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Updated: Jul 5, 2025

Identification of Kinase-substrate Pairs Using High Throughput Screening

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

Using explainable machine learning to uncover the kinase-substrate interaction landscape.

Zhongliang Zhou¹, Wayland Yeung², Saber Soleymani¹

¹School of Computing, University of Georgia, Athens, GA 30602, United States.

Bioinformatics (Oxford, England)

|January 20, 2024

Summary

This summary is machine-generated.

We developed an explainable AI model to predict kinase-substrate relationships, improving accuracy and interpretability for phosphorylation research. This tool aids in understanding cellular signaling by analyzing kinase specificity.

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Last Updated: Jul 5, 2025

Identification of Kinase-substrate Pairs Using High Throughput Screening

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

Identification of Novel CK2 Kinase Substrates Using a Versatile Biochemical Approach

Identification of Novel CK2 Kinase Substrates Using a Versatile Biochemical Approach

Published on: February 21, 2019

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

Area of Science:

Bioinformatics
Cellular Signaling
Proteomics

Background:

Phosphorylation is a critical post-translational modification regulating cellular processes.
Predicting kinase-substrate relationships is vital for understanding cell signaling.
Existing deep learning models for kinase-substrate prediction lack interpretability and are trained on biased datasets.

Purpose of the Study:

To develop an explainable Transformer model for predicting kinase-peptide interactions.
To leverage peptide library datasets for training on a large number of serine/threonine kinases.
To provide insights into the model's decision-making process using explainable AI methods.

Main Methods:

Developed an explainable Transformer model trained solely on primary sequences.
Utilized multitask learning for broad kinase-peptide interaction predictions.
Employed SHapley Additive exPlanation (SHAP) for residue-level analysis.

Main Results:

Achieved state-of-the-art performance in kinase-peptide interaction prediction.
Enabled predictions for kinases not included in training datasets.
Identified key specificity-determining residues and revealed the model's substrate prediction strategy.

Conclusions:

The developed model offers a highly accurate and interpretable approach to predicting kinase-substrate associations.
The model's ability to generalize to unseen kinases and provide mechanistic insights advances the field of cell signaling.
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