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通过显式溶剂-蛋白质相互作用增强可溶性和膜蛋白的结构预测和设计。

Enhancing Structure Prediction and Design of Soluble and Membrane Proteins with Explicit Solvent-Protein Interactions.

机构信息

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.

出版信息

Structure. 2017 Nov 7;25(11):1758-1770.e8. doi: 10.1016/j.str.2017.09.002. Epub 2017 Sep 28.

Abstract

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.

摘要

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。

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本文引用的文献

1
Orphan receptor ligand discovery by pickpocketing pharmacological neighbors.
Nat Chem Biol. 2017 Feb;13(2):235-242. doi: 10.1038/nchembio.2266. Epub 2016 Dec 19.
2
Structure of CC chemokine receptor 2 with orthosteric and allosteric antagonists.
Nature. 2016 Dec 15;540(7633):458-461. doi: 10.1038/nature20605. Epub 2016 Dec 7.
3
Enthalpy-entropy compensation: the role of solvation.
Eur Biophys J. 2017 May;46(4):301-308. doi: 10.1007/s00249-016-1182-6. Epub 2016 Oct 28.
4
G-protein coupled receptors: advances in simulation and drug discovery.
Curr Opin Struct Biol. 2016 Dec;41:83-89. doi: 10.1016/j.sbi.2016.06.008. Epub 2016 Jun 22.
5
X-ray and Neutron Scattering of Water.
Chem Rev. 2016 Jul 13;116(13):7570-89. doi: 10.1021/acs.chemrev.5b00663. Epub 2016 May 19.
6
Electrolytes induce long-range orientational order and free energy changes in the H-bond network of bulk water.
Sci Adv. 2016 Apr 8;2(4):e1501891. doi: 10.1126/sciadv.1501891. eCollection 2016 Apr.
7
De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity.
Science. 2016 May 6;352(6286):680-7. doi: 10.1126/science.aad8865.
8
The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis.
Nucleic Acids Res. 2016 Jul 8;44(W1):W410-5. doi: 10.1093/nar/gkw348. Epub 2016 Apr 29.
9
Extra-helical binding site of a glucagon receptor antagonist.
Nature. 2016 May 12;533(7602):274-7. doi: 10.1038/nature17414. Epub 2016 Apr 25.
10
Statistical survey of the buried waters in the Protein Data Bank.
Amino Acids. 2016 Jan;48(1):193-202. doi: 10.1007/s00726-015-2064-4. Epub 2015 Aug 28.

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