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Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors.

ABSTRACT: MOTIVATION:Accurate prediction and interpretation of ligand bioactivities are essential for virtual screening and drug discovery. Unfortunately, many important drug targets lack experimental data about the ligand bioactivities; this is particularly true for G protein-coupled receptors (GPCRs), which account for the targets of about a third of drugs currently on the market. Computational approaches with the potential of precise assessment of ligand bioactivities and determination of key substructural features which determine ligand bioactivities are needed to address this issue. RESULTS:A new method, SED, was proposed to predict ligand bioactivities and to recognize key substructures associated with GPCRs through the coupling of screening for Lasso of long extended-connectivity fingerprints (ECFPs) with deep neural network training. The SED pipeline contains three successive steps: (i) representation of long ECFPs for ligand molecules, (ii) feature selection by screening for Lasso of ECFPs and (iii) bioactivity prediction through a deep neural network regression model. The method was examined on a set of 16 representative GPCRs that cover most subfamilies of human GPCRs, where each has 300-5000 ligand associations. The results show that SED achieves excellent performance in modelling ligand bioactivities, especially for those in the GPCR datasets without sufficient ligand associations, where SED improved the baseline predictors by 12% in correlation coefficient (r2) and 19% in root mean square error. Detail data analyses suggest that the major advantage of SED lies on its ability to detect substructures from long ECFPs which significantly improves the predictive performance. AVAILABILITY AND IMPLEMENTATION:The source code and datasets of SED are freely available at https://zhanglab.ccmb.med.umich.edu/SED/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Wu J

PROVIDER: S-EPMC6612825 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors.

Wu Jiansheng J Liu Ben B Chan Wallace K B WKB Wu Weijian W Pang Tao T Hu Haifeng H Yan Shancheng S Ke Xiaoyan X Zhang Yang Y

Bioinformatics (Oxford, England) 20190701 14

<h4>Motivation</h4>Accurate prediction and interpretation of ligand bioactivities are essential for virtual screening and drug discovery. Unfortunately, many important drug targets lack experimental data about the ligand bioactivities; this is particularly true for G protein-coupled receptors (GPCRs), which account for the targets of about a third of drugs currently on the market. Computational approaches with the potential of precise assessment of ligand bioactivities and determination of key s ...[more]

PMID: 31510691

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Project description:BackgroundG protein-coupled receptors (GPCRs) are abundant, activate complex signalling and represent the targets for up to approximately 60% of pharmaceuticals but there is a paucity of structural data. Bovine rhodopsin is the first GPCR for which high-resolution structures have been completed but significant variations in structure are likely to exist among the GPCRs. Because of this, considerable effort has been expended on developing in silico tools for refining structures of individual GPCRs. We have developed REPIMPS, a modification of the inverse-folding software Profiles-3D, to assess and predict the rotational orientation and vertical position of helices within the helix bundle of individual GPCRs. We highlight the value of the method by applying it to the Baldwin GPCR template but the method can, in principle, be applied to any low- or high-resolution membrane protein template or structure.Results3D models were built for transmembrane helical segments of 493 GPCRs based on the Baldwin template, and the models were then scored using REPIMPS and Profiles-3D. The compatibility scores increased significantly using REPIMPS because it takes into account the physicochemical properties of the (lipid) environment surrounding the helix bundle. The arrangement of helices in the helix bundle of the 493 models was then altered systematically by rotating the individual helices. For most GPCRs in the set, changes in the rotational position of one or more helices resulted in significant improvement in the compatibility scores. In particular, for most GPCRs, a rotation of helix VII by 240-300 degrees resulted in improved scores. Bovine rhodopsin modelled using this method showed 3.31 A RMSD to its crystal structure for 198 Calpha atom pairs, suggesting the utility of the method even when starting with idealised structures such as the Baldwin template.ConclusionWe have developed an in silico tool which can be used to test the validity of, and refine, models of GPCRs with respect to helix rotation and vertical position based on the physicochemical properties of amino acids and the surrounding environment. The method can be applied to any multi-pass membrane protein and potentially can be used in combination with other high-throughput methodologies to generate and refine models of membrane proteins.

Dataset Information

Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors.

Publications

Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors.

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