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Protein Microarrays

ProtoArray® Human Protein Microarray

Access the proteome

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Protein Microarrays

Immune response biomarker profilingRapidly discover and identify novel autoimmune biomarkers

→ Discover novel biomarker proteins specific for cancer, autoimmune, and other diseases (Figure 1)

→ Rapidly discover and identify autoantigens from over 9,000 unique human proteins present on the ProtoArray® Human Protein Microarray

→ Obtain valuable, high-quality information difficult to obtain using alternative discovery methods

Many disease states, including autoim-

mune diseases and cancer, are charac-

terized by the presence of antibodies

directed against self-antigens (autoanti-

gens). These circulating antibodies or their

antigen targets serve as potential bio-

markers that form the basis of diagnostic

and theranostic assays. A high-content,

functional human protein microarray has

been developed as a biomarker discovery

tool for identifying the presence of auto-

antibodies directed against over 9,000 full-

length native proteins.

Figure 1—Discovering autoimmune biomarkers. The ProtoArray® Human Protein Microarray may be used to design studies for the discovery of disease-speci!c biomarkers. These novel biomarker candidates may have signifi-cance in early-stage cancer diagnostics, as well as in monitoring drug efficacy and the progress of treat-ment regimens.

Comparison

Sera from healthy or untreated controls

Sera from diseasedor treated patients

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Protein microarraysEnabling a broad range of discovery projectsA number of discovery projects are possible using the Immune

Response Biomarker Profiling application (Figure 2). Investigators

have the ability to uncover biomarker candidates at an unprece-

dented pace. With the ProtoArray® Human Protein Microarray, you

have the power to discover biomarkers for:

→ Disease diagnosis and prognosis1–3

→ Disease classification

→ Disease pathogenesis4,5

→ Drug efficacy and toxicity

→ Responses to therapy

High-content human protein microarraysProteins on our high-content arrays are expressed as N-terminal

GST fusion proteins using a baculovirus-based expression system.

Proteins are purified under nondenaturing conditions and printed

in duplicate on a 1 x 3 inch ultrathin nitrocellulose (nc)-coated

glass slide. Unique bar code labeling on each array enables easy

tracking. ProtoArray® microarrays can be read with commercially

available fluorescent microarray scanners.

Analysis of antibody–antigen interactions is rapidly facilitated

with Invitrogen’s ProtoArray® Prospector, a free data analysis software

tool available on our website at www.invitrogen.com/protoarray

(Figure 3). The ProtoArray® Prospector software includes a linear

normalization algorithm that facilitates inter-assay data analysis and

M-statistics algorithms for cross-group comparisons important for

biomarker identification. These statistical tools allow you to rapidly

detect candidate biomarkers with accuracy and sensitivity.

Figure 2. Simple pro!ling protocol for biomarker discovery. ProtoArray® microar-rays are blocked with a synthetic blocking agent and probed with the biologi-cal fluid containing autoantibodies. The slides are then washed to remove any unbound proteins/antibodies. The autoantibodies bound to their cognate are then detected with isotype-specific secondary antibodies conjugated with Alexa Fluor® 647 dye.

Figure 3—ProtoArray® Human Protein Micorarray technology. A. The high-content ProtoArray® human microarray contains thousands of full-length human proteins for rapid profiling and easy biomarker discovery. B. ProtoArray® Prospector software is a user-friendly tool that enables rapid data analysis and cross-group comparisons.

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Advantages of ProtoArray® technologyBiomarker discovery projects require highly sensitive, reproduc-

ible methodologies that query a significant portion of the human

proteome. Historically, 2D gel electrophoresis coupled with mass

spectrometry–based identification was commonly used to dis-

cover differentially expressed proteins in healthy and disease

samples. However, this technology is costly and time consuming,

requires large amounts of sample, and is biased toward identifying

high-abundance proteins. In contrast, ELISA technology is widely

established and automatable, and can detect low-abundance pro-

teins; still, its use requires prior knowledge of biomarker identity,

as it does not serve as a discovery tool for unknown biomarkers.

ProtoArray® technology addresses several of these challenges and

is a revolutionary tool for biomarker discovery efforts, providing:

→ High-content discovery

→ Immediate protein identification

→ Low sample and reagent requirements

→ High sensitivity

→ Functional, native proteins

→ Rapid, cost-effective profiling

→ Reproducible screening

Specifically, the high level of sensitivity and reproducibility pro-

vided by ProtoArray® technology differentiates it from other pro-

teomics-based discovery platforms. As little as 10 μl of serum or

plasma can be applied to the array in a dilution buffer, providing

detection of specific autoantibody–antigen interactions directly

on the array. No prelabeling or tagging is required for this applica-

tion, making it ideal for proteomics discovery projects.

ProtoArray® protein microarray technology enables reproduc-

ible profiling of multiple samples. Inter- and intra-assay reproduc-

ibility is highly important to the success of biomarker discovery

projects in which numerous samples are profiled on different days

and by multiple operators. Specific protocol steps, normalization

algorithms, and protein QC standards have been included in the

development and production of ProtoArray® microarrays to ensure

reproducible profiling capabilities important to the success of bio-

marker discovery projects. Table 1 shows assay reproducibility

within and between ProtoArray® microarray lots.

Table 1—ProtoArray® Immune Response Biomarker Pro!ling assay reproducibility. A panel of values was calculated for each human protein spot across the replicates. Reproducibility values include the coefficient of variation (CV) and Pearson correlation coefficient. Values reflect both inter- and intra-assay conditions.

Performance factor CV Pearson correlation coe"cient

Intra-lot variability 11–14% 0.97–0.99

Inter-lot variability 16–19% 0.89–0.98

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Immune Response Biomarker Pro!ling optionsThe ProtoArray® Immune Response Biomarker Profiling applica-

tion is available either as an individual product or as a custom

service. The service provides analysis of your biological samples

and supplies a comprehensive report within 4–6 weeks. The pro-

filing service includes:

→ A complete report detailing the experimental protocol,

your results, and population statistics

→ A list of the top biomarker candidates and their prevalence

among diseased or treated groups

→ ProtoArray® image files and raw data formatted as FTP-

accessible electronic files

→ Responsive support and dedicated staff available to you

through the completion of your project

To order the Immune Response Biomarker Pro!ling custom service, simply:

→ Complete the online order form at

www.invitrogen.com/protoarray or contact us at 800 955

6288 x45682 (toll free in the USA only)

→ Receive confirmation regarding the scope of the project

and pricing

→ Submit your purchase order to custom.services@invitrogen.com

or call 800 955 6288 x45682 with your information (toll free

in the USA only)

Upon placing your order, a Customer Service representative will

provide you with shipping instructions for your serum samples.

You will receive notification of receipt of your materials and an

estimated delivery date for your analysis report.

Ordering informationProduct Cat. no.ProtoArray® Control Protein Microarray v5.0 PA10057

ProtoArray® Human Protein Microarray v5.0 PAH052501

ProtoArray® Human Protein Microarray v5.0, 20-pack PAH0525020

ProtoArray® Human Protein Microarray v5.0 PPI Kit (for biotinylated proteins) PAH0525011

ProtoArray® Human Protein Microarray v5.0 PPI Kit (for V5-tagged proteins) PAH0525013

ProtoArray® Human Protein Microarray v5.0 KSI Kit PAH0525065

ProtoArray Blocking Buffer Kit PA055

10X Synthetic Block PA017

Array Control Protein 451096

Alexa Fluor® 647 Anti-V5 Antibody for ProtoArray® 451098

Alexa Fluor® 647 goat anti-human IgM (µ chain) A-21249

Alexa Fluor® 647 goat anti-human IgG (H+L) A-21445

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References1. Auger, I. et al. (2008) New autoantigens in rheumatoid arthritis: screening 8268 protein arrays with RA patients’ sera. Ann Rheum Dis (Epub ahead of print).

2. Schnack, C. et al. (2008) Protein array analysis of oligomerization-induced changes in alpha-synuclein protein-protein interactions points to an interference with Cdc42 effector proteins. Neuroscience 154:1450–1457.

3. Gunawardana, C. et al. (2008) Identifying novel autoantibody signatures in ovarian cancer using high-density protein microarrays. Clin Biochem (Epub ahead of print).

4. Gnjatic, S. et al. (2008) Seromic analysis of antibody responses in non-small cell lung cancer patients and healthy donors using conformational protein arrays. J Immunol Methods (available online).

5. Singh, J. et al. (2008) DcpS as a therapeutic target for spinal muscular atrophy. ACS Chem Biol 3:711–722.

6. Satoh, J. et al. (2008) Protein microarray analysis identifies human cellular prion protein interactors. Neuropathol Appl Neurobiol 35:16–35.

7. Marina, O. et al. (2008) A concentration-dependent analysis method for high density protein microarrays. J Proteome Res 7:2059–2068.

8. Schnack, C. et al. (2008) Identification of novel substrates for Cdk5 and new targets for Cdk5 inhibitors using high-density protein microarrays. Proteomics 8:1980–1986.

9. Li, Z. et al. (2008) Cdc34p ubiquitin-conjugating enzyme is a component of the tombusvirus replicase complex and ubiquitinates p33 replication protein. J Virol 82:6911–6926.

10. Roche, S. et al. (2008) Autoantibody profiling on high-density protein microarrays for biomarker discovery in the cerebrospinal fluid. J Immunol Methods 338:75–78.

For research use only. Not intended for any animal or human therapeutic or diagnostic use, unless otherwise stated. © 2009 Life Technologies Corporation. All rights reserved. The trademarks mentioned herein are the property of Life Technologies Corporation or their respective owners. These products may be covered by one or more Limited Use Label Licenses (see Invitrogen catalog or www.invitrogen.com). By use of these products you accept the terms and conditions of all applicable Limited Use Label Licenses. B-084977 0509

www.invitrogen.com

Publication List

References for ProtoArray® protein microarrays ProtoArray® microarrays are high-density, functional protein microarrays that enable rapid elucidation of protein interactions on a proteome scale. For your convenience, we have complied references that describe applications of this powerful technology. Immune Response Biomarker Profiling

1. Ghevaria N et al. (2012) Quality control for a large-scale study using protein arrays and protein beads to measure immune response in serum and plasma. Proteomics 12:2802-2807.

2. Sidgel T et al. (2012) Non-HLA Antibodies to Immunogenic Epitopes Predict the Evolution of Chronic Renal Allograft Injury. J Am Soc Nephrol. 23: 750-763.

3. Chen R et al. (2012) Personalized Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes. Cell 148: 1293-1307.

4. Liu M et al. (2012) Immune responses to self-antigens in asthma patients: clinical and immunopathological implications. Hum Immunol. 73: 511-516.

5. Postow MA et al. (2012) Immunologic correlates of the abscopal effect in a patient with melanoma. NEJM. 366: 925-931.

6. Han M et al. (2012) Diagnosis of Parkinson’s disease based on disease-specific autoantibody profiles in human sera. PLoS One. 7: e32383.

7. Mangé A et al. (2012) Serum autoantibody signature of ductal carcinoma in situ progression to invasive breast cancer. Clin Cancer Res. 18: 1992-2000.

8. Wood JD et al. (2012) Anti-Enteric Neuronal Antibodies and the Irritable Bowel Syndrome. J Neurogastroenterol Motil. 18: 78-85.

9. Biernacki MA et al. (2012) Novel myeloma-associated antigens revealed in the context of syngeneic hematopoietic stem cell transplantation. Blood. 119: 3142-3150.

10. Sigdel TK et al. (2011) Profiling of Autoantibodies in IgA Nephropathy, an Integrative Antibiomics Approach. Clin J Am Soc Nephrol. 6: 2775-2784.

11. Jansen FH et al. (2011) Profiling of antibody production against xenograft-released proteins by protein microarrays discovers prostate cancer markers. J. Proteome Res. 11: 728-735.

12. Winer DA et al. (2011) B cells promote insulin resistance through modulation of T cells and production of pathogenic IgG antibodies. Nat. Med 17: 610-618.

13. Hu S et al. (2011) Identification of autoantibody biomarkers for primary Sjögren’s syndrome using protein microarrays. Proteomics. 11: 1499-1507.

14. Nagele E et al. (2011) Diagnosis of Alzheimer's disease based on disease-specific autoantibody profiles in human sera. PLoS One. 6: e23112.

15. Dinavahi R et al. (2011) Antibodies reactive to non-HLA antigens in transplant glomerulopathy. J Am Soc Nephrol. 22: 1168-1178.

16. Vermeulen N et al. (2011) Identification of a novel autoantigen in inflammatory bowel disease by a protein microarray. Inflamm Bowel Dis. 17: 1291-1300.

17. Butte AJ et al. (2011) Protein Microarrays Discover Angiotensinogen and PRKRIP1 as Novel Targets for Autoantibodies in Chronic Renal Disease. Mol Cell Proteomics. 10: M110.000497.

18. Gnjatic S et al. (2010) Seromic profiling of ovarian and pancreatic cancer. Proc. Natl. Acad. Sci. USA, 107: 5088–5093.

19. Le Roux S et al. (2010) Biomarkers for the Diagnosis of the Stable Kidney Transplant and Chronic Transplant Injury Using the ProtoArray® Technology. Transplant Proc. 42: 3475-3481

20. Nguyen MC et al. (2010) Antibody responses to galectin-8, TARP and TRAP1 in prostate cancer patients treated with a GM-CSF-secreting cellular immunotherapy. Cancer Immunol Immunother. 59:1313-1323.

21. Porcheray F et al. (2010) Chronic Humoral Rejection of Human Kidney Allografts Associates With Broad Autoantibody Responses. Transplantation. 89: 1239-1246.

22. Jarius S et al. (2010) A new Purkinje cell antibody (anti-Ca) associated with subacute cerebellar ataxia: immunological characterization. J Neuroinflammation. 7: 21.

23. Orenes-Piñero E et al. (2010) Serum and Tissue Profiling in Bladder Cancer Combining Protein and Tissue Arrays. J Proteome Res. 9: 164-173.

24. Marina O et al. (2010) Serologic Markers of Effective Tumor Immunity against Chronic Lymphocytic Leukemia Include Nonmutated B-Cell Antigens. Cancer Res. 70: 1344-1355.

25. Li L et al. (2010) Compartmental Localization and Clinical Relevance of MICA Antibodies After Renal Transplantation. Transplantation. 89: 312-319.

26. Wadia PP et al. (2010) Antibodies specifically target AML antigen NuSAP1 after allogeneic bone marrow transplantation. Blood. 115: 2077-2087.

27. Biernacki MA et al. (2010) Efficacious immune therapy in chronic myelogenous leukemia (CML) recognizes antigens that are expressed on CML progenitor cells. Cancer Res. 70: 906–915.

28. Schweitzer B et al. (2010) Immune response biomarker profiling application on ProtoArray protein microarrays. Methods Mol Biol. 641:243-52.

29. Babel I et al. (2009) Identification of tumor-associated autoantigens for the diagnosis of colorectal cancer in serum using high density protein microarrays. Mol Cell Proteomics. 8: 2382–2395.

30. Sutherland SM et al. (2009) Protein microarrays identify antibodies to protein kinase C zeta that are associated with a greater risk of allograft loss in pediatric renal transplant recipients. Kidney Int. 76: 1277–1283.

31. Li L et al. (2009) Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and “antibodyome” measures. Proc Natl Acad Sci USA. 106: 4148–4153.

32. Auger I et al. (2009) New autoantigens in rheumatoid arthritis: screening 8268 protein arrays with RA patients’ sera. Ann Rheum Dis. 68: 591–594.

33. Gunawardana CG et al. (2009) Identifying novel autoantibody signatures in ovarian cancer using high-density protein microarrays. Clin Biochem. 42: 426–429.

34. Gnjatic S et al. (2009) Seromic analysis of antibody responses in non-small cell lung cancer patients and healthy donors using conformational protein arrays. J Immunol Methods. 341: 50–58.

35. Roche S et al. (2008) Autoantibody profiling on high-density protein microarrays for biomarker discovery in the cerebrospinal fluid. J Immunol Methods. 338: 75-78.

36. Hudson ME et al. (2007) Identification of differentially expressed proteins in ovarian cancer using high-density protein microarrays. Proc Natl Acad Sci USA. 104: 17494-17499.

37. Lubomirski M et al. (2007) A consolidated approach to analyzing data from high-throughput protein microarrays with an application to immune response profiling in humans. J Comput Biol. 14: 350-359.

38. Lalive PH et al. (2006) Identification of new serum autoantibodies in neuromyelitis optica using protein microarrays. Neurology. 67: 176-177.

39. Mattoon D et al. (2005) Biomarker discovery using protein microarray technology platforms: Antibody-antigen complex profiling. Expert Rev Proteomics. 2: 879-889.

Protein-Protein Interactions

1. Pitek AS et al (2012) Transferrin Coated Nanoparticles: Study of the Bionano Interface in Human Plasma. PLoS One 7: e40685.

2. Meehan M et al. (2012) Protein tyrosine phosphatase receptor delta acts as a neuroblastoma tumor suppressor by destabilizing the aurora kinase oncogene. Mol Cancer. 11: 6.

3. Storm P et al. (2011) Conserved features of cancer cells define their sensitivity to HAMLET-induced death; c-Myc and glycolysis. Oncogene. 30: 4765–4779.

4. De Denato M et al. (2011) Class III ! -tubulin and the cytoskeletal gateway for drug resistance in ovarian cancer. J Cell Physiol. 227: 1034-1041.

5. Babij C et al. (2011) STK33 kinase activity is nonessential in KRAS-dependent cancer cells. Cancer Res. 71: 5818-5826.

6. Lee HK et al. (2011) Odontogenic ameloblasts-associated protein (ODAM), via phosphorylation by bone morphogenetic protein receptor type IB (BMPR–IB), is implicated in ameloblast differentiation. J Cell Biochem. 113: 1754-1765.

7. Virok DP et al. (2011) Protein Array Based Interactome Analysis of Amyloid-! Indicates an Inhibition of Protein Translation. J. Proteome Res. 10: 1538–1547.

8. Wu YY et al. (2011) SCUBE3 is an endogenous TGF-b receptor ligand and regulates the epithelial-mesenchymal transition in lung cancer. Oncogene 30: 3682–3693.

9. Wolting CD et al. (2011) Biochemical and Computational Analysis Of LNX1 Interacting Proteins. PLoS One 6: e26248.

10. Echtenkamp FJ et al. (2011) Global Functional Map of the p23 Molecular Chaperone Reveals an Extensive Cellular Network. Molecular Cell 43: 229–241.

11. Bauer M et al. (2011) Protein networks involved in vesicle fusion, transport, and storage revealed by array-based proteomics. Methods Mol Biol. 781: 47-58.

12. Olah J et al. (2011) Interactions of Pathological Hallmark Proteins: Tubulin Polymerization Promoting protein/p25, E-Amyloid, and D-Synuclein. J Biol Chem. 286: 34088-34100.

13. Li D et al (2011) Binding of Lactoferrin to IGBP1 Triggers Apoptosis in a Lung Adenocarcinoma Cell Line. Anticancer Res. 31: 529-534.

14. Al-Mulla F et al (2011) Raf Kinase Inhibitor Protein RKIP Enhances Signaling by Glycogen Synthase Kinase-3 E� Cancer Res. 71: 1334-1343.

15. Morikawa H et al. (2010) The bacterial effector Cif interferes with SCF ubiquitin ligase function by inhibiting deneddylation of Cullin1. Biochem Biophys Res Commun. 401: 268-274.

16. Paidas MJ et al. (2010) A genomic and proteomic investigation of the impact of preimplantation factor on human decidual cells. Am J Obstet Gynecol. 202:459.e1-8.

17. Sumiyoshi K et al. (2010) Protein microarray analysis identifies cyclic nucleotide phosphodiesterase as an interactor of Nogo-A. Neuropathology. 30: 7–14.

18. Fenner BJ et al. (2010) Expanding the Substantial Interactome of NEMO Using Protein Microarrays. PLoS One. 5: e8799.

19. Tong Y et al. (2008) Pituitary tumor transforming gene 1 regulates Aurora kinase A activity. Oncogene. 27: 6385–6395.

20. Schnack C et al. (2008) Protein array analysis of oligomerizatin-induced changes in alpha-synuclein protein-protein interactions points to an interference with Cdc42 effector proteins. Neuroscience. 154: 1450–1457.

21. Li Z. et al. (2008). Cdc34p ubiquitin-conjugating enzyme is a component of the tombusvirus replicase complex and ubiquitinates p33 replication protein. J Virol. 82: 6911-6926.

22. Satoh J et al. (2008). Protein microarray analysis identifies human cellular prion protein interactors. Neuropathol Appl Neurobiol. 35: 16–35.

23. Poltermann S et al. (2007) Gpm1p is a Factor H-, FHL-1-, and plasminogen-binding surface protein of Candida albicans. J Biol Chem. 282: 37537-37544.

24. Salamat-Miller N et al. (2007) A network-based analysis of polyanion-binding proteins utilizing human protein arrays. J Biol Chem. 282: 10153-10163.

25. Satoh J et al. (2006) Rapid identification of 14-3-3-binding proteins by protein microarray analysis. J Neurosci Methods. 152: 278-288.

26. Salamat-Miller N et al. (2006) A network-based analysis of polyanion-binding proteins utilizing yeast protein arrays. Mol Cell Proteomics. 5: 2263-2278.

27. Jin F. et al. (2006) A pooling-deconvolution strategy for biological network elucidation. Nat Methods. 3: 183-189.

28. Hesselberth J et al. (2006) Comparative analysis of Saccharomyces cerevisiae WW domains and their interacting proteins. Genome Biol. 7: R30.

Kinsase Substrate Interactions 1. Kinuka I et al. (2012) cGMP-dependent protein kinase I promotes cell apoptosis through

hyperactivation of death-associated protein kinase 2. Biochem and Biophys Res Commun. 422: 2. 2. Kottom TJ and Limper AH. (2011) Substrate analysis of the Pneumocystis carinii protein kinases

PcCbk1 and PcSte20 using yeast proteome microarrays provides a novel method for Pneumocystis signalling biology. Yeast. 10: 707-719.

3. De la Mota-Peynado A et al. (2011) Identification of the atypical MAPK Erk3 as a novel substrate for p21-activated kinase (Pak) activity. J Biol Chem. 286: 13603-13611.

4. Schnack C et al. (2008). Identification of novel substrates for Cdk5 and new targets for Cdk5 inhibitors using high-density protein microarrays. Proteomics. 8: 1980-1986.

5. Meng L et al. (2008) Protein kinase substrate identification on functional protein arrays. BMC Biotechnol. 8: 22.

6. Boyle SN et al. (2007) A critical role for cortactin phosphorylation by Abl-family kinases in PDGF-induced dorsalwave formation. Curr Biol. 17: 1-7.

7. Ptacek J et al. (2005) Global analysis of protein phosphorylation in yeast. Nature. 438: 679-684. 8. Mah A et al. (2005) Substrate specificity analysis of protein kinase complex Dbf2-Mob1 by peptide

library and proteome array screening. BMC Biochem. 6: 22.

Antibody Specificity Profiling 1. Stafford P et al. (2012) Physical Characterization of the “Immunosignaturing Effect”. Mol Cell

Proteomics 11: M111.011593. 2. Diehnelt CW et al. (2010) Discovery of High-Affinity Protein Binding Ligands – Backwards. PLoS One.

5: e10728. 3. Predki PF et al. (2005) Protein microarrays: A new tool for profiling antibody cross-reactivity. Hum

Antibodies. 14: 7-15. 4. Bangham R et al. (2005) Protein microarray-based screening of antibody specificity. Methods Mol

Med. 114: 173-182. 5. Michaud GA et al. (2003) Analyzing antibody specificity with whole proteome microarrays. Nat

Biotechnol. 21: 1509-1512. Small Molecule Interactions

1. To C et al. (2010) Synthetic Triterpenoids Target the ARP2/3 Complex and Inhibit Branched Actin Polymerization. JBC. 285: 27944-27957.

2. Conrad A et al. (2010) Proteomic Analysis of Potential Keratan Sulfate, Chondroitin Sulfate A, and Hyaluronic Acid Molecular Interactions. Invest Ophthalmol Vis Sci. 51: 4500-4515.

3. Singh J et al. (2008) DcpS as a therapeutic target for spinal muscular atrophy. ACS Chem Biol. 3: 711–722.

Post Translation Modification Profiling

1. Levy D et al. (2011) A proteomic approach for the identification of novel lysine methyltransferase substrates. Epigenetics & Chromatin. 4: 19.

2. Loch CM et al. (2011) Protein Microarrays for the Identification of Praja1 E3 Ubiquitin Ligase Substrates Cell Biochem Biophys. 60: 127–135.

3. Troiani S et al. (2011) Identification of candidate substrates for Poly(ADP-ribose) polymerase-2 (PARP2) in the absence of DNA damage using high-density protein microarrays. FEBS J. 278: 3676-3687.

4. Rincón et al. (2010) Development and Validation of a Method for Profiling Post-Translational Modification Activities Using Protein Microarrays. PLoS One. 5: e11332.

5. Merbl Y and Kirschner MW. (2009) Large-scale detection of ubiquitination substrates using cell extracts and protein microarrays. Proc Natl Acad Sci USA. 106: 2543-8.

6. Tao et al. (2009) Studies of the Expression of Human Poly(ADP-ribose) Polymerase-1 in Saccharomyces cerevisiae and Identification of PARP-1 Substrates by Yeast Proteome Microarray Screening. Biochemistry 48: 11745–11754.

7. Lin Y et al. (2009) Protein acetylation microarray reveals that NuA4 controls key metabolic target regulating gluconeogenesis. Cell 136: 1073–1084.

8. Gupta R. et al. (2007) Ubiquitination screen using protein microarrays for comprehensive identification of Rsp5 substrates in yeast. Mol Systems Biol. 3: 1-12.

DNA/RNA-Protein Interactions

1. Scherrer T et al (2010) A screen for RNA-binding proteins in yeast indicates dual functions for many enzymes. PLoS One. 5: e15499.

2. Hu S et al. (2009) Profiling the Human Protein-DNA Interactome Reveals ERK2 as a Transcriptional Repressor of Interferon Signaling. Cell. 139: 610-622.

3. Hall D. et al. (2004) Regulation of gene expression by a metabolic enzyme. Science. 306: 482-484. Pathogen Detection

1. Fernandez S et al. (2011) Antibody Recognition of the Dengue Virus Proteome and Implications for Development of Vaccines. Clin. Vaccine Immunol. 18: 523-532.

2. Keasey SL et al. (2009) Extensive Antibody Cross-reactivity among Infectious Gram-negative Bacteria Revealed by Proteome Microarray Analysis. Mol Cell Proteomics. 8: 924-935.

Reviews

1. Luo M. (2012) Current Chemical Biology Approaches to Interrogate Protein Methyltransferases. ACS Chem Biol. 7: 443-463.

2. DesMetz C et al. (2011) Autoantibody signatures: progress and perspectives for early cancer detection.J Cell Mol Med. 15: 2013–2024.

3. Yang L et al. (2011) Protein microarrays for systems biology. Acta Biochim Biophys. Sin 43: 161–171. 4. Martin K et al (2011) Exploring the Immunoproteome for Ovarian Cancer Biomarker Discovery Int. J.

Mol. Sci. 12: 410-428. 5. Vaughan R and Sacks S. (2010) Genomics in human renal transplantation. Curr Opin Immunol. 22:

689-93. 6. Wu T et al. (2010) Biomarkers of rheumatoid arthritis: recent progress. Exp Op Med Diag. 4: 293-305. 7. Naesens M and Sarwal MM. (2010) Molecular Diagnostics in Transplantation. Nat Rev Nephrol. 6:

614-628. 8. Michaud GA et al. (2006) Functional protein arrays to facilitate drug discovery and development.

IDrugs. 9: 266-272. 9. Michaud GA et al. (2006) Applications of protein arrays for small molecule drug discovery and

characterization. Biotechnol Genet Eng Rev. 22: 197-211. 10. Merkel J et al. (2005) Functional protein microarrays: Just how functional are they? Curr Opin

Biotechnol Rev. 16: 447-452. 11. Zhou FX et al. (2004) Development of functional protein microarrays for drug discovery: Progress and

challenges. Comb Chem High Throughput Screen. 7: 539-546. 12. Predki PF (2004) Functional protein microarrays: Ripe for discovery. Curr Opin Chem Biol. 8: 8-13.

13. Schweitzer B et al. (2003) Microarrays to characterize protein interactions on a whole-proteome scale. Proteomics. 3: 2190-2199.

14. Michaud GA and Snyder M (2002) Proteomic approaches for the global analysis of proteins. Biotechniques. 33: 1308-1316.

15. Zhu H et al. (2001) Global analysis of protein activities using proteome chips. Science. 293: 2101-2105. Book Chapters

1. DeLuca DS, Marina O, Ray S, Zhang GL, Wu CJ, and Brusic V (2011) Data Processing and Analysis for Protein Microarrays. In Catherine J. Wu (ed.), Protein Microarray for Disease Analysis: Methods and Protocols, Methods in Molecular Biology, vol. 723 (pp 337-347) Springer Science+Business Media.

2. Smith MG, Ptacek J, & Snyder M (2011) Kinase Substrate Interactions. In Catherine J. Wu (ed.), Protein Microarray for Disease Analysis: Methods and Protocols, Methods in Molecular Biology, vol. 723 (pp 201-212) Springer Science+Business Media.

3. Wadia PP, Sahaf B, & Miklos DB (2011) Recombinant Antigen Microarrays for Serum/Plasma Antibody Detection. In Catherine J. Wu (ed.), Protein Microarray for Disease Analysis: Methods and Protocols, Methods in Molecular Biology, vol. 723 (pp 81-104) Springer Science+Business Media.

4. Persaud A & Rotin D (2011) Use of Proteome Arrays to Globally Identify Substrates for E3 Ubiquitin Ligases. In Castrillo JI & Oliver SG (eds.), Yeast Systems Biology, Methods in Molecular Biology, vol 759 (pp 215-224) Springer Science+Business Media.

For research use only. Not intended for any animal or human therapeutic or diagnostic use. ©2012 Life Technologies Corporation. All rights reserved. The trademarks mentioned herein are the property of Life Technologies Corporation or their respective owners.