Logistic Regression of Ligands of Chemotaxis Receptors Offers Clues about Their Recognition by Bacteria - Sagawa, Takashi; Mashiko, Ryota; Yokota, Yusuke; Naruse, Yasushi; Okada, Masato; Kojima, Hiroaki (Frontiers in Bioengineering and Biotechnology, 2018)
Because of relative simplicity of signal transduction pathway, bacterial chemotaxis sensory systems have been expected to be applied to biosensor. Tar and Tsr receptors mediate chemotaxis of Escherichia coli and have been studied extensively as models of chemoreception by bacterial two-transmembrane receptors. Such studies are typically conducted using two canonical ligands: L-aspartate for Tar, and L-serine for Tsr. However, Tar and Tsr also recognize various analogs of aspartate and serine; it remains unknown whether the mechanism by which the canonical ligands are recognized is also common to the analogs. Moreover, in terms of engineering, it is important to know a single species of receptor can recognize various ligands to utilize bacterial receptor as the sensor for wide range of substances. To answer these question, we tried to extract the features that are common to the recognition of the different analogs by constructing classification models based on machine-learning. We computed 20 physicochemical parameters for each of 38 well-known attractants that act as chemoreception ligands, and 15 known non-attractants. The classification models were generated by utilizing one or more of the 7 physicochemical properties as descriptors. From the classification models, we identified the most effective physicochemical parameter for classification: the minimum electron potential. This descriptor that occurred repeatedly in classification models with the highest accuracies, This descriptor used alone could accurately classify 42/53 of compounds. Among the 11 misclassified compounds, eight contained two carboxyl groups, which is analogous to the structure of characteristic of aspartate analog. When considered separately, 16 of the 17 aspartate analogs could be classified accurately based on the distance between their two carboxyl groups. As shown in these results, we succeed to predict the ligands for bacterial chemoreceptors using only a few descriptors; single descriptor for single receptor. This result might be due to the relatively simple topology of bacterial two-transmembrane receptors compared to the GPCRs of seven-transmembrane receptors. Moreover, this distance between carboxyl groups correlated with the receptor binding affinity of the aspartate analogs. In view of this correlation, we propose a common mechanism underlying ligand recognition by Tar of compounds with two carboxyl groups.