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 questions, 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 seven 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 G-protein-coupled receptors 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.

Actin filaments are involved in various cell motility processes. Actin polymerization is primarily governed by monomer association and dissociation occurring at the rapid-growing end called the barbed end, which generates the force to push the plasma membrane forward. Individual actin filaments bind to one nucleotide and its hydrolysis energy is used to maintain the filamentous form by changing the characteristics of the subunits. The asymmetry of the individual actin filaments is also important for detecting the asymmetry of the cell. However, asymmetry at the subunit level including conformational and temporal changes in actin filaments has not been visualized yet. Here, we used "Forster (or fluorescence) resonance energy transfer (FRET)-actin filament" by copolymerizing an equal amount of donor and acceptor labelled actin. FRET efficiency change was measured along each actin filament under a light microscope. The FRET efficiency was lower near the end region than in the interior regions. Fluctuations in the FRET efficiency (fFRET) were used to monitor local flexibilities along each actin filament. The fFRET was larger near the end region than the interior region. Our quantified data showed that spatial change of fFRET along actin filaments was rapidly decayed from the barbed end from near the pointed end toward internal region, suggesting that the behaviour of actin subunits near ends is affected from each end. Our result revealed that actin filaments have different orientations locally. These orientations appear when actin forms filaments, which may contribute the cell motility.