Limits...
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 primary metrics of the six types of L-descriptor.(a) Sensitivity, (b) precision, (c) specificity, and (d) accuracy.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4383629&req=5

pone.0122787.g010: Box plots by primary metrics of the six types of L-descriptor.(a) Sensitivity, (b) precision, (c) specificity, and (d) accuracy.

Mentions: Fig. 10 compares the six L-descriptor types with four primary metrics; the sensitivity S, the precision P, the specificity SP, and the accuracy AC. The horizontal axis denotes the L-descriptors in the order given in Equation (8). The vertical axis denotes the metric values. Fig. 10(a) shows that a bigger L-descriptor tends to produce a higher sensitivity value than a smaller one. This implies that a bigger L-descriptor tends to produce a larger recognized pocket which has a higher chance to have more correct atoms. On the other hand, Fig. 10(b) shows that a smaller L-descriptor tends to have a higher value of precision than a bigger one. This implies that a larger pocket has a higher chance to have incorrect atoms in a recognized pocket. This observation thus shows the trade-offs among the sensitivity and the precision. Fig. 10(c) and (d) shows that the specificity and the accuracy cannot properly discriminate the L-descriptor types.


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 primary metrics of the six types of L-descriptor.(a) Sensitivity, (b) precision, (c) specificity, and (d) accuracy.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0122787.g010: Box plots by primary metrics of the six types of L-descriptor.(a) Sensitivity, (b) precision, (c) specificity, and (d) accuracy.
Mentions: Fig. 10 compares the six L-descriptor types with four primary metrics; the sensitivity S, the precision P, the specificity SP, and the accuracy AC. The horizontal axis denotes the L-descriptors in the order given in Equation (8). The vertical axis denotes the metric values. Fig. 10(a) shows that a bigger L-descriptor tends to produce a higher sensitivity value than a smaller one. This implies that a bigger L-descriptor tends to produce a larger recognized pocket which has a higher chance to have more correct atoms. On the other hand, Fig. 10(b) shows that a smaller L-descriptor tends to have a higher value of precision than a bigger one. This implies that a larger pocket has a higher chance to have incorrect atoms in a recognized pocket. This observation thus shows the trade-offs among the sensitivity and the precision. Fig. 10(c) and (d) shows that the specificity and the accuracy cannot properly discriminate the L-descriptor types.

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.