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Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

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Training PBertKla on an Integrated Multi-Source Dataset with a Machine-Learning Layer for Lysine Lactylation Site

Seung Beom Jin¹, Junghee Park^2,3, Summer Dabin Lee¹

¹LNPsolution, 199-9 Dugaebisan-ro, Hongcheon-gun 25114, Gangwon-do, Republic of Korea.

International Journal of Molecular Sciences

|July 15, 2026

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We developed a new computational tool to predict lysine lactylation (Kla) sites, achieving high accuracy and generalization. Our model, PBertKla + ML, is a valuable resource for studying this important post-translational modification.

Area of Science:

Biochemistry
Computational Biology
Genomics

Background:

Lysine lactylation (Kla) is a critical post-translational modification impacting cellular processes like metabolism and reprogramming.
Understanding Kla site regulation is vital for disease research.

Purpose of the Study:

To develop and validate an accurate computational model for predicting lysine lactylation (Kla) sites.
To create and release the largest curated Kla dataset for community use.

Main Methods:

Trained an enhanced ProteinBERT-based predictor (PBertKla + ML) using an integrated multi-source dataset.
Assembled and quality-controlled a large Kla dataset (Multi) from nine sources.
Validated model generalization using leakage-controlled splits (protein-level, homology, leave-one-study-out).

Keywords:

AlphaFold ProteinBERT curated benchmark dataset leakage-controlled validation lysine lactylation machine learning post-translational modification prediction protein language model

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Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Published on: April 4, 2018

Related Experiment Videos

Last Updated: Jul 16, 2026

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Published on: November 29, 2014

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Published on: April 4, 2018

Main Results:

PBertKla + ML achieved an AUROC of 0.9126 on the integrated dataset, comparable to state-of-the-art tools.
The model demonstrated strong generalization, with AUROC ≈0.90 under stringent leakage-controlled conditions.
Ablation and SHAP analyses confirmed the primary contribution of ProteinBERT features, with a modest gain from the ML layer.

Conclusions:

The PBertKla + ML model is a highly accurate and generalizable tool for Kla site prediction.
The released Multi dataset serves as a valuable community resource for Kla research.
The study provides insights into the predictive signal for Kla sites and their potential structural distribution.