Computational prediction shines light on type III secretion origins.

Area of Science:

• Microbiology
• Computational Biology
• Genomics

Background:

• The type III secretion system (TTSS) is crucial for bacterial symbiosis and pathogenicity.
• TTSS facilitates the transport of effector proteins into host cells, conferring a bacterial advantage.
• Existing methods for identifying TTSS effectors have limitations in performance and scope.

Purpose of the Study:

• To develop a novel computational method for identifying type III effectors.
• To improve the accuracy and performance of effector identification compared to existing tools.
• To investigate the distribution of effector recognition and transport signals within protein sequences.

Main Methods:

• A hybrid approach combining homology-based inference and de novo predictions.
• Application of the method to scan hundreds of prokaryotic genomes.
• Evaluation of performance on short sequence fragments without requiring genome assembly.

Main Results:

• The developed method achieves up to 3-fold higher performance than current tools.
• Effector recognition and transport signals are distributed across the entire protein sequence, challenging previous assumptions.
• Identification of numerous previously unknown effectors across diverse prokaryotic genomes.
• The method demonstrates efficacy on short sequence fragments, enabling microbial community analysis.

Conclusions:

• The new computational method significantly enhances the identification of type III effectors.
• The findings suggest a broader evolutionary history for TTSS, potentially predating the archaea/bacteria split.
• This tool facilitates rapid bacterial pathogenicity assessment and microbial community evaluation.