Protein Mass Spectrometry

UPS1 & UPS2 Proteomic Standards

Sigma now offers both the Universal Proteomics Standard and the Proteomics Dynamic range Standard as complex, well-defined, well characterized reference standards for mass spectrometry. Both standards contain the same 48 human proteins ranging in molecular mass from 6,000 to 83,000 Daltons. Each constituent protein has been HPLC purified and AAA quantitated prior to formulation.

  • Troubleshoot and optimize your analytical protocol
  • Confirm system suitability before analyzing critical samples
  • Normalize analytical results day-to-day or lab-to-lab
  • Determine your limit of detection

UPS1 | UPS2 | 2006 ABRF Study | List of UPS Proteins | References


Download the UPS FASTA File (17 Kb)

Universal Proteomics Standard, UPS1

Developed in collaboration with the Association of Biomolecular Resource Facilities Proteomics Standards Research Group (sPRG), the Universal Proteomics Standard (UPS1) contains 48 human proteins (5 pmols of each) ranging in molecular weight from 6,000 to 83,000 daltons.

Proteomics Dynamic Range Standard, UPS2

This standard is an enhancement of Sigma's original Universal Proteomics Standard (UPS1). The same complex mixture of 48 human proteins has been formulated into a dynamic range of concentrations, ranging from 500 amoles to 50 pmoles.


Ordering Information
Product No. Product Name Package Size Add to Cart
UPS1 Universal Proteomics Standard 1 set
UPS2 Proteomics Dynamic Range Standard 1 set

Each set contains one vial of Universal Proteomics Standard and one vial (20 µg) of Proteomics Grade Trypsin (T6567)

The ABRF sPRG 2006 Study

In the Fall and Winter of 2005/2006, the ABRF sPRG (Proteomics Standards Research Group) conducted a study to assess the analytical capabilities of proteomics laboratories. Approximately 125 labs from across the world volunteered to participate. Each lab received a complex mixture of 49 unknown proteins and were asked to identify as many of these proteins as possible using their best analytical strategies. The results, presented in February 2006, were quite impressive and in some cases, surprising. Learn more about the sPRG’s 2006 study.

List of UPS Proteins

UniProt Accession Number UPS1 Amount (fmol) UPS2 Amount (fmol) UniProt Protein Name [Synonym] MW (Da)
Source or recombinant Host Tag Potential PTMs
P00915 5,000 50,000 Carbonic anhydrase 1 28,738 Erythrocytes     Acetylation
P00918 5,000 50,000 Carbonic anhydrase 2 29,115 Erythrocytes     Acetylation
P01031 5,000 50,000 Complement C5 [Complement C5a] 8,536 Recombinant E. coli    
P69905 5,000 50,000 Hemoglobin alpha chain 15,126 Erythrocytes      
P68871 5,000 50,000 Hemoglobin beta chain 15,867 Erythrocytes     Acetylation, nitrosylation, glycosylation
P41159 5,000 50,000 Leptin 16,158 Recombinant E. coli    
P02768 5,000 50,000 Serum Albumin 66,357 Recombinant Pichia pastoris    
P62988 5,000 50,000 Ubiquitin 10,597 Recombinant E. coli 6-His  
P04040 5,000 5,000 Catalase 59,625 Erythrocytes      
P00167 5,000 5,000 Cytochrome b5 16,022 Recombinant E. coli 6-His  
P01133 5,000 5,000 Epidermal Growth Factor 6,353 Recombinant E. coli    
P02144 5,000 5,000 Myoglobin C 17,053 Heart      
P15559 5,000 5,000 NAD(P)H dehydrogenase [quinone] 1 [DT Diaphorase] C 30,736 Recombinant E. coli    
P62937 5,000 5,000 Peptidyl-prolyl cis-trans isomerase A [Cyclophilin A] 20,176 Recombinant E. coli    
Q06830 5,000 5,000 Peroxiredoxin 1 21,979 Recombinant E. coli    
P63165 5,000 5,000 Small ubiquitin-related modifier 1 [SUMO-1] 38,815 Recombinant E. coli GST  
P00709 5,000 500 Alpha-lactalbumin 14,078 Milk     Glycosylation
P06732 5,000 500 Creatine kinase M-type [CK-MM] 43,101 Heart      
P12081 5,000 500 Histidyl-tRNA synthetase [Jo-1] 58,233 Recombinant E. coli    
P61626 5,000 500 Lysozyme C 14,701 Milk      
Q15843 5,000 500 Neddylin [Nedd8] 9,072 Recombinant E. coli    
P02753 5,000 500 Retinol-binding protein 21,071 Urine      
P16083 5,000 500 Ribosyldihydronicotinamide dehydrogenase [quinone] [Quinone oxidoreductase 2] [NQO2] 25,821 Recombinant E. coli    
P63279 5,000 500 Ubiquitin-conjugating enzyme E2 I [UbcH9] 18,007 Recombinant E. coli    
P01008 5,000 50 Antithrombin-III 49,039 Plasma     Glycosylation
P61769 5,000 50 Beta-2-microglobulin 11,731 Urine      
P55957 5,000 50 BH3 Interacting domain death agonist [BID] 21,995 Recombinant E. coli    
O76070 5,000 50 Gamma-synuclein 15,363 Recombinant E. coli    
P08263 5,000 50 Glutathione S-transferase A1 [GST A1-1] 25,500 Recombinant E. coli    
P01344 5,000 50 Insulin-like growth factor II 7,475 Recombinant E. coli    
P01127 5,000 50 Platelet-derived growth factor B chain 12,294 Recombinant E. coli    
P10599 5,000 50 Thioredoxin 12,429 Recombinant E. coli 6-His  
P99999 5,000 5 Cytochrome c[Apocytochrome c] 11,618 Recombinant E. coli    
P06396 5,000 5 Gelsolin 82,959 Plasma     Phosphorylation
P09211 5,000 5 Glutathione S-transferase P [GST] 23,225 Placenta      
P01112 5,000 5 GTPase HRas [Ras protein] 21,298 Recombinant E. coli    
P01579 5,000 5 Interferon gamma (IFN-gamma) 16,879 Recombinant E. coli    
P02787 5,000 5 Serotransferrin [Apotransferrin] 75,181 Plasma     Glycosylation
O00762 5,000 5 Ubiquitin-conjugating enzyme E2 C [UbcH10] 20,475 Recombinant E. coli 6-His  
P51965 5,000 5 Ubiquitin-conjugating enzyme E2 E1 [UbcH6] 22,227 Recombinant E. coli 6-His  
P08758 5,000 0.5 Annexin A 5 35,806 Placenta     Acetylation
P02741 5,000 0.5 C-reactive protein 23,047 Plasma      
P05413 5,000 0.5 Fatty acid-binding protein 14,727 Plasma     Acetylation, phosphorylation
P10145 5,000 0.5 Interleukin-8 8,386 Recombinant E. coli    
P02788 5,000 0.5 Lactotransferrin 76,165 Milk     Glycosylation
P10636 5,000 0.5 Microtubule-associated protein tau [Tau protein] 45,719 Recombinant E. coli 6-His  
P00441 5,000 0.5 Superoxide dismutase [Cu-Zn] 15,805 Erythrocytes     Acetylation
P01375 5,000 0.5 Tumor necrosis factor [TNF-alpha] 17,353 Recombinant E. coli    


 References for UPS1

  1. Tabb, D.L., et al., MyriMatch: Highly Accurate Tandem Mass Spectral Peptide Identification by Multivariate Hypergeometric Analysis.  J. Protein Res., 6(2), 654-661 (2007). Abstract
  2. Uwaje, N.C., et al., Interrogation of MS/MS search data with an pI Filter algorithm to increase protein identification success.  Electrophoresis, 28(12), 1867-1874 (2007). Abstract
  3. Brosch, M., et al., Comparison of Mascot and X!Tandem performance for low and high accuracy mass spectrometry and the development of an adjusted Mascot threshold.  Mol. Cell. Proteomics, 7(5), 962-970 (2008). Abstract
  4. Molina, H., et al., Comprehensive comparison of collision induced dissociation and electron transfer dissociation.  Anal. Chem., 80(13), 4825-4835 (2008). Abstract
  5. Fusaro, V.A., et al., Prediction of high-responding peptides for targeted protein assays by mass spectrometry.  Nat. Biotechnol., 27(2), 190-198 (2009). Abstract
  6. Davidson, W.S., et al., Proteomic Analysis of Defined HDL Subpopulations Reveals Particle-Specific Protein Clusters.  Arterioscler. Thromb. Vasc. Biol., 29(6), 870-876 (2009). Abstract
  7. Paulovich, A.G., et al., Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance.  Mol. Cell. Proteomics, 9(2), 242-254 (2010). Abstract
  8. Tabb, D.L., et al., Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography-Tandem Mass Spectrometry.  J. Proteome Res., 9(2), 761-776 (2010). Abstract
  9. Kim, M.S., et al., Assessment of Resolution Parameters for CID-Based Shotgun Proteomic Experiments on the LTQ-Orbitrap Mass Spectrometer.  J. Am. Soc. Mass. Spectrom., 21(9), 1606-1611 (2010). Abstract
  10. Li, B., et al., Confident identification of 3-nitrotyrosine modifications in mass spectral data across multiple mass spectrometry platforms.  J. Proteomics, 74(11), 2510-2521 (2011). Abstract
  11. Yen, C.Y., et al., Spectrum-to-Spectrum Searching Using a Proteome-wide Spectral Library.  Mol. Cell. Proteomics, 10(7), M111.007666 (2011). Abstract
  12. Ma, X., et al., Processing methods for signal suppression of FTMS data.  Proteome Sci., 9 Suppl 1, S2 (2011). Abstract
  13. Wu, Q., et al., NSI and NSMT: usages of MS/MS fragment ion intensity for sensitive differential proteome detection and accurate protein fold change calculation in relative label-free proteome quantification.  Analyst, 137(13), 3146-3153 (2012). Abstract
  14. Wright, J.C., et al., Enhanced Peptide Identification by Electron Transfer Dissociation Using an Improved Mascot Percolator.  Mol. Cell. Proteomics, 11(8), 479-491 (2012). Abstract
  15. Nahnsen, S., and Kohlbacher, O., In silico design of targeted SRM-based experiments.  BMC Bioinformatics, 13 Suppl 16, S8 (2012). Abstract
  16. Reiz, B., et al., Chemical rule-based filtering of MS/MS spectra.  Bioinformatics, 29(7), 925-932 (2013). Abstract
  17. Augustsson, P., et al., Acoustophoretic microfluidic chip for sequential elution of surface bound molecules from beads or cells.  Biomicrofluidics, 6(3), 34115 (2012). Abstract
  18. Milac, T.I., et al., Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies.  Stat. Interface, 5(1), 75-87 (2012). Abstract
  19. Jian, L., et al., A Novel Algorithm for Validating Peptide Identification from a Shotgun Proteomics Search Engine.  J. Proteome Res., 12(3), 1108-1119 (2013). Abstract
  20. Zerck, A., et al., Optimal precursor ion selection for LC-MALDI MS/MS.  BMC Bioinformatics, 14, 56 (2013). Abstract
  21. Higgs, R.E., et al., Quantitative Proteomics via High Resolution MS Quantification: Capabilities and Limitations.  Int. J. Proteomics, 2013, 674282 (2013). Abstract                                                                                       
  22. Kertész-Farkas, A., et al., PTMTreeSearch: a novel two-stage tree-search algorithm with pruning rules for the identification of post-translational modification of proteins in MS/MS spectra.  Bioinformatics, 30(2), 234-241 (2014). Abstract
  23. Gatto, L., and Christoforou, A., Using R and Bioconductor for proteomics data analysis.  Biochim. Biophys. Acta, 1844(1 Pt A), 42-51 (2014). Abstract
  24. Van Riper, S.K., et al., Improved Intensity-Based Label-Free Quantification via Proximity-Based Intensity Normalization (PIN).  J. Proteome Res., 13(3), 1281-1292 (2014). Abstract
  25. Rudnick, P.A., et al., Improved Normalization of Systematic Biases Affecting Ion Current Measurements in Label-free Proteomics Data.  Mol. Cell. Proteomics, 13(5), 1341-1351 (2014). Abstract
  26. Ivanov, M.V., et al., Empirical multi-dimensional space for scoring peptide spectrum matches in shotgun proteomics.  J. Proteome Res., 13(4), 1911-1920 (2014). Abstract
  27. Baba, T., et al., Phosphatidic Acid (PA)-Preferring Phospholipase A1 Regulates Mitochondrial Dynamics.  J. Biol. Chem., 289(16), 11497-11511 (2014). Abstract
  28. Kannaste, O., et al., Cross-correlation of spectral count ranking to validate quantitative proteome measurements.  J. Proteome Res., 13(4), 1957-1968 (2014). Abstract
  29. Tu, C., et al., Systematic Assessment of Survey Scan and MS2-Based Abundance Strategies for Label-Free Quantitative Proteomics Using High-Resolution MS Data.  J. Proteome Res., 13(4), 2069-2079 (2014). Abstract
  30. Prokai, L., et al., Selective chemoprecipitation to enrich nitropeptides from complex proteomes for mass-spectrometric analysis.  Nat. Protoc., 9(4), 882-895 (2014). Abstract
  31. Chawade, A., et al., Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets.  J. Proteome Res., 13(6), 3114-3120 (2014). Abstract
  32. Ebhardt, H.A., et al., Enzymatic generation of peptides flanked by basic amino acids to obtain MS/MS spectra with 2× sequence coverage.  Rapid Commun. Mass Spectrom., 28(24), 2735-2743 (2014). Abstract
  33. Koh, H.W., et al., EBprot: Statistical analysis of labeling-based quantitative proteomics data.  Proteomics, 15(15), 2580-2591 (2015). Abstract
  34. Qi, D., et al., The mzqLibrary - An open source Java library supporting the HUPO-PSI quantitative proteomics standard.  Proteomics, 15(18), 3152-3162 (2015). Abstract
  35. Pursiheimo, A., et al., Optimization of Statistical Methods Impact on Quantitative Proteomics Data.  J. Proteome Res., 14(10), 4118-4126 (2015). Abstract
  36. Suomi, T., et al., Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins.  J. Proteome Res., 14(11), 4564-4570 (2015). Abstract
  37. Liang, X., et al., An adaptive classification model for peptide identification.  BMC Genomics, 16(Suppl 11):S1 (2015). Abstract
  38. Ramus, C., et al., Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset.  J. Proteomics, 132, 51-62 (2016). Abstract
  39. Ramus, C., et al., Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods.  Data Brief, 6, 286-294 (2016). Abstract

 References for UPS2

  1. Dicker, L., et al., Increased power for the analysis of label-free LC-MS/MS proteomics data by combining spectral counts and peptide peak attributes.  Mol .Cell. Proteomics, 9(12), 2704-2718 (2010). Abstract
  2. Zhang, J., and Haskins, W., ICPD - A New Peak Detection Algorithm for LC/MS.  BMC Genomics, 11 Suppl 3, S8 (2010). Abstract
  3. Kwon, T., et al., MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines.  J. Proteome Res., 10(7), 2949-2958 (2011). Abstract
  4. Schwanhäusser, B., et al., Global quantification of mammalian gene expression control.  Nature, 473, 337-342 (2011). Abstract
  5. Forshed, J., et al., Enhanced information output from shotgun proteomics data by 5protein quantification and peptide quality control (PQPQ).  Mol. Cell. Proteomics, 10(10), M111.010264 (2011). Abstract
  6. Arike, L., et al., Comparison and applications of label-free absolute proteome quantification methods on Escherichia coliJ. Proteomics, 75(17), 5437-5448 (2012). Abstract
  7. Shin, J.B., et al., Molecular architecture of the chick vestibular hair bundle.  Nat. Neurosci., 16(3), 365-374 (2013). Abstract
  8. Wu, Q., et al., Improved accuracy for label-free absolute quantification of proteome by combining the Absolute Protein EXpression profiling algorithm and summed tandem mass spectrometric total ion current.  Analyst, 139, 138-146 (2014). Abstract
  9. Brendel, J., et al., A Complex of Cas Proteins 5, 6, and 7 Is Required for the Biogenesis and Stability of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-derived RNAs (crRNAs) in Haloferax volcaniiJ. Biol. Chem., 289(10), 7164-7177 (2014). Abstract
  10. Sandberg, A., et al., Quantitative accuracy in mass spectrometry based proteomics of complex samples: The impact of labeling and precursor interference.  J. Proteomics, 96, 133-144 (2014). Abstract
  11. Smits, A.H., et al., Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs.  Nucleic Acids Res., 42(15), 9880-9891 (2014). Abstract
  12. Hutchinson, E.C., et al., Conserved and host-specific features of influenza virion architecture.  Nat. Commun., 5, 4816 (2014). Abstract
  13. Böhm, Juila Wiebke, “A comprehensive C/EBPβ interactome”.  Dr. rer. nat. dissertation, Humboldt University Berlin, 2015.
  14. Tsou, C.C., et al., DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics.  Nat. Methods, 12(3), 258-264 (2015). Abstract
  15. Soufi, B., et al., Characterization of the E. coli proteome and its modifications during growth and ethanol stress.  Front. Microbiol., 6, 103 (2015). Abstract
  16. Ma, B., Novor: Real-Time Peptide de Novo Sequencing Software.  J. Am. Soc. Mass Spectrom., 26(11), 1885-1894 (2015). Abstract

 References for UPS1 and UPS2

  1. Geiger, T., et al., Proteomics on an Orbitrap Benchtop Mass Spectrometer Using All-ion Fragmentation.  Mol. Cell. Proteomics, 9(10), 2252-2261 (2010). Abstract
  2. Wiśniewski, J.R., et al., Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma.  Mol. Syst. Biol., 8, 611 (2012). Abstract
  3. Ivanov, A.R., et al., Interlaboratory studies and initiatives developing standards for proteomics.  Proteomics, 13(6), 904-909 (2013). Abstract
  4. Krey, J.F., et al.,  Accurate Label-Free Protein Quantitation with High- and Low-Resolution Mass Spectrometers.  J. Proteome Res., 13(2), 1034-1044 (2014). Abstract
  5. Cox, J., et al., Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ.  Mol. Cell. Proteomics, 13(9), 2513-2526 (2014). Abstract
  6. Laskay, Ü.A., et al., Extended bottom-up proteomics with secreted aspartic protease Sap9.  J. Proteomics, 110, 20-31 (2014). Abstract
  7. Beck, S., et al., The Impact II, a Very High-Resolution Quadrupole Time-of-Flight Instrument (QTOF) for Deep Shotgun Proteomics.  Mol. Cell. Proteomics, 14(7), 2014-2029 (2015). Abstract
  8. Kirli, K., et al., A deep proteomics perspective on CRM1-mediated nuclear export and nucleocytoplasmic partitioning.  eLIFE, 4, e11466 (2015). Abstract