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

Kinase-Associated Neural Signaling data-driven research database pathway analysis protein phosphorylation

Journal

Frontiers in molecular neuroscience
ISSN: 1662-5099
Titre abrégé: Front Mol Neurosci
Pays: Switzerland
ID NLM: 101477914

Informations de publication

Date de publication:
2024
Historique:
received: 31 01 2024
accepted: 11 03 2024
medline: 17 4 2024
pubmed: 17 4 2024
entrez: 17 4 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 38628370
doi: 10.3389/fnmol.2024.1379089
pmc: PMC11018961
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1379089

Informations de copyright

Copyright © 2024 Kannon, Murashige, Nishioka, Amano, Funahashi, Tsuboi, Yamahashi, Nagai, Kaibuchi and Yoshimoto.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Takayuki Kannon (T)

Department of Biomedical Data Science, Fujita Health University School of Medicine, Toyoake, Japan.
Division of Computational Science, International Center for Brain Science, Fujita Health University, Toyoake, Japan.

Satoshi Murashige (S)

Department of Biomedical Data Science, Fujita Health University School of Medicine, Toyoake, Japan.

Tomoki Nishioka (T)

Division of Cell Biology, International Center for Brain Science, Fujita Health University, Toyoake, Japan.

Mutsuki Amano (M)

Department of Cell Pharmacology, Graduate School of Medicine, Nagoya University, Nagoya, Japan.

Yasuhiro Funahashi (Y)

Division of Cell Biology, International Center for Brain Science, Fujita Health University, Toyoake, Japan.

Daisuke Tsuboi (D)

Division of Cell Biology, International Center for Brain Science, Fujita Health University, Toyoake, Japan.

Yukie Yamahashi (Y)

Division of Cell Biology, International Center for Brain Science, Fujita Health University, Toyoake, Japan.

Taku Nagai (T)

Division of Behavioral Neuropharmacology, International Center for Brain Science, Fujita Health University, Toyoake, Japan.

Kozo Kaibuchi (K)

Division of Cell Biology, International Center for Brain Science, Fujita Health University, Toyoake, Japan.

Junichiro Yoshimoto (J)

Department of Biomedical Data Science, Fujita Health University School of Medicine, Toyoake, Japan.
Division of Computational Science, International Center for Brain Science, Fujita Health University, Toyoake, Japan.

Classifications MeSH