Related Experiment Video
Updated: Dec 28, 2025
Identification of Kinase-substrate Pairs Using High Throughput Screening
Published on: August 29, 2015
DeepKinZero: zero-shot learning for predicting kinase-phosphosite associations involving understudied kinases
Iman Deznabi1,2, Busra Arabaci1, Mehmet Koyutürk3,4
1Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.
Motivation:
Protein phosphorylation is a key regulator of protein function in signal transduction pathways. Kinases are the enzymes that catalyze the phosphorylation of other proteins in a target-specific manner. The dysregulation of phosphorylation is associated with many diseases including cancer. Although the advances in phosphoproteomics enable the identification of phosphosites at the proteome level, most of the phosphoproteome is still in the dark: more than 95% of the reported human phosphosites have no known kinases. Determining which kinase is responsible for phosphorylating a site remains an experimental challenge. Existing computational methods require several examples of known targets of a kinase to make accurate kinase-specific predictions, yet for a large body of kinases, only a few or no target sites are reported.
Results:
We present DeepKinZero, the first zero-shot learning approach to predict the kinase acting on a phosphosite for kinases with no known phosphosite information. DeepKinZero transfers knowledge from kinases with many known target phosphosites to those kinases with no known sites through a zero-shot learning model. The kinase-specific positional amino acid preferences are learned using a bidirectional recurrent neural network. We show that DeepKinZero achieves significant improvement in accuracy for kinases with no known phosphosites in comparison to the baseline model and other methods available. By expanding our knowledge on understudied kinases, DeepKinZero can help to chart the phosphoproteome atlas.
Availability And Implementation:
The source codes are available at https://github.com/Tastanlab/DeepKinZero.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
DeepKinZero is a novel zero-shot learning method that predicts kinases for understudied phosphosites. This approach advances phosphoproteome mapping by identifying kinases without prior target information.
Area of Science:
- Biochemistry
- Computational Biology
- Genomics
Background:
- Protein phosphorylation is crucial for cell signaling, with kinases catalyzing this process.
- Dysregulated phosphorylation is linked to diseases like cancer.
- Most human phosphosites lack identified kinases, hindering research.
Purpose of the Study:
- To develop a computational method for predicting kinases acting on phosphosites, especially for kinases with limited or no known targets.
- To address the challenge of identifying kinase-phosphosite relationships for understudied kinases.
Main Methods:
- Introduced DeepKinZero, a zero-shot learning model.
- Utilized a bidirectional recurrent neural network to learn kinase-specific amino acid preferences.
- Transferred knowledge from well-characterized kinases to those with unknown targets.
Main Results:
- DeepKinZero demonstrated significant accuracy improvements for kinases with no known phosphosites.
- Outperformed baseline models and existing methods in predicting kinase-target relationships.
- Successfully identified kinases for previously uncharacterized phosphosites.
Conclusions:
- DeepKinZero is the first zero-shot learning approach for kinase-phosphosite prediction.
- The method enhances the understanding of understudied kinases and contributes to mapping the phosphoproteome.
- Enables broader phosphoproteome analysis by providing kinase information for uncharacterized sites.
Related Concept Videos
02:54Protein Kinases and Phosphatases
Protein kinases
Many proteins in the cell are regulated by phosphorylation, the addition of a phosphate group. A family of enzymes called kinases...
02:54Protein Kinases and Phosphatases
01:02Phosphorylation
During phosphorylation, protein kinases transfer the terminal phosphate group of ATP to specific amino acid side chains of substrate proteins. Serine, threonine, and tyrosine are the most commonly...
02:04Protein-protein Interfaces
01:49Conserved Binding Sites
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
02:26Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...