Related Experiment Video
Updated: Sep 13, 2025
08:48
Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays
Published on: November 29, 2014
14.1K
HybridKla: a hybrid deep learning framework for lactylation site prediction
Wanshan Ning1, Feibo Qin2, Ziwei Zhou2
1Institute for Clinical Medical Research, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian 361003, China.
Briefings in Bioinformatics
|July 30, 2025
Summary
Researchers developed HybridKla, a new computational tool to identify lysine lactylation (Kla) sites. This method significantly improves prediction accuracy using a large dataset and advanced feature encoding, aiding research into this important biological modification.
Area of Science:
- Biochemistry
- Molecular Biology
- Computational Biology
Background:
- Lysine lactylation (Kla) is a crucial post-translational modification involved in various biological processes and diseases.
- Existing computational methods for identifying Kla sites are limited by small datasets.
Purpose of the Study:
- To construct a comprehensive benchmark dataset for Kla site identification.
- To develop an accurate and efficient computational predictor for Kla sites.
Main Methods:
- Compiled a large dataset of 23,984 Kla sites from 7,297 proteins.
- Developed a multi-feature hybrid system integrating eight encoding strategies.
- Employed deep learning with the hybrid system to create the HybridKla predictor.
Main Results:
- HybridKla achieved a high Area Under the Curve (AUC) of 0.8460.
- Demonstrated a significant improvement (>28.90%) in AUC compared to existing tools (0.6563).
- Performed a proteome-wide prediction of Kla sites.
Conclusions:
- HybridKla offers a powerful and accurate tool for identifying lysine lactylation sites.
- The developed benchmark dataset and predictor advance the study of Kla.
- An online service for HybridKla is available for academic research.
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