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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.


Box plots by Precision-based metrics of the six shape descriptors.(a) F-measure, (b) geometric mean 1 and (c) predictive summary index (d) negative predictive value.
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pone.0122787.g007: Box plots by Precision-based metrics of the six shape descriptors.(a) F-measure, (b) geometric mean 1 and (c) predictive summary index (d) negative predictive value.

Mentions: Fig. 6 shows the results of the ROC-based metrics which is based on sensitivity and specificity. Fig. 7 shows the results of metrics based on precision. These PR-based metrics mislead because the metrics cannot reflect the low precision of larger L-descriptor. Negative predictive value cannot discriminate among the L-descriptor types at all, because an optimal pocket has larger negative cases than positive cases. In all metrics, it turns out that the van der Waals volume consistently belongs to the group of L-descriptors showing better performance.


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

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

Box plots by Precision-based metrics of the six shape descriptors.(a) F-measure, (b) geometric mean 1 and (c) predictive summary index (d) negative predictive value.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0122787.g007: Box plots by Precision-based metrics of the six shape descriptors.(a) F-measure, (b) geometric mean 1 and (c) predictive summary index (d) negative predictive value.
Mentions: Fig. 6 shows the results of the ROC-based metrics which is based on sensitivity and specificity. Fig. 7 shows the results of metrics based on precision. These PR-based metrics mislead because the metrics cannot reflect the low precision of larger L-descriptor. Negative predictive value cannot discriminate among the L-descriptor types at all, because an optimal pocket has larger negative cases than positive cases. In all metrics, it turns out that the van der Waals volume consistently belongs to the group of L-descriptors showing better performance.

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.