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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

The accuracy graphs.The red circle corresponds to the proposed method. The black triangle and blue square correspond to the average value (of the 85 structures of the Astex Diverse Set) for the STP and Random methods for each threshold value, respectively. The horizontal and the vertical axes denote the thresholds and the computed values of accuracy, respectively. (a) Accuracy for “Without (component)” and (b) one for “With (component).”
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pone.0122787.g020: The accuracy graphs.The red circle corresponds to the proposed method. The black triangle and blue square correspond to the average value (of the 85 structures of the Astex Diverse Set) for the STP and Random methods for each threshold value, respectively. The horizontal and the vertical axes denote the thresholds and the computed values of accuracy, respectively. (a) Accuracy for “Without (component)” and (b) one for “With (component).”

Mentions: We computed the five measures: The precision P (Fig. 18), the specificity SP (Fig. 19), the accuracy AC (Fig. 20), the sensitivity S (Fig. 21), and the normalized likelihood ratio LR (Fig. 22).


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 accuracy graphs.The red circle corresponds to the proposed method. The black triangle and blue square correspond to the average value (of the 85 structures of the Astex Diverse Set) for the STP and Random methods for each threshold value, respectively. The horizontal and the vertical axes denote the thresholds and the computed values of accuracy, respectively. (a) Accuracy for “Without (component)” and (b) one for “With (component).”
© Copyright Policy
Related In: Results  -  Collection

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

pone.0122787.g020: The accuracy graphs.The red circle corresponds to the proposed method. The black triangle and blue square correspond to the average value (of the 85 structures of the Astex Diverse Set) for the STP and Random methods for each threshold value, respectively. The horizontal and the vertical axes denote the thresholds and the computed values of accuracy, respectively. (a) Accuracy for “Without (component)” and (b) one for “With (component).”
Mentions: We computed the five measures: The precision P (Fig. 18), the specificity SP (Fig. 19), the accuracy AC (Fig. 20), the sensitivity S (Fig. 21), and the normalized likelihood ratio LR (Fig. 22).

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