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TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.

Md Toki Tahmid¹, A K M Mehedi Hasan¹, Md Shamsuzzoha Bayzid²

¹Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh.

Communications Biology

|April 4, 2025

Summary

TransBind is a new deep learning method that predicts DNA-binding proteins and residues from single sequences. This alignment-free approach improves accuracy and efficiency, overcoming limitations of existing methods.

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Area of Science:

Computational biology
Bioinformatics
Molecular biology

Background:

Identifying DNA-binding proteins and residues is crucial for understanding biological processes.
Experimental methods are slow and costly.
Current machine learning methods struggle with accuracy, data imbalance, and reliance on multiple sequence alignments (MSAs).

Purpose of the Study:

To develop an accurate and efficient alignment-free deep learning framework for predicting DNA-binding proteins and residues.
To overcome the limitations of existing methods, particularly for orphan or rapidly evolving proteins.

Main Methods:

Developed TransBind, an alignment-free deep learning framework.
Utilized features from pre-trained protein language models.
Predicted DNA-binding proteins and residues directly from single primary sequences.

Main Results:

TransBind significantly outperforms state-of-the-art methods in accuracy and computational efficiency.
The framework effectively handles data imbalance issues.
Demonstrated superior performance through extensive evaluations and case studies.

Conclusions:

TransBind offers a powerful and efficient solution for identifying DNA-binding proteins and residues.
The alignment-free approach makes it suitable for a wider range of proteins, including orphan and rapidly evolving ones.
TransBind is accessible via a web server for broader research application.