Automated Computational Inference of Multi-protein Assemblies from Biochemical Co-purification Data

Florian Goebels; Lucas Hu; Gary Bader; Andrew Emili

doi:10.1007/978-1-4939-7759-8_25

Automated Computational Inference of Multi-protein Assemblies from Biochemical Co-purification Data

Methods Mol Biol. 2018:1764:391-399. doi: 10.1007/978-1-4939-7759-8_25.

Authors

Florian Goebels¹, Lucas Hu¹, Gary Bader¹, Andrew Emili²

Affiliations

¹ Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
² Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada. andrew.emili@utoronto.ca.

PMID: 29605929
DOI: 10.1007/978-1-4939-7759-8_25

Abstract

Biology has amassed a wealth of information about the function of a multitude of protein-coding genes across species. The challenge now is to understand how all these proteins work together to form a living organism, and a crucial step for gaining this knowledge is a complete description of the molecular "wiring circuits" that underlie cellular processes. In this chapter, we describe a general computational framework for predicting multi-protein assemblies from biochemical co-fractionation data.

Keywords: Bioinformatics; Cytoscape; Docker; Machine learning; Protein complex prediction; Protein interaction prediction; Protein-protein interaction; Python; Systems biology.

MeSH terms

Computational Biology / methods*
Electronic Data Processing / methods*
Humans
Multiprotein Complexes / chemistry*
Multiprotein Complexes / metabolism*
Protein Interaction Domains and Motifs*
Proteins / isolation & purification*

Substances

Multiprotein Complexes
Proteins