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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 overlapping spheres algorithm places a sphere around each  and places points on the surface of the spheres. The points that do not overlap with the spheres of any other amino acids are used as a measure of relative exposure. The  atoms are colored in black and the points that do not overlap with any other spheres are colored in gray. a) the exterior of the protein b) a cut away of the protein
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Fig6: The overlapping spheres algorithm places a sphere around each and places points on the surface of the spheres. The points that do not overlap with the spheres of any other amino acids are used as a measure of relative exposure. The atoms are colored in black and the points that do not overlap with any other spheres are colored in gray. a) the exterior of the protein b) a cut away of the protein

Mentions: Overlapping spheres (OLS) The overlapping spheres algorithm is a variant of the Shrake and Rupley [38] algorithm for calculating molecular surfaces with the exception that spheres surround amino acids rather than atoms. In this algorithm, a sphere is placed around each and points are placed on the surface of the sphere surrounding the amino acid of interest. The fraction of points on an amino acid’s sphere that do not overlap with any other sphere is used as a measure of exposure (see Fig. 6). The spheres where chosen to have a uniform size regardless of amino acid type. Usage of amino acid specific radii did not lead to a significant improvement in rSASA calculation (data not shown). While the optimal number of points placed on the sphere has been investigated [52], this parameter was not optimized. Points were distributed uniformly every 5° along the surface of the sphere.Fig. 6


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 overlapping spheres algorithm places a sphere around each  and places points on the surface of the spheres. The points that do not overlap with the spheres of any other amino acids are used as a measure of relative exposure. The  atoms are colored in black and the points that do not overlap with any other spheres are colored in gray. a) the exterior of the protein b) a cut away of the protein
© Copyright Policy
Related In: Results  -  Collection

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

Fig6: The overlapping spheres algorithm places a sphere around each and places points on the surface of the spheres. The points that do not overlap with the spheres of any other amino acids are used as a measure of relative exposure. The atoms are colored in black and the points that do not overlap with any other spheres are colored in gray. a) the exterior of the protein b) a cut away of the protein
Mentions: Overlapping spheres (OLS) The overlapping spheres algorithm is a variant of the Shrake and Rupley [38] algorithm for calculating molecular surfaces with the exception that spheres surround amino acids rather than atoms. In this algorithm, a sphere is placed around each and points are placed on the surface of the sphere surrounding the amino acid of interest. The fraction of points on an amino acid’s sphere that do not overlap with any other sphere is used as a measure of exposure (see Fig. 6). The spheres where chosen to have a uniform size regardless of amino acid type. Usage of amino acid specific radii did not lead to a significant improvement in rSASA calculation (data not shown). While the optimal number of points placed on the sphere has been investigated [52], this parameter was not optimized. Points were distributed uniformly every 5° along the surface of the sphere.Fig. 6

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