Chemosensory proteins (CSPs) are believed to play a key role in the chemosensory process in insects. Sequencing genomic DNA and RNA encoding CSP1, CSP2 and CSP3 in the sweet potato whitefly Bemisia tabaci showed strong variation between B and Q biotypes. Analyzing CSP-RNA levels showed not only biotype, but also age and developmental stage-specific expression. Interestingly, applying neonicotinoid thiamethoxam insecticide using twenty-five different dose/time treatments in B and Q young adults showed that Bemisia CSP1, CSP2 and CSP3 were also differentially regulated over insecticide exposure. In our study one of the adult-specific gene (CSP1) was shown to be significantly up-regulated by the insecticide in Q, the most highly resistant form of B. tabaci. Correlatively, competitive binding assays using tryptophan fluorescence spectroscopy and molecular docking demonstrated that CSP1 protein preferentially bound to linoleic acid, while CSP2 and CSP3 proteins rather associated to another completely different type of chemical, i.e. α-pentyl-cinnamaldehyde (jasminaldehyde). This might indicate that some CSPs in whiteflies are crucial to facilitate the transport of fatty acids thus regulating some metabolic pathways of the insect immune response, while some others are tuned to much more volatile chemicals known not only for their pleasant odor scent, but also for their potent toxic insecticide activity.

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Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov’SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2n combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (29), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found.