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

Authors
  • Lai, Jason K1
  • Ambia, Joaquin1
  • Wang, Yumeng2
  • Barth, Patrick3
  • 1 Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
  • 2 Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
  • 3 Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA; Verna and Marrs McLean Department of Biochemistry and Molecular Biology, 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. Electronic address: [email protected]
Type
Published Article
Journal
Structure
Publisher
Elsevier
Publication Date
Nov 07, 2017
Volume
25
Issue
11
Identifiers
DOI: 10.1016/j.str.2017.09.002
PMID: 28966016
Source
Medline
Keywords
License
Unknown

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.

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