氨酸，且优先筛选出更具选择性而非仅具备反应活性的半胱氨酸位点。我们将此工具以CovCysPredictor为名开源分享给全球科研社区。