Background: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has also been well established. Currently, steroid preparations of estrogen and progesterone are being used for managing fertility, in spite of the harmful side effects, as there has not been much success in identification of effective FSHR modulators. Structure-based drug design initiatives for identification of potent and specific FSHR modulators have been impeded due to the non-availability of the complete crystal structure of hFSHR complexed with FSH. Methods: In this study, we have modeled the 3D structure of transmembrane domain (TMD) of hFSHR and identified molecules that demonstrate good binding affinity by virtual screening of drug-like library of compounds. The 3D structural and pharmacophoric features of the binders and non-binders obtained from virtual screening were further used to develop Support Vector Machine based classifier for TMD binding. Based on the observations from docking and SVM classification, a small molecule was identified for extensive MD simulations and in vitro assays for FSHR modulatory activity. Results: The molecule selected based on docking score and SVM prediction was found to inhibit FSH-induced cAMP activity by 80% at 300 μM concentration. Conclusion: The study proposes 1,3-diphenyl-1H-pyrazole-5-carboxylate as a promising scaffold for the design of new and potent FSHR allosteric inhibitors.
Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms / B. Sahu, S. Shah, K. Prabhudesai, A. Contini, S. Idicula-Thomas. - In: JOURNAL OF MOLECULAR GRAPHICS & MODELLING. - ISSN 1093-3263. - 89(2019 Jun), pp. 156-166.
|Titolo:||Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms|
CONTINI, ALESSANDRO (Penultimo)
|Parole Chiave:||FSHR; GPCR; machine learning; virtual screening; docking; molecular dynamics; transmembrane receptor|
|Settore Scientifico Disciplinare:||Settore CHIM/06 - Chimica Organica|
Settore BIO/10 - Biochimica
Settore CHIM/08 - Chimica Farmaceutica
|Data di pubblicazione:||giu-2019|
|Data ahead of print / Data di stampa:||5-mar-2019|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.jmgm.2019.02.001|
|Appare nelle tipologie:||01 - Articolo su periodico|