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Protein Networks

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Protein Families

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism. Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members. If these new proteins contain similar amino acids in key...

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...

Protein and Protein Structures

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Updated: Jun 15, 2025

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, USA.

Biorxiv : the Preprint Server for Biology

|June 4, 2025

Summary

This summary is machine-generated.

Predicting protein functions computationally is essential for disease research. Our novel ProtFun model, using deep learning and protein embeddings, accurately predicts protein functions, outperforming existing methods.

Keywords:

Graph Attention Networks Graph Neural Networks Large Language Models Protein Family Network Protein function prediction

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Area of Science:

Bioinformatics
Computational Biology
Genomics

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 crucial for disease research and drug discovery.

Purpose of the Study:

To develop an advanced computational method for automatic protein function prediction.
To introduce ProtFun, a multi-modal deep learning architecture for enhanced protein function prediction.

Main Methods:

Integrated protein large language model (LLM) embeddings into a protein family network.
Utilized graph attention networks (GAT) to learn protein embeddings.
Combined LLM embeddings with InterPro signatures for function prediction model training.

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 achieved high accuracy in predicting protein functions.

Conclusions:

ProtFun offers a powerful and efficient approach for protein function prediction.
The developed method aids in understanding disease mechanisms and identifying therapeutic targets.
Open-sourced data and code facilitate further research and application.