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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 visualization of pockets (PDB accession code: 1jd0).(a) The optimal pocket, (b) the best matched component produced by the proposed method, (c), (d), (e), and (f) are the atoms recognized by the STP method for the threshold values 80, 60, 40, and 20, respectively.
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pone.0122787.g016: The visualization of pockets (PDB accession code: 1jd0).(a) The optimal pocket, (b) the best matched component produced by the proposed method, (c), (d), (e), and (f) are the atoms recognized by the STP method for the threshold values 80, 60, 40, and 20, respectively.

Mentions: Fig. 16 shows the optimal pocket (Fig. 16(a)), the pocket computed by the proposed method (Fig. 16(b)), and the one by the STP method (Fig. 16(c) through (f)) for a protein (PDB Accession code: 1jd0). The bound compound is visualized as a set of blue sticks (for the reference purpose), the atoms belonging to pockets are visualized as colored balls, and the rest of the protein structure is visualized as gray line segments. The red balls in Fig. 16 (a) are the atoms of the optimal pocket; The green balls in Fig. 16 (b) are the atoms of the best matched component produced from the proposed algorithm; The yellow balls in Fig. 16 (c), (d), (e), and (f) are the atoms recognized by the STP method for the threshold values 80, 60, 40, and 20, respectively. Fig. 17 shows another example (PDB Accession code: 1s19). Experiments with other proteins show similar results.


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 visualization of pockets (PDB accession code: 1jd0).(a) The optimal pocket, (b) the best matched component produced by the proposed method, (c), (d), (e), and (f) are the atoms recognized by the STP method for the threshold values 80, 60, 40, and 20, respectively.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0122787.g016: The visualization of pockets (PDB accession code: 1jd0).(a) The optimal pocket, (b) the best matched component produced by the proposed method, (c), (d), (e), and (f) are the atoms recognized by the STP method for the threshold values 80, 60, 40, and 20, respectively.
Mentions: Fig. 16 shows the optimal pocket (Fig. 16(a)), the pocket computed by the proposed method (Fig. 16(b)), and the one by the STP method (Fig. 16(c) through (f)) for a protein (PDB Accession code: 1jd0). The bound compound is visualized as a set of blue sticks (for the reference purpose), the atoms belonging to pockets are visualized as colored balls, and the rest of the protein structure is visualized as gray line segments. The red balls in Fig. 16 (a) are the atoms of the optimal pocket; The green balls in Fig. 16 (b) are the atoms of the best matched component produced from the proposed algorithm; The yellow balls in Fig. 16 (c), (d), (e), and (f) are the atoms recognized by the STP method for the threshold values 80, 60, 40, and 20, respectively. Fig. 17 shows another example (PDB Accession code: 1s19). Experiments with other proteins show similar results.

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.