The social architecture of an in-depth cellular protein interactome

Nearly all cellular functions are mediated by protein-protein interactions and mapping the interactome provides fundamental insights into the regulation and structure of biological systems. In principle, affinity purification coupled to mass spectrometry (AP-MS) is an ideal and scalable tool, however, it has been difficult to identify low copy number complexes, membrane complexes and those disturbed by protein-tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive, high-throughput, and highly reproducible AP-MS technology combined with a quantitative two-dimensional analysis strategy for comprehensive interactome mapping of Saccharomyces cerevisiae. We reduced required cell culture volumes thousand-fold and employed 96-well formats throughout, allowing replicate analysis of the endogenous green fluorescent protein (GFP) tagged library covering the entire expressed yeast proteome. The 4159 pull-downs generated a highly structured network of 3,909 proteins connected by 29,710 interactions. Compared to previous large-scale studies, we double the number of proteins (nodes in the network) and triple the number of reliable interactions (edges), including very low abundant epigenetic complexes, organellar membrane complexes and non-taggable complexes interfered by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 15 interactors, the majority of them unreported so far. Similar to social networks between humans, the average shortest distance is 4.2 interactions. A web portal (www.yeast-interactome.org) enables exploration of our dataset by the network and biological communities and variations of our AP-MS technology can be employed in any organism or dynamic conditions.

[1]  J. Christopher Fromme,et al.  Structures of core eukaryotic protein complexes , 2021, bioRxiv.

[2]  Gyu Rie Lee,et al.  Accurate prediction of protein structures and interactions using a 3-track neural network , 2021, Science.

[3]  Oriol Vinyals,et al.  Highly accurate protein structure prediction with AlphaFold , 2021, Nature.

[4]  Isabell Bludau Discovery–Versus Hypothesis–Driven Detection of Protein–Protein Interactions and Complexes , 2021, International journal of molecular sciences.

[5]  R. Aebersold,et al.  Proteomic and interactomic insights into the molecular basis of cell functional diversity , 2020, Nature Reviews Molecular Cell Biology.

[6]  Yan Han,et al.  Cryo-EM structure of SWI/SNF chromatin remodeling complex with nucleosome , 2020, Nature.

[7]  Huanchen Wang,et al.  Control of XPR1-dependent cellular phosphate efflux by InsP8 is an exemplar for functionally-exclusive inositol pyrophosphate signaling , 2020, Proceedings of the National Academy of Sciences.

[8]  Ramin Rad,et al.  Dual proteome-scale networks reveal cell-specific remodeling of the human interactome , 2020, Cell.

[9]  L. Hothorn,et al.  Inositol pyrophosphates promote the interaction of SPX domains with the coiled-coil motif of PHR transcription factors to regulate plant phosphate homeostasis , 2019, Nature Communications.

[10]  T. Inada,et al.  The Ccr4-Not complex monitors the translating ribosome for codon optimality , 2019, Science.

[11]  Ruedi Aebersold,et al.  Complex‐centric proteome profiling by SEC‐SWATH‐MS , 2019, Nature Protocols.

[12]  J. Grantham,et al.  The role of the molecular chaperone CCT in protein folding and mediation of cytoskeleton-associated processes: implications for cancer cell biology , 2018, Cell Stress and Chaperones.

[13]  Kara Dolinski,et al.  The BioGRID interaction database: 2019 update , 2018, Nucleic Acids Res..

[14]  Melvin A. Park,et al.  Online Parallel Accumulation–Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer* , 2018, Molecular & Cellular Proteomics.

[15]  Matthias Mann,et al.  A Novel LC System Embeds Analytes in Pre-formed Gradients for Rapid, Ultra-robust Proteomics* , 2018, Molecular & Cellular Proteomics.

[16]  Anastasia Baryshnikova,et al.  Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome. , 2018, Cell systems.

[17]  Matthias Mann,et al.  Loss-less Nano-fractionator for High Sensitivity, High Coverage Proteomics * , 2017, Molecular & Cellular Proteomics.

[18]  Philip Lössl,et al.  The diverse and expanding role of mass spectrometry in structural and molecular biology , 2016, The EMBO journal.

[19]  Ruedi Aebersold,et al.  Mass-spectrometric exploration of proteome structure and function , 2016, Nature.

[20]  Gary D. Bader,et al.  AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations , 2016, F1000Research.

[21]  Quentin Giai Gianetto,et al.  Uses and misuses of the fudge factor in quantitative discovery proteomics , 2016, Proteomics.

[22]  A. Saiardi,et al.  Control of eukaryotic phosphate homeostasis by inositol polyphosphate sensor domains , 2016, Science.

[23]  Matthias Meurer,et al.  One library to make them all: streamlining the creation of yeast libraries via a SWAp-Tag strategy , 2016, Nature Methods.

[24]  Matthias Mann,et al.  Parallel Accumulation-Serial Fragmentation (PASEF): Multiplying Sequencing Speed and Sensitivity by Synchronized Scans in a Trapped Ion Mobility Device. , 2015, Journal of proteome research.

[25]  Marco Y. Hein,et al.  A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances , 2015, Cell.

[26]  Chengchao Xu,et al.  Glycosylation-directed quality control of protein folding , 2015, Nature Reviews Molecular Cell Biology.

[27]  Edward L. Huttlin,et al.  The BioPlex Network: A Systematic Exploration of the Human Interactome , 2015, Cell.

[28]  Marco Y. Hein,et al.  Accurate Protein Complex Retrieval by Affinity Enrichment Mass Spectrometry (AE-MS) Rather than Affinity Purification Mass Spectrometry (AP-MS)* , 2014, Molecular & Cellular Proteomics.

[29]  Philipp Korber,et al.  The yeast PHO5 promoter: from single locus to systems biology of a paradigm for gene regulation through chromatin , 2014, Nucleic acids research.

[30]  Andrew R. Jones,et al.  ProteomeXchange provides globally co-ordinated proteomics data submission and dissemination , 2014, Nature Biotechnology.

[31]  C. Kyriacou,et al.  Glutathione peroxidase activity is neuroprotective in models of Huntington's disease , 2013, Nature Genetics.

[32]  Tsutomu Suzuki,et al.  A cyclic form of N6-threonylcarbamoyladenosine as a widely distributed tRNA hypermodification. , 2013, Nature chemical biology.

[33]  Franco J. Vizeacoumar,et al.  Interaction landscape of membrane-protein complexes in Saccharomyces cerevisiae , 2012, Nature.

[34]  Gary D. Bader,et al.  WordCloud: a Cytoscape plugin to create a visual semantic summary of networks , 2011, Source Code for Biology and Medicine.

[35]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[36]  A. Burlingame,et al.  The RasGAP Proteins Ira2 and Neurofibromin Are Negatively Regulated by Gpb1 in Yeast and ETEA in Humans , 2010, Molecular and Cellular Biology.

[37]  Gipsi Lima-Mendez,et al.  The powerful law of the power law and other myths in network biology. , 2009, Molecular bioSystems.

[38]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

[39]  R. Shamir,et al.  From E-MAPs to module maps: dissecting quantitative genetic interactions using physical interactions , 2008, Molecular systems biology.

[40]  Sean R. Collins,et al.  Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae*S , 2007, Molecular & Cellular Proteomics.

[41]  K. Willison,et al.  Quantitative actin folding reactions using yeast CCT purified via an internal tag in the CCT3/gamma subunit. , 2006, Journal of molecular biology.

[42]  P. Uetz,et al.  The elusive yeast interactome , 2006, Genome Biology.

[43]  P. Bork,et al.  Proteome survey reveals modularity of the yeast cell machinery , 2006, Nature.

[44]  Sean R. Collins,et al.  Global landscape of protein complexes in the yeast Saccharomyces cerevisiae , 2006, Nature.

[45]  M. Glickman,et al.  A window of opportunity: timing protein degradation by trimming of sugars and ubiquitins. , 2005 .

[46]  F. Giostra,et al.  Methods and Results , 2014 .

[47]  E. O’Shea,et al.  Global analysis of protein localization in budding yeast , 2003, Nature.

[48]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[49]  P. Bork,et al.  Functional organization of the yeast proteome by systematic analysis of protein complexes , 2002, Nature.

[50]  Gary D Bader,et al.  Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry , 2002, Nature.

[51]  M. Münsterkötter,et al.  Chromatin remodelling at the PHO8 promoter requires SWI–SNF and SAGA at a step subsequent to activator binding , 1999, The EMBO journal.

[52]  J. Rappsilber,et al.  The NOT proteins are part of the CCR4 transcriptional complex and affect gene expression both positively and negatively , 1998, The EMBO journal.

[53]  T. Biederer,et al.  Role of Cue1p in ubiquitination and degradation at the ER surface. , 1997, Science.

[54]  Sean R. Collins,et al.  Supporting Online Material : Comprehensive characterization of genes required for protein folding in the endoplasmic reticulum , 2009 .

[55]  A. Barabasi,et al.  Emergence of Scaling in Random Networks , 1999 .