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Optimal ligand descriptor for pocket recognition based on the Beta-shape.

Kim JK, Won CI, Cha J, Lee K, Kim DS - PLoS ONE (2015)

Bottom Line: Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex.In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms.The performance of the proposed algorithm is verified by a benchmark test.

View Article: PubMed Central - PubMed

Affiliation: Voronoi Diagram Research Center, Hanyang University, Seoul, Korea.

ABSTRACT
Structure-based virtual screening is one of the most important and common computational methods for the identification of predicted hit at the beginning of drug discovery. Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex. In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms. We investigate six candidates for a shape descriptor for a ligand using statistical analysis: the minimum enclosing sphere, three measures from the principal component analysis of atoms, the van der Waals volume, and the beta-shape volume. Among them, the van der Waals volume of a ligand is the optimal shape descriptor for pocket recognition and best tunes the pocket recognition algorithm based on the beta-shape for efficient virtual screening. The performance of the proposed algorithm is verified by a benchmark test.

No MeSH data available.


Related in: MedlinePlus

L-descriptor curves with respect to the ligand size.R2 (the coefficient of determination) is a statistical measure of how close the data are to the fitted regression line. The p-values of the six linear regressions are all less than 10−11.
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pone.0122787.g009: L-descriptor curves with respect to the ligand size.R2 (the coefficient of determination) is a statistical measure of how close the data are to the fitted regression line. The p-values of the six linear regressions are all less than 10−11.

Mentions: Fig. 9 shows the curves for the L-descriptors vs. the ligands ordered in their sizes. The six L-descriptors are divided into two graphs: Fig. 9(a) for the PC1, PC2, and PC3; Fig. 9(b) for the minimum enclosing sphere, the van der Waals volume, and the beta-shape volume. The L-descriptors tend to increase with respect to the ligand size, and their average values are in the following order (Within the parentheses are the averages):βθ_beta(3.35)<βθ_PC3(3.60)<βθ_vdW(4.04)<βθ_PC2(4.96)<βθ_PC1(7.21)<βθ_mes(7.41).(8)When βX < βY in Equation (8), we say that βX is smaller than βY and βY is bigger than βX.


Optimal ligand descriptor for pocket recognition based on the Beta-shape.

Kim JK, Won CI, Cha J, Lee K, Kim DS - PLoS ONE (2015)

L-descriptor curves with respect to the ligand size.R2 (the coefficient of determination) is a statistical measure of how close the data are to the fitted regression line. The p-values of the six linear regressions are all less than 10−11.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383629&req=5

pone.0122787.g009: L-descriptor curves with respect to the ligand size.R2 (the coefficient of determination) is a statistical measure of how close the data are to the fitted regression line. The p-values of the six linear regressions are all less than 10−11.
Mentions: Fig. 9 shows the curves for the L-descriptors vs. the ligands ordered in their sizes. The six L-descriptors are divided into two graphs: Fig. 9(a) for the PC1, PC2, and PC3; Fig. 9(b) for the minimum enclosing sphere, the van der Waals volume, and the beta-shape volume. The L-descriptors tend to increase with respect to the ligand size, and their average values are in the following order (Within the parentheses are the averages):βθ_beta(3.35)<βθ_PC3(3.60)<βθ_vdW(4.04)<βθ_PC2(4.96)<βθ_PC1(7.21)<βθ_mes(7.41).(8)When βX < βY in Equation (8), we say that βX is smaller than βY and βY is bigger than βX.

Bottom Line: Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex.In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms.The performance of the proposed algorithm is verified by a benchmark test.

View Article: PubMed Central - PubMed

Affiliation: Voronoi Diagram Research Center, Hanyang University, Seoul, Korea.

ABSTRACT
Structure-based virtual screening is one of the most important and common computational methods for the identification of predicted hit at the beginning of drug discovery. Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex. In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms. We investigate six candidates for a shape descriptor for a ligand using statistical analysis: the minimum enclosing sphere, three measures from the principal component analysis of atoms, the van der Waals volume, and the beta-shape volume. Among them, the van der Waals volume of a ligand is the optimal shape descriptor for pocket recognition and best tunes the pocket recognition algorithm based on the beta-shape for efficient virtual screening. The performance of the proposed algorithm is verified by a benchmark test.

No MeSH data available.


Related in: MedlinePlus