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Computing van der Waals energies in the context of the rotamer approximation.

Publication ,  Journal Article
Grigoryan, G; Ochoa, A; Keating, AE
Published in: Proteins
September 1, 2007

The rotamer approximation states that protein side-chain conformations can be described well using a finite set of rotational isomers. This approximation is often applied in the context of computational protein design and structure prediction to reduce the complexity of structural sampling. It is an effective way of reducing the structure space to the most relevant conformations. However, the appropriateness of rotamers for sampling structure space does not imply that a rotamer-based energy landscape preserves any of the properties of the true continuous energy landscape. Specifically, because the energy of a van der Waals interaction can be very sensitive to small changes in atomic separation, meaningful van der Waals energies are particularly difficult to calculate from rotamer-based structures. This presents a problem for computational protein design, where the total energy of a given structure is often represented as a sum of precalculated rigid rotamer self and pair contributions. A common way of addressing this issue is to modify the van der Waals function to reduce its sensitivity to atomic position, but excessive modification may result in a strongly nonphysical potential. Although many different van der Waals modifications have been used in protein design, little is known about which performs best, and why. In this paper, we study 10 ways of computing van der Waals energies under the rotamer approximation, representing four general classes, and compare their performance using a variety of metrics relevant to protein design and native-sequence repacking calculations. Scaling van der Waals radii by anywhere from 85 to 95% gives the best performance. Linearizing and capping the repulsive portion of the potential can give additional improvement, which comes primarily from getting rid of unrealistically large clash energies. On the other hand, continuously minimizing individual rotamer pairs prior to evaluating their interaction works acceptably in native-sequence repacking, but fails in protein design. Additionally, we show that the problem of predicting relevant van der Waals energies from rotamer-based structures is strongly nonpairwise decomposable and hence further modifications of the potential are unlikely to give significant improvement.

Duke Scholars

Published In

Proteins

DOI

EISSN

1097-0134

Publication Date

September 1, 2007

Volume

68

Issue

4

Start / End Page

863 / 878

Location

United States

Related Subject Headings

  • Thermodynamics
  • Reproducibility of Results
  • Proteins
  • Protein Conformation
  • Models, Molecular
  • Enzymes
  • Computational Biology
  • Bioinformatics
  • 49 Mathematical sciences
  • 31 Biological sciences
 

Citation

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Grigoryan, G., Ochoa, A., & Keating, A. E. (2007). Computing van der Waals energies in the context of the rotamer approximation. Proteins, 68(4), 863–878. https://doi.org/10.1002/prot.21470
Grigoryan, Gevorg, Alejandro Ochoa, and Amy E. Keating. “Computing van der Waals energies in the context of the rotamer approximation.Proteins 68, no. 4 (September 1, 2007): 863–78. https://doi.org/10.1002/prot.21470.
Grigoryan G, Ochoa A, Keating AE. Computing van der Waals energies in the context of the rotamer approximation. Proteins. 2007 Sep 1;68(4):863–78.
Grigoryan, Gevorg, et al. “Computing van der Waals energies in the context of the rotamer approximation.Proteins, vol. 68, no. 4, Sept. 2007, pp. 863–78. Pubmed, doi:10.1002/prot.21470.
Grigoryan G, Ochoa A, Keating AE. Computing van der Waals energies in the context of the rotamer approximation. Proteins. 2007 Sep 1;68(4):863–878.
Journal cover image

Published In

Proteins

DOI

EISSN

1097-0134

Publication Date

September 1, 2007

Volume

68

Issue

4

Start / End Page

863 / 878

Location

United States

Related Subject Headings

  • Thermodynamics
  • Reproducibility of Results
  • Proteins
  • Protein Conformation
  • Models, Molecular
  • Enzymes
  • Computational Biology
  • Bioinformatics
  • 49 Mathematical sciences
  • 31 Biological sciences