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Solvent accessible surface area approximations for rapid and accurate protein structure prediction.

Durham E, Dorr B, Woetzel N, Staritzbichler R, Meiler J - J Mol Model (2009)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Center for Structural Biology, Vanderbilt University, 465 21st Ave South, Nashville, TN 37232-8725, USA.

ABSTRACT
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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The enrichment is shown for each algorithm over all benchmark proteins. There are some proteins for which none of the exposure algorithms provided an enrichment (for example 1scj) while there are some benchmark proteins for which many of the exposure algorithms provided good enrichments. There are also proteins for which the enrichment produced by each algorithm increased with algorithm complexity as expected (for example 1enh)
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Fig9: The enrichment is shown for each algorithm over all benchmark proteins. There are some proteins for which none of the exposure algorithms provided an enrichment (for example 1scj) while there are some benchmark proteins for which many of the exposure algorithms provided good enrichments. There are also proteins for which the enrichment produced by each algorithm increased with algorithm complexity as expected (for example 1enh)

Mentions: As evidenced by the enrichment values in Fig. 8, the rSASA reference standard and the neighbor vector, artificial neural network, and overlapping spheres algorithms perform similarly (enrichment = ∼3.0) and all outperform the NC method (enrichment <2.5). While no single method clearly dominates the others, some trends can be seen (Fig. 9). In several cases (i.e., 1bq9, 1iib, 1enh), the neighbor count algorithm does not perform as well as the other algorithms. While the rSASA reference standard algorithm often provides the greatest enrichment (i.e., 1bq9, 1iib, 1a19), there are several cases in which the neighbor vector algorithm provides the better results (i.e., 1ail, 1b3a, 1e6i).Fig. 8


Solvent accessible surface area approximations for rapid and accurate protein structure prediction.

Durham E, Dorr B, Woetzel N, Staritzbichler R, Meiler J - J Mol Model (2009)

The enrichment is shown for each algorithm over all benchmark proteins. There are some proteins for which none of the exposure algorithms provided an enrichment (for example 1scj) while there are some benchmark proteins for which many of the exposure algorithms provided good enrichments. There are also proteins for which the enrichment produced by each algorithm increased with algorithm complexity as expected (for example 1enh)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2712621&req=5

Fig9: The enrichment is shown for each algorithm over all benchmark proteins. There are some proteins for which none of the exposure algorithms provided an enrichment (for example 1scj) while there are some benchmark proteins for which many of the exposure algorithms provided good enrichments. There are also proteins for which the enrichment produced by each algorithm increased with algorithm complexity as expected (for example 1enh)
Mentions: As evidenced by the enrichment values in Fig. 8, the rSASA reference standard and the neighbor vector, artificial neural network, and overlapping spheres algorithms perform similarly (enrichment = ∼3.0) and all outperform the NC method (enrichment <2.5). While no single method clearly dominates the others, some trends can be seen (Fig. 9). In several cases (i.e., 1bq9, 1iib, 1enh), the neighbor count algorithm does not perform as well as the other algorithms. While the rSASA reference standard algorithm often provides the greatest enrichment (i.e., 1bq9, 1iib, 1a19), there are several cases in which the neighbor vector algorithm provides the better results (i.e., 1ail, 1b3a, 1e6i).Fig. 8

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Center for Structural Biology, Vanderbilt University, 465 21st Ave South, Nashville, TN 37232-8725, USA.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus