SecretoMyc

SecretoMyc identifies and integrates computational predictions and Alphafold-based structural homology to characterize secreted proteins from six mycobacterial genomes, with emphasis on Mycobacterium tuberculosis and the SEC, TAT, and T7SS secretion systems to support studies of host–pathogen interactions and antigen discovery.

Key Features:

• Comprehensive Secretome Identification: Uses a combination of bioinformatics servers and custom-developed software to predict potentially secreted proteins across six mycobacterial genomes by analyzing the SEC, TAT, and T7SS pathways.
• Integration with Experimental Data: Cross-references computational predictions with selected proteomics and transcriptomics studies to support validation of predicted secreted proteins.
• Structural Homology Identification: Incorporates Alphafold-generated models to identify structural homologues among mycobacterial genomes for evolutionary and functional inference.

Scientific Applications:

• Host-Pathogen Interaction Studies: Identification of secreted proteins implicated in host–pathogen interactions to elucidate mechanisms of immune evasion and infection by M. tuberculosis.
• Vaccine Development: Characterization of secreted proteins to highlight potential antigenic targets for vaccine design.
• Drug Discovery: Structural and functional analysis of secreted proteins to reveal potential therapeutic targets for antimicrobial development.

Methodology:

Systematic genome-wide scanning to predict proteins secreted via SEC, TAT, and T7SS; cross-referencing of computational predictions with proteomics and transcriptomics data; and use of Alphafold-generated structural models for structure-based homology identification.