期刊
JOURNAL OF MOLECULAR MODELING
卷 15, 期 9, 页码 1093-1108出版社
SPRINGER
DOI: 10.1007/s00894-009-0454-9
关键词
Environment free energy; Protein structure prediction; Solvent accessible surface area
类别
资金
- National Institute of General Medical Sciences [R01-GM080403]
- National Library of Medicine [2-T15 LM07450-06]
- Div Of Molecular and Cellular Bioscience
- Direct For Biological Sciences [0742762] Funding Source: National Science Foundation
The burial of hydrophobic amino acids in the protein core is a driving force in protein folding. The extent to which an amino acid interacts with the solvent and the protein core is naturally proportional to the surface area exposed to these environments. However, an accurate calculation of the solvent-accessible surface area (SASA), a geometric measure of this exposure, is numerically demanding as it is not pair-wise decomposable. Furthermore, it depends on a full-atom representation of the molecule. This manuscript introduces a series of four SASA approximations of increasing computational complexity and accuracy as well as knowledge-based environment free energy potentials based on these SASA approximations. Their ability to distinguish correctly from incorrectly folded protein models is assessed to balance speed and accuracy for protein structure prediction. We find the newly developed Neighbor Vector algorithm provides the most optimal balance of accurate yet rapid exposure measures.
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