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An efficient machine-learning framework for predicting protein post-translational modification sites.

Heba M Elreify1, Fathi E Abd El-Samie2,3, Moawad I Dessouky4

  • 1Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt. hebamohamedibrahim09@gmail.com.

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|August 25, 2025
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Summary
This summary is machine-generated.

HyLightKhib accurately predicts lysine 2-hydroxyisobutyrylation (Khib) sites using a novel computational framework. This advancement aids in understanding protein regulation and developing targeted therapies.

Keywords:
ESMLightGBMLysine 2-hydroxyisobutyrylationMachine learningMutual informationPost-translational modificationProtein language models

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Proteomics

Background:

  • Post-Translational Modifications (PTMs) regulate protein function, with lysine 2-hydroxyisobutyrylation (Khib) implicated in vital cellular processes.
  • Experimental Khib site identification is challenging due to resource-intensive methods and transient modification nature.

Purpose of the Study:

  • To develop an accurate and efficient computational tool for predicting Khib sites.
  • To overcome limitations of current experimental Khib identification techniques.

Main Methods:

  • Introduced HyLightKhib, a computational framework utilizing Light Gradient Boosting Machine.
  • Employed a hybrid feature extraction strategy combining Evolutionary Scale Modeling (ESM-2) embeddings, Composition, Transition, and Distribution (CTD) descriptors, and amino acid physicochemical properties.
  • Optimized feature selection using mutual information.

Main Results:

  • HyLightKhib achieved high predictive performance across diverse organisms (human, parasite, rice) with AUC-ROC scores of 0.893, 0.876, and 0.847, respectively.
  • Outperformed existing predictors like DeepKhib and ResNetKhib.
  • Demonstrated superior performance compared to XGBoost and CatBoost implementations.

Conclusions:

  • HyLightKhib offers an advanced computational approach for Khib site prediction.
  • Provides enhanced predictive accuracy and valuable biological insights for functional proteomics.
  • Has direct implications for developing PTM-targeted therapies.