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Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery

Autor
Latek, Dorota
Mizera, Mikołaj
Data publikacji
2021
Abstrakt (EN)

The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a receptor subtype was compared with explicit information on ligand-receptor interactions from induced-fit docking. Both methods have proved their usefulness in drug response predictions. Yet, their successful combination still requires allosteric/orthosteric assignment of ligands from datasets. Our ligand datasets included activities of two members of the secretin receptor family: GCGR and GLP-1R. Simultaneous activation of two or three receptors of this family by dual or triple agonists is not a typical kind of information included in compound databases. A precise allosteric/orthosteric ligand assignment requires a continuous update based on new structural and biological data. This data incompleteness remains the main obstacle for current ML methods applied to class B GPCR drug discovery. Even so, for these two class B receptors, our ligand-based ML model demonstrated high accuracy (5-fold cross-validation Q2 > 0.63 and Q2 > 0.67 for GLP-1R and GCGR, respectively). In addition, we performed a ligand annotation using recent cryogenic-electron microscopy (cryo-EM) and X-ray crystallographic data on small-molecule complexes of GCGR and GLP-1R. As a result, we assigned GLP-1R and GCGR actives deposited in ChEMBL to four small-molecule binding sites occupied by positive and negative allosteric modulators and a full agonist. Annotated compounds were added to our recently released repository of GPCR data.

Słowa kluczowe EN
G protein-coupled receptors
machine learning
gradient boosting
induced-fit docking
virtual screening
molecular docking
scoring functions
drug discovery
glucagon receptor family
GCGR
GLP-1R
secretin receptor family
class B GPCRs
Dyscyplina PBN
nauki chemiczne
Czasopismo
International Journal of Molecular Sciences
Tom
22
Zeszyt
8
Strony od-do
4060
ISSN
1422-0067
Data udostępnienia w otwartym dostępie
2021-04-14
Licencja otwartego dostępu
Uznanie autorstwa