Publications

@article{Jimenez-Roses2022-ss,

title = {Combined docking and machine learning identify key molecular 

 determinants of ligand pharmacological activity on β2 

 adrenoceptor},

author = {Mireia Jim\'{e}nez-Ros\'{e}s and Bradley Angus Morgan and Maria Jimenez Sigstad and Thuy Duong Zoe Tran and Rohini Srivastava and Asuman Bunsuz and Leire Borrega-Rom\'{a}n and Pattarin Hompluem and Sean A Cullum and Clare R Harwood and Eline J Koers and David A Sykes and Iain B Styles and Dmitry B Veprintsev},

year  = {2022},

date = {2022-10-01},

journal = {Pharmacol. Res. Perspect.},

volume = {10},

number = {5},

pages = {e00994},

publisher = {Wiley},

abstract = {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 K972.68$times$67 , F194ECL2 , 

 S2035.42$times$43 , S2045.43$times$44 , S2075.46$times$641 , 

 H2966.58$times$58 , and K3057.32$times$31 . Meanwhile, the 

 antagonist ligands made interactions with W2866.48$times$48 and 

 Y3167.43$times$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.},

keywords = {adrenoceptor, docking, drug discovery, GPCRs, Machine learning, structure-activity relationship},

pubstate = {published},

tppubtype = {article}

}