Computational reverse chemical ecology: virtual screening and predicting behaviorally active semiochemicals for...

Area of Science:

• Chemical ecology
• Insect behavior
• Computational chemistry

Background:

• Semiochemicals are vital for insect communication, influencing mate finding, host selection, and habitat identification.
• Traditional methods for discovering semiochemicals are time-consuming and labor-intensive.
• A reverse chemical ecology approach, targeting odorant binding proteins (OBPs), is emerging for identifying behaviorally active compounds.

Purpose of the Study:

• To develop and validate a computational reverse chemical ecology approach for rapid screening of potential semiochemicals.
• To accelerate the discovery of behaviorally active compounds for pest management.

Main Methods:

• Utilized molecular docking and molecular dynamics (in silico) to predict the binding potential of 25 semiochemicals to a GOBP of B. dorsalis.
• Validated in silico predictions using experimental fluorescent quenching assays.
• Conducted behavioral bioassays to confirm the attractiveness of predicted compounds to insects.

Main Results:

• The computational method demonstrated high prediction accuracy, with significant correlation (r2 = 0.9408; P < 0.0001) between in silico and experimental data.
• Screened semiochemicals showed significant binding potential to the target GOBP.
• Predicted compounds were confirmed as highly attractive to insects in behavioral bioassays.

Conclusions:

• The developed computational methodology enables rapid screening and prediction of behaviorally active semiochemicals.
• This approach offers a viable and efficient alternative to laborious traditional methods for semiochemical discovery.
• The methodology holds promise for developing effective, environmentally friendly pest control strategies.