Mapping differential interactomes by affinity purification coupled with data independent mass spectrometry acquisition

Characterizing changes in protein-protein interactions associated with sequence variants (e.g., disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies in which cost and time are major considerations. We have coupled AP to data-independent mass spectrometric acquisition (sequential window acquisition of all theoretical spectra, SWATH) and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. We used AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes and propose a scalable pipeline for systems biology studies.

[1]  S. Lindquist,et al.  HSP90 at the hub of protein homeostasis: emerging mechanistic insights , 2010, Nature Reviews Molecular Cell Biology.

[2]  John D. Venable,et al.  Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra , 2004, Nature Methods.

[3]  Ludovic C. Gillet,et al.  Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system , 2013, Nature Methods.

[4]  V. Vacic,et al.  Disease mutations in disordered regions--exception to the rule? , 2012, Molecular bioSystems.

[5]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[6]  T. Ideker,et al.  Differential network biology , 2012, Molecular systems biology.

[7]  Ludovic C. Gillet,et al.  Quantitative measurements of N‐linked glycoproteins in human plasma by SWATH‐MS , 2013, Proteomics.

[8]  A. Lowell,et al.  Epidermal growth factor receptors harboring kinase domain mutations associate with the heat shock protein 90 chaperone and are destabilized following exposure to geldanamycins. , 2005, Cancer research.

[9]  Ludovic C. Gillet,et al.  Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* , 2012, Molecular & Cellular Proteomics.

[10]  Mike Wood,et al.  4,5-diarylisoxazole Hsp90 chaperone inhibitors: potential therapeutic agents for the treatment of cancer. , 2007, Journal of medicinal chemistry.

[11]  R. Young,et al.  BET Bromodomain Inhibition as a Therapeutic Strategy to Target c-Myc , 2011, Cell.

[12]  Tony Pawson,et al.  Temporal regulation of EGF signaling networks by the scaffold protein Shc1 , 2013, Nature.

[13]  T. Pawson,et al.  Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor , 2011, Nature Biotechnology.

[14]  M. Tyers,et al.  CIF-1, a Shared Subunit of the COP9/Signalosome and Eukaryotic Initiation Factor 3 Complexes, Regulates MEL-26 Levels in the Caenorhabditis elegans Embryo , 2007, Molecular and Cellular Biology.

[15]  A. Gingras,et al.  Beyond hairballs: The use of quantitative mass spectrometry data to understand protein–protein interactions , 2012, FEBS letters.

[16]  Teresa Blasco Máñez a structural perspective, , 2011 .

[17]  O. M. Grbovic,et al.  V600E B‐Rafはその安定性にHsp90シャペロンを要求し,Hsp90阻害剤への応答として分解される , 2006 .

[18]  R. Aebersold,et al.  Analysis of protein complexes using mass spectrometry , 2007, Nature Reviews Molecular Cell Biology.

[19]  Brett Larsen,et al.  Label-free quantitative proteomics trends for protein-protein interactions. , 2013, Journal of proteomics.

[20]  J. Thornton,et al.  Molecular basis of inherited diseases: a structural perspective. , 2003, Trends in genetics : TIG.

[21]  Sean L Seymour,et al.  The Paragon Algorithm, a Next Generation Search Engine That Uses Sequence Temperature Values and Feature Probabilities to Identify Peptides from Tandem Mass Spectra*S , 2007, Molecular & Cellular Proteomics.

[22]  G. Hannon,et al.  A new regulatory motif in cell-cycle control causing specific inhibition of cyclin D/CDK4 , 1993, Nature.

[23]  N. Rosen,et al.  V600E B-Raf requires the Hsp90 chaperone for stability and is degraded in response to Hsp90 inhibitors. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Mathieu Blanchette,et al.  Systematic analysis of the protein interaction network for the human transcription machinery reveals the identity of the 7SK capping enzyme. , 2007, Molecular cell.

[25]  N. Schork,et al.  Kinase mutations in human disease: interpreting genotype–phenotype relationships , 2010, Nature Reviews Genetics.

[26]  M. Serrano,et al.  A p16INK4a-insensitive CDK4 mutant targeted by cytolytic T lymphocytes in a human melanoma , 1995, Science.

[27]  C. Prodromou Strategies for stalling malignancy: targeting cancer's addiction to Hsp90. , 2009, Current topics in medicinal chemistry.

[28]  Brett Larsen,et al.  A cost–benefit analysis of multidimensional fractionation of affinity purification‐mass spectrometry samples , 2011, Proteomics.

[29]  Amber L. Couzens,et al.  The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data , 2013, Nature Methods.

[30]  N. Hayward,et al.  Germline mutations in the p16INK4a binding domain of CDK4 in familial melanoma , 1996, Nature Genetics.

[31]  K. G. Coleman,et al.  Identification of CDK4 Sequences Involved in Cyclin D1 and p16 Binding* , 1997, The Journal of Biological Chemistry.

[32]  Natasa Przulj,et al.  High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells , 2005, Science.

[33]  Birgit Schilling,et al.  Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. , 2010, Journal of proteome research.

[34]  A. Gingras,et al.  eIF4 initiation factors: effectors of mRNA recruitment to ribosomes and regulators of translation. , 1999, Annual review of biochemistry.

[35]  Susan Lindquist,et al.  Quantitative Analysis of Hsp90-Client Interactions Reveals Principles of Substrate Recognition , 2012, Cell.

[36]  P. Workman,et al.  Activated B-RAF is an Hsp90 client protein that is targeted by the anticancer drug 17-allylamino-17-demethoxygeldanamycin. , 2005, Cancer research.

[37]  M. Vidal,et al.  Edgetic perturbation models of human inherited disorders , 2009, Molecular systems biology.

[38]  A. Bateman,et al.  Protein interactions in human genetic diseases , 2008, Genome Biology.

[39]  R. Aebersold,et al.  Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions , 2012, Nature Methods.

[40]  Amber L. Couzens,et al.  Mass spectrometry approaches to study mammalian kinase and phosphatase associated proteins. , 2012, Methods.

[41]  N. Sonenberg,et al.  The Transformation Suppressor Pdcd4 Is a Novel Eukaryotic Translation Initiation Factor 4A Binding Protein That Inhibits Translation , 2003, Molecular and Cellular Biology.

[42]  Christoph H Borchers,et al.  Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma , 2009, Nature Biotechnology.

[43]  R. Aebersold,et al.  mProphet: automated data processing and statistical validation for large-scale SRM experiments , 2011, Nature Methods.

[44]  B. Simons,et al.  Performance characteristics of a new hybrid quadrupole time-of-flight tandem mass spectrometer (TripleTOF 5600). , 2011, Analytical chemistry.