Bio-activity of aminosulfonyl ureas in the light of nucleic acid bases and DNA base pair interaction. (August 2018)

• Record Type: Journal Article
• Authors: Mondal Roy, Sutapa
• Is Part Of: Computational biology and chemistry. Volume 75(2018)
• Page Start: 91
• Page End: 100
• Publication Date: 2018-08
• Subjects: QSAR -- Density functional theory -- ACAT Inhibitors -- Aminosulfonyl urea -- NA Bases -- DNA Base Pairs; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.04.015
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Bio-activity of a series of Amynosulfonyl Ureas (AU) is explained successfully. QSAR investigation is performed under Density Functional Theory. Global ( E.A., ΔN ) and local (∑ Q Sul ) are developed as structural descriptors. ΔN finds charge transfer between AU and NA/DNA bases/base pair interactions. Developed QSAR models are validated successfully with more than 90% efficacy. Abstract: The quantum chemical descriptors based on density functional theory (DFT) are applied to predict the biological activity (log IC50 ) of one class of acyl-CoA: cholesterol O-acyltransferase (ACAT) inhibitors, viz. aminosulfonyl ureas. ACAT are very effective agents for reduction of triglyceride and cholesterol levels in human body. Successful two parameter quantitative structure-activity relationship (QSAR) models are developed with a combination of relevant global and local DFT based descriptors for prediction of biological activity of aminosulfonyl ureas. The global descriptors, electron affinity of the ACAT inhibitors ( EA ) and/or charge transfer (Δ N ) between inhibitors and model biosystems (NA bases and DNA base pairs) along with the local group atomic charge on sulfonyl moiety (∑ Q Sul ) of the inhibitors reveals more than 90% efficacy of the selected descriptors for predicting the experimental log (IC50 ) values.