Comprehensive identification of ubiquitin-like 3 (UBL3)-interacting proteins in the mouse brain.

Discovery of novel post-translational modifications provides new insights into changes in protein function, localization, and stability. They are also key elements in understanding disease mechanisms and developing therapeutic strategies. We have previously reported that ubiquitin-like 3 (UBL3) serves as a novel post-translational modifier that is highly expressed in the cerebral cortex and hippocampus, in addition to various other organs, and that 60% of proteins contained in small extracellular vesicles (sEVs), including exosomes, are influenced by UBL3. In this study, we generated transgenic mice expressing biotinylated UBL3 in the forebrain under control of the alpha-CaMKII promoter (Ubl3Tg/+). Western blot analysis revealed that the expression of UBL3 in the cerebral cortex and hippocampus was 6- to 7-fold higher than that in the cerebellum. Therefore, we performed immunoprecipitation of protein extracts from the cerebral cortex of Ubl3+/+ and Ubl3Tg/+ mice using avidin beads to comprehensively discover UBL3 interacting proteins, identifying 35 new UBL3 interacting proteins. Nine proteins were annotated as extracellular exosomes. Gene Ontology (GO) analysis suggested a new relationship between sEVs and RNA metabolism in neurodegenerative diseases. We confirmed the association of endogenous UBL3 with the RNA-binding proteins FUS and HPRT1-both listed in the Neurodegenerative Diseases Variation Database (NDDVD)-and with LYPLA1, which is involved in Huntington's disease, using immunoprecipitation (IP)-western blotting analysis. These UBL3 interacting proteins will accelerate the continued elucidation of sEV research about proteins regulated by novel post-translational modifications by UBL3 in the brain.

KANPHOS: Kinase-associated neural phospho-signaling database for data-driven research.

Protein phosphorylation, a key regulator of cellular processes, plays a central role in brain function and is implicated in neurological disorders. Information on protein phosphorylation is expected to be a clue for understanding various neuropsychiatric disorders and developing therapeutic strategies. Nonetheless, existing databases lack a specific focus on phosphorylation events in the brain, which are crucial for investigating the downstream pathway regulated by neurotransmitters. To overcome the gap, we have developed a web-based database named "Kinase-Associated Neural PHOspho-Signaling (KANPHOS)." This paper presents the design concept, detailed features, and a series of improvements for KANPHOS. KANPHOS is designed to support data-driven research by fulfilling three key objectives: (1) enabling the search for protein kinases and their substrates related to extracellular signals or diseases; (2) facilitating a consolidated search for information encompassing phosphorylated substrate genes, proteins, mutant mice, diseases, and more; and (3) offering integrated functionalities to support pathway and network analysis. KANPHOS is also equipped with API functionality to interact with external databases and analysis tools, enhancing its utility in data-driven investigations. Those key features represent a critical step toward unraveling the complex landscape of protein phosphorylation in the brain, with implications for elucidating the molecular mechanisms underlying neurological disorders. KANPHOS is freely accessible to all researchers at https://kanphos.jp.

Asking the right questions for mutagenicity prediction from BioMedical text.

Assessing the mutagenicity of chemicals is an essential task in the drug development process. Usually, databases and other structured sources for AMES mutagenicity exist, which have been carefully and laboriously curated from scientific publications. As knowledge accumulates over time, updating these databases is always an overhead and impractical. In this paper, we first propose the problem of predicting the mutagenicity of chemicals from textual information in scientific publications. More simply, given a chemical and evidence in the natural language form from publications where the mutagenicity of the chemical is described, the goal of the model/algorithm is to predict if it is potentially mutagenic or not. For this, we first construct a golden standard data set and then propose MutaPredBERT, a prediction model fine-tuned on BioLinkBERT based on a question-answering formulation of the problem. We leverage transfer learning and use the help of large transformer-based models to achieve a Macro F1 score of >0.88 even with relatively small data for fine-tuning. Our work establishes the utility of large language models for the construction of structured sources of knowledge bases directly from scientific publications.