Chronic pain is an unpleasant experience caused by sensory and emotional instability, sometimes independent of actual tissue damage. Pain relief can greatly impact psychologic, social, and economic well-being. Aromatherapy has long been used to alleviate pain and previous studies demonstrated that odors alter cerebral blood flow. In the present study, we used near-infrared spectroscopy to test our hypothesis that olfactory stimulation contributes to pain relief by altering cerebral blood flow in brain regions associated with pain. Pain was induced by transcutaneous electrical stimulation and assessed using a visual analog scale. Peppermint and lavender olfactory stimuli were used. Based on previous results, we focused on the prefrontal cortex. A placebo experiment in which only air stimulation was presented revealed minimal changes in blood flow in the ventromedial prefrontal cortex when comparing pain stimulation alone and a combination of placebo and pain stimulation. We then examined changes in blood flow following the presentation of peppermint or lavender scents. Significant differences in blood flow were observed in the dorsolateral prefrontal cortex (DLPFC) between pain stimulation alone and pain stimulation combined with odor stimulation. These findings supported our previous finding that the DLPFC is involved in pain relief by patch-adhered stimulation, but odor stimulation activated the right DLPFC whereas patch-adhered stimulation suppressed the left DLPFC. One interpretation of the discrepancy is that the contrast of activation between the right and left DLPFC is important in pain relief. Our research will help to elucidate the neurologic mechanisms underlying pain relief.

Transport networks spanning the entire body of an organism are key infrastructures for achieving a functional system and facilitating the distribution of nutrients and signals. The large amoeba-like organism Physarum polycephalum has gained attention as a useful model for studying biological transport networks owing to its visible and rapidly adapting vein structure. Using particle-tracking velocimetry, we measured the flow velocity of protoplasmic streaming over the entire body of Physarum plasmodia during the development of its intricate vein network. Based on these measurements, we estimated how the protoplasm is transported and mixed throughout the body. Our findings suggest that the vein network significantly enhances effective mixing of the protoplasm throughout the organism, which may have important physiological implications for nutrient distribution and signaling.

Cyanobacteria can produce alkanes equivalent to diesel fuels through a two-step enzymatic process involving acyl-(acyl carrier protein) reductase (AAR) and aldehyde deformylating oxygenase (ADO), providing a potential renewable biofuel source. AAR binds to ADO for efficient delivery of an aldehyde substrate and they have been proposed to dissociate when the alkane product is released from the same site as the substrate entrance of ADO. However, the dynamics of the substrate and product in ADO during substrate entry and product release are poorly understood. Here, we performed molecular dynamics (MD) simulations of ADO in the presence of substrate or product. We found that while the aldehyde substrate remains close to the active center of ADO before catalysis, the alkane product can dynamically rotate within the hydrophobic tunnel inside ADO toward the product exit after catalysis. Furthermore, the parallel cascade selection (PaCS)-MD simulations of ADO and the AAR/ADO complex identified the locations of the substrate entrance and the multiple exits for product release on ADO. Strikingly, the PaCS-MD simulations revealed that the alkane product can be released from the exit different from the substrate entrance without dissociation of AAR. Based on these results, we propose a reaction model for efficient alkane production by the AAR/ADO complex in which aldehydes and alkanes are synthesized simultaneously while AAR and ADO remain bound, and the aldehyde substrate can be delivered to ADO immediately after alkane release. Our study will be useful in improving the efficiency of bioalkane production using AAR and ADO.

Visualization of hydration structures over the entire protein surface is necessary to understand why the aqueous environment is essential for protein folding and functions. However, it is still difficult for experiments. Recently, we developed a convolutional neural network (CNN) to predict the probability distribution of hydration water molecules over protein surfaces and in protein cavities. The deep network was optimized using solely the distribution patterns of protein atoms surrounding each hydration water molecule in high-resolution X-ray crystal structures and successfully provided probability distributions of hydration water molecules. Despite the effectiveness of the probability distribution, the positional differences of the predicted positions obtained from the local maxima as predicted sites remained inadequate in reproducing the hydration sites in the crystal structure models. In this work, we modified the deep network by subdividing atomic classes based on the electronic properties of atoms composing amino acids. In addition, the exclusion volumes of each protein atom and hydration water molecule were taken to predict the hydration sites from the probability distribution. These information on chemical properties of atoms leads to an improvement in positional prediction accuracy. We selected the best CNN from 47 CNNs constructed by systematically varying the number of channels and layers of neural networks. Here, we report the improvements in prediction accuracy by the reorganized CNN together with the details in the architecture, training data, and peak search algorithm.

Proteins typically fold into unique three-dimensional structures largely driven by interactions between hydrophobic amino acids. This understanding has helped improve our knowledge of protein folding. However, recent research has shown an exception to this idea, demonstrating that specific threonine-rich peptides have a strong tendency to form β-hairpin structures, even in the highly hydrophilic amino acid sequences. This finding suggests that the hydrophilic amino acid sequence space still leaves room for exploring foldable amino acid sequences. In this study, we conducted a systematic exploration of the repetitive amino acid sequence space by AlphaFold2 (AF2), with a focus on sequences composed exclusively of hydrophilic residues, to investigate their potential for adopting unique structures. As a result, the sequence space exploration suggested that several repetitive threonine-rich sequences adopt distinctive conformations and these conformational shapes can be influenced by the length of the sequence unit. Moreover, the analysis of structural dataset suggested that threonine contributes to the structural stabilization by forming non-polar atom packing that tolerates unsatisfied hydrogen bonds, and while also supporting other residues in forming hydrogen bonds. Our findings will broaden the horizons for the discovery of foldable amino acid sequences consisting solely of hydrophilic residues and help us clarify the unknown mechanisms of protein structural stabilization.