Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
Structure. 2017 Nov 7;25(11):1758-1770.e8. doi: 10.1016/j.str.2017.09.002. Epub 2017 Sep 28.
Solvent molecules interact intimately with proteins and can profoundly regulate their structure and function. However, accurately and efficiently modeling protein solvation effects at the molecular level has been challenging. Here, we present a method that improves the atomic-level modeling of soluble and membrane protein structures and binding by efficiently predicting de novo protein-solvent molecule interactions. The method predicted with unprecedented accuracy buried water molecule positions, solvated protein conformations, and challenging mutational effects on protein binding. When applied to homology modeling, solvent-bound membrane protein structures, pockets, and cavities were recapitulated with near-atomic precision even from distant homologs. Blindly refined atomic-level structures of evolutionary distant G protein-coupled receptors imply strikingly different functional roles of buried solvent between receptor classes. The method should prove useful for refining low-resolution protein structures, accurately modeling drug-binding sites in structurally uncharacterized receptors, and designing solvent-mediated protein catalysis, recognition, ligand binding, and membrane protein signaling.
溶剂分子与蛋白质密切相互作用,可以深刻调节它们的结构和功能。然而,在分子水平上准确高效地模拟蛋白质溶剂化效应一直具有挑战性。在这里,我们提出了一种方法,通过有效预测从头开始的蛋白质-溶剂分子相互作用,来提高可溶性和膜蛋白结构和结合的原子级建模。该方法以前所未有的精度预测了埋藏水分子的位置、溶剂化蛋白质构象以及对蛋白质结合的挑战性突变效应。当应用于同源建模时,即使来自远缘同源物,溶剂结合的膜蛋白结构、口袋和腔也可以近乎原子精度再现。对进化上差异很大的 G 蛋白偶联受体进行盲目精细原子水平结构表明,受体类别之间埋藏溶剂的功能作用明显不同。该方法应该有助于精细低分辨率蛋白质结构,准确模拟结构未知受体中的药物结合位点,并设计溶剂介导的蛋白质催化、识别、配体结合和膜蛋白信号转导。