G protein‐coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands that bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesized that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over‐represented. We computationally docked ~2700 known β2AR ligands to multiple β2AR structures, generating ca 75 000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. We demonstrate with the application of ML methods that it is possible to identify the key interactions associated with agonism or antagonism of ligands. The most representative interactions for agonist ligands involve K97
2.68×67
, F194
ECL2
, S203
5.42×43
, S204
5.43×44
, S207
5.46×641
, H296
6.58×58
, and K305
7.32×31
. Meanwhile, the antagonist ligands made interactions with W286
6.48×48
and Y316
7.43×42
, both residues considered to be important in GPCR activation. The interpretation of ML analysis in human understandable form allowed us to construct an exquisitely detailed structure‐activity relationship that identifies small changes to the ligands that invert their pharmacological activity and thus helps to guide the drug discovery process. This approach can be readily applied to any drug target.
Hücre yüzey glikoproteinleri olan Temel Doku Uygunluk Kompleks (MHC) molekülleri yabancı antijenlere bağlanır ve onları uygun immün tanınma için antijen sunucu hücrelerin yüzeyindeki T lenfosit hücrelerine sunar. İlk olarak insanlarda lökosit hücrelerinde tanımlanmış oldukları için, aynı zamanda İnsan Lökosit Antijenleri (HLA) olarak da isimlendirilirler. Son zamanlarda peptit bazlı aşıların tasarlanması üzerine odaklanan çalışmalar, peptitin sitotoksik T hücre aracılı immün cevabı uyarma yeteneği olarak tanımlanan peptit immunojenite mekanizmasının anlaşılmasına olanak sağlamaktadır. Peptit immünojenisitesinin, peptit-HLA kompleksinin stabilitesi ile ilişkili olduğu bilinmektedir. Bu çalışmada, AIFQSSMTK and QVPLRPMTYK peptitlerini bağlayan HLA-A*03:01 ve HLA-A*11:01 alellerinin stabilitesinin temel moleküler mekanizmalarını ortaya çıkarmak için moleküler dinamik simülasyonları gerçekleştirilmiştir ve ENCOM sunucusu kullanılarak peptit rezidüleri üzerinde gerçekleştirilen tek nokta mutasyonlarının protein termostabilitesine olan tahmini etkisi araştırılmıştır.
G protein coupled receptors (GPCRs) form one of the largest families of proteins in humans, and are valuable therapeutic targets for a variety of different diseases. One central question of drug discovery surrounding GPCRs is what determines the agonism or antagonism exhibited by ligands which bind these important targets. Ligands exert their action via the interactions they make in the ligand binding pocket. We hypothesised that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action. We reasoned that among a large dataset of different ligands, the functionally important interactions will be over-represented. To investigate this hypothesis, we assembled a database of ~2700 known β2AR ligands and computationally docked them to multiple experimentally determined β2AR structures, generating ca 75,000 docking poses. For each docking pose, we predicted all interactions between the atoms of the receptor and the atoms of the ligand. Using Machine Learning (ML) we identified specific interactions that correlated with the agonist or antagonist activity of these ligands, and developed ML-based predictors of agonist/antagonist activity with up to 90% accuracy. This approach can be readily applied to other GPCRs and drug targets beyond GPCRs.
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