High throughout prediction of toxin-antitoxin protein interfaces with AlphaFold2
Title: High throughout prediction of toxin-antitoxin protein interfaces with AlphaFold2
SNIC Project: Berzelius-2022-107
Project Type: LiU Berzelius
Principal Investigator: Gemma Atkinson <gemma.atkinson@med.lu.se>
Affiliation: Lunds universitet
Duration: 2022-06-01 – 2022-12-01
Classification: 30109
Homepage: https://kaw.wallenberg.org/en/research/bacterias-emergency-stop-buttons
Keywords:

Abstract

Toxin-antitoxin (TA) systems are ubiquitous, diverse and highly mobile gene pairs of microbes. They consist of a gene encoding a a toxin that dramatically inhibits bacterial growth and an adjacent gene encoding an antitoxin that protects against, and neutralises the toxic effect. In this project we are using deep learning methods to understand the structure, function and evolution of thousands of TA systems that we have predicted in microbial genomes. As a mechanism of defence against bacteriophages, TAs have significance for developing new biotechnological tools, as well as understanding and eventually overcoming natural barriers to phage therapy for treating antibiotic resistant infections.