Insight into the relationships between chemical, protein and functional variables in the PBP/GOBP family in moths based on machine learning

López-Cortés, Xaviera A.; Lara, Gabriel; Fernández, Nicolás; Manríquez-Troncoso, José M.; Venthur, Herbert

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dc.contributor.author	López-Cortés, Xaviera A.
dc.contributor.author	Lara, Gabriel
dc.contributor.author	Fernández, Nicolás
dc.contributor.author	Manríquez-Troncoso, José M.
dc.contributor.author	Venthur, Herbert
dc.date.accessioned	2025-06-05T15:25:45Z
dc.date.available	2025-06-05T15:25:45Z
dc.date.issued	2025
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/6095
dc.description.abstract	During their lives, insects must cope with a plethora of chemicals, of which a few will have an impact at the behavioral level. To detect these chemicals, insects use several protein families located in their main olfactory organs, the antennae. Inside the antennae, odorant-binding proteins (OBPs), as the most studied protein family, bind volatile chemicals to transport them. Pheromone-binding proteins (PBPs) and general-odorant-binding proteins (GOPBs) are two subclasses of OBPs and have evolved in moths with a putative olfactory role. Predictions for OBP–chemical interactions have remained limited, and functional data collected over the years unused. In this study, chemical, protein and functional data were curated, and related datasets were created with descriptors. Regression algorithms were implemented and their performance evaluated. Our results indicate that XGBoostRegressor exhibits the best performance (R2 of 0.76, RMSE of 0.28 and MAE of 0.20), followed by GradientBoostingRegressor and LightGBMRegressor. To the best of our knowledge, this is the first study showing a correlation among chemical, protein and functional data, particularly in the context of the PBP/GOBP family of proteins in moths.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	International Journal of Molecular Sciences, 26(5), 2302	es_CL
dc.subject	Chemical ecology	es_CL
dc.subject	Lepidoptera	es_CL
dc.subject	Odorant-binding proteins	es_CL
dc.subject	Artificial intelligence	es_CL
dc.subject	Ligand binding	es_CL
dc.subject	Regression algorithm	es_CL
dc.title	Insight into the relationships between chemical, protein and functional variables in the PBP/GOBP family in moths based on machine learning	es_CL
dc.type	Article	es_CL
dc.ucm.facultad	Facultad de Ciencias de la Ingeniería	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.indexacion	Isi	es_CL
dc.ucm.uri	mdpi.com/1422-0067/26/5/2302	es_CL
dc.ucm.doi	doi.org/10.3390/ijms26052302	es_CL