A computational approach for the prediction of relevant chemosensory proteins in a broad range of insect taxa

Olfactory receptors in insects are being employed as novel targets to develop strategies to control the damaging behaviors of crop pests and disease vectors. Traditional methodologies for their identification are laborious and time-consuming. Thus, identifying these receptors at the genomic level provides a broader overview to compare different species, use of available databases, and increasing the number of candidate proteins in non-model insects. Here, we propose an in-silico approach using a combination of the genomic and functionally characterized dataset available in public databases to predict chemosensory receptors relevant for insect ecology. Using Hidden Markov Models, we generated 103 potential models capable of detecting chemosensory receptors, including olfactory receptors, odorantbinding proteins, and gustatory receptors involved in the detection of volatile organic compounds with high accuracy and covering a broader range of insect taxa. Our database set of predicted chemosensory includes a total of 3,708 ORs, 2,014 OBPs, 781 GRs, and 3,193 IRs proteins that should be useful for future experiments

• Autores: Montes Ortiz, Zaide Katherine
• Fecha de publicación: 2020
• Institución: Universidad de los Andes
• Repositorio: Séneca: repositorio Uniandes
• Idioma: eng
• OAI Identifier: oai:repositorio.uniandes.edu.co:1992/48495
• Acceso en línea: http://hdl.handle.net/1992/48495
• Palabra clave: Quimiosensores; Ecología química animal; Proteínas de insectos; Biología
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/