Evidence map›Paper›PMID 25835497›Full record

ArticlePloS one2015

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

Jae-Kwan Kim, Chung-In Won, Jehyun Cha, Kichun Lee, Deok-Soo Kim

Abstract read
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In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jae-Kwan KimVoronoi Diagram Research Center, Hanyang University, Seoul, Korea.
Chung-In WonVoronoi Diagram Research Center, Hanyang University, Seoul, Korea.
Jehyun ChaSchool of Mechanical Engineering, Hanyang University, Seoul, Korea.
Kichun LeeDepartment of Industrial Engineering, Hanyang University, Seoul, Korea.
Deok-Soo KimVoronoi Diagram Research Center, Hanyang University, Seoul, Korea; School of Mechanical Engineering, Hanyang University, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsAcetazolamideBenchmarkingBinding SitesCalcitriolCarbonic AnhydrasesDrug DiscoveryHigh-Throughput Screening AssaysHumansLigandsPrincipal Component AnalysisProtein BindingProtein ConformationReceptors, CalcitriolStructure-Activity RelationshipUser-Computer InterfaceAcetazolamidecalcipotrieneCalcitriolCarbonic Anhydrasescarbonic anhydrase XIILigandsReceptors, CalcitriolVDR protein, human

Identifiers

PMID25835497
PMCPMC4383629

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.