Prediction models for the discovery of insect repellents that interfere with olfaction

Disease vector insects rely on chemosensors to locate hosts, find mates and choose where to lay their eggs. Currently, the most efficient method of preventing and controlling the outbreak of insect-borne diseases has been the use of insect repellents (IRs). However, they do not meet the necessary conditions, such as protecting a broad spectrum of mosquitoes; many of them have unpleasant odors or produce unpleasant sensations on the skin, some of them are even carcinogens. In other words, current IRs have significant drawbacks. Therefore, the need for new, more effective, safer, and longer-lasting broad-spectrum IRs than conventional IRs is evident. Here, classifiers for predicting IRs will be developed by using QuBiLS Suite 0-3D molecular descriptors and shallow machine learning techniques. The best individual models were used to obtain ensemble models with suitable statistical parameters for the learning series. In the present work, we intend to introduce, for the first time, the ability of QSAR- (Quantitative Structure-Activity Relationships) and structure-based models to describe the interaction of IRs with the olfactory response of the sensilla of the mosquito Culex quinquefasciatus as well as with repellent activities by using four datasets that take into consideration the two most relevant IR scaffolds: carboxamides and plant-derived compounds with repellent effect on A. aegypti (and also A. gambiae) and the two most common species of cockroach (Blattella germanica and Periplaneta americana). A non-commercial and cross-platform software named “SiliS-PAPACS” was developed for the IRs-prediction and is freely available at http://tomocomd.com/apps. This software will be used for the screening of datasets containing diverse chemotypes like essential oils constituents, chemicals, and FDA-approved drugs. The purpose is to assess the usefulness of the developed models in the IR-labeling of organic substances and show the system's ability to identify novel IR leads (new IR chemical Scaffold). Here, we report 23 novel compounds found through virtual screening that may have potential repellent activity. The results suggest that the proposed method will be an excellent computer-assisted system that could increase the chance of finding new insect control agents and assist those researchers in screening and/or designing new chemotype IRs.

• Үндсэн зохиолч: Pulgar Sánchez, Mary Estefanía (author)
• Формат: bachelorThesis
• Хэл сонгох: eng
• Хэвлэсэн: 2021
• Нөхцлүүд: Repelente de insectos; Proteína de unión a odorantes; Software SiliS-PAPACS; Índices algebraicos 0-3D; Aprendizaje automático; Insect repellent; Odorant binding protein; Machine learning; Docking; Atom- and Bond-based 0-3D algebraic indices
• Онлайн хандалт: http://repositorio.yachaytech.edu.ec/handle/123456789/343