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J Chem Inf Model. 2018 May 29;58(5):933-942. doi: 10.1021/acs.jcim.7b00574. Epub 2018 Apr 18.

Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays.

Author information

1
Institute of Structural Biology , Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH) , Ingolstaedter Landstrasse 1 , 85764 Neuherberg , Germany.
2
Lead Discovery Center GmbH , Otto-Hahn-Straße 15 , 44227 Dortmund , Germany.
3
Assay Development and Screening Platform , Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH) , Ingolstaedter Landstrasse 1 , 85764 Neuherberg , Germany.
4
Bayerisches NMR-Zentrum, Department of Chemistry , Technical University of Munich , Ernst-Otto-Fischer-Straße 2 , 85747 Garching , Germany.
5
BIGCHEM GmbH , Ingolstaedter Landstrasse 1 b. 60w , 85764 Neuherberg , Germany.

Abstract

Firefly luciferase is an enzyme that has found ubiquitous use in biological assays in high-throughput screening (HTS) campaigns. The inhibition of luciferase in such assays could lead to a false positive result. This issue has been known for a long time, and there have been significant efforts to identify luciferase inhibitors in order to enhance recognition of false positives in screening assays. However, although a large amount of publicly accessible luciferase counterscreen data is available, to date little effort has been devoted to building a chemoinformatic model that can identify such molecules in a given data set. In this study we developed models to identify these molecules using various methods, such as molecular docking, SMARTS screening, pharmacophores, and machine learning methods. Among the structure-based methods, the pharmacophore-based method showed promising results, with a balanced accuracy of 74.2%. However, machine-learning approaches using associative neural networks outperformed all of the other methods explored, producing a final model with a balanced accuracy of 89.7%. The high predictive accuracy of this model is expected to be useful for advising which compounds are potential luciferase inhibitors present in luciferase HTS assays. The models developed in this work are freely available at the OCHEM platform at http://ochem.eu .

PMID:
29667823
DOI:
10.1021/acs.jcim.7b00574
[Indexed for MEDLINE]

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