Jordy Schifferstein, Andrius Bernatavicius, Antonius P A Janssen
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引用次数: 0
摘要
激酶抑制剂是一类重要的抗癌药物,目前已有 80 种抑制剂获得临床批准,超过 100 种正在进行临床试验。大多数抑制剂与 ATP 结合位点竞争性结合,导致对特定激酶的选择性面临挑战,从而产生毒性和一般脱靶效应的风险。评估抑制剂与整个激酶组的结合在实验上是可行的,但成本高昂。对激酶选择性进行可靠、可解释的计算预测,将大大有利于抑制剂的发现和优化过程。在此,我们利用对接姿势的机器学习来满足这一需求。为此,我们汇总了所有已知的抑制剂-激酶亲和力,并通过将所有抑制剂与各自的高质量 X 射线结构对接,生成了完整的伴随三维相互作用组。然后,我们利用这一资源训练神经网络作为激酶特异性评分函数,该函数在整个激酶组的未见抑制剂上的总体性能(R2)达到了 0.63-0.74。从分子到基于三维的亲和力预测的整个流程已经完全自动化,并封装在一个免费提供的软件包中。它的图形用户界面与 PyMOL 紧密集成,可立即用于药物化学实践。
Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.
Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, resulting in risks for toxicity and general off-target effects. Assessing the binding of an inhibitor for the entire kinome is experimentally possible but expensive. A reliable and interpretable computational prediction of kinase selectivity would greatly benefit the inhibitor discovery and optimization process. Here, we use machine learning on docked poses to address this need. To this end, we aggregated all known inhibitor-kinase affinities and generated the complete accompanying 3D interactome by docking all inhibitors to the respective high-quality X-ray structures. We then used this resource to train a neural network as a kinase-specific scoring function, which achieved an overall performance (R2) of 0.63-0.74 on unseen inhibitors across the kinome. The entire pipeline from molecule to 3D-based affinity prediction has been fully automated and wrapped in a freely available package. This has a graphical user interface that is tightly integrated with PyMOL to allow immediate adoption in the medicinal chemistry practice.
期刊介绍:
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