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Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector

Tao Feng^1,2, Xirao Chen¹, Shufang Wu¹

¹Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, PR China.

Microbial Genomics

|February 11, 2025

Summary

This summary is machine-generated.

HRPredict accurately predicts plasmid host ranges using a novel machine learning approach. This tool improves understanding of gene transfer and bacterial adaptability by overcoming limitations of existing methods.

Keywords:

language model machine learning one-class SVM plasmid host range

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Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Plasmid host range prediction is vital for understanding gene dissemination (resistance, virulence).
Existing machine learning tools underestimate host ranges due to limited database annotations.
Mobile plasmids can transfer genes across diverse bacterial species.

Purpose of the Study:

To develop a novel method, HRPredict, for accurate prediction of plasmid host ranges.
To address the underestimation of host ranges by existing tools.
To provide insights into the genetic basis of plasmid host adaptability.

Main Methods:

HRPredict uses a word vector model to represent plasmid-encoded proteins.
A no-negative samples learning approach with one-class support vector machine (SVM) models was employed.
Host range prediction was performed across various taxonomic levels (families, genera, species).

Main Results:

HRPredict achieved high performance metrics (AUC, F1-score, recall, precision, accuracy > 0.9) on benchmark datasets.
The method demonstrated superior host range coverage compared to HOTSPOT and PlasmidHostFinder.
Identified genes related to bacterial adaptability, pathogenicity, and survival are key determinants of plasmid host range.

Conclusions:

HRPredict offers a more comprehensive prediction of plasmid host ranges, overcoming limitations of previous methods.
The findings enhance understanding of bacterial adaptation mechanisms mediated by plasmids.
HRPredict is expected to advance research on broad-host-range plasmid dissemination and facilitate novel plasmid analysis from microbiome data.