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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Updated: Jan 12, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

ProtFun: a protein function prediction model using graph attention networks with a protein large language model.

Muhammed Talo^1,2,3, Serdar Bozdag^1,2,3,4

¹Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, United States.

Bioinformatics Advances

|October 31, 2025

Summary

This summary is machine-generated.

We developed ProtFun, a novel deep learning method for automatic protein function prediction. This approach integrates protein embeddings from large language models and graph attention networks, outperforming existing methods for identifying protein functions and aiding disease research.

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Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Experimental protein function determination is costly and time-consuming.
High-throughput technologies generate vast amounts of protein sequence data.
Accurate protein function prediction is vital for disease research and drug discovery.

Purpose of the Study:

To develop an automated computational method for predicting protein functions.
To leverage multimodal deep learning for enhanced protein function prediction accuracy.

Main Methods:

Proposed ProtFun, a multimodal deep learning architecture.
Integrated protein large language model embeddings and InterPro signatures.
Utilized graph attention networks on protein family networks.

Main Results:

ProtFun demonstrated superior performance compared to state-of-the-art methods on benchmark datasets.
An ablation study confirmed the significance of individual components within the ProtFun architecture.
The model effectively predicts protein functions using integrated sequence and network information.

Conclusions:

ProtFun offers a powerful and efficient computational approach for protein function prediction.
This method can accelerate the identification of disease mechanisms and therapeutic targets.
The developed model and data are publicly available for research use.