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Identification of type VI secretion system effector-immunity pairs using structural bioinformatics.

Alexander M Geller1, Maor Shalom1, David Zlotkin1

  • 1Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.

Molecular Systems Biology
|April 24, 2024
PubMed
Summary

Structural bioinformatics tools identified 517 type VI secretion system effector (T6SS) families and their immunity proteins. This study provides a valuable database for understanding bacterial interactions and developing novel antimicrobials.

Keywords:
Alphafold-multimerEffector-immunity PairsFoldseekStructural BioinformaticsType VI Secretion System (T6SS)

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Area of Science:

  • Microbiology
  • Structural Bioinformatics
  • Genomics

Background:

  • The type VI secretion system (T6SS) is crucial for bacterial interactions, delivering toxic effectors (T6Es) into target cells.
  • T6SS effectors require cognate immunity proteins to prevent self-intoxication.
  • Discovering and annotating these effector-immunity pairs is vital for understanding bacterial virulence and inter-bacterial competition.

Purpose of the Study:

  • To systematically discover and functionally annotate T6SS effectors (T6Es) and their cognate immunity proteins using novel structural bioinformatic approaches.
  • To develop predictive models for T6E-immunity protein interactions.
  • To create a comprehensive, annotated database of T6E-immunity pairs.

Main Methods:

  • Applied structural clustering to a dataset of 17,920 T6SS-encoding bacterial genomes to identify T6E families.
  • Developed a logistic regression model to predict protein-protein interactions for T6E-immunity pairs.
  • Utilized structure-based annotation for functional characterization of T6Es.
  • Performed experimental validation of selected T6E-immunity pairs in E. coli.

Main Results:

  • Identified 517 putative T6E families using structural clustering, surpassing sequence-based methods.
  • Predicted candidate immunity proteins for 231 T6E families via a logistic regression model.
  • Achieved functional annotations for 51% of T6E families using structure-based annotation.
  • Validated four novel T6E-immunity pairs, including DUF3289 as a Colicin M homolog and DUF943 as its immunity protein.
  • Discovered a novel T6E homologous to SleB and identified its putative catalytic residue.

Conclusions:

  • Novel structural bioinformatic tools enable efficient and accurate discovery and annotation of T6SS effectors and immunity proteins.
  • The study provides an extensive, annotated database of T6E-immunity pairs, advancing the understanding of bacterial secretion systems.
  • Findings contribute to the potential development of new antibacterial strategies targeting T6SS mechanisms.