From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach

We propose the Kuramoto Graph Neural Network (KuramotoGNN), a novel class of continuous-depth graph neural networks (GNNs) that employs the Kuramoto model to mitigate the over-smoothing phenomenon, in which node features in GNNs become indistinguishable as the number of layers increases. The Kuramoto model captures the synchronization behavior of non-linear coupled oscillators. Under the view of coupled oscillators, we first show the connection between Kuramoto model and basic GNN and then over-smoothing phenomenon in GNNs can be interpreted as phase synchronization in Kuramoto model. The KuramotoGNN replaces this phase synchronization with frequency synchronization to prevent the node features from converging into each other while allowing the system to reach a stable synchronized state. We experimentally verify the advantages of the KuramotoGNN over the baseline GNNs and existing methods in reducing over-smoothing on various graph deep learning benchmark tasks

Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs). We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC) curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved

Comprehensive study of liposome-assisted synthesis of membrane proteins using a reconstituted cell-free translation system

Membrane proteins play pivotal roles in cellular processes and are key targets for drug discovery. However, the reliable synthesis and folding of membrane proteins are significant problems that need to be addressed owing to their extremely high hydrophobic properties, which promote irreversible aggregation in hydrophilic conditions. Previous reports have suggested that protein aggregation could be prevented by including exogenous liposomes in cell-free translation processes. Systematic studies that identify which membrane proteins can be rescued from irreversible aggregation during translation by liposomes would be valuable in terms of understanding the effects of liposomes and developing applications for membrane protein engineering in the context of pharmaceutical science and nanodevice development. Therefore, we performed a comprehensive study to evaluate the effects of liposomes on 85 aggregation-prone membrane proteins from Escherichia coli by using a reconstituted, chemically defined cell-free translation system. Statistical analyses revealed that the presence of liposomes increased the solubility of >90% of the studied membrane proteins, and ultimately improved the yields of the synthesized proteins. Bioinformatics analyses revealed significant correlations between the liposome effect and the physicochemical properties of the membrane proteins

Periodontal disease and atherosclerosis from the viewpoint of the relationship between community periodontal index of treatment needs and brachial-ankle pulse wave velocity

BACKGROUND: It has been suggested that periodontal disease may be an independent risk factor for the development of atherosclerosis. However, the relationship between periodontal disease and atherosclerosis has not been fully elucidated. This study aimed to assess the effects of periodontal disease on atherosclerosis. METHODS: The study design was a cross-sectional study. Subjects were 291 healthy male workers in Japan. We used the Community Periodontal Index of Treatment Needs (CPITN) score, average probing depth and gingival bleeding index (rate of bleeding gums) to assess the severity of periodontal disease. We also used the Brachial-Ankle Pulse Wave Velocity (baPWV) as the index for the development of atherosclerosis. RESULTS: The unadjusted odds ratio (OR) of atherosclerosis in relation to the CPITN score was 1.41 [95% CI: 1.16–1.73]. However, after adjustment for age, systolic blood pressure and smoking, the CPITN score had no relationship with atherosclerosis (adjusted OR: 0.91 [0.68–1.20]). CONCLUSION: Our results show no relationship between mild periodontal disease and atherosclerosis after appropriate adjustments