title: Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data.
abstract: Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about their biology. Yet, as more experiments are considered, a complex's signature may become conditional and less identifiable. Previously we successfully distinguished the general proteomic signature of genuine chromosomal proteins from hitchhikers using the Random Forests (RF) machine learning algorithm. Here we test whether small protein complexes can define distinguishable signatures of their own, despite the assumption that machine learning needs large training sets. We show, with simulated and real proteomics data, that RF can detect small protein complexes and relationships between them. We identify several complexes in quantitative proteomics results of wild-type and knockout mitotic chromosomes. Other proteins covary strongly with these complexes, suggesting novel functional links for later study. Integrating the RF analysis for several complexes reveals known interdependences among kinetochore subunits and a novel dependence between the inner kinetochore and condensin. Ribosomal proteins, although identified, remained independent of kinetochore subcomplexes. Together these results show that this complex-oriented RF (NanoRF) approach can integrate proteomics data to uncover subtle protein relationships. Our NanoRF pipeline is available online.
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom.
authors.forename: Luis F
authors.initials: LF
authors.lastname: Montaño-Gutierrez
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom.|Center for Innovative and Translational Medicine, Medical School, Kochi University, Kochi 783-8505, Japan.
authors.forename: Shinya
authors.initials: S
authors.lastname: Ohta
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom.
authors.forename: Georg
authors.initials: G
authors.lastname: Kustatscher
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom Bill.Earnshaw@ed.ac.uk Juri.Rappsilber@ed.ac.uk.
authors.forename: William C
authors.initials: WC
authors.lastname: Earnshaw
authors.affiliation: Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom Bill.Earnshaw@ed.ac.uk Juri.Rappsilber@ed.ac.uk.|Department of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany.
authors.forename: Juri
authors.initials: J
authors.lastname: Rappsilber
mesh_terms: D000251:Adenosine Triphosphatases / Q000737:chemistry; D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000737:chemistry; D066264:Datasets as Topic; D018386:Kinetochores / Q000737:chemistry; D000069550:Machine Learning; D046912:Multiprotein Complexes / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D013329:Structure-Activity Relationship
chemical_list: D004268:DNA-Binding Proteins; D046912:Multiprotein Complexes; C108312:condensin complexes; D000251:Adenosine Triphosphatases