PXD018905 is an
original dataset announced via ProteomeXchange.
Dataset Summary
Title | Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs |
Description | Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder). |
HostingRepository | PRIDE |
AnnounceDate | 2022-02-20 |
AnnouncementXML | Submission_2022-02-20_06:23:11.394.xml |
DigitalObjectIdentifier | |
ReviewLevel | Peer-reviewed dataset |
DatasetOrigin | Original dataset |
RepositorySupport | Unsupported dataset by repository |
PrimarySubmitter | David Matallanas |
SpeciesList | scientific name: Homo sapiens (Human); NCBI TaxID: 9606; |
ModificationList | biotinylated residue; monohydroxylated residue; acetylated residue |
Instrument | Q Exactive |
Dataset History
Revision | Datetime | Status | ChangeLog Entry |
0 | 2020-04-29 08:11:06 | ID requested | |
⏵ 1 | 2022-02-20 06:23:11 | announced | |
2 | 2024-10-22 05:32:57 | announced | 2024-10-22: Updated project metadata. |
Publication List
Dataset with its publication pending |
Keyword List
submitter keyword: machine learning, cell signaling, statistical relational learning, kinase-substrate predictions, LinkPhinder |
Contact List
David Gomez |
contact affiliation | Systems Biology Ireland, University College Dublin |
contact email | david.gomez@ucd.ie |
lab head | |
David Matallanas |
contact affiliation | Systems Biology Ireland, University College Dublin |
contact email | david.gomez@ucd.ie |
dataset submitter | |
Full Dataset Link List
Dataset FTP location
NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD018905 |
PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD018905
- Label: PRIDE project
- Name: Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs