PXD018905

PXD018905 is an original dataset announced via ProteomeXchange.

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	2024-10-22
AnnouncementXML	Submission_2024-10-22_05:32:56.863.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

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.

Dataset with its publication pending

submitter keyword: statistical relational learning, cell signaling, kinase-substrate predictions,machine learning, LinkPhinder

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

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

• PRIDE
  1. PXD018905
     1. Label: PRIDE project
     2. Name: Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs