Enhancing structure prediction and design of soluble and membrane proteins with explicit solvent-protein interactions
By J. Lai, Joaquin Ambia, Yumeng Wang, Patrick Barth. 2017
23 citations recorded by the source corpus.
Summary 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.
Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Rebecca F. Alford
- Jeffrey J. Gray
- Patrick J. Fleming
- K. Fleming
- Brian G. Pierce
- Brian D. Weitzner
- Aroop Sircar
- Tanja Kortemme
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. (2020, background): “A further challenge is accounting for local properties such as specific protein interactions with lipids and cholesterol, which may be captured by a hybrid implicit-explicit approach such as SPadES (73) or HMMM (74).”