title: QSAR analyses of DDT analogues and their in silico validation using molecular docking study against voltage-gated sodium channel of Anopheles funestus.
abstract: DDT has enjoyed the reputation of a successful pesticide in disease control programme and agricultural practices along with the serious opposition and ban later on due to its biomagnification and toxic action against non-target organisms. The present work was carried out to develop QSAR models for analysing DDT analogues for their pesticidal activity and in silico validation of these models. A 2D-QSAR model was generated using stepwise with multiple regression, and the model with a value of r(2) = 0.7324; q(2) = 0.6215; pred r(2) = 0.7038, containing five descriptors, was selected for further study. The 3D QSAR with CoMFA analysis showed that steric contribution of 21% and electrostatic contribution of about 79% were required for larvicidal activity of DDT analogues. A set of 3430 molecules was generated using the basic DDT skeleton as template, and these were evaluated for their mosquito larvicidal activity using the generated QSAR models and DDT as standard. Eleven molecules were selected for in silico validation of these models. For this, a docking study of the selected molecules against the homology-modelled voltage-gated sodium channel of Anopheles funestus was conducted. The study showed that the activities of these analogues as predicted by 2D-QSAR, 3D-QSAR with CoMFA and dock score were observed to be well correlated.
authors.affiliation: a Toxicology & Computational Biology Group, Centre for Bioinformatics , M.D. University , Rohtak , Haryana , India.
authors.forename: V
authors.initials: V
authors.lastname: Saini
authors.forename: A
authors.initials: A
authors.lastname: Kumar
mesh_terms: D000818:Animals; D000852:Anopheles / Q000187:drug effects*; D003198:Computer Simulation; D003634:DDT / Q000031:analogs & derivatives / Q000737:chemistry* / Q000494:pharmacology; D007306:Insecticides / Q000737:chemistry* / Q000494:pharmacology; D007814:Larva / Q000187:drug effects; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship; D015222:Sodium Channels / Q000378:metabolism*; D055672:Static Electricity
chemical_list: D007306:Insecticides; D015222:Sodium Channels; D003634:DDT
keywords: CoMFA; combinatorial library; docking; homology modelling; voltage-gated sodium channel (VGSC)