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1.
Pharmacol Res Perspect ; 10(5): e00994, 2022 10.
Artículo en Inglés | MEDLINE | ID: mdl-36029004

RESUMEN

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 ß2AR ligands to multiple ß2AR 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 K972.68×67 , F194ECL2 , S2035.42×43 , S2045.43×44 , S2075.46×641 , H2966.58×58 , and K3057.32×31 . Meanwhile, the antagonist ligands made interactions with W2866.48×48 and Y3167.43×42 , 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.


Asunto(s)
Descubrimiento de Drogas , Aprendizaje Automático , Receptores Adrenérgicos beta 2 , Humanos , Ligandos , Simulación del Acoplamiento Molecular , Receptores Adrenérgicos beta 2/química
2.
Comput Biol Chem ; 84: 107195, 2020 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-31877499

RESUMEN

Major Histocompatibility Complex (MHC) is a cell surface glycoprotein that binds to foreign antigens and presents them to T lymphocyte cells on the surface of Antigen Presenting Cells (APCs) for appropriate immune recognition. Recently, studies focusing on peptide-based vaccine design have allowed a better understanding of peptide immunogenicity mechanisms, which is defined as the ability of a peptide to stimulate CTL-mediated immune response. Peptide immunogenicity is also known to be related to the stability of peptide-loaded MHC (pMHC) complex. In this study, ENCoM server was used for structure-based estimation of the impact of single point mutations on pMHC complex stabilities. For this purpose, two human MHC molecules from the HLA-B*27 group (HLA-B*27:05 and HLA-B*27:09) in complex with four different peptides (GRFAAAIAK, RRKWRRWHL, RRRWRRLTV and IRAAPPPLF) and three HLA-B*44 molecules (HLA-B*44:02, HLA-B*44:03 and HLA-B*44:05) in complex with two different peptides (EEYLQAFTY and EEYLKAWTF) were analyzed. We found that the stability of pMHC complexes is dependent on both peptide sequence and MHC allele. Furthermore, we demonstrate that allele-specific peptide-binding preferences can be accurately revealed using structure-based computational methods predicting the effect of mutations on protein stability.


Asunto(s)
Antígeno HLA-B27/metabolismo , Antígeno HLA-B44/metabolismo , Péptidos/metabolismo , Alelos , Bases de Datos de Proteínas/estadística & datos numéricos , Antígeno HLA-B27/química , Antígeno HLA-B27/genética , Antígeno HLA-B44/química , Antígeno HLA-B44/genética , Humanos , Mutación , Unión Proteica , Estabilidad Proteica
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