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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 area under the ROC curve (AUC) is shown for each exposure algorithm over all benchmark proteins. The AUC varies widely over the benchmark proteins. There are some proteins for which all algorithms perform very well (for example, 1c9o) while there are some proteins for which none of the algorithms perform well (for example, 1scj)
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Fig10: The area under the ROC curve (AUC) is shown for each exposure algorithm over all benchmark proteins. The AUC varies widely over the benchmark proteins. There are some proteins for which all algorithms perform very well (for example, 1c9o) while there are some proteins for which none of the algorithms perform well (for example, 1scj)

Mentions: Additionally, the area under the ROC curve (AUC) is examined for the KBPs over the benchmark proteins (see Fig. 10). Again, the AUC values vary widely across benchmark proteins. However, the neighbor count algorithm (AUC = 0.7) lags a bit behind the neighbor vector, artificial neural network, overlapping spheres, and reference standard rSASA algorithms (AUCs ≥0.75).Fig. 10


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 area under the ROC curve (AUC) is shown for each exposure algorithm over all benchmark proteins. The AUC varies widely over the benchmark proteins. There are some proteins for which all algorithms perform very well (for example, 1c9o) while there are some proteins for which none of the algorithms perform well (for example, 1scj)
© Copyright Policy
Related In: Results  -  Collection

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

Fig10: The area under the ROC curve (AUC) is shown for each exposure algorithm over all benchmark proteins. The AUC varies widely over the benchmark proteins. There are some proteins for which all algorithms perform very well (for example, 1c9o) while there are some proteins for which none of the algorithms perform well (for example, 1scj)
Mentions: Additionally, the area under the ROC curve (AUC) is examined for the KBPs over the benchmark proteins (see Fig. 10). Again, the AUC values vary widely across benchmark proteins. However, the neighbor count algorithm (AUC = 0.7) lags a bit behind the neighbor vector, artificial neural network, overlapping spheres, and reference standard rSASA algorithms (AUCs ≥0.75).Fig. 10

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