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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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This figure depicts a shortcoming of the neighbor count algorithm. Lines are drawn from the amino acid of interest in this case to all neighboring (as defined by the neighbor count algorithm) amino acids. Two scenarios are shown for which the neighbor count algorithm returns a value of five. However, these two scenarios depict two very different exposure states
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Fig3: This figure depicts a shortcoming of the neighbor count algorithm. Lines are drawn from the amino acid of interest in this case to all neighboring (as defined by the neighbor count algorithm) amino acids. Two scenarios are shown for which the neighbor count algorithm returns a value of five. However, these two scenarios depict two very different exposure states

Mentions: Several approximations for burial are based upon “neighborhood densities [47],” a weighted sum of neighboring atoms, which take advantage of the idea that neighborhood density is inversely related to SASA. The method used to approximate burial in an early version of Rosetta, a state-of-the-art protein structure prediction algorithm, uses the number of atoms within 10 Å of the of the amino acid of interest [18]. Since that time, this has been modified slightly so that centroids, pseudo-atoms located at the side chain’s center of mass, rather than are used [48]. Other work has examined various burial approximations and found that the number of atoms within 14 Å of the of the amino acid of interest is most conserved in structural alignments, most predictable from amino acid sequence, and provides the greatest utility in fold recognition and sequence alignment [49]. A shortcoming of burial approximations is their inability to take into account the spatial orientation of neighboring atoms (illustrated in Fig. 3). A method that calculates burial by examining neighborhood densities in four different tetrahedral directions attempts to address this shortcoming [50]. The “neighbor vector” algorithm introduced in this manuscript attempts to address this shortcoming as well.


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)

This figure depicts a shortcoming of the neighbor count algorithm. Lines are drawn from the amino acid of interest in this case to all neighboring (as defined by the neighbor count algorithm) amino acids. Two scenarios are shown for which the neighbor count algorithm returns a value of five. However, these two scenarios depict two very different exposure states
© Copyright Policy
Related In: Results  -  Collection

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

Fig3: This figure depicts a shortcoming of the neighbor count algorithm. Lines are drawn from the amino acid of interest in this case to all neighboring (as defined by the neighbor count algorithm) amino acids. Two scenarios are shown for which the neighbor count algorithm returns a value of five. However, these two scenarios depict two very different exposure states
Mentions: Several approximations for burial are based upon “neighborhood densities [47],” a weighted sum of neighboring atoms, which take advantage of the idea that neighborhood density is inversely related to SASA. The method used to approximate burial in an early version of Rosetta, a state-of-the-art protein structure prediction algorithm, uses the number of atoms within 10 Å of the of the amino acid of interest [18]. Since that time, this has been modified slightly so that centroids, pseudo-atoms located at the side chain’s center of mass, rather than are used [48]. Other work has examined various burial approximations and found that the number of atoms within 14 Å of the of the amino acid of interest is most conserved in structural alignments, most predictable from amino acid sequence, and provides the greatest utility in fold recognition and sequence alignment [49]. A shortcoming of burial approximations is their inability to take into account the spatial orientation of neighboring atoms (illustrated in Fig. 3). A method that calculates burial by examining neighborhood densities in four different tetrahedral directions attempts to address this shortcoming [50]. The “neighbor vector” algorithm introduced in this manuscript attempts to address this shortcoming as well.

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