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Protein Kinases and Phosphatases

Protein Kinases and Phosphatases

Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
Protein kinases
Many proteins in the cell are regulated by phosphorylation, the addition of a phosphate group. A family of enzymes called kinases...

Protein Kinases and Phosphatases

Protein Kinases and Phosphatases

Phosphorylation

Phosphorylation

The addition or removal of phosphate groups from proteins is the most common chemical modification that regulates cellular processes. These modifications can affect the structure, activity, stability, and localization of proteins within cells as well as their interactions with other proteins.
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...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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...

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Related Experiment Video

Updated: Dec 8, 2025

Oligopeptide Competition Assay for Phosphorylation Site Determination

Oligopeptide Competition Assay for Phosphorylation Site Determination

Published on: May 18, 2017

DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence

Saeed Ahmed¹, Muhammad Kabir¹, Muhammad Arif¹

¹School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

Analytical Biochemistry

|September 19, 2020

Summary

DeepPPSite, a novel deep learning predictor, accurately identifies phosphorylation sites on proteins. This tool overcomes limitations of existing methods, improving disease research and drug development through efficient computational analysis.

Keywords:

Deep learning Phosphorylation sites Post-translation modification Sequence feature information Stacked long short term memory

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Last Updated: Dec 8, 2025

Oligopeptide Competition Assay for Phosphorylation Site Determination

Oligopeptide Competition Assay for Phosphorylation Site Determination

Published on: May 18, 2017

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Published on: May 3, 2018

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

Biochemistry
Computational Biology
Bioinformatics

Background:

Phosphorylation, a key post-translational modification, regulates diverse cellular functions.
Abnormal phosphorylation is linked to diseases like cancer and neurodegenerative disorders.
Accurate identification of phosphorylation sites is crucial for biological research and drug discovery.

Purpose of the Study:

To develop a novel deep learning-based computational tool for accurate phosphorylation site prediction.
To address the limitations of existing methods in predicting serine (S), threonine (T), and tyrosine (Y) phosphorylation sites.
To provide an efficient and cost-effective alternative to time-consuming wet-lab techniques.

Main Methods:

A stacked long short-term memory recurrent neural network was employed to construct the DeepPPSite predictor.
The model learns protein representations from conjoint protein descriptors.
Performance was evaluated using 10-fold cross-validation and independent testing.

Main Results:

DeepPPSite demonstrated superior performance on training data, achieving high Matthews Correlation Coefficient (MCC) values for S, T, and Y sites (0.608, 0.602, 0.558, respectively).
Independent testing confirmed the predictor's generalization efficacy with MCC values of 0.358, 0.356, and 0.350 for S, T, and Y sites.
The tool significantly outperformed existing state-of-the-art methods in predicting phosphorylation sites.

Conclusions:

DeepPPSite offers a significant advancement in computational prediction of phosphorylation sites.
The tool's high accuracy and efficiency support its application in large-scale biological research and drug development.
This deep learning approach provides a valuable resource for understanding phosphorylation's role in health and disease.