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The transfer matrix (TM) method was applied to calculate the transmission probability (TP) for proton transfer reactions. The tunneling factors in the reaction rate constants were also evaluated using the TPs. To test this method, TPs for the Eckart potentials modeled as a guanine-cytosine base pair were calculated by the TM method and compared to TPs by the analytical solution. As a result, the errors in the TPs by the TM method were quite small. The tunneling factors for the guanine-thymine (G-T) and adenine-cytosine (A-C) mispair reactions were then evaluated by the TM method. A shoulder appears on each potential for these reactions [Odai, K.; Umesaki,K. J. Phys. Chem. A. 2021, 125, 8196-8204]. As a result, the shoulder for the G-T mispair reaction contributes significantly to the tunneling, while the shoulder for the A-C mispair reaction contributes little to the tunneling. These results are difficult to obtain with methods such as Wigner's tunneling factor.

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According to the Löwdin model [ Rev. Mod. Phys. 1963, 35, 724-732], the Watson-Crick guanine-cytosine (G-C) base pair is tautomerized (G*-C*) with a small probability and then replication of G*-C* produces G*-thymine (T) and adenine (A)-C* base pairs. On the basis of this model and our previous work [ J. Phys. Chem. B 2020, 124, 1715-1722], we first calculated the intrinsic reaction coordinates from G*-T to G-T* using density functional theory and evaluated the probability of G*-T tautomerization to G-T* by double proton transfer (DPT) on the basis of the transition state theory. Similarly, we calculated the probability of A-C* tautomerization to A*-C by DPT. Then, according to these probabilities, we calculated the probability of transition mutations from G-C to A-T after 2 replications. The calculated probability was 1.31 × 10-8, a value consistent with the mutation rate previously reported by Drake et al. [ Proc. Natl. Acad. Sci. U.S.A. 1991, 88, 7160-7164]. Our results suggest that DPT is one cause of the G-C → A-T transition. To investigate differences in the optical properties between G*-T and G-T* and between A-C* and A*-C, we also evaluated the infrared absorption spectra and Raman intensities for these base pairs.

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Double proton transfer (DPT) in guanine-cytosine (GC) base pairs and adenine-thymine (AT) base pairs produces tautomeric forms, denoted G*C* and A*T*. To examine DPT, (i) intrinsic reaction coordinates for DPT, (ii) probabilities of change from GC to G*C* and from AT to A*T*, and (iii) infrared absorption intensities of GC and G*C* were investigated on the basis of density functional theory and Eyring's chemical kinetics. The probabilities of change from GC to G*C* were 3 × 10-8, and those from AT to A*T* were 2 × 10-10. These values are consistent with the rate of mutation previously reported by Drake et al. (Proc. Natl. Acad. Sci. U.S.A. 1991, 88, 7160-7164). G*C* exhibited two vibrational modes around 3000 cm-1, whereas GC exhibited no vibrational modes around the same frequency. The infrared intensity calculated for G*C* showed that the strong absorption obtained around 3000 cm-1 was caused by one of the two modes.

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Our goal was to develop an automated system to determine whether animals have learned and changed their behavior in real-time using a low calculation-power central processing unit (CPU). The bottleneck of real-time analysis is the speed of image recognition. For fast image recognition, 99.5% of the image was excluded from image recognition by distinguishing between the subject and the background. We achieved this by applying a binarization and connected-component labeling technique. This task is important for developing a fully automated learning apparatus. The use of such an automated system can improve the efficiency and accuracy of biological studies. The pond snail Lymnaea stagnails can be classically conditioned to avoid food that naturally elicits feeding behavior, and to consolidate this aversion into long-term memory. Determining memory status in the snail requires real-time analysis of the number of bites the snail makes in response to food presentation. The main algorithm for counting bites comprises two parts: extracting the mouth images from the recorded video and measuring the bite rate corresponding to the memory status. Reinforcement-supervised learning and image recognition were used to extract the mouth images. A change in the size of the mouth area was used as the cue for counting the number of bites. The accuracy of the final judgment of whether or not the snail had learned was the same as that determined by human observation. This method to improve the processing speed of image recognition has the potential for broad application beyond biological fields.

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5-Hydroxytryptamine (5-HT; serotonin) regulates metabolism and various homeostatic mechanisms in the body, and is involved in depression. These complicated functions of 5-HT are supported by several 5-HT receptor and 5-HT transporter subtypes. The development of agonists/antagonists and activators/inhibitors of 5-HT receptors and transporters is a strong target for drug studies. Toward this purpose, we calculated the conformations and electrical states of ionized 5-HT in aqueous solution using ab-initio methods. When we assumed an ionized 5-HT molecule and three surrounding water molecules, the hydrogen bond network for these four molecules formed a ring shape, resulting in deformation of the pyrrole ring in the indole group of 5-HT. To our knowledge, this is the first finding demonstrating deformation of the indole skeleton. The findings suggest that the direct involvement of water in the binding between 5-HT and its receptors and transporters should be taken account when designing candidate 5-HT active compounds.

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The gating of ion channel of ionotropic glutamate receptors is controlled by the structural change of the ligand-binding domain of GluR2. We examined the roles of residues in the glutamate-binding and cleft-closing mechanisms by molecular dynamics (MD) simulations. A glutamate entered the cleft deeply within the order of nanoseconds and the cleft locked the glutamate completely at 15 ns in an MD run. TYR450 seemed to regulate the orientation of the glutamate upon binding by cation-π interaction. A semi-open state was identified in the free energy profile evaluated with the structures on the spontaneously glutamate-bound and cleft-closed pathway by the unbiased MD simulations for the first time to our knowledge. In the semi-open state, the two sub-domains were bridged by two hydrogen bonds of GLU705 in the sub-domain 2 with TYR732 in the sub-domain 1 and with the glutamate bound to the sub-domain 1 until the transition to the closed state.

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Conformational changes of proteins are dominated by the excitation and relaxation processes of their vibrational states. To elucidate the mechanism of receptor activation, the conformation dynamics of receptors must be analyzed in response to agonist-induced vibrational excitation. In this study, we chose the bending vibrational mode of the guanidinium group of Arg485 of the glutamate receptor subunit GluR2 based on our previous studies, and we investigated picosecond dynamics of the glutamate receptor caused by the vibrational excitation of Arg485 via molecular dynamics simulations. The vibrational excitation energy in Arg485 in the ligand-binding site initially flowed into Lys730, and then into the J-helix at the subunit interface of the ligand-binding domain. Consequently, the atomic displacement in the subunit interface around an intersubunit hydrogen bond was evoked in about 3 ps. This atomic displacement may perturb the subunit packing of the receptor, triggering receptor activation.

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Even though glutamic acid contains only one more carboxyl group than gamma-aminobutyric acid (GABA), these neurotransmitters are recognized by their own specific receptors. To understand the ligand-recognition mechanism of the receptors, we must determine the geometric and electronic structures of GABA and glutamic acid in aqueous conditions using the ab initio calculation. The results of the present study showed that the stable structure of GABA was the extended form, and it attracted both cations and anions. Glutamic acid only attracted cations and was stabilized in four forms in aqueous conditions: Type 1 (an extended form), Type 2 (a rounded form), and Types 3 and 4 (twisted forms of Type 1). The former two types had low energy and the energy barrier between them was estimated to be small. These results showed that most free glutamic acid is present as Type 1, Type 2, and transient forms. The present results therefore suggest that the flexibility of the geometric structures of ligands should be taken into account when we attempt to elucidate the mechanism of recognition between ligands and receptors, in addition to the physicochemical characteristics of ligands and receptors.

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