Open AccessEPIC: software toolkit for elution profile-based inference of protein complexes
Author(s) -
Lucas Hu,
Florian Goebels,
Tan Jh,
Eric J. Wolf,
Uroš Kuzmanov,
Cuihong Wan,
Sadhna Phanse,
Changjiang Xu,
Mike Schertzberg,
Andrew Fraser,
Gary D. Bader,
Andrew Emili
Publication year - 2019
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-019-0461-4
Subject(s) - interactome , epic , elution , fractionation , computer science , computational biology , software , caenorhabditis elegans , inference , component (thermodynamics) , data mining , biology , chemistry , biological system , chromatography , artificial intelligence , biochemistry , art , literature , programming language , physics , gene , thermodynamics
Protein complexes are key macromolecular machines of the cell, but their description remains incomplete. We and others previously reported an experimental strategy for global characterization of native protein assemblies based on chromatographic fractionation of biological extracts coupled to precision mass spectrometry analysis (chromatographic fractionation-mass spectrometry, CF-MS), but the resulting data are challenging to process and interpret. Here, we describe EPIC (elution profile-based inference of complexes), a software toolkit for automated scoring of large-scale CF-MS data to define high-confidence multi-component macromolecules from diverse biological specimens. As a case study, we used EPIC to map the global interactome of Caenorhabditis elegans, defining 612 putative worm protein complexes linked to diverse biological processes. These included novel subunits and assemblies unique to nematodes that we validated using orthogonal methods. The open source EPIC software is freely available as a Jupyter notebook packaged in a Docker container (https://hub.docker.com/r/baderlab/bio-epic/).