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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.


The ROC-graph of the L-descriptors.(a) the beta-shape volume, (b) the PC3, (c) the van der Waals volume, (d) the PC2, (e) the PC1, and (f) the minimum enclosing sphere.
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pone.0122787.g011: The ROC-graph of the L-descriptors.(a) the beta-shape volume, (b) the PC3, (c) the van der Waals volume, (d) the PC2, (e) the PC1, and (f) the minimum enclosing sphere.

Mentions: Fig. 11 and Fig. 12 show the ROC-graphs and the PR-graphs of the six L-descriptor types, respectively, in the order as before. In the ROC-graphs in Fig. 11, the FPR tends to be small because there are many boundary atoms which do not belong to the optimal pocket. Note that the window of the horizontal-axis is given between 0 and 0.2. From these graphs, we observe that Fig. 11(c) and (d) shows the best distribution of the FPR and TPR values. Fig. 11(a) and (b) shows rather widely distributed TPR values and Fig. 11(e) and (f) shows rather widely distributed FPR values. Recall that the perfect match occurs at the point (FPR = 0,TPR = 1). In the PR-graphs in Fig. 12, we observe that Fig. 12(c) (βθ_vdW) and (d) (βθ_PC2) show the best distribution of the R and P values. Fig. 12(a) and (b) shows rather widely distributed R values and Fig. 12(e) and (f) shows that the P values are rather downward distributed. Recall that a perfect match occurs at the point (R = 1,P = 1).


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

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

The ROC-graph of the L-descriptors.(a) the beta-shape volume, (b) the PC3, (c) the van der Waals volume, (d) the PC2, (e) the PC1, and (f) the minimum enclosing sphere.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4383629&req=5

pone.0122787.g011: The ROC-graph of the L-descriptors.(a) the beta-shape volume, (b) the PC3, (c) the van der Waals volume, (d) the PC2, (e) the PC1, and (f) the minimum enclosing sphere.
Mentions: Fig. 11 and Fig. 12 show the ROC-graphs and the PR-graphs of the six L-descriptor types, respectively, in the order as before. In the ROC-graphs in Fig. 11, the FPR tends to be small because there are many boundary atoms which do not belong to the optimal pocket. Note that the window of the horizontal-axis is given between 0 and 0.2. From these graphs, we observe that Fig. 11(c) and (d) shows the best distribution of the FPR and TPR values. Fig. 11(a) and (b) shows rather widely distributed TPR values and Fig. 11(e) and (f) shows rather widely distributed FPR values. Recall that the perfect match occurs at the point (FPR = 0,TPR = 1). In the PR-graphs in Fig. 12, we observe that Fig. 12(c) (βθ_vdW) and (d) (βθ_PC2) show the best distribution of the R and P values. Fig. 12(a) and (b) shows rather widely distributed R values and Fig. 12(e) and (f) shows that the P values are rather downward distributed. Recall that a perfect match occurs at the point (R = 1,P = 1).

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.