Universität zu Köln

Li, Li, Koh, Ching Chiek ORCID: 0000-0002-3840-4954, Reker, Daniel ORCID: 0000-0003-4789-7380, Brown, J. B., Wang, Haishuai, Lee, Nicholas Keone, Liow, Hien-haw, Dai, Hao, Fan, Huai-Meng, Chen, Luonan and Wei, Dong-Qing (2019). Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees. Sci Rep, 9. LONDON: NATURE PUBLISHING GROUP. ISSN 2045-2322

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Abstract

Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates, as well as de-orphans phenotypic hits. For the rapid identification of protein-ligand interactions, we here present a novel chemogenomics algorithm for the prediction of protein-ligand interactions using a new machine learning approach and novel class of descriptor. The algorithm applies Bayesian Additive Regression Trees (BART) on a newly proposed proteochemical space, termed the bow-pharmacological space. The space spans three distinctive sub-spaces that cover the protein space, the ligand space, and the interaction space. Thereby, the model extends the scope of classical target prediction or chemogenomic modelling that relies on one or two of these subspaces. Our model demonstrated excellent prediction power, reaching accuracies of up to 94.5-98.4% when evaluated on four human target datasets constituting enzymes, nuclear receptors, ion channels, and G-protein-coupled receptors . BART provided a reliable probabilistic description of the likelihood of interaction between proteins and ligands, which can be used in the prioritization of assays to be performed in both discovery and vigilance phases of small molecule development.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Li, Li UNSPECIFIED UNSPECIFIED UNSPECIFIED Koh, Ching Chiek UNSPECIFIED orcid.org/0000-0002-3840-4954 UNSPECIFIED Reker, Daniel UNSPECIFIED orcid.org/0000-0003-4789-7380 UNSPECIFIED Brown, J. B. UNSPECIFIED UNSPECIFIED UNSPECIFIED Wang, Haishuai UNSPECIFIED UNSPECIFIED UNSPECIFIED Lee, Nicholas Keone UNSPECIFIED UNSPECIFIED UNSPECIFIED Liow, Hien-haw UNSPECIFIED UNSPECIFIED UNSPECIFIED Dai, Hao UNSPECIFIED UNSPECIFIED UNSPECIFIED Fan, Huai-Meng UNSPECIFIED UNSPECIFIED UNSPECIFIED Chen, Luonan UNSPECIFIED UNSPECIFIED UNSPECIFIED Wei, Dong-Qing UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-147969
DOI:	10.1038/s41598-019-43125-6
Journal or Publication Title:	Sci Rep
Volume:	9
Date:	2019
Publisher:	NATURE PUBLISHING GROUP
Place of Publication:	LONDON
ISSN:	2045-2322
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language DIVERSITY-ORIENTED SYNTHESIS; DE-NOVO DESIGN; COUPLED RECEPTORS; DRUG DESIGN; LIBRARIES; MODEL; ROC Multiple languages Multidisciplinary Sciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/14796