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HomeProtein Mass SpectrometryUPS1 & UPS2 Proteomic Standards

UPS1 & UPS2 Proteomic Standards

We now offer 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

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 our original Universal Proteomics Standard (UPS2). The same complex mixture of 48 human proteins has been formulated into a dynamic range of concentrations, ranging from 500 amoles to 50 pmoles.

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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

References for UPS1

1.
Tabb DL, Fernando CG, Chambers MC. 2007. MyriMatch:  Highly Accurate Tandem Mass Spectral Peptide Identification by Multivariate Hypergeometric Analysis. J. Proteome Res.. 6(2):654-661. https://doi.org/10.1021/pr0604054
2.
Uwaje NC, Mueller NS, Maccarrone G, Turck CW. 2007. Interrogation of MS/MS search data with anpI Filter algorithm to increase protein identification success. Electrophoresis. 28(12):1867-1874. https://doi.org/10.1002/elps.200700022
3.
Brosch M, Swamy S, Hubbard T, Choudhary J. 2008. Comparison of Mascot and X!Tandem Performance for Low and High Accuracy Mass Spectrometry and the Development of an Adjusted Mascot Threshold. Molecular & Cellular Proteomics. 7(5):962-970. https://doi.org/10.1074/mcp.m700293-mcp200
4.
Molina H, Matthiesen R, Kandasamy K, Pandey A. 2008. Comprehensive Comparison of Collision Induced Dissociation and Electron Transfer Dissociation. Anal. Chem.. 80(13):4825-4835. https://doi.org/10.1021/ac8007785
5.
Fusaro VA, Mani DR, Mesirov JP, Carr SA. 2009. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol. 27(2):190-198. https://doi.org/10.1038/nbt.1524
6.
Davidson WS, Silva RGD, Chantepie S, Lagor WR, Chapman MJ, Kontush A. 2009. Proteomic Analysis of Defined HDL Subpopulations Reveals Particle-Specific Protein Clusters. ATVB. 29(6):870-876. https://doi.org/10.1161/atvbaha.109.186031
7.
Paulovich AG, Billheimer D, Ham AL, Vega-Montoto L, Rudnick PA, Tabb DL, Wang P, Blackman RK, Bunk DM, Cardasis HL, et al. 2010. Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance*. Molecular & Cellular Proteomics. 9(2):242-254. https://doi.org/10.1074/mcp.m900222-mcp200
8.
Tabb DL, Vega-Montoto L, Rudnick PA, Variyath AM, Ham AL, Bunk DM, Kilpatrick LE, Billheimer DD, Blackman RK, Cardasis HL, et al. 2010. Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography?Tandem Mass Spectrometry. J. Proteome Res.. 9(2):761-776. https://doi.org/10.1021/pr9006365
9.
Kim M, Kandasamy K, Chaerkady R, Pandey A. 2010. 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. https://doi.org/10.1016/j.jasms.2010.04.011
10.
Li B, Held JM, Schilling B, Danielson SR, Gibson BW. 2011. Confident identification of 3-nitrotyrosine modifications in mass spectral data across multiple mass spectrometry platforms. Journal of Proteomics. 74(11):2510-2521. https://doi.org/10.1016/j.jprot.2011.04.007
11.
Yen C, Houel S, Ahn NG, Old WM. 2011. Spectrum-to-Spectrum Searching Using a Proteome-wide Spectral Library. Molecular & Cellular Proteomics. 10(7):M111.007666. https://doi.org/10.1074/mcp.m111.007666
12.
Ma X, Cui J, Zhang J. 2011. Processing methods for signal suppression of FTMS data. Proteome Sci. 9(Suppl 1):S2. https://doi.org/10.1186/1477-5956-9-s1-s2
13.
Wu Q, Zhao Q, Liang Z, Qu Y, Zhang L, Zhang Y. 2012. 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. https://doi.org/10.1039/c2an35173k
14.
Wright JC, Collins MO, Yu L, Käll L, Brosch M, Choudhary JS. 2012. Enhanced Peptide Identification by Electron Transfer Dissociation Using an Improved Mascot Percolator*. Molecular & Cellular Proteomics. 11(8):478-491. https://doi.org/10.1074/mcp.o111.014522
15.
Nahnsen S, Kohlbacher O. 2012. In silico design of targeted SRM-based experiments. BMC Bioinformatics. 13(S16): https://doi.org/10.1186/1471-2105-13-s16-s8
16.
Reiz B, Kertész-Farkas A, Pongor S, Myers MP. 2013. Chemical rule-based filtering of MS/MS spectra. 29(7):925-932. https://doi.org/10.1093/bioinformatics/btt061
17.
Augustsson P, Malm J, Ekström S. 2012. Acoustophoretic microfluidic chip for sequential elution of surface bound molecules from beads or cells. Biomicrofluidics. 6(3):034115. https://doi.org/10.1063/1.4749289
18.
Milac TI, Randolph TW, Wang P. 2012. Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies. 5(1):75-87. https://doi.org/10.4310/sii.2012.v5.n1.a7
19.
Jian L, Niu X, Xia Z, Samir P, Sumanasekera C, Mu Z, Jennings JL, Hoek KL, Allos T, Howard LM, et al. 2013. A Novel Algorithm for Validating Peptide Identification from a Shotgun Proteomics Search Engine. J. Proteome Res.. 12(3):1108-1119. https://doi.org/10.1021/pr300631t
20.
Zerck A, Nordhoff E, Lehrach H, Reinert K. 2013. Optimal precursor ion selection for LC-MALDI MS/MS. BMC Bioinformatics. 14(1): https://doi.org/10.1186/1471-2105-14-56
21.
Higgs RE, Butler JP, Han B, Knierman MD. 2013. Quantitative Proteomics via High Resolution MS Quantification: Capabilities and Limitations. International Journal of Proteomics. 20131-10. https://doi.org/10.1155/2013/674282
22.
Kertész-Farkas A, Reiz B, Vera R, Myers MP, Pongor S. 2014. PTMTreeSearch: a novel two-stage tree-search algorithm with pruning rules for the identification of post-translational modification of proteins in MS/MS spectra. 30(2):234-241. https://doi.org/10.1093/bioinformatics/btt642
23.
Gatto L, Christoforou A. 2014. Using R and Bioconductor for proteomics data analysis. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1844(1):42-51. https://doi.org/10.1016/j.bbapap.2013.04.032
24.
Van Riper SK, de Jong EP, Higgins L, Carlis JV, Griffin TJ. 2014. Improved Intensity-Based Label-Free Quantification via Proximity-Based Intensity Normalization (PIN). J. Proteome Res.. 13(3):1281-1292. https://doi.org/10.1021/pr400866r
25.
Rudnick PA, Wang X, Yan X, Sedransk N, Stein SE. 2014. Improved Normalization of Systematic Biases Affecting Ion Current Measurements in Label-free Proteomics Data. Molecular & Cellular Proteomics. 13(5):1341-1351. https://doi.org/10.1074/mcp.m113.030593
26.
Ivanov MV, Levitsky LI, Lobas AA, Panic T, Laskay ÜA, Mitulovic G, Schmid R, Pridatchenko ML, Tsybin YO, Gorshkov MV. 2014. Empirical Multidimensional Space for Scoring Peptide Spectrum Matches in Shotgun Proteomics. J. Proteome Res.. 13(4):1911-1920. https://doi.org/10.1021/pr401026y
27.
Baba T, Kashiwagi Y, Arimitsu N, Kogure T, Edo A, Maruyama T, Nakao K, Nakanishi H, Kinoshita M, Frohman MA, et al. 2014. Phosphatidic Acid (PA)-preferring Phospholipase A1 Regulates Mitochondrial Dynamics. Journal of Biological Chemistry. 289(16):11497-11511. https://doi.org/10.1074/jbc.m113.531921
28.
Kannaste O, Suomi T, Salmi J, Uusipaikka E, Nevalainen O, Corthals GL. 2014. Cross-Correlation of Spectral Count Ranking to Validate Quantitative Proteome Measurements. J. Proteome Res.. 13(4):1957-1968. https://doi.org/10.1021/pr401096z
29.
Tu C, Li J, Sheng Q, Zhang M, Qu J. 2014. 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. https://doi.org/10.1021/pr401206m
30.
Prokai L, Guo J, Prokai-Tatrai K. 2014. Selective chemoprecipitation to enrich nitropeptides from complex proteomes for mass-spectrometric analysis. Nat Protoc. 9(4):882-895. https://doi.org/10.1038/nprot.2014.052
31.
Chawade A, Alexandersson E, Levander F. 2014. Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets. J. Proteome Res.. 13(6):3114-3120. https://doi.org/10.1021/pr401264n
32.
Ebhardt HA, Nan J, Chaulk SG, Fahlman RP, Aebersold R. 2014. 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. https://doi.org/10.1002/rcm.7069
33.
Koh HWL, Swa HLF, Fermin D, Ler SG, Gunaratne J, Choi H. 2015. EBprot: Statistical analysis of labeling-based quantitative proteomics data. Proteomics. 15(15):2580-2591. https://doi.org/10.1002/pmic.201400620
34.
Qi D, Zhang H, Fan J, Perkins S, Pisconti A, Simpson DM, Bessant C, Hubbard S, Jones AR. 2015. The mzqLibrary - An open source Java library supporting the HUPO-PSI quantitative proteomics standard. Proteomics. 15(18):3152-3162. https://doi.org/10.1002/pmic.201400535
35.
Pursiheimo A, Vehmas AP, Afzal S, Suomi T, Chand T, Strauss L, Poutanen M, Rokka A, Corthals GL, Elo LL. 2015. Optimization of Statistical Methods Impact on Quantitative Proteomics Data. J. Proteome Res.. 14(10):4118-4126. https://doi.org/10.1021/acs.jproteome.5b00183
36.
Suomi T, Corthals GL, Nevalainen OS, Elo LL. 2015. Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins. J. Proteome Res.. 14(11):4564-4570. https://doi.org/10.1021/acs.jproteome.5b00363
37.
Liang X, Xia Z, Jian L, Niu X, Link A. 2015. An adaptive classification model for peptide identification. BMC Genomics. 16(Suppl 11):S1. https://doi.org/10.1186/1471-2164-16-s11-s1
38.
Ramus C, Hovasse A, Marcellin M, Hesse A, Mouton-Barbosa E, Bouyssié D, Vaca S, Carapito C, Chaoui K, Bruley C, et al. 2016. Benchmarking quantitative label-free LC?MS data processing workflows using a complex spiked proteomic standard dataset. Journal of Proteomics. 13251-62. https://doi.org/10.1016/j.jprot.2015.11.011
39.
Ramus C, Hovasse A, Marcellin M, Hesse A, Mouton-Barbosa E, Bouyssié D, Vaca S, Carapito C, Chaoui K, Bruley C, et al. 2016. Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods. Data in Brief. 6286-294. https://doi.org/10.1016/j.dib.2015.11.063

References for UPS2

1.
Dicker L, Lin X, Ivanov AR. 2010. Increased Power for the Analysis of Label-free LC-MS/MS Proteomics Data by Combining Spectral Counts and Peptide Peak Attributes. Molecular & Cellular Proteomics. 9(12):2704-2718. https://doi.org/10.1074/mcp.m110.002774
2.
Zhang J, Haskins W. 2010. ICPD-A New Peak Detection Algorithm for LC/MS. BMC Genomics. 11(Suppl 3):S8. https://doi.org/10.1186/1471-2164-11-s3-s8
3.
Kwon T, Choi H, Vogel C, Nesvizhskii AI, Marcotte EM. 2011. MSblender: A Probabilistic Approach for Integrating Peptide Identifications from Multiple Database Search Engines. J. Proteome Res.. 10(7):2949-2958. https://doi.org/10.1021/pr2002116
4.
Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M. 2011. Global quantification of mammalian gene expression control. Nature. 473(7347):337-342. https://doi.org/10.1038/nature10098
5.
Forshed J, Johansson HJ, Pernemalm M, Branca RM, Sandberg A, Lehtiö J. 2011. Enhanced Information Output From Shotgun Proteomics Data by Protein Quantification and Peptide Quality Control (PQPQ). Molecular & Cellular Proteomics. 10(10):M111.010264. https://doi.org/10.1074/mcp.m111.010264
6.
Arike L, Valgepea K, Peil L, Nahku R, Adamberg K, Vilu R. 2012. Comparison and applications of label-free absolute proteome quantification methods on Escherichia coli. Journal of Proteomics. 75(17):5437-5448. https://doi.org/10.1016/j.jprot.2012.06.020
7.
Shin J, Krey JF, Hassan A, Metlagel Z, Tauscher AN, Pagana JM, Sherman NE, Jeffery ED, Spinelli KJ, Zhao H, et al. 2013. Molecular architecture of the chick vestibular hair bundle. Nat Neurosci. 16(3):365-374. https://doi.org/10.1038/nn.3312
8.
Wu Q, Shan Y, Qu Y, Jiang H, Yuan H, Liu J, Zhang S, Liang Z, Zhang L, Zhang Y. 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(1):138-146. https://doi.org/10.1039/c3an01738a
9.
Brendel J, Stoll B, Lange SJ, Sharma K, Lenz C, Stachler A, Maier L, Richter H, Nickel L, Schmitz RA, et al. 2014. 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 volcanii. Journal of Biological Chemistry. 289(10):7164-7177. https://doi.org/10.1074/jbc.m113.508184
10.
Sandberg A, Branca RM, Lehtiö J, Forshed J. 2014. Quantitative accuracy in mass spectrometry based proteomics of complex samples: The impact of labeling and precursor interference. Journal of Proteomics. 96133-144. https://doi.org/10.1016/j.jprot.2013.10.035
11.
Smits AH, Lindeboom RG, Perino M, van Heeringen SJ, Veenstra G, Vermeulen M. 2014. Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs. 42(15):9880-9891. https://doi.org/10.1093/nar/gku661
12.
Hutchinson EC, Charles PD, Hester SS, Thomas B, Trudgian D, Martínez-Alonso M, Fodor E. 2014. Conserved and host-specific features of influenza virion architecture. Nat Commun. 5(1): https://doi.org/10.1038/ncomms5816
13.
Böhm, Juila Wiebke,. 2015. “A comprehensive C/EBPβ interactome”. Dr. rer. nat. dissertation, Humboldt University Berlin,. [dissertation].
14.
Tsou C, Avtonomov D, Larsen B, Tucholska M, Choi H, Gingras A, Nesvizhskii AI. 2015. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods. 12(3):258-264. https://doi.org/10.1038/nmeth.3255
15.
Soufi B, Krug K, Harst A, Macek B. Characterization of the E. coli proteome and its modifications during growth and ethanol stress. Front. Microbiol.. 6 https://doi.org/10.3389/fmicb.2015.00103
16.
Ma B. 2015. Novor: Real-Time Peptide de Novo Sequencing Software. J. Am. Soc. Mass Spectrom.. 26(11):1885-1894. https://doi.org/10.1007/s13361-015-1204-0

References for UPS1 and UPS2

1.
Geiger T, Cox J, Mann M. 2010. Proteomics on an Orbitrap Benchtop Mass Spectrometer Using All-ion Fragmentation. Molecular & Cellular Proteomics. 9(10):2252-2261. https://doi.org/10.1074/mcp.m110.001537
2.
Wi?niewski JR, Ostasiewicz P, Du? K, Zieli?ska DF, Gnad F, Mann M. 2012. Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma. Mol Syst Biol. 8(1):611. https://doi.org/10.1038/msb.2012.44
3.
Ivanov AR, Colangelo CM, Dufresne CP, Friedman DB, Lilley KS, Mechtler K, Phinney BS, Rose KL, Rudnick PA, Searle BC, et al. 2013. Interlaboratory studies and initiatives developing standards for proteomics. Proteomics. 13(6):904-909. https://doi.org/10.1002/pmic.201200532
4.
Krey JF, Wilmarth PA, Shin J, Klimek J, Sherman NE, Jeffery ED, Choi D, David LL, Barr-Gillespie PG. 2014. Accurate Label-Free Protein Quantitation with High- and Low-Resolution Mass Spectrometers. J. Proteome Res.. 13(2):1034-1044. https://doi.org/10.1021/pr401017h
5.
Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. 2014. Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ. Molecular & Cellular Proteomics. 13(9):2513-2526. https://doi.org/10.1074/mcp.m113.031591
6.
Laskay ÜA, Srzenti? K, Monod M, Tsybin YO. 2014. Extended bottom-up proteomics with secreted aspartic protease Sap9. Journal of Proteomics. 11020-31. https://doi.org/10.1016/j.jprot.2014.07.035
7.
Beck S, Michalski A, Raether O, Lubeck M, Kaspar S, Goedecke N, Baessmann C, Hornburg D, Meier F, Paron I, et al. 2015. The Impact II, a Very High-Resolution Quadrupole Time-of-Flight Instrument (QTOF) for Deep Shotgun Proteomics *. Molecular & Cellular Proteomics. 14(7):2014-2029. https://doi.org/10.1074/mcp.m114.047407
8.
K?rl? K, Karaca S, Dehne HJ, Samwer M, Pan KT, Lenz C, Urlaub H, Görlich D. A deep proteomics perspective on CRM1-mediated nuclear export and nucleocytoplasmic partitioning. 4 https://doi.org/10.7554/elife.11466
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