Dipartimento di Chimica, Università di Pavia, Via Taramelli 12, 27100 Pavia, Italy.
Dipartimento di Matematica "F. Casorati", Università di Pavia, Via Ferrata 5, 27100 Pavia, Italy.
J Chem Theory Comput. 2024 Oct 22;20(20):9209-9229. doi: 10.1021/acs.jctc.4c01097. Epub 2024 Oct 10.
The modulation of protein function via designed small molecules is providing new opportunities in chemical biology and medicinal chemistry. While drugs have traditionally been developed to block enzymatic activities through active site occupation, a growing number of strategies now aim to control protein functions in an allosteric fashion, allowing for the tuning of a target's activation or deactivation via the modulation of the populations of conformational ensembles that underlie its function. In the context of the discovery of new active leads, it would be very useful to generate hypotheses for the functional impact of new ligands. Since the discovery and design of allosteric modulators (inhibitors/activators) is still a challenging and often serendipitous target, the development of a rapid and robust approach to predict the functional profile of a new ligand would significantly speed up candidate selection. Herein, we present different machine learning (ML) classifiers to distinguish between potential orthosteric and allosteric binders. Our approach integrates information on the chemical fingerprints of the ligands with descriptors that recapitulate ligand effects on protein functional motions. The latter are derived from molecular dynamics (MD) simulations of the target protein in complex with orthosteric or allosteric ligands. In this framework, we train and test different ML architectures, which are initially probed on the classification of orthosteric versus allosteric ligands for cyclin-dependent kinases (CDKs). The results demonstrate that different ML methods can successfully partition allosteric versus orthosteric effectors (although to different degrees). Next, we further test the models with FDA-approved CDK drugs, not included in the original dataset, as well as ligands that target other kinases, to test the range of applicability of these models outside of the domain on which they were developed. Overall, the results show that enriching the training dataset with chemical physics-based information on the protein-ligand dynamic cross-talk can significantly expand the reach and applicability of approaches for the prediction and classification of the mode of action of small molecules.
通过设计小分子来调节蛋白质功能为化学生物学和药物化学提供了新的机会。虽然传统上药物的开发是通过占据活性位点来阻断酶的活性，但现在越来越多的策略旨在以变构的方式控制蛋白质的功能，通过调节构成其功能基础的构象集合的种群来调节靶标的激活或失活。在发现新的活性先导物的背景下，生成关于新配体功能影响的假设将非常有用。由于变构调节剂（抑制剂/激活剂）的发现和设计仍然是一个具有挑战性且常常是偶然的目标，因此开发一种快速而稳健的方法来预测新配体的功能特征将显著加快候选物的选择。在此，我们提出了不同的机器学习 (ML) 分类器来区分潜在的正构和变构配体。我们的方法将配体的化学指纹信息与描述符集成在一起，这些描述符概括了配体对蛋白质功能运动的影响。后者源自与正构或变构配体结合的靶蛋白的分子动力学 (MD) 模拟。在这个框架中，我们训练和测试不同的 ML 架构，最初在构象激酶 (CDK) 的正构与变构配体分类上对其进行测试。结果表明，不同的 ML 方法可以成功地将变构与正构效应物分开（尽管程度不同）。接下来，我们使用未包含在原始数据集中的美国食品和药物管理局批准的 CDK 药物以及针对其他激酶的配体进一步测试模型，以测试这些模型在其开发的领域之外的适用性范围。总体而言，结果表明，通过在蛋白质 - 配体动态相互作用方面丰富基于化学物理的训练数据集，可以显著扩大小分子作用模式预测和分类方法的范围和适用性。
