LIPO

LIPO identifies polypeptide sequences containing lipo-box motifs to predict Gram-negative outer membrane lipoproteins and support comparative analysis with proteomic data.

Key Features:

• Lipo-box recognition: Scans entire predicted proteomes for lipo-box motifs associated with signal peptidase II and categorizes sequences by similarity to known Gram-negative lipoproteins.
• Proteome-wide categorization: Classifies candidate lipoproteins based on lipo-box similarity to provide comprehensive lists of potential OM lipoproteins.
• Comparative analysis with experimental data: Compares bioinformatic predictions to experimental proteomic results to reveal discrepancies and species-specific features.
• Integration of proteomic evidence: Uses high-resolution two-dimensional gel electrophoresis and mass spectrometry findings from the OM subproteome of Methylococcus capsulatus (Bath) to inform interpretation of candidates.
• Beta-barrel OMP prediction: Implements novel bioinformatic algorithms to predict beta-barrel outer membrane proteins (OMPs) alongside OM lipoproteins.

Scientific Applications:

• Outer membrane proteomics: Identification and characterization of outer membrane lipoproteins in Gram-negative bacterial proteomes.
• Comparative genomics: Cross-species analysis of predicted proteomes to elucidate evolutionary differences, including in obligate methanotrophs such as Methylococcus capsulatus (Bath).
• Functional annotation: Assignment of putative functions to hypothetical proteins by comparison with known lipoproteins and OM protein signatures.

Methodology:

LIPO employs novel bioinformatic algorithms to predict beta-barrel outer membrane proteins and OM lipoproteins, compares these predictions with experimental two-dimensional gel electrophoresis and mass spectrometry data, and categorizes sequences based on lipo-box similarity.