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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.
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature

Heba M Elreify¹, Fathi E Abd El-Samie^2,3, Moawad I Dessouky²

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

Scientific Reports

|November 6, 2025

Summary

This summary is machine-generated.

A new deep-learning tool, BLOS-Khib, accurately predicts Lysine 2-hydroxyisobutyrylation (Khib) sites across species. This computational approach enhances understanding of this crucial post-translational modification in diverse organisms.

Keywords:

Blosum62 Cross-species Deep learning Lysine 2-hydroxyisobutyrylation Post-translational modifications

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Area of Science:

Biochemistry and Molecular Biology
Computational Biology
Genomics

Background:

Lysine 2-hydroxyisobutyrylation (Khib) is a vital post-translational modification (PTM) involved in gene expression and metabolic regulation.
Accurate computational prediction of Khib sites is currently limited, hindering research in this area.

Purpose of the Study:

To develop and validate a novel deep-learning framework, BLOS-Khib, for accurate cross-species prediction of Khib sites.
To identify conserved and species-specific sequence features associated with Khib modification.

Main Methods:

A deep-learning framework (Convolutional Neural Network - CNN) was developed, incorporating evolutionary information from the BLOSUM62 matrix.
Systematic optimization determined the optimal peptide length (43 amino acids) for sequence context.
Performance was evaluated across six diverse organisms, including human, wheat, rice, T. gondii, Candida albicans, and Botrytis cinerea.

Main Results:

BLOS-Khib achieved high predictive performance, with Area Under the ROC Curve (AUC) values ranging from 0.885 to 0.913 on independent test sets.
The framework outperformed existing state-of-the-art methods, including traditional machine learning and other deep learning architectures.
Sequence signature analysis revealed conserved lysine-rich regions and species-specific flanking amino acid preferences.
High transferability of the model across evolutionarily distant organisms was observed.

Conclusions:

BLOS-Khib offers a robust and accurate computational tool for predicting Khib sites across diverse species.
The study provides evolutionary insights into the sequence determinants of Khib modification, suggesting convergent evolution.
BLOS-Khib advances the field of PTM prediction and understanding of Khib's regulatory roles.