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Open Access Methodology article

Computational reverse chemical ecology: Virtual screening and predicting behaviorally active semiochemicals for Bactrocera dorsalis

Kamala Jayanthi P D1, Vivek Kempraj1*, Ravindra M Aurade1, Tapas Kumar Roy2, Shivashankara K S2 and Abraham Verghese1

Author Affiliations

1 National Fellow Lab, Division of Entomology and Nematology, Indian Institute of Horticultural Research, Bangalore, India

2 Division of Plant Physiology and Biochemistry, Indian Institute of Horticultural Research, Bangalore, India

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BMC Genomics 2014, 15:209  doi:10.1186/1471-2164-15-209

Published: 19 March 2014

Abstract

Background

Semiochemical is a generic term used for a chemical substance that influences the behaviour of an organism. It is a common term used in the field of chemical ecology to encompass pheromones, allomones, kairomones, attractants and repellents. Insects have mastered the art of using semiochemicals as communication signals and rely on them to find mates, host or habitat. This dependency of insects on semiochemicals has allowed chemical ecologists to develop environment friendly pest management strategies. However, discovering semiochemicals is a laborious process that involves a plethora of behavioural and analytical techniques, making it expansively time consuming. Recently, reverse chemical ecology approach using odorant binding proteins (OBPs) as target for elucidating behaviourally active compounds is gaining eminence. In this scenario, we describe a “computational reverse chemical ecology” approach for rapid screening of potential semiochemicals.

Results

We illustrate the high prediction accuracy of our computational method. We screened 25 semiochemicals for their binding potential to a GOBP of B. dorsalis using molecular docking (in silico) and molecular dynamics. Parallely, compounds were subjected to fluorescent quenching assays (Experimental). The correlation between in silico and experimental data were significant (r2 = 0.9408; P < 0.0001). Further, predicted compounds were subjected to behavioral bioassays and were found to be highly attractive to insects.

Conclusions

The present study provides a unique methodology for rapid screening and predicting behaviorally active semiochemicals. This methodology may be developed as a viable approach for prospecting active semiochemicals for pest control, which otherwise is a laborious process.