Accelerated Discovery, IBM Research Europe, 8803 Rüschlikon, Switzerland.
Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
J Chem Inf Model. 2022 Sep 26;62(18):4295-4299. doi: 10.1021/acs.jcim.2c00840. Epub 2022 Sep 13.
Recent work showed that active site rather than full-protein-sequence information improves predictive performance in kinase-ligand binding affinity prediction. To refine the notion of an "active site", we here propose and compare multiple definitions. We report significant evidence that our novel definition is superior to previous definitions and better models of ATP-noncompetitive inhibitors. Moreover, we leverage the discontiguity of the active site sequence to motivate novel protein-sequence augmentation strategies and find that combining them further improves performance.
最近的研究表明，在激酶-配体结合亲和力预测中，活性位点而不是完整的蛋白质序列信息可以提高预测性能。为了完善“活性位点”的概念，我们在这里提出并比较了多种定义。我们有充分的证据表明，我们的新定义优于以前的定义，并且更好地模拟了非竞争性 ATP 抑制剂。此外，我们利用活性位点序列的不连续性来激发新的蛋白质序列增强策略，并发现将它们结合使用可以进一步提高性能。
