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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 radar charts of the proposed algorithm, the STP algorithm, and the Random method for the five statistical measures.(a) The case corresponding to the five best pockets recognized by the proposed algorithm, and (b) the case corresponding to the best pocket recognized by the proposed algorithm.
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pone.0122787.g023: The radar charts of the proposed algorithm, the STP algorithm, and the Random method for the five statistical measures.(a) The case corresponding to the five best pockets recognized by the proposed algorithm, and (b) the case corresponding to the best pocket recognized by the proposed algorithm.

Mentions: We performed another test as follows. Let ABeta be the set of all atoms belonging to the best five pockets recognized by the proposed algorithm. Let ASTP′ be the set of n(ABeta) atoms recognized by the STP method. This means that we collect the best n(ABeta) atoms from the one with the highest patch score to the ones with lower score, without considering the threshold. Let ARandom′ be the set of n(ABeta) atoms randomly selected. Fig. 23(a) shows the distribution of the five statistical measures for the three methods. Suppose that we find the best matching component among the five pockets recognized by the proposed algorithm and let ABeta* be the set of the atoms belonging to this pocket. Let ASTP′* and ARandom′* be the sets of n(ABeta*) atoms recognized by the STP and the Random methods, respectively. Fig. 23(b) shows the distribution of the five statistical measures for the three methods with the three atom sets ABeta*, ASTP′* and ARandom′*.


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 radar charts of the proposed algorithm, the STP algorithm, and the Random method for the five statistical measures.(a) The case corresponding to the five best pockets recognized by the proposed algorithm, and (b) the case corresponding to the best pocket recognized by the proposed algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0122787.g023: The radar charts of the proposed algorithm, the STP algorithm, and the Random method for the five statistical measures.(a) The case corresponding to the five best pockets recognized by the proposed algorithm, and (b) the case corresponding to the best pocket recognized by the proposed algorithm.
Mentions: We performed another test as follows. Let ABeta be the set of all atoms belonging to the best five pockets recognized by the proposed algorithm. Let ASTP′ be the set of n(ABeta) atoms recognized by the STP method. This means that we collect the best n(ABeta) atoms from the one with the highest patch score to the ones with lower score, without considering the threshold. Let ARandom′ be the set of n(ABeta) atoms randomly selected. Fig. 23(a) shows the distribution of the five statistical measures for the three methods. Suppose that we find the best matching component among the five pockets recognized by the proposed algorithm and let ABeta* be the set of the atoms belonging to this pocket. Let ASTP′* and ARandom′* be the sets of n(ABeta*) atoms recognized by the STP and the Random methods, respectively. Fig. 23(b) shows the distribution of the five statistical measures for the three methods with the three atom sets ABeta*, ASTP′* and ARandom′*.

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.