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Ligand binding and homology modelling of insect odorant‐binding proteins
Author(s) -
Venthur Herbert,
Mutis Ana,
Zhou JingJiang,
Quiroz Andrés
Publication year - 2014
Publication title -
physiological entomology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 57
eISSN - 1365-3032
pISSN - 0307-6962
DOI - 10.1111/phen.12066
Subject(s) - homology modeling , biology , odorant binding protein , computational biology , docking (animal) , homology (biology) , sequence homology , insect , template , binding site , context (archaeology) , ligand (biochemistry) , biological system , biophysics , biochemistry , receptor , peptide sequence , nanotechnology , amino acid , ecology , gene , materials science , enzyme , medicine , paleontology , nursing
This review describes the main characteristics of odorant‐binding proteins ( OBP s) for homology modelling and presents a summary of structure prediction studies on insect OBP s, along with the steps involved and some limitations and improvements. The technique involves a computing approach to model protein structures and is based on a comparison between a target (unknown structure) and one or more templates (experimentally determined structures). As targets for structure prediction, OBP s are considered to play a functional role for recognition, desorption, scavenging, protection and transportation of hydrophobic molecules (odourants) across an aqueous environment (lymph) to olfactory receptor neurones ( ORN s) located in sensilla, the main olfactory units of insect antennae. Lepidopteran pheromone‐binding proteins, a subgroup of OBP s, are characterized by remarkable structural features, in which high sequence identities (approximately 30%) among these OBP s and a large number of available templates can facilitate the prediction of precise homology models. Approximately 30 studies have been performed on insect OBP s using homology modelling as a tool to predict their structures. Although some of the studies have assessed ligand‐binding affinity using structural information and biochemical measurements, few have performed docking and molecular dynamic ( MD ) simulations as a virtual method to predict best ligands. Docking and MD simulations are discussed in the context of discovery of novel semiochemicals (super‐ligands) using homology modelling to conceive further strategies in insect management.