Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Cell Chem Biol. 2018 Jul 19;25(7):916-924.e2. doi: 10.1016/j.chembiol.2018.05.002. Epub 2018 May 31.
Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved αC helix and DFG motif to define the conformational space of the catalytic domain of protein kinases. We then construct Kinformation, a random forest classifier, to annotate the conformation of 3,708 kinase structures in the PDB. Our classification scheme captures known active and inactive kinase conformations and defines an additional conformational state, thereby refining the current understanding of the kinase conformational space. Furthermore, network analysis of the small molecules recognized by each conformation captures chemical substructures that are associated with each conformation type. Our description of the kinase conformational space is expected to improve modeling of protein kinase structures, as well as guide the development of conformation-specific kinase inhibitors with optimal pharmacological profiles.
蛋白激酶是动态的，会采用不同的构象状态，这些状态对其催化活性至关重要。我们评估了一系列源自保守的 αC 螺旋和 DFG 模体的结构特征，以定义蛋白激酶催化结构域的构象空间。然后，我们构建了 Kinformation，这是一个随机森林分类器，用于注释 PDB 中 3708 个激酶结构的构象。我们的分类方案捕获了已知的活性和非活性激酶构象，并定义了一个额外的构象状态，从而细化了对激酶构象空间的现有理解。此外，通过对每种构象识别的小分子进行网络分析，捕获了与每种构象类型相关的化学亚结构。我们对激酶构象空间的描述有望改善蛋白激酶结构的建模，并指导开发具有最佳药理学特性的构象特异性激酶抑制剂。
