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Updated: Jul 29, 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

SAP: 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, Broekmanweg 6, 2628 XE, Delft, The Netherlands.

Biorxiv : the Preprint Server for Biology

|May 19, 2023

Summary

Predicting bacterial gene function is challenging. A new tool, SAP, uses protein language models and bacterial operon structure to accurately predict gene functions, even for novel proteins, and identified potential new toxins.

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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
Bioinformatics
Microbial genomics

Background:

The function of most bacterial proteins remains unknown, hindering our understanding of microbial biology and potential applications.
Existing gene function prediction methods often fail for bacteria due to a lack of homologous sequences in databases and a focus on eukaryotic models.

Approach:

Developed SAP, a novel synteny-aware gene function prediction tool utilizing protein embeddings from advanced protein language models.
Integrated bacterial operon structure and conserved synteny into the prediction model to enhance accuracy for prokaryotes.

Key Points:

SAP significantly outperforms conventional sequence-based and current state-of-the-art methods in bacterial gene function prediction.
The tool demonstrates high efficacy in detecting distant homologs, even with sequence similarity as low as 40% to known proteins.
SAP successfully identified 11 previously unrecognized putative novel toxins in diverse enterococci species.

Conclusions:

SAP offers an improved approach for bacterial gene function prediction, addressing limitations of existing methods.
The tool has identified potential novel toxins in clinically relevant enterococci, highlighting its significance for human and animal health research.