Seshan, G and Kanagasabai, S and Ananthasri, S and Kannappan, B and Suvitha, A and Jaimohan, S M and Kanagaraj, S and Kothandan, G (2020) Insights of structure-based pharmacophore studies and inhibitor design against Gal3 receptor through molecular dynamics simulations. In: Journal of Biomolecular Structure and Dynamics .
Full text not available from this repository.Abstract
Our present work studies the structure-based pharmacophore modeling and designing inhibitor against Gal3 receptor through molecular dynamics (MD) simulations extensively. Pharmacophore models play a key role in computer-aided drug discovery like in the case of virtual screening of chemical databases, de novo drug design and lead optimization. Structure-based methods for developing pharmacophore models are important, and there have been a number of studies combining such methods with the use of MD simulations to model protein�s flexibility. The two potential antagonists SNAP 37889 and SNAP 398299 were docked and simulated for 250 ns and the results are analyzed and carried for the structure-based pharmacophore studies. This helped in identification of the subtype selectivity of the binding sites of the Gal3 receptor. Our work mainly focuses on identifying these binding site residues and to design more potent inhibitors compared to the previously available inhibitors through pharmacophore models. The study provides crucial insight into the binding site residues Ala2, Asp3, Ala4, Gln5, Phe24, Gln79, Ala80, Ile82, Tyr83, Trp88, His99, Ile102, Tyr103, Met106, Tyr157, Tyr161, Pro174, Trp176, Arg181, Ala183, Leu184, Asp185, Thr188, Trp248, His251, His252, Ile255, Leu256, Phe258, Trp259, Tyr270, Arg273, Leu274 and His277, which plays a significant role in the conformational changes of the receptor and helps to understand the inhibition mechanism. Communicated by Ramaswamy H. Sarma. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
| Item Type: | Journal Article |
|---|---|
| Publication: | Journal of Biomolecular Structure and Dynamics |
| Publisher: | Taylor and Francis Ltd. |
| Additional Information: | The copyright for this article belongs to Taylor and Francis Ltd. |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 11 Aug 2021 10:04 |
| Last Modified: | 11 Aug 2021 10:04 |
| URI: | http://eprints.iisc.ac.in/id/eprint/66337 |
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