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Updated: Jun 25, 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

SAFPred: synteny-aware gene function prediction for bacteria using protein embeddings.

Aysun Urhan^1,2, Bianca-Maria Cosma¹, Ashlee M Earl²

¹Delft Bioinformatics Lab, Delft University of Technology Van Mourik, Delft XE 2628, The Netherlands.

Bioinformatics (Oxford, England)

|May 22, 2024

Summary

This summary is machine-generated.

We developed SAFPred, a new tool using advanced protein language models and bacterial gene synteny to predict bacterial gene functions. SAFPred improves accuracy, especially for novel proteins, and identified new toxins in clinical threat bacteria.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Area of Science:

Genomics and Bioinformatics
Microbial Genomics
Computational Biology

Background:

A significant portion of bacterial protein sequences lack functional annotation due to limitations in current prediction methods.
Existing gene function prediction algorithms primarily focus on eukaryotes and rely on sequence similarity, which is often absent for novel bacterial proteins.
The phylogenetic and metabolic diversity of bacteria necessitates specialized gene function prediction tools.

Purpose of the Study:

To develop an improved gene function prediction method specifically tailored for bacterial genomes.
To leverage protein embeddings from advanced language models and bacterial conserved synteny for enhanced annotation accuracy.

Main Methods:

Developed SAFPred, a novel synteny-aware gene function prediction tool utilizing protein embeddings from state-of-the-art protein language models.
Incorporated conserved synteny and bacterial operon structures into the prediction model.
Evaluated SAFPred performance against conventional sequence-based methods and existing state-of-the-art approaches across multiple bacterial species.

Main Results:

SAFpred demonstrated superior performance compared to conventional and state-of-the-art methods in bacterial gene function prediction.
The tool achieved high accuracy in detecting distant homologs, even with sequence similarities as low as 40%.
Application of SAFPred to Enterococcus species identified 11 putative novel toxins, highlighting its potential for discovering clinically relevant genes.

Conclusions:

SAFpred represents a significant advancement in bacterial gene function prediction, outperforming existing methods.
The integration of protein embeddings and synteny information effectively addresses the challenge of annotating novel bacterial proteins.
The discovery of novel toxins in clinical threat bacteria underscores the tool's utility for advancing human and animal health research.