Dataset Information

The discovery of hidden guanylate cyclases (GCs) in the Homo sapiens proteome.

ABSTRACT: Recent discoveries have established functional guanylate cyclase (GC) catalytic centers with low activity within kinase domains in plants. These crypto GCs generate guanosine 3',5'-cyclic monophosphate (cGMP) essential for both intramolecular and downstream signaling. Here, we have set out to search for such crypto GCs moonlighting in kinases in the H. sapiens proteome and identified 18 candidates, including the neurotropic receptor tyrosine kinase 1 (NTRK1). NTRK1 shows a domain architecture much like plant receptor kinases such as the phytosulfokine receptor, where a functional GC essential for downstream signaling is embedded within a kinase domain. In vitro characterization of the NTRK1 shows that the embedded NTRK1 GC is functional with a marked preference for Mn²⁺ over Mg²⁺. This therefore points to hitherto unsuspected roles of cGMP in intramolecular and downstream signaling of NTRK1 and the role of cGMP in NTRK1-dependent growth and neoplasia.

SUBMITTER: Turek I

PROVIDER: S-EPMC10665587 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The discovery of hidden guanylate cyclases (GCs) in the <i>Homo sapiens</i> proteome.

Turek Ilona I Freihat Lubna L Vyas Jignesh J Wheeler Janet J Muleya Victor V Manallack David T DT Gehring Chris C Irving Helen H

Computational and structural biotechnology journal 20231104

Recent discoveries have established functional guanylate cyclase (GC) catalytic centers with low activity within kinase domains in plants. These crypto GCs generate guanosine 3',5'-cyclic monophosphate (cGMP) essential for both intramolecular and downstream signaling. Here, we have set out to search for such crypto GCs moonlighting in kinases in the <i>H. sapiens</i> proteome and identified 18 candidates, including the neurotropic receptor tyrosine kinase 1 (NTRK1). NTRK1 shows a domain architec ...[more]

PMID: 38022692

Similar Datasets

Project description:BackgroundHuman T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported.ResultsIn this work we develop Multi-instance Adaboost method to conduct homolog knowledge transfer for computationally reconstructing proteome-wide HTLV-human PPI networks. In this method, the homolog knowledge in the form of gene ontology (GO) is treated as auxiliary homolog instance to address the problems of data scarcity and data unavailability, while the potential negative knowledge transfer is automatically attenuated by AdaBoost instance reweighting. The cross validation experiments show that the homolog knowledge transfer in the form of independent homolog instances can effectively enrich the feature information and substitute for the missing GO information. Moreover, the independent tests show that the method can validate 70.3% of the recently curated interactions, significantly exceeding the 2.1% recognition rate by the HT-Y2H experiment. We have used the method to reconstruct the proteome-wide HTLV-human PPI networks and further conducted gene ontology based clustering of the predicted networks for further biomedical research. The gene ontology based clustering analysis of the predictions provides much biological insight into the pathogenesis of HTLV retroviruses.ConclusionsThe Multi-instance AdaBoost method can effectively address the problems of data scarcity and data unavailability for the proteome-wide HTLV-human PPI interaction networks reconstruction. The gene ontology based clustering analysis of the predictions reveals some important signaling pathways and biological modules that HTLV retroviruses are likely to target.

Dataset Information

The discovery of hidden guanylate cyclases (GCs) in the Homo sapiens proteome.

Publications

The discovery of hidden guanylate cyclases (GCs) in the <i>Homo sapiens</i> proteome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets