Decrypting Orphan GPCR Drug Discovery via Multitask Learning

Wei-Cheng Huang 1, Wei-Ting Lin 1, Ming-Shiu Hung 1, Jinq-Chyi Lee 1, Chun-Wei Tung 1*

1 Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, Taiwan

• Correspondence: cwtung@nhri.edu.tw

Abstract

The drug discovery of G protein-coupled receptors (GPCRs) superfamily using computational models is often limited by the availability of protein three-dimensional (3D) structures and chemicals with experimentally measured bioactivities. Orphan GPCRs without known ligands further complicate the process. To enable drug discovery for human orphan GPCRs, multitask models were proposed for predicting half maximal effective concentrations (EC50) of the pairs of chemicals and GPCRs. Protein multiple sequence alignment features, and physicochemical properties and fingerprints of chemicals were utilized to encode the protein and chemical information, respectively. The protein features enabled the transfer of data-rich GPCRs to orphan receptors and transferability based on similarity of protein features. The final model was trained using both agonist and antagonist data from 200 GPCRs and showed an excellent mean squared error (MSE) of 0.24 in the validation dataset. An independent test using the orphan dataset consisting of 16 receptors associated with less than 8 bioactivities showed a reasonably good MSE of 1.51 that can be further improved to 0.53 by considering transferability based on protein features. The informative features were identified and mapped to corresponding 3D structures to gain insights into the mechanism of GPCR-ligand interactions across the GPCR family. The proposed method provides a novel perspective on learning ligand bioactivity within the diverse human GPCR superfamily and can potentially accelerate the discovery of therapeutic agents for orphan GPCRs.

Keywords

Multitask Learning; G Protein-Coupled Receptors; GPCR; Feature Selection; Ligand-Based Virtual Screening

Python installation and environment setup

You may download the whole directory with script files and compressed result files.

1. use the python_env_setup.sh file to setup all dependencies
2. use the extract_files.sh file to extract all the result files for this task.

git clone git@github.com:drhuangwc/GPCR.git
sh python_env_setup.sh
sh extract_files.sh > files.out &

Script files:

1. GPCR_MTL_dataprocess.ipynb: script + original dataset
2. GPCR_MTL_autogluontrain.ipynb: script for separate protein Agonist training using Autogluon AutoML & separate protein Antagonist training using Autogluon AutoML & separate protein Agonist & Antagonist training using Autogluon AutoML
3. GPCR_MTL_Mol2vec.ipynb: script for using Mol2vec replace chemical features + result data
4. GPCR_MTL_FeatureSelection.ipynb: script for using mRMR feature selection of the files + FeatureSelection_GPCRtrain + result data
5. GPCR_MTL_TRAINevaluate.ipynb: script for evaluation of validation data and orphan data + result data
6. GPCR_MTL_similarity.ipynb: script for calculate Tanimoto similarity with or without mRMR selected features in 7 parts of the GPCR
7. GPCR_MTL_colorPDB.ipynb: script for mapping the mRMR selected features into the protein structure files
8. GPCR_MTL_GPRC5A_pred.ipynb: script for the prediction of GPRC5A activities