pEffect

pEffect predicts type III secreted effector proteins by combining homology-based inference with de novo sequence-based prediction to detect effectors, including from short sequence fragments, across prokaryotic genomes.

Key Features:

• Enhanced Prediction Accuracy: Integrates homology-based inference with de novo predictions and reports up to three times higher performance compared to existing tools.
• Distributed Signal Recognition: Identifies effector recognition and transport signals distributed throughout the entire protein sequence rather than confined to the N-terminus.
• Genomic Insights: Scans hundreds of prokaryotic genomes to identify previously unknown effector proteins, suggesting a history of type III secretion systems that may predate the divergence of archaea and bacteria.
• Fragment Analysis Capability: Analyzes short sequence fragments to enable assessment of microbial communities without complete genome assemblies and identification of bacterial pathogenicity factors.

Scientific Applications:

• Pathogen Research: Identifies and characterizes type III effectors involved in diverse infectious diseases.
• Microbial Ecology: Enables analysis of microbial community sequence fragments to study bacterial interactions within ecosystems and symbioses.
• Evolutionary Biology: Provides data on previously unknown effectors to investigate the evolutionary origins and adaptations of type III secretion systems across prokaryotic lineages.

Methodology:

pEffect employs a hybrid computational approach combining homology-based inference using known effector sequences with de novo sequence-based predictions.