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CC-BY - Uznanie autorstwaLigand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery
| dc.abstract.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. |
| dc.affiliation | Uniwersytet Warszawski |
| dc.contributor.author | Latek, Dorota |
| dc.contributor.author | Mizera, Mikołaj |
| dc.date.accessioned | 2024-01-25T05:10:36Z |
| dc.date.available | 2024-01-25T05:10:36Z |
| dc.date.copyright | 2021-04-14 |
| dc.date.issued | 2021 |
| dc.description.accesstime | AT_PUBLICATION |
| dc.description.finance | Publikacja bezkosztowa |
| dc.description.number | 8 |
| dc.description.version | FINAL_PUBLISHED |
| dc.description.volume | 22 |
| dc.identifier.doi | 10.3390/IJMS22084060 |
| dc.identifier.issn | 1422-0067 |
| dc.identifier.uri | https://repozytorium.uw.edu.pl//handle/item/111230 |
| dc.identifier.weblink | https://www.mdpi.com/1422-0067/22/8/4060 |
| dc.language | eng |
| dc.pbn.affiliation | chemical sciences |
| dc.relation.ispartof | International Journal of Molecular Sciences |
| dc.relation.pages | 4060 |
| dc.rights | CC-BY |
| dc.sciencecloud | nosend |
| dc.subject.en | G protein-coupled receptors |
| dc.subject.en | machine learning |
| dc.subject.en | gradient boosting |
| dc.subject.en | induced-fit docking |
| dc.subject.en | virtual screening |
| dc.subject.en | molecular docking |
| dc.subject.en | scoring functions |
| dc.subject.en | drug discovery |
| dc.subject.en | glucagon receptor family |
| dc.subject.en | GCGR |
| dc.subject.en | GLP-1R |
| dc.subject.en | secretin receptor family |
| dc.subject.en | class B GPCRs |
| dc.title | Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery |
| dc.type | JournalArticle |
| dspace.entity.type | Publication |