1. EPIC: software toolkit for elution profile-based inference of protein complexes
- Author
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Lucas Zhongming Hu, Changjiang Xu, Sadhna Phanse, June H. Tan, Eric J. Wolf, Mike Schertzberg, Uros Kuzmanov, Andrew Emili, Florian Goebels, Andrew G. Fraser, Cuihong Wan, and Gary D. Bader
- Subjects
Proteome ,Computer science ,Inference ,Computational biology ,EPIC ,Mass spectrometry ,Biochemistry ,Interactome ,Article ,03 medical and health sciences ,Software ,Protein Interaction Mapping ,Native protein ,Animals ,Caenorhabditis elegans ,Caenorhabditis elegans Proteins ,Molecular Biology ,030304 developmental biology ,0303 health sciences ,business.industry ,Elution ,Cell Biology ,Open source ,Multiprotein Complexes ,business ,Biotechnology - Abstract
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/).
- Published
- 2018