Journal
PROTEIN SCIENCE
Volume 16, Issue 10, Pages 2123-2139Publisher
WILEY
DOI: 10.1110/ps.072939707
Keywords
protein folding; statistical potentials; reduced models; decoy sets
Categories
Funding
- NIGMS NIH HHS [R29 GM055694, R01 GM055694] Funding Source: Medline
Ask authors/readers for more resources
We developed a series of statistical potentials to recognize the native protein from decoys, particularly when using only a reduced representation in which each side chain is treated as a single C-beta atom. Beginning with a highly successful all-atom statistical potential, the Discrete Optimized Protein Energy function ( DOPE), we considered the implications of including additional information in the all-atom statistical potential and subsequently reducing to the Cb representation. One of the potentials includes interaction energies conditional on backbone geometries. A second potential separates sequence local from sequence nonlocal interactions and introduces a novel reference state for the sequence local interactions. The resultant potentials perform better than the original DOPE statistical potential in decoy identification. Moreover, even upon passing to a reduced Cb representation, these statistical potentials outscore the original (all-atom) DOPE potential in identifying native states for sets of decoys. Interestingly, the backbone-dependent statistical potential is shown to retain nearly all of the information content of the all-atom representation in the Cb representation. In addition, these new statistical potentials are combined with existing potentials to model hydrogen bonding, torsion energies, and solvation energies to produce even better performing potentials. The ability of the Cb statistical potentials to accurately represent protein interactions bodes well for computational efficiency in protein folding calculations using reduced backbone representations, while the extensions to DOPE illustrate general principles for improving knowledge-based potentials.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available