Functional protein microarray technology
Identifieur interne : 000995 ( Pmc/Corpus ); précédent : 000994; suivant : 000996Functional protein microarray technology
Auteurs : Shaohui Hu ; Zhi Xie ; Jiang Qian ; Seth Blackshaw ; Heng ZhuSource :
- Wiley Interdisciplinary Reviews. Systems Biology and Medicine [ 1939-5094 ] ; 2010.
Abstract
Functional protein microarrays are emerging as a promising new tool for large‐scale and high‐throughput studies. In this article, we review their applications in basic proteomics research, where various types of assays have been developed to probe binding activities to other biomolecules, such as proteins, DNA, RNA, small molecules, and glycans. We also report recent progress of using functional protein microarrays in profiling protein post‐translational modifications, including phosphorylation, ubiquitylation, acetylation, and nitrosylation. Finally, we discuss potential of functional protein microarrays in biomarker identification and clinical diagnostics. We strongly believe that functional protein microarrays will soon become an indispensible and invaluable tool in proteomics research and systems biology.
This article is categorized under:
Laboratory Methods and Technologies > Proteomics Methods
Url:
DOI: 10.1002/wsbm.118
PubMed: 20872749
PubMed Central: 3044218
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PMC:3044218Le document en format XML
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<front><journal-meta><journal-id journal-id-type="nlm-ta">Wiley Interdiscip Rev Syst Biol Med</journal-id><journal-id journal-id-type="iso-abbrev">Wiley Interdiscip Rev Syst Biol Med</journal-id><journal-id journal-id-type="doi">10.1002/(ISSN)1939-005X</journal-id><journal-id journal-id-type="publisher-id">WSBM</journal-id><journal-title-group><journal-title>Wiley Interdisciplinary Reviews. Systems Biology and Medicine</journal-title></journal-title-group><issn pub-type="ppub">1939-5094</issn><issn pub-type="epub">1939-005X</issn><publisher><publisher-name>John Wiley & Sons, Inc.</publisher-name><publisher-loc>Hoboken, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">20872749</article-id><article-id pub-id-type="pmc">3044218</article-id><article-id pub-id-type="doi">10.1002/wsbm.118</article-id><article-id pub-id-type="publisher-id">WSBM118</article-id><article-categories><subj-group subj-group-type="article-subject-classification"><subject>Proteomics Methods</subject></subj-group><subj-group subj-group-type="overline"><subject>Overview</subject></subj-group><subj-group subj-group-type="heading"><subject>Overview</subject></subj-group></article-categories><title-group><article-title>Functional protein microarray technology</article-title><alt-title alt-title-type="right-running-head">Functional protein microarray technology</alt-title></title-group><contrib-group><contrib id="au1" contrib-type="author"><name><surname>Hu</surname><given-names>Shaohui</given-names></name><xref ref-type="aff" rid="af1"><sup>1</sup></xref><xref ref-type="aff" rid="af2"><sup>2</sup></xref></contrib><contrib id="au2" contrib-type="author"><name><surname>Xie</surname><given-names>Zhi</given-names></name><xref ref-type="aff" rid="af3"><sup>3</sup></xref></contrib><contrib id="au3" contrib-type="author"><name><surname>Qian</surname><given-names>Jiang</given-names></name><xref ref-type="aff" rid="af3"><sup>3</sup></xref><xref ref-type="aff" rid="af4"><sup>4</sup></xref></contrib><contrib id="au4" contrib-type="author"><name><surname>Blackshaw</surname><given-names>Seth</given-names></name><xref ref-type="aff" rid="af5"><sup>5</sup></xref></contrib><contrib id="au5" contrib-type="author" corresp="yes"><name><surname>Zhu</surname><given-names>Heng</given-names></name><xref ref-type="aff" rid="af1"><sup>1</sup></xref><xref ref-type="aff" rid="af2"><sup>2</sup></xref><xref ref-type="aff" rid="af4"><sup>4</sup></xref><address><email>heng.zhu@jhmi.edu</email></address></contrib></contrib-group><aff id="af1"><label><sup>1</sup></label>Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA</aff><aff id="af2"><label><sup>2</sup></label>The Center for High‐Throughput Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USA</aff><aff id="af3"><label><sup>3</sup></label>Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA</aff><aff id="af4"><label><sup>4</sup></label>The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA</aff><aff id="af5"><label><sup>5</sup></label>The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA</aff><author-notes><corresp id="correspondenceTo"><label>*</label>Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA</corresp></author-notes><pub-date pub-type="ppub"><season>May-Jun</season><year>2011</year></pub-date><pub-date pub-type="epub"><day>24</day><month>9</month><year>2010</year></pub-date><volume>3</volume><issue>3</issue><issue-id pub-id-type="doi">10.1002/wsbm.v3.3</issue-id><fpage>255</fpage><lpage>268</lpage><permissions><copyright-statement content-type="article-copyright">Copyright © 2010 John Wiley & Sons, Inc.</copyright-statement><license><license-p>This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="file:WSBM-3-255.pdf"></self-uri><abstract><title>Abstract</title><p>Functional protein microarrays are emerging as a promising new tool for large‐scale and high‐throughput studies. In this article, we review their applications in basic proteomics research, where various types of assays have been developed to probe binding activities to other biomolecules, such as proteins, DNA, RNA, small molecules, and glycans. We also report recent progress of using functional protein microarrays in profiling protein post‐translational modifications, including phosphorylation, ubiquitylation, acetylation, and nitrosylation. Finally, we discuss potential of functional protein microarrays in biomarker identification and clinical diagnostics. We strongly believe that functional protein microarrays will soon become an indispensible and invaluable tool in proteomics research and systems biology. <italic>WIREs Syst Biol Med</italic> 2011 3 255–268 DOI: 10.1002/wsbm.118</p><p>This article is categorized under:
<list list-type="simple"><list-item><label>1</label><p>Laboratory Methods and Technologies > Proteomics Methods</p></list-item></list></p></abstract><counts><fig-count count="0"></fig-count><table-count count="1"></table-count><ref-count count="88"></ref-count><page-count count="14"></page-count></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>May/June 2011</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.0 mode:remove_FC converted:15.04.2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="sec1-1"><title>INTRODUCTION</title><p>The fundamental principle of microarray technology was first put forward by Ekins et al. over 20 years ago.<xref rid="bib1" ref-type="ref">1</xref> Their ambient analyte theory stated that a tiny spot of purified antibody (or any other macromolecule) provides substantially better sensitivity than when used in conventional immunoassay formats. Fueled by large‐scale genome sequencing projects, DNA microarray technology became the first application of this theory and has been widely used in gene expression profiling.<xref rid="bib2" ref-type="ref">2</xref>, <xref rid="bib3" ref-type="ref">3</xref>, <xref rid="bib4" ref-type="ref">4</xref>, <xref rid="bib5" ref-type="ref">5</xref>, <xref rid="bib6" ref-type="ref">6</xref> However, biological functions are carried out primarily by proteins rather than nucleic acids. Furthermore, RNA expression levels do not always correlate with protein expression levels, and it is almost impossible to predict biochemical properties of a protein encoded by a given gene simply based on its expression profiles.<xref rid="bib7" ref-type="ref">7</xref>,<xref rid="bib8" ref-type="ref">8</xref> Therefore, by focusing on studies of protein structures, functionalities, and protein–protein interactions one can more directly characterize biological function of a given gene.</p><p>Large‐scale protein‐centered analyses of gene function, however, have not been generally as fruitful as their DNA‐centered counterparts for several reasons. First, the biochemical properties of proteins are far more diverse and complex than those of nucleic acids. Second, there is no ready method to amplify proteins for analysis, unlike PCR‐based amplification of nucleic acids. Third, many proteins are prone to denature or degrade in standard buffer conditions and at ambient temperature, making them substantially more challenging to study. Therefore, technology for systematically assaying protein function that is both high‐throughput and highly flexible is urgently needed. The past success of the DNA microarray technology highlights the power of a highly parallel, high‐throughput platform that allows profiling of thousands of molecular targets in a single experiment. By the same token, protein microarray technology is now emerging as a promising new tool that can push proteomic studies to a new level. In the past decade, many methodologies based on the protein microarray technology have been successfully developed and applied to proteomic studies, including protein identification, quantification, and functional analysis of signaling pathways and networks, as well as clinical diagnostics and antibody characterizations.</p><p>A protein microarray, also known as a protein chip, is a solid surface (typically glass) on which thousands of different proteins (e.g., antigens, antibodies, enzymes, substrates, etc.) are immobilized in discrete spatial locations, forming a high‐density protein dot matrix. Depending on their applications, protein microarrays can be classified into two types: the analytical and functional protein microarrays. Analytical protein microarrays are usually composed of well‐characterized biomolecules with specific binding activities, such as antibodies, to analyze the components of complex biological samples (e.g., serum and cell lysates) or to determine whether a sample contains a specific protein of interest. They have been used for protein expression profiling, biomarker identification, cell surface marker/glycosylation profiling, clinical diagnosis, and environmental/food safety analysis. On the other hand, functional protein microarrays are constructed by printing a large number of individually purified proteins, and are mainly used to comprehensively query biochemistry properties and activities of those immobilized proteins. In principle, it is feasible to print arrays comprised of virtually all annotated proteins of a given organism, effectively comprising a whole proteome microarray. Functional protein microarrays have been successfully applied to identify protein–protein, protein–lipid, protein–antibody, protein–small molecules, protein–DNA, protein–RNA, lectin–glycan, and lectin–cell interactions, and to identify substrates or enzymes in phosphorylation, ubiquitylation, acetylation, and nitrosylation, as well as to profile immune response. In this review, we will mainly focus on the fabrication and application of functional protein microarrays.</p></sec><sec id="sec1-2"><title>FABRICATION OF FUNCTIONAL PROTEIN MICROARRAYS</title><p>Capture molecules with high specificity and selectivity are the essential prerequisite for the design and fabrication of both protein and DNA microarrays. Regardless of their sequences, the biochemical properties of DNA molecules are essentially the same, which allows the same chemistries to be applied to either immobilize to or synthesize <italic>in situ</italic> DNA strands on a solid surface. Therefore, the design and construction of oligonucleotide DNA microarrays are relatively straightforward. However, the protein world is much more complicated than DNA because of the vast differences among the structures, charge and hydrophobicity of individual proteins. This implies that the fabrication and analysis of protein microarrays is substantially less straightforward and standardizable than that of DNA microarrays. Unlike DNA or even RNA molecules, full‐length proteins cannot be directly synthesized <italic>in vitro</italic> at high efficiency because of their complex chemistry. Although <italic>in vitro</italic> synthesis of peptides has been feasible for decades, it still suffers from low yield, high cost, and effective limitation to short sequences. Moreover, the vast majority of proteins must be correctly folded and modified to be functional after translation, which may require a complex molecular machinery of chaperones and other accessory molecules that cannot be fully recapitulated <italic>in vitro</italic>. Thus far, proteins used to construct high‐content proteome microarrays have been all individually expressed and purified from live cells (see below).</p><p>Because proteins must fold correctly in order to be active, proteins are prone to inactivation due to loss of their native conformations when directly immobilized on a solid surface. Proteins vary greatly in categories and properties, while a carrier surface can only be modified by one or two kinds of chemical or biological group. All these factors pose challenges for optimizing the physical attachment of proteins used in microarray construction to the slide surface.</p><sec id="sec2-1"><title>Surface Chemistry</title><p>Choosing a proper surface for protein immobilization is crucial to the success of any assay performed using protein microarrays. An ideal surface should be able to retain protein functionality with relatively high signal‐to‐noise ratios, and possess both high protein‐binding capacity and long shelf‐life.<xref rid="bib9" ref-type="ref">9</xref> Glass slides covered with polyvinylidene fluoride (PVDF), nitrocellulose membrane, or polystyrene were popular for protein microarray fabrication in the early days of the technology.<xref rid="bib10" ref-type="ref">10</xref>, <xref rid="bib11" ref-type="ref">11</xref>, <xref rid="bib12" ref-type="ref">12</xref> However, PVDF and polystyrene are relatively soft, allowing lateral spread of printed proteins, and hence limited density of proteins to be printed. Nitrocellulose membranes, in addition, tend to generate high background and low signal‐to‐noise ratio for most applications.</p><p>To bypass these shortcomings, researchers developed three‐dimensional matrix arrays, in which glass slides are coated with polyacrylamide or agarose to form a porous hydrophilic matrix in which proteins or antibodies are trapped within the pores and lateral diffusion is restricted, reducing the size of printed protein spots and thus increasing the maximal complexity of the array.<xref rid="bib13" ref-type="ref">13</xref>,<xref rid="bib14" ref-type="ref">14</xref> Protein activity is generally well preserved in such matrix arrays, and their protein‐binding capacity is relatively high. For instance, Zhu et al. utilized soft lithography to generate nanowells on a polydimethylsiloxane sheet placed on top of microscope slides.<xref rid="bib15" ref-type="ref">15</xref> These nanowell chips were used to immobilize substrate proteins to profile phosphorylation specificity of 119 kinases encoded by budding yeast. The open structure of nanowells provides physical barriers and allows for sequential adding of different buffers, which is critical for multistep experiments. The main disadvantage of this method is the requirement of specialized equipment needed to load nanowells at high density.</p><p>Other researchers printed proteins, antigens, or antibodies directly onto plain glass slides, which are usually coated with a bifunctional cross‐linker with two functional groups, one reacting with the glass surface and the other with the desired proteins. For example, Schweitzer et al. demonstrated in their study that protein microarrays fabricated on glass surface possess high sensitivities, wide dynamic range, and decent spot‐to‐spot reproducibility.<xref rid="bib16" ref-type="ref">16</xref> MacBeath and Schreiber demonstrated with three proteins that thousands of protein spots could be immobilized to aldehyde‐activated plain glass surfaces to form a high‐density protein microarray that was suitable for a range of different classes of assays.<xref rid="bib17" ref-type="ref">17</xref></p></sec><sec id="sec2-2"><title>High‐Throughput Protein Production</title><p>Although the technologies of arraying proteins on various types of surfaces at high‐density were starting to mature by the end of last millennium, the main hurdle to their more general use remained the difficulty in producing the large number of different proteins needed for construction of a truly high‐content array. Obviously, a readily useable high‐throughput protocol for parallel production of thousands of different proteins is the key to this.</p><p>An early attempt led by the Lehrach group was to express human proteins in bacteria using a library consisting of random cDNAs.<xref rid="bib10" ref-type="ref">10</xref> Individual cDNA clones of this library were robotically arrayed onto PVDF membrane laid on top of agar media and allowed to grow to full size. These cells were then lysed <italic>in situ</italic> to extract proteins, and then by incubation with a labeled test protein to identify interacting partners. Strictly speaking, human proteins bound to the nitrocellulose membranes were not purified—the vast majority of the proteins in every spot were bacterial proteins. Furthermore, the proteins were neither unique nor in native conformation, given the redundancy of the library and denaturing conditions used to break cells open. Though powerful as a screening technique in early days, this particular experimental strategy had limited general application.</p><p>To overcome these hurdles, the Snyder group created a high‐throughput protein purification protocol in the budding yeast.<xref rid="bib18" ref-type="ref">18</xref> Using a homologous recombination‐based strategy, more than 5800 full‐length yeast open reading frames (ORFs) were cloned into a yeast expression vector that, upon galactose induction, produces glutathione‐<italic>S</italic>‐transferase (GST)‐tagged N‐terminal fusion proteins. The purification protocol took advantage of both a 96‐well format and immobilized affinity chromatography. This strategy allowed parallel purification of unprecedented numbers of proteins—up to 1152 per day. The success of this approach is built upon several unique aspects: First, it utilizes a eukaryotic expression system that both generates high levels of recombinant proteins and tends to produce a high fraction of soluble proteins. Compared with bacterial expression systems, in which a large fraction of recombinant proteins end up in inclusion bodies, this is a huge advantage when a large number of eukaryotic proteins are being generated. Second, the expression of recombinant proteins is only induced over about two total cell cycles, which greatly reduces toxicity and cell death. Third, a foreign eukaryotic protein purified from yeast is more likely to be active because post‐translational modifications (PTMs) necessary for function are more likely to occur correctly than in either bacteria or a cell‐free system. Forth, the use of an N‐terminal GST tag helps protein fold correctly and therefore, improve its stability and solubility. Other commonly used tags include the so‐called tandem affinity purification (TAP‐tag), maltose binding protein (MBP), and 6xHis, to name a few. In fact, the same group later went on to build a TAP‐tagged yeast ORF collection and purified >5000 yeast proteins.<xref rid="bib19" ref-type="ref">19</xref></p><p>Another commonly used expression system is <italic>Escherichia coli</italic>. The procedures for automatic high‐throughput protein expression/purification using the 6xHis tag has been developed.<xref rid="bib20" ref-type="ref">20</xref> The subsequent protein purification takes advantage of immobilized Ni–NTA affinity chromatography.<xref rid="bib21" ref-type="ref">21</xref> The 6xHis tag usually does not alter the properties of the fusion proteins, and the increment of molecular weight is less than 1 kDa. Furthermore, it is selective and stable even under severe denaturing conditions.<xref rid="bib22" ref-type="ref">22</xref>,<xref rid="bib23" ref-type="ref">23</xref> Our group has recent reported a high‐throughput protein purification protocol for 6xHis‐tagged proteins in <italic>E. coli</italic>.<xref rid="bib24" ref-type="ref">24</xref></p><p>Despite the fact that high‐throughput protein production in both prokaryotes and eukaryotes is now increasingly feasible, these protocols are still both labor‐intensive and costly. Aside from the cost of protein production, fabrication of a proteome microarray requires construction of an expressible collection of full‐length ORFs, which can be both challenging and expensive when dealing with higher eukaryotes with a large number of genes, such as humans. To explore alternative approaches, several groups have attempted to test the <italic>in vitro</italic> transcription/translation systems, such as the <italic>E. coli</italic>, wheat germ, and rabbit reticulocyte systems. In these systems, proteins can be expressed directly from cDNA templates,<xref rid="bib25" ref-type="ref">25</xref> which can be obtained through PCR amplification without the lengthy and costly process of subcloning. For example, the <italic>E</italic>. <italic>coli</italic> cell‐free protein expression system has been used to synthesize proteins in a 96‐well format,<xref rid="bib26" ref-type="ref">26</xref> and the improved wheat germ cell‐free protein synthesis system has been applied to the <italic>in vitro</italic> expression of 13,364 human proteins.<xref rid="bib27" ref-type="ref">27</xref> Furthermore, these systems can significantly decrease the reaction volume required for generation of recombinant proteins,<xref rid="bib28" ref-type="ref">28</xref> which is also one of the advantages because the cost of <italic>in vitro</italic> expression system is rather high.</p><p>Such systems can also be applied to directly synthesize proteins on glass slides to fabricate so‐called ‘<italic>in situ</italic> protein microarrays.’ In the Protein <italic>In Situ</italic> Array method, proteins are expressed directly from DNA <italic>in vitro</italic> and become attached to the array surfaces through recognition of a sequence that serves as an affinity tag.<xref rid="bib29" ref-type="ref">29</xref> Similarly, in the Nucleic Acid Programmable Protein Array (NAPPA) technology, biotinylated cDNA plasmids encoding proteins as GST fusions are printed onto avidin‐coated slides, together with anti‐GST antibodies as the capture molecules.<xref rid="bib30" ref-type="ref">30</xref> The cDNA array is then incubated with rabbit reticulocyte lysate to express the proteins, which become trapped by the antibodies adjacent to each DNA spot. Recently, NAPPA has been successfully expanded to high‐density arrays of 1000 different proteins.<xref rid="bib31" ref-type="ref">31</xref> In addition, Tao et al. developed a different method in which ribosomes are installed at the end of an RNA template to allow for the capture of the nascent polypeptides by a puromycin moiety that is grafted at one end of an oligonucleotide immobilized on a solid surface.<xref rid="bib32" ref-type="ref">32</xref></p><p>Another similar method is called DNA Array to Protein Array (DAPA), in which proteins are synthesized between two glass slides, one of which is arrayed with DNA while the other carries a specific affinity reagent to capture the proteins.<xref rid="bib33" ref-type="ref">33</xref> In this approach, tagged proteins are synthesized in parallel from the DNA array, spread across the gap between the two slides, and then bind to the tag‐capturing reagents on the other slide to form a protein array. Unlike the NAPPA method in which proteins are present together with DNA and the DNA array can only be used once, DAPA generates multiple copies of ‘pure’ protein arrays on a separate surface from the same DNA template, with at least 20 copies capable of being produced from a single template.</p></sec><sec id="sec2-3"><title>Protein Printing</title><p>With regard to spotting proteins the two major mechanisms are contact and noncontact printing. Adapted from DNA microarray fabrication, the robotic contact printing tool is the most suitable for producing protein microarrays of high content due to the requirement to array large numbers of different proteins. Metal pins with solid or quill tips are used in contact printers to deliver subnanoliter of protein samples to the slide surface. Quill pins, which have a larger sample capacity, can print hundreds of spots continuously after each sample loading. The printed spots are typically circular and the size depends largely on the pin tip dimension, surface chemistry, and the printing buffer. A significant advantage of this type of microarrayers is their speed and throughput—up to 48 pins can be loaded and more than 200 slides printed at a time. However, the pins are very fragile and expensive, and the pin tips may damage the slide surface, especially complex 3D substrates (e.g., nitrocellulose‐coated slides). Furthermore, some proteins are sticky to metal and the general washing steps may not clean them completely from the pins, and thus give rise to cross‐contamination of protein samples and carry‐over problem.</p><p>To address these issues, noncontact dispensing techniques have been developed for printing protein microarrays, by which a small droplet of protein sample is delivered to the slide surface without touching it. Droplets can be generated by conventional ink‐jet, piezoelectric pulsing, or electrospray deposition.<xref rid="bib34" ref-type="ref">34</xref>, <xref rid="bib35" ref-type="ref">35</xref>, <xref rid="bib36" ref-type="ref">36</xref> Unlike contact printing, the amount of liquid deposited by noncontact printers is not dependent on surface properties of the slide, and significant better spots morphology has been observed on hydrophobic surfaces using noncontact printing compared to contact printing.<xref rid="bib37" ref-type="ref">37</xref> In addition to standard glass slides, noncontact printers can also print on membranes. However, such instruments usually suffer from longer printing time and fewer pins, which is a significant drawback when printing a large number of protein samples. Moreover, noncontact printers can sometimes misplace spots and/or generate satellite spots, resulting in a high failure rate.<xref rid="bib38" ref-type="ref">38</xref> An additional disadvantage of noncontact printers is that they usually require a larger sample volume, which is challenging and expensive for high‐throughput protein production.</p></sec><sec id="sec2-4"><title>Protein Immobilization</title><p>The physical and chemical properties of different proteins vary greatly, and protein activities are closely related to their structures. Therefore, the development of a stable, universal immobilization method that does not change protein structures is one of the difficulties of protein microarray fabrication. So far several different methods have been used for protein immobilization on solid carrier surfaces, such as noncovalent adsorption, covalent binding, and affinity capture.</p><p>Noncovalent adsorption provides both high protein capacities and low impact on protein structures, but cannot control the amount and orientation of immobilized proteins. Thus the reaction efficiency, accuracy, and reproducibility of arrays produced in this manner are variable. Covalent binding, on the other hand, results in chemically cross‐linked proteins via reactive residues (e.g., lysine and cystine) to surface‐grafted ligands, such as aldehyde, epoxy, reactive ester, etc.<xref rid="bib17" ref-type="ref">17</xref>, <xref rid="bib39" ref-type="ref">39</xref>, <xref rid="bib40" ref-type="ref">40</xref> Lee et al. developed novel calixcrown derivatives as a ProLinker that permits efficient immobilization of captured proteins on solid matrixes, and the immobilized proteins showed both consistent directionality and functionality.<xref rid="bib41" ref-type="ref">41</xref> Covalent binding is suitable for immobilization of a wide range of proteins with strong conjunctions to the carrier surfaces. However, the modification of chemical groups can sometimes both alter the activities of target proteins and their binding to specific ligands.</p><p>Affinity capture is an attractive way to immobilize proteins that avoids many of the shortcomings of the previously detailed approaches. For example, biotinylated proteins have been used for protein immobilization to streptavidin‐coated slides. The use of genetically encoded affinity tags, which can be fused to target proteins and bind to a specific slide surface, is an analogous approach. For example, 6xHis‐tags have been utilized to immobilize proteins on Ni–NTA coated glass slides.<xref rid="bib18" ref-type="ref">18</xref> Presumably, affinity‐based protein immobilization should result in immobilization of proteins in relatively uniform orientation with minimum interruption of protein structure, and thus may be the best approach to preserving the structure and function of printed proteins. One important caveat to bear in mind, however, is that the incorporation of affinity tags may alter the protein structures.</p><p>One way to deal with this challenge was demonstrated by Zhang et al., who developed a flexible polypeptide scaffold consisting of a surface immobilization domain and a protein capture domain, which allows much greater flexibility in the immobilization of proteins on a microarray.<xref rid="bib42" ref-type="ref">42</xref> Wacker et al. compared the DNA‐directed immobilization (DDI) method with both direct spotting and with biotin–streptavidin affinity immobilization for antibodies.<xref rid="bib43" ref-type="ref">43</xref> DDI is based on the self‐assembly of semisynthetic DNA–streptavidin conjugates that converts a DNA oligomer array into an antibody array.<xref rid="bib44" ref-type="ref">44</xref> DDI and direct spotting showed the highest fluorescence intensities. DDI also performed the best in spot homogeneity and intra‐ and interexperimental reproducibility. Moreover, DDI required the lowest amount of antibodies, at least 100‐fold less than direct spotting. The drawback of DDI is that proteins have to be linked to DNA prior to immobilization, which increases the workload involved in generating microarrays.</p><p>The orientation of immobilized proteins may influence both their activity and their affinity for specific ligands. Peluso et al. compared randomly versus specifically oriented capture agents based on both full‐sized antibodies and Fab' fragments.<xref rid="bib45" ref-type="ref">45</xref> The specific orientation of capture agents consistently increased the analyte‐binding capacity of the surfaces up to 10‐fold relative to surfaces with randomly oriented capture agents. When specifically oriented, Fab' fragments formed a dense monolayer and 90% of them were active, while randomly attached Fab's both packed at lower density and had lower specific activity.</p></sec></sec><sec id="sec1-3"><title>SIGNAL DETECTION</title><p>In addition to optimized surface modification and optimized reaction condition, the detection sensitivity of samples bound on microarrays is another key parameter in the design of protein microarray assays. There are two basic detection methods: label‐dependent and label‐free detections.</p><sec id="sec2-5"><title>Label‐Dependent Detection Methods</title><p>Radioisotopes and fluorescent dyes are the two most common labeling methods for signal detection in protein microarray assays. Fluorescent dyes, such as Cy‐3/5 and their equivalent, have been used as a popular labeling method. Because most good dyes have relatively narrow excitation and emission spectra, multicolor scheme can be readily implemented for simultaneous detection and direct comparison of different samples, both reducing cost and avoiding chip‐to‐chip variation. Semiconductor quantum dot labeling, which is brighter and more stable than organic dyes, has also been applied to protein microarrays.<xref rid="bib46" ref-type="ref">46</xref>,<xref rid="bib47" ref-type="ref">47</xref></p><p>In addition to fluorescent labeling, Huang et al. detected multiple cytokines on an antibody array with enhanced chemiluminescence, providing an alternative detection method.<xref rid="bib48" ref-type="ref">48</xref> Enzymatic signal amplification is also a valuable labeling method. Rolling circle amplification (RCA) has been developed for protein microarray assays. For low abundance protein samples, the sensitivity of traditional fluorescence or chemiluminescence detection is relatively low, while RCA can detect captured proteins at fmol level and is promising to improve the sensitivity of fluorescent detection.<xref rid="bib16" ref-type="ref">16</xref>,<xref rid="bib49" ref-type="ref">49</xref>, <xref rid="bib50" ref-type="ref">50</xref>, <xref rid="bib51" ref-type="ref">51</xref>, <xref rid="bib52" ref-type="ref">52</xref> Tyramide signal amplification is another way to amplify signals with enzymes, which utilizes the horseradish peroxidase conjugated on secondary antibodies to convert the labeled substrates (tyramide) into short‐lived, extremely reactive intermediates, which then very rapidly react with and covalently bind to adjacent proteins.<xref rid="bib53" ref-type="ref">53</xref></p><p>For some biochemical assays, especially enzymatic reactions, use of radioisotopes is the only detection method available (see below for more details). They still offer the most sensitive and reliable detection of PTM events when there is a lack of high quality and high affinity detection reagents, such as antibodies. We and others have successfully applied <sup>32</sup>P‐, <sup>33</sup>P‐, and <sup>14</sup>C‐labeled substrates to detect protein phosphorylation and acetylation events.<xref rid="bib54" ref-type="ref">54</xref>,<xref rid="bib55" ref-type="ref">55</xref></p></sec><sec id="sec2-6"><title>Label‐Free Detection Methods</title><p>One obvious disadvantage of label‐dependent detection is the requirement of manipulating structure of either a probe or a specific antibody. It is not amenable to real‐time detection, which can provide important information when analyzing reaction dynamics. Therefore, label‐free detection methods have also been investigated for protein microarrays. Surface plasmon resonance is a label‐free technology to analyze biomolecular interactions in real‐time, and has been adapted for protein microarray signal detection.<xref rid="bib56" ref-type="ref">56</xref>,<xref rid="bib57" ref-type="ref">57</xref> Based on the principle that incident light can resonate with plasma on a metal surface in total internal reflection, the resonance signals will change when ligands bind to (and dissociate from) ligands on the array surface. Binding event can thus be monitored and the kinetic parameters calculated in real‐time. Mass spectrometry has also been used for detecting ligands bound to individual proteins printed on protein microarrays, with such approaches as MALDI‐MS, SELDI‐TOF‐MS, and MALDI‐TOF‐MS used for this purpose.<xref rid="bib58" ref-type="ref">58</xref>, <xref rid="bib59" ref-type="ref">59</xref>, <xref rid="bib60" ref-type="ref">60</xref> The analysis is rapid and simple, requires small sample amount, and can be used for direct detection of analytes bound from complex samples, such as urine, serum, plasma, and cell lysates. Atomic force microscopy (AFM) uses surface topological changes to identify the analytes bound on the array.<xref rid="bib61" ref-type="ref">61</xref>,<xref rid="bib62" ref-type="ref">62</xref> More specifically, AFM detects the increase in height of the proteins/antibodies on the array, and thus is able to measure binding interactions.</p></sec></sec><sec id="sec1-4"><title>APPLICATIONS OF FUNCTIONAL PROTEIN MICROARRAYS</title><p>Unlike the DNA/oligo microarray or analytical protein microarrays, functional protein microarrays provide a flexible platform that allows development and detection of a wide range of protein biochemical properties. To date, well‐developed assays include detection of various types of protein–ligand interactions, such as protein–protein, protein–DNA, protein–RNA, protein–lipid, protein–drug, and protein–glycan interactions,<xref rid="bib17" ref-type="ref">17</xref>,<xref rid="bib18" ref-type="ref">18</xref>,<xref rid="bib24" ref-type="ref">24</xref>,<xref rid="bib63" ref-type="ref">63</xref>, <xref rid="bib64" ref-type="ref">64</xref>, <xref rid="bib65" ref-type="ref">65</xref>, <xref rid="bib66" ref-type="ref">66</xref>, <xref rid="bib67" ref-type="ref">67</xref>, <xref rid="bib68" ref-type="ref">68</xref>, <xref rid="bib69" ref-type="ref">69</xref> and identification of substrates of various classes of enzymes, such as protein kinase, ubiquitin E3 ligase, and acetyltransferase, to name a few.<xref rid="bib15" ref-type="ref">15</xref>,<xref rid="bib54" ref-type="ref">54</xref>,<xref rid="bib55" ref-type="ref">55</xref>,<xref rid="bib70" ref-type="ref">70</xref>,<xref rid="bib71" ref-type="ref">71</xref> Application of these assays has had a profound impact on a wide range of research areas. This is especially true when they are used in large‐scale, high‐throughput projects, exemplified in both network construction and biomarker identification (see below and Table <xref rid="tbl1" ref-type="table">1</xref>).</p><table-wrap id="tbl1" xml:lang="en" orientation="portrait" position="float"><label>Table 1</label><caption><p>Application of Functional Protein Microarrays in Large‐Scale Projects</p></caption><table frame="hsides" rules="groups"><col span="1"></col><col span="1"></col><col span="1"></col><col span="1"></col><col span="1"></col><col span="1"></col><thead><tr style="border-bottom:solid 1px #000000"><th align="left" rowspan="1" colspan="1">Assay Type</th><th align="center" rowspan="1" colspan="1">Array Content</th><th align="center" rowspan="1" colspan="1">Type of Probe</th><th align="center" rowspan="1" colspan="1">No. of Probe</th><th align="center" rowspan="1" colspan="1">Application</th><th align="center" rowspan="1" colspan="1">Reference </th></tr></thead><tbody><tr><td align="left" rowspan="1" colspan="1">Protein–peptide interaction</td><td align="left" rowspan="1" colspan="1">159 human SH2 and PTB domains</td><td align="left" rowspan="1" colspan="1">Peptide</td><td align="center" rowspan="1" colspan="1">61</td><td align="left" rowspan="1" colspan="1">Protein interaction network</td><td align="center" rowspan="1" colspan="1"><xref rid="bib74" ref-type="ref">74</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Protein–DNA interaction</td><td align="left" rowspan="1" colspan="1">282 yeast TFs</td><td align="left" rowspan="1" colspan="1">DNA motif</td><td align="center" rowspan="1" colspan="1">75</td><td align="left" rowspan="1" colspan="1">Protein–DNA interaction network</td><td align="center" rowspan="1" colspan="1"><xref rid="bib65" ref-type="ref">65</xref></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">4191 human proteins</td><td align="left" rowspan="1" colspan="1">DNA motif</td><td align="center" rowspan="1" colspan="1">460</td><td align="left" rowspan="1" colspan="1">Protein–DNA interaction network</td><td align="center" rowspan="1" colspan="1"><xref rid="bib66" ref-type="ref">66</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Kinase assay</td><td align="left" rowspan="1" colspan="1">Yeast proteome</td><td align="left" rowspan="1" colspan="1">Protein kinase</td><td align="center" rowspan="1" colspan="1">87</td><td align="left" rowspan="1" colspan="1">Phosphorylation network</td><td align="center" rowspan="1" colspan="1"><xref rid="bib54" ref-type="ref">54</xref></td></tr><tr><td align="left" rowspan="1" colspan="1">Antigen–antibody interaction</td><td align="left" rowspan="1" colspan="1">196 human autoantigens</td><td align="left" rowspan="1" colspan="1">Autoimmune disease patient sera</td><td align="center" rowspan="1" colspan="1">>50</td><td align="left" rowspan="1" colspan="1">Antibody profiling</td><td align="center" rowspan="1" colspan="1"><xref rid="bib78" ref-type="ref">78</xref></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">82 coronavirus proteins</td><td align="left" rowspan="1" colspan="1">SARS patient sera</td><td align="center" rowspan="1" colspan="1">602</td><td align="left" rowspan="1" colspan="1">Antibody profiling</td><td align="center" rowspan="1" colspan="1"><xref rid="bib84" ref-type="ref">84</xref></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1"><italic>E. coli</italic> proteome</td><td align="left" rowspan="1" colspan="1">IBD patient sera</td><td align="center" rowspan="1" colspan="1">134</td><td align="left" rowspan="1" colspan="1">Biomarker identification</td><td align="center" rowspan="1" colspan="1"><xref rid="bib81" ref-type="ref">81</xref></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">5005 human proteins</td><td align="left" rowspan="1" colspan="1">Ovarian cancer patient sera</td><td align="center" rowspan="1" colspan="1">60</td><td align="left" rowspan="1" colspan="1">Biomarker identification</td><td align="center" rowspan="1" colspan="1"><xref rid="bib80" ref-type="ref">80</xref></td></tr><tr><td align="left" rowspan="1" colspan="1"></td><td align="left" rowspan="1" colspan="1">5011 human proteins</td><td align="left" rowspan="1" colspan="1">AIH patient sera</td><td align="center" rowspan="1" colspan="1">278</td><td align="left" rowspan="1" colspan="1">Biomarker identification</td><td align="center" rowspan="1" colspan="1"><xref rid="bib83" ref-type="ref">83</xref></td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Inc.</copyright-holder><license><license-p>This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.</license-p></license></permissions></table-wrap><sec id="sec2-7"><title>Protein–Protein and Protein–Lipid Interactions</title><p>Among the first applications of protein microarrays was in the analysis of protein–protein and protein–lipid interactions, where test ligands were directly or indirectly labeled with fluorescent dyes. For example, Zhu et al. developed the first proteome microarray composed of ∼5800 recombinant yeast proteins (>85% of the yeast proteome) and identified binding partners of calmodulin and phosphatidylinositides (PIPs).<xref rid="bib18" ref-type="ref">18</xref> They first incubated the microarrays with biotinylated bovine calmodulin and discovered 39 new calmodulin binding partners. In addition, using liposomes as a carrier for various PIPs, they identified more than 150 binding proteins, >50% of which were known membrane‐associated proteins. Popescu et al. developed a protein microarray containing 1133 <italic>Arabidopsis thaliana</italic> proteins and also used it to globally identify proteins bind to calmodulins or calmodulin‐like proteins in <italic>Arabidopsis.</italic><xref rid="bib63" ref-type="ref">63</xref> A large number of previously known and novel targets were identified, including transcription factors (TFs), receptor and intracellular protein kinases, F‐box proteins, RNA‐binding proteins, and proteins of unknown function. Alternative approaches to identifying protein–protein interactions, such as the yeast two‐hybrid system and protein complex purification coupled with mass spectrometry analysis, are well established, however, and are used as standard high‐throughput methods to detect protein–protein interactions in higher eukaryotes.<xref rid="bib72" ref-type="ref">72</xref>,<xref rid="bib73" ref-type="ref">73</xref> Thus, while protein microarray‐based approaches provide a rapid approach to characterizing protein–protein interactions, they have much competition in this arena.</p></sec><sec id="sec2-8"><title>Protein–Peptide Interaction</title><p>MacBeath and colleagues fabricated protein domain microarrays to investigate protein–peptide interactions in a semiquantitative fashion that might play an important role in signaling.<xref rid="bib74" ref-type="ref">74</xref> They constructed an array by printing 159 human Src homology 2 (SH2) and phosphotyrosine binding (PTB) domains on the aldehyde‐modified glass substrates, and incubated the arrays with 61 peptides representing tyrosine phosphorylation sites on the four ErbB receptors. Eight concentrations of each peptide (10 nM–5 mM) were tested in the assay, allowing quantitative measurement of the binding affinity of each peptide to its protein ligand.</p></sec><sec id="sec2-9"><title>Protein–DNA Interaction</title><p>Protein microarrays have also been applied extensively and productively to characterize protein–DNA interactions (PDIs). In an earlier study, Snyder and colleagues screened for novel DNA‐binding proteins by probing the yeast proteome microarrays with fluorescently labeled yeast genomic DNA.<xref rid="bib64" ref-type="ref">64</xref> Of the ∼200 positive proteins, half were not previously known to bind to DNA. By focusing on a single yeast gene, <italic>ARG5,6</italic>, encoding two enzymes involved in arginine biosynthesis, they discovered that it bound to a specific DNA motif and associated with specific nuclear and mitochondrial loci <italic>in vivo</italic>.</p><p>In a later report, the Snyder and Johnston group constructed a protein microarray with 282 known and predicted yeast TFs to identify their interactions with 75 evolutionarily conserved DNA motifs.<xref rid="bib65" ref-type="ref">65</xref> Over 200 specific PDIs were identified and >60% of them are previously unknown. The binding site of a previously uncharacterized DNA‐binding protein, Yjl103p, was defined and a number of its target genes were identified, many of which are involved in stress response and oxidative phosphorylation.</p><p>Our team developed a bacterial proteome microarray composed of 4256 proteins encoded by the <italic>E. coli</italic> K12 strain (∼99% coverage of the proteome) using a bacterial high‐throughput protein purification protocol.<xref rid="bib24" ref-type="ref">24</xref> To demonstrate the usefulness, end‐labeled, double‐stranded (ds) DNA probes carrying abasic or mismatched base pairs were used to identify proteins involved in DNA damage recognition. A small number of proteins were specifically recognized by each type of the probes with high affinity. Two of them, YbaZ and YbcN, were further characterized to encode base‐flipping activity using biochemical assays.</p><p>Recently, our group also undertook a large‐scale analysis of human PDIs using a protein microarray composed of 4191 unique human proteins in full‐length, including ∼90% of the annotated TFs and a wide range of other protein categories, such as RNA‐binding proteins, chromatin‐associated proteins, nucleotide‐binding proteins, transcription co‐regulators, mitochondrial proteins, and protein kinases.<xref rid="bib66" ref-type="ref">66</xref> The protein microarrays were probed with 400 predicted and 60 known DNA motifs and a total of 17,718 PDIs were identified. Many known PDIs and a large number of new PDIs for both well characterized and predicted TFs were recovered, and new consensus sites for over 200 TFs were determined, which doubled the number of previously reported consensus sites for human TFs.<xref rid="bib66" ref-type="ref">66</xref>, <xref rid="bib75" ref-type="ref">75</xref> Surprisingly, over 300 proteins that were previously unknown to specifically interact with DNA showed sequence‐specific PDIs, suggesting that many human proteins may bind specific DNA sequences as a moonlighting function. To further investigate whether the DNA‐binding activities of these unconventional DNA‐binding proteins (uDBPs) were physiologically relevant, we carried out in‐depth analysis on a well studied protein kinase, Erk2, to determine the potential mechanism behind its DNA‐binding activity. Using a series of <italic>in vitro</italic> and <italic>in vivo</italic> approaches, such as electrophoretic mobility shift assay (EMSA), luciferase assay, mutagenesis, and chromatin immunoprecipitation (ChIP), we demonstrated that the DNA‐binding activity of Erk2 is independent of its protein kinase activity and it acts as a transcription repressor of transcripts induced by interferon gamma signaling.<xref rid="bib66" ref-type="ref">66</xref> Our study suggests that moonlighting functions of uDBPs based on their sequence‐specific DNA‐binding activity may be a widespread phenomenon in humans.</p></sec><sec id="sec2-10"><title>Protein–Small‐Molecule Interaction</title><p>Discovering new drug molecules and drug targets is another field in which protein microarrays have shown its potential. For example, Huang et al. incubated biotinylated small‐molecule inhibitors of rapamycin (SMIRs) on the yeast proteome microarrays, and obtained the binding profiles of the SMIRs across the entire yeast proteome.<xref rid="bib68" ref-type="ref">68</xref> They identified candidate target proteins of the SMIRs, including Tep1p, a homolog of the mammalian PTEN tumor suppressor, and Ybr077cp (Nir1p), a protein of previously unknown function, both of which are validated to associate with PI(3,4)P2, suggesting a novel mechanism by which phosphatidylinositides might modulate the target of rapamycin pathway.</p></sec><sec id="sec2-11"><title>Protein–RNA Interaction</title><p>The yeast proteome microarray has been used to identify specific RNA‐binding proteins for antiviral activities.<xref rid="bib67" ref-type="ref">67</xref> In these experiments, arrays were incubated with a fluorescently tagged small RNA hairpin containing a clamped adenine motif, which is required for the replication of Brome Mosaic Virus (BMV), a plant‐infecting RNA virus that can also replicate in the budding yeast. Two of the candidate proteins, Pseudouridine Synthase 4 (Pus4) and the Actin Patch Protein 1 (App1), were further characterized in <italic>Nicotiana benthamiana.</italic> Both of them modestly reduced BMV genomic plus‐strand RNA accumulation and dramatically inhibited the spread of BMV in plants.</p></sec><sec id="sec2-12"><title>Protein–Glycan Interaction</title><p>Protein glycosylation, a general PTM of proteins involved in cell membrane formation, is crucial to dictate proper conformation of many membrane proteins, retain stability on some secreted glycoproteins, and play a role in cell‐cell adhesion. To further understand the roles of protein glycosylation in yeast, the Zhu and Snyder teams reasoned that since proteins on the yeast proteome microarrays are expressed in their original host and therefore, should maintain most of their PTMs, these arrays can be used to profile glycosylation using fluorescently labeled lectins, such as Concanavalin A and Wheat‐Germ Agglutinin.<xref rid="bib69" ref-type="ref">69</xref> A total of 534 proteins were identified, 406 of which were previously not known to be glycosylated. Many proteins in the secretory pathway were identified, as well as other functional classes of proteins, including TFs and mitochondrial proteins. Upon treatment with tunicamycin, an inhibitor of N‐linked protein glycosylation, two of the four mitochondrial proteins identified showed partial distribution to the cytosol and reduced localization to the mitochondria, suggesting a new role of protein glycosylation in mitochondrial protein function and localization.</p></sec><sec id="sec2-13"><title>Protein Phosphorylation</title><p>Protein phosphorylation plays a central role in most, if not all, aspects of cellular processes. The application of protein microarray technology to protein phosphorylation was first demonstrated by Zhu et al.<xref rid="bib15" ref-type="ref">15</xref> They immobilized 17 different substrates on a nanowell protein microarray, followed by individual kinase assays with almost all of the yeast kinases (119/122). This approach allowed them to determine the substrate specificity of the yeast kinome and identify new tyrosine phosphorylation activity.</p><p>In a later report, Snyder's group accomplished a large scale “Phosphorylome Project” using the yeast proteome microarrays.<xref rid="bib54" ref-type="ref">54</xref> Eighty‐seven purified yeast kinases or kinase complexes were individually incubated on the yeast proteome arrays in a kinase buffer in the presence of <sup>33</sup>P‐<italic>γ</italic>‐ATP and a total of 1325 distinct protein substrates were identified, representing a total of 4129 phosphorylation events. These results provided a global network that connect kinases to their potential substrates and offered a new opportunity to identify new signaling pathways or cross‐talk between pathways.</p><p>Several smaller scale studies of kinase–substrate interactions have been reported. For instance, Popescu et al. probed 10 <italic>Arabidopsis</italic> mitogen‐activated protein kinases (MPKs) to protein microarrays containing 2158 <italic>Arabidopsis</italic> proteins and identified 570 putative MPK phosphorylation targets, which were enriched in transcription factors involved in the regulation of development, defense, and stress responses.<xref rid="bib76" ref-type="ref">76</xref> A commercially available human protein microarray comprised of approximately 3000 individual proteins was used to identify substrates of cyclin‐dependent kinase 5 (Cdk5), a serine/threonine kinase that plays an important role during central nervous system development.<xref rid="bib70" ref-type="ref">70</xref></p></sec><sec id="sec2-14"><title>Protein Ubiquitylation</title><p>Ubiquitylation is one of the most prevalent PTMs and controls almost all types of cellular events in eukaryotes. To establish a protein microarray‐based approach for identification of ubiquitin E3 ligase substrates, Lu et al. developed an assay for yeast proteome microarrays that utilizes a HECT‐domain E3 ligase, Rsp5, in combination with the E1 and E2 enzymes.<xref rid="bib71" ref-type="ref">71</xref> More than 90 new substrates were identified, eight of which were validated as <italic>in vivo</italic> substrates of Rsp5. Further <italic>in vivo</italic> characterization of two substrates, Sla1 and Rnr2, demonstrated that Rsp5‐dependent ubiquitylation affects either post‐translational process of the substrate or subcellular localization.</p></sec><sec id="sec2-15"><title>Protein Acetylation</title><p>Histone acetylation and deacetylation, which are catalyzed by histone acetyltransferases (HATs) and histone deacetylases (HDACs), respectively, are emerging as critical regulators of chromatin structure and transcription. However, it has been hypothesized that many HATs and HDACs might also modify nonhistone substrates. For example, the core enzyme, Esa1, of the essential nucleosome acetyltransferase of H4 (NuA4) complex, is the only essential HAT in yeast, which strongly suggested that it may target additional nonhistone proteins that are crucial for cell to survive. To identify nonhistone substrates of the NuA4 complex, Lin et al. established and performed acetylation reactions on the yeast proteome microarrays using the NuA4 complex in the presence of [<sup>14</sup>C]‐Acetyl‐CoA as a donor.<xref rid="bib55" ref-type="ref">55</xref> Surprisingly, 91 proteins were found to be readily acetylated by the NuA4 complex on the array. To further validate these <italic>in vitro</italic> results, 20 of them were randomly chosen and 13 of them showed Esa1‐dependent acetylation in cells. One of them, phosphoenolpyruvate carboxykinase (Pck1p), was further characterized to explore the possible link between acetylation and metabolism. Mass spectrometry assay revealed Lys19 and 514 as the acetylation sites of Pck1p, and mutagenesis analyses demonstrated that acetylation on K514 is critical to enhance Pck1p's enzyme activity and results in longer life span for yeast cells growing under starvation. This study offers a molecular link between the HDAC Sir2 and yeast longevity.</p></sec><sec id="sec2-16"><title>S‐Nitrosylation</title><p>S‐nitrosylation is independent of enzyme catalysis but an important PTM that affects a wide range of proteins involved in many cellular processes. Recently, Foster et al. developed a protein microarray‐based approach to detecting proteins reactive to S‐nitrosothiol (SNO), the donor of NO<sup>+</sup> in S‐nitrosylation, and to investigating determinants of S‐nitrosylation.<xref rid="bib77" ref-type="ref">77</xref> S‐nitrosocysteine (CysNO), a highly reactive SNO, was added to the yeast proteome microarray and the nitrosylated proteins were then detected using a modified biotin switch technique. The top 300 proteins with the highest relative signal intensity were further analyzed and the results revealed that proteins with active‐site Cys thiols residing at N termini of alpha‐helices or within catalytic loops were particularly prominent. However, substantial variations of S‐nitrosylation were observed even within these protein families, indicating that secondary structure or intrinsic nucleophilicity of Cys thiols was not sufficient to interpret the specificity of S‐nitrosylation. Further analyses revealed that NO‐donor stereochemistry and structure had significant impact on S‐nitrosylation efficiency.</p></sec><sec id="sec2-17"><title>Biomarker Identification</title><p>Though the applications described above are most useful in basic research, functional protein microarrays may have enormous impacts on clinical diagnosis and prognosis. When proteins on a functional protein microarray are viewed as potential antigens that may or may not associate with a particular disease, it becomes a powerful tool in biomarker identification. The principle is straightforward: when an auto‐antibody presented in human sera associated with a human disease (e.g., autoimmune diseases) recognizes a human protein spotted on the array, it can be readily detected with fluorescently labeled antihuman immunoglobulin antibodies (e.g., anti‐IgG) and a profile of auto‐antibodies associated with a disease thus created, providing a rapid approach to identifying potential disease biomarkers. For example, Robinson et al. reported the first application of protein microarray technology to profile multiple human disease sera.<xref rid="bib78" ref-type="ref">78</xref> They constructed a microarray with 196 biomolecules shown to be autoantigens in eight human autoimmune diseases, including proteins, peptides, enzyme complexes, ribonucleoprotein complexes, DNA, and post‐translationally modified antigens. The arrays were incubated with patient sera to study the specificity and pathogenesis of auto‐antibody responses, and were used to identify and define relevant autoantigens in human autoimmune diseases, including systemic lupus erythematosus and rheumatoid arthritis (RA).</p><p>Hu et al. reported a new approach for high‐throughput characterization of monoclonal antibody (mAb) target specificity using a protein microarray composed of 1058 unique human liver proteins.<xref rid="bib79" ref-type="ref">79</xref> They immunized mice with live cells from human livers, isolated 54 hybridomas with binding activities to human cells, and identified the corresponding antigens for five mAbs via screening on the protein microarray. Expression profiles of the corresponding antigens of the five antibodies were characterized by using tissue microarrays and one of the antigens, eIF1A, was found to be expressed in normal human liver but not in hepatocellular carcinoma. Other applications include biomarker identification for ovarian cancer,<xref rid="bib80" ref-type="ref">80</xref> inflammatory bowel disease (IBD),<xref rid="bib81" ref-type="ref">81</xref> alopecia areata,<xref rid="bib82" ref-type="ref">82</xref> and autoimmune hepatitis (AIH),<xref rid="bib83" ref-type="ref">83</xref> etc.</p><p>Protein microarrays can also be used for detection of infectious diseases. Zhu et al. developed a coronavirus protein microarray for the diagnosis of severe acute respiratory syndrome (SARS), which included all the SARS‐CoV proteins as well as proteins from five additional coronaviruses that can infect humans (HCoV‐229E and HCoV‐OC43), cows (BCV), cats (FIPV), and mice (MHVA59).<xref rid="bib84" ref-type="ref">84</xref> These microarrays could quickly distinguish patient serum samples as SARS‐positive or SARS‐negative based on the presence of human IgG and IgM antibodies against SARS‐CoV proteins, with a 94% accuracy compared to standard diagnostic methods. Patients carrying antibodies against other coronavirus proteins were also identified. The advantages of this microarray‐based assay to standard ELISA‐based diagnostic methods include at least 100‐fold higher sensitivity and the need for substantially less sample for analysis.</p></sec></sec><sec id="sec1-5"><title>OUTLOOK</title><p>Recent years have witnessed a rapid growth in using functional protein microarrays for basic research.<xref rid="bib85" ref-type="ref">85</xref> Although the technology is still at a relatively early stage of development, it has become obvious that the protein microarray platform can and will act as a versatile tool suitable for the large‐scale, high‐throughput biology, especially in the areas of profiling PTMs and in analysis of signal transduction networks and pathways.<xref rid="bib54" ref-type="ref">54</xref>, <xref rid="bib66" ref-type="ref">66</xref> As another crucial proteomics technology, recent progress in mass spectrometry has allowed global profiling of PTMs using a shotgun approach. For example, the Zhao, Mann, and Guan groups recently identified numerous acetylated lysine residues in metabolic enzymes in mice and human cells without knowing the upstream HATs.<xref rid="bib86" ref-type="ref">86</xref>, <xref rid="bib87" ref-type="ref">87</xref>, <xref rid="bib88" ref-type="ref">88</xref> In parallel, our team also identified many yeast metabolic enzymes as substrates of the NuA4 acetylation complex without knowing the actual modified sites. Therefore, we envision that the combination of the two technologies will have enormous potential to both identify critical regulatory PTMs at the resolution of modified individual amino acids and to identify the enzymes that mediate these effects. Another emerging direction is in the forefront of understanding the molecular mechanisms of pathogen–host interactions. In the same manner in which we identified host proteins that recognized the SLD loop of the BMV virus, functional protein microarrays (e.g., a human protein microarray) can be used to discover those host proteins targeted by pathogens (e.g., HIV, HCV, and SARS‐CoV). The identification of the host targets of a virus will provide alternative therapeutics that cannot be rapidly evaded <italic>via</italic> mutation of the viral genomes. In conclusion, the potential of functional protein microarrays is only just now starting to reveal itself. It is expected that it will become an indispensible and invaluable tool in proteomics and systems biology research.</p></sec></body><back><ack id="sec1-ack-1"><title>Acknowledgements</title><p>We thank the NIH for funding support. 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