EPIC: software toolkit for elution profile-based inference of protein complexes.

Area of Science:

• Cellular biology
• Proteomics
• Bioinformatics

Background:

• Protein complexes are essential cellular machinery, but their complete characterization remains a challenge.
• Existing methods for analyzing native protein assemblies, like chromatographic fractionation-mass spectrometry (CF-MS), generate complex data that is difficult to interpret.

Purpose of the Study:

• To introduce EPIC (elution profile-based inference of complexes), a novel software toolkit designed for automated analysis of large-scale CF-MS data.
• To enable high-confidence identification of multi-component protein complexes from diverse biological samples.

Main Methods:

• Development of the EPIC software toolkit, a user-friendly Jupyter notebook packaged in a Docker container.
• Application of EPIC to analyze CF-MS data for global interactome mapping.
• Validation of identified protein complexes using orthogonal experimental methods.

Main Results:

• EPIC successfully automated the scoring of large-scale CF-MS data, facilitating the definition of protein complexes.
• A global interactome map of Caenorhabditis elegans was generated, identifying 612 putative protein complexes.
• Novel nematode-specific protein assemblies and subunits were discovered and experimentally validated.

Conclusions:

• EPIC provides an effective computational solution for interpreting complex CF-MS data, advancing the study of protein assemblies.
• The software facilitates the discovery of novel protein complexes and expands our understanding of cellular machinery.
• The open-source availability of EPIC promotes broader research in proteomics and interactomics.