Developing a variation of 3D-QSAR/MD method in drug design.


Journal

Journal of computational chemistry
ISSN: 1096-987X
Titre abrégé: J Comput Chem
Pays: United States
ID NLM: 9878362

Informations de publication

Date de publication:
15 05 2021
Historique:
revised: 22 02 2021
received: 19 01 2021
accepted: 26 02 2021
pubmed: 16 3 2021
medline: 28 9 2021
entrez: 15 3 2021
Statut: ppublish

Résumé

In continuation of the previous reports on a combination of 3D-quantitative structure-activity relationships (QSAR) with computational molecular dynamics (MD) studies, a new variation of 3D-QSAR/MD method has been employed for drug-design as an alternative or supplementary for the typical experimental methods. The presented method is more cost-effective and less time-consuming than the previous methods and avoids several restrictions of experimental methods, such as validity estimation, and predictability. For this purpose, seven inhibitors for bromodomain (BRD)-containing protein, as an important protein in the development of different types of cancer and responsible for oncogenic rearrangements, have been selected to study of their interactions by docking and MD simulations using molecular mechanics/generalized born surface area (MM/GBSA) method. To build the proposed model, a common variant of 3D-QSAR methods, comparative molecular field analysis has been employed using a dataset of 100 MD-extracted ligand conformations and their corresponding MM/GBSA BRD4-binding energies. The results showed excellent predictability of the generated model for both the training set and test groups. Finally, two new inhibitors were selected among total 4000 designed derivatives (generated through evolutionary techniques) using the proposed 3D-QSAR-MD model. The potentials of these inhibitors were assessed by MD simulations, which showed the higher inhibitory of these compounds than the previous inhibitors. Therefore, this method showed high potentials for acceleration of the procedure of drug design and a basis for joining researchers in computational biology and pharmaceutical sciences.

Identifiants

pubmed: 33719136
doi: 10.1002/jcc.26514
doi:

Substances chimiques

Organic Chemicals 0
Transcription Factors 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

917-929

Informations de copyright

© 2021 Wiley Periodicals LLC.

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Auteurs

Hamed Haghshenas (H)

Division of Biochemistry, Department of Biology, Faculty of Sciences, Shahrekord University, Shahrekord, Iran.

Bita Kaviani (B)

Division of Genetics, Department of Biology, Faculty of Sciences, Islamic Azad University, Shahrekord, Iran.

Monireh Firouzeh (M)

Department of Nanobiotechnology, NourDanesh Institute of Higher Education, Isfahan, Iran.

Hossein Tavakol (H)

Department of Chemistry, Isfahan University of Technology, Isfahan, Iran.

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