title: Hydrophobic potential of mean force as a solvation function for protein structure prediction.
abstract: We have developed a solvation function that combines a Generalized Born model for polarization of protein charge by the high dielectric solvent, with a hydrophobic potential of mean force (HPMF) as a model for hydrophobic interaction, to aid in the discrimination of native structures from other misfolded states in protein structure prediction. We find that our energy function outperforms other reported scoring functions in terms of correct native ranking for 91% of proteins and low Z scores for a variety of decoy sets, including the challenging Rosetta decoys. This work shows that the stabilizing effect of hydrophobic exposure to aqueous solvent that defines the HPMF hydration physics is an apparent improvement over solvent-accessible surface area models that penalize hydrophobic exposure. Decoys generated by thermal sampling around the native-state basin reveal a potentially important role for side-chain entropy in the future development of even more accurate free energy surfaces.
authors.affiliation: UCSF/UCB Joint Graduate Group in Bioengineering, University of California-Berkeley, Berkeley, CA 94720, USA.
authors.forename: Matthew S
authors.initials: MS
authors.lastname: Lin
authors.forename: Nicolas Lux
authors.initials: NL
authors.lastname: Fawzi
authors.forename: Teresa
authors.initials: T
authors.lastname: Head-Gordon
mesh_terms: D019277:Entropy; D008962:Models, Theoretical; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012997:Solvents / Q000737:chemistry; D014867:Water / Q000737:chemistry
chemical_list: D011506:Proteins; D012997:Solvents; D014867:Water