Improving inverse docking target identification with Z-score selection.
Z-score
inverse docking
molecular docking
virtual screening
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
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
1105-1116Subventions
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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