Improving inverse docking target identification with Z-score selection.


Journal

Chemical biology & drug design
ISSN: 1747-0285
Titre abrégé: Chem Biol Drug Des
Pays: England
ID NLM: 101262549

Informations de publication

Date de publication:
06 2019
Historique:
received: 31 08 2018
revised: 22 10 2018
accepted: 17 11 2018
pubmed: 4 1 2019
medline: 6 5 2020
entrez: 4 1 2019
Statut: ppublish

Résumé

The utilization of inverse docking methods for target identification has been driven by an increasing demand for efficient tools for detecting potential drug side-effects. Despite impressive achievements in the field of inverse docking, identifying true positives from a pool of potential targets still remains challenging. Notably, most of the developed techniques have low accuracies, limit the pool of possible targets that can be investigated or are not easy to use for non-experts due to a lack of available scripts or webserver. Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel "combined Z-score" method that used a weighted fraction of ligand and receptor-based Z-scores to identify the most likely binding target of a ligand. With our combined Z-score method, an additional 14%, 3.6%, and 6.3% of all ligand-protein pairs of the Astex, DUD, and DUD-E databases, respectively, were correctly predicted compared to a docking score-based selection. The combined Z-score had the highest area under the curve in a ROC curve analysis of all three datasets and the enrichment factor for the top 1% predictions using the combined Z-score analysis was the highest for the Astex and DUD-E datasets. Additionally, we developed a user-friendly python script (compatible with both Python2 and Python3) that enables users to employ the combined Z-score analysis for target identification using a user-defined list of ligands and targets. We are providing this python script and a user tutorial as part of the supplemental information.

Identifiants

pubmed: 30604454
doi: 10.1111/cbdd.13453
pmc: PMC6606408
mid: NIHMS1002283
doi:

Substances chimiques

Ligands 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

1105-1116

Subventions

Organisme : NIAID NIH HHS
ID : R01 AI140541
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL137015
Pays : United States
Organisme : NIA NIH HHS
ID : R03 AG054904
Pays : United States
Organisme : NIGMS NIH HHS
ID : P41 GM128577
Pays : United States

Informations de copyright

© 2019 John Wiley & Sons A/S.

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Auteurs

Stephanie S Kim (SS)

Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio.

Melanie L Aprahamian (ML)

Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio.

Steffen Lindert (S)

Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio.

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Classifications MeSH