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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 knowledge-based potentials based upon each exposure algorithm are shown and colored by value where white represents low values and dark gray represents high values. A visual inspection of the KBPs confirms that the energies shown in the KBPs agree with expectations. For example, one expects a hydrophobic amino acid, for example valine (V), to prefer a low exposure value, a large number of neighbors, and a low neighbor vector magnitude. This is in fact what is seen as indicated by the minima in the plots. Conversely, one expects a hydrophilic amino acid, such as lysine (K) to prefer a high exposure value, a small number of neighbors and a high neighbor vector magnitude. This is also what is seen in the plots
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Fig7: The knowledge-based potentials based upon each exposure algorithm are shown and colored by value where white represents low values and dark gray represents high values. A visual inspection of the KBPs confirms that the energies shown in the KBPs agree with expectations. For example, one expects a hydrophobic amino acid, for example valine (V), to prefer a low exposure value, a large number of neighbors, and a low neighbor vector magnitude. This is in fact what is seen as indicated by the minima in the plots. Conversely, one expects a hydrophilic amino acid, such as lysine (K) to prefer a high exposure value, a small number of neighbors and a high neighbor vector magnitude. This is also what is seen in the plots

Mentions: A visual inspection of the KBPs ensures that the potentials agree with expectations (see Fig. 7). For example, one expects for hydrophobic amino acids in solution to prefer burial. This is in fact what is seen. Consider the preference of hydrophobic amino acids, such as valine (V), methionine (M), and phenylalanine (F) for a large number of neighbors, a small neighbor vector magnitude, and small relative exposures. Additionally, one expects hydrophilic amino acids to prefer exposure in solution. This is also the case. Consider the preference of the hydrophilic amino acids lysine (K), asparagine (N), and glutamine (Q) for low neighbor counts, a large neighbor vector magnitude, and large relative exposures.Fig. 7


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 knowledge-based potentials based upon each exposure algorithm are shown and colored by value where white represents low values and dark gray represents high values. A visual inspection of the KBPs confirms that the energies shown in the KBPs agree with expectations. For example, one expects a hydrophobic amino acid, for example valine (V), to prefer a low exposure value, a large number of neighbors, and a low neighbor vector magnitude. This is in fact what is seen as indicated by the minima in the plots. Conversely, one expects a hydrophilic amino acid, such as lysine (K) to prefer a high exposure value, a small number of neighbors and a high neighbor vector magnitude. This is also what is seen in the plots
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Related In: Results  -  Collection

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

Fig7: The knowledge-based potentials based upon each exposure algorithm are shown and colored by value where white represents low values and dark gray represents high values. A visual inspection of the KBPs confirms that the energies shown in the KBPs agree with expectations. For example, one expects a hydrophobic amino acid, for example valine (V), to prefer a low exposure value, a large number of neighbors, and a low neighbor vector magnitude. This is in fact what is seen as indicated by the minima in the plots. Conversely, one expects a hydrophilic amino acid, such as lysine (K) to prefer a high exposure value, a small number of neighbors and a high neighbor vector magnitude. This is also what is seen in the plots
Mentions: A visual inspection of the KBPs ensures that the potentials agree with expectations (see Fig. 7). For example, one expects for hydrophobic amino acids in solution to prefer burial. This is in fact what is seen. Consider the preference of hydrophobic amino acids, such as valine (V), methionine (M), and phenylalanine (F) for a large number of neighbors, a small neighbor vector magnitude, and small relative exposures. Additionally, one expects hydrophilic amino acids to prefer exposure in solution. This is also the case. Consider the preference of the hydrophilic amino acids lysine (K), asparagine (N), and glutamine (Q) for low neighbor counts, a large neighbor vector magnitude, and large relative exposures.Fig. 7

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