Name: an integrated approach for microprotein identification and sequence analysis
Uploaded: 2022-07-12T13:15:00.000+00:00
Duration: 9 min 37 s
Description: Watch this Scientific Journal Video about An Integrated Approach for Microprotein Identification and Sequence Analysis at JoVE.com

This work was supported by grants from the National Institutes of Health (HL-141630 and HL-160569) and Cincinnati Children&#39;s Research Foundation (Trustee Award).

The protocol outlined below details steps to load and navigate the PhyloCSF browser tracks on the UCSC Genome Browser (generated by Mudge et al.49). For general questions regarding the UCSC Genome Browser, an extensive Genome Browser User&#39;s Guide can be found here:&#160;<a href="https://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html">https://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html</a>.
1. Loading the PhyloCSF Track Hub to the UCSC Genome Browser
<ol>
	<li>Open an internet browser window and navigate to the UCSC Genome Browser (<a href="https://genome.ucsc.edu/">https://genome.ucsc.edu/</a>).</li>
	<li>Under the Our tools heading, select the Track Hubs option. 
	NOTE: The Track Hubs option can also be found under the My Data tab.</li>
	<li>In the Public Hubs tab, type PhyloCSF into the Search terms box. Click on the Search Public Hubs button.</li>
	<li>Connect to PhyloCSF by clicking on the Connect button for the Hub Name PhyloCSF (Description: Evolutionary protein-coding potential as measured by PhyloCSF). 
	&#8203;NOTE: This Track Hub will load to numerous assemblies, including human (hg19 and hg38) and mouse (mm10 and mm39).</li>
	<li>After clicking on connect, wait to be redirected to the UCSC Genome Browser Gateway page (<a href="https://genome.ucsc.edu/cgi-bin/hgGateway">https://genome.ucsc.edu/cgi-bin/hgGateway</a>).</li>
</ol>
2. Navigating to genes of interest using Gene Identifiers
<ol>
	<li>Select the species and genome assembly to query. To query a different species (e.g., mouse), select the species of interest under the Browse/Select Species heading by clicking on the appropriate icon, or type the species into the text box that says, Enter species, common name or assembly ID. 
	NOTE: The assembly is listed directly under the Find Position heading. Typically, the default is the Human Assembly (e.g., Dec. 2009 [GRCh37/hg19]).</li>
	<li>Choose the assembly to search under the Find Position heading using the dropdown menu.</li>
	<li>Enter the position, gene symbol, or search terms in the Position/Search Term box and click on Go to navigate to a gene of interest on the Genome Browser.</li>
	<li>If the search resulted in multiple matches, wait to be redirected to a page that requires the selection of a position of interest. Click on the appropriate gene of interest.</li>
</ol>
3. Navigating to genomic regions of interest using sequence information
<ol>
	<li>Navigate to the UCSC Genome Browser (<a href="https://genome.ucsc.edu/">https://genome.ucsc.edu/</a>) and select the BLAST-Like Alignment Tool (BLAT) under the&#160;Our tools heading to query a specific DNA or protein sequence. Alternatively, hover the cursor over the Tools tab and select the Blat option or follow this link:&#160;<a href="https://genome.ucsc.edu/cgi-bin/hgBlat">https://genome.ucsc.edu/cgi-bin/hgBlat</a>.</li>
	<li>Select the species (Genome) and Assembly of interest using the dropdown menus.</li>
	<li>Define the Query type using the dropdown menu.</li>
	<li>Paste the sequence of interest into the BLAT Search Genome text box and click Submit.</li>
	<li>Click on the browser link under the ACTIONS heading to navigate to the genomic region of interest.</li>
</ol>
4. Identifying conserved sORFs using PhyloCSF Track Data
<ol>
	<li>Visually scan the genomic area of interest for positively scoring PhyloCSF regions (Figure 1). 
	NOTE: For a detailed explanation of how to visually interpret PhyloCSF scores on the UCSC Genome Browser, see the representative results section below.</li>
	<li>Use the zoom feature to magnify regions of interest to examine sequence characteristics and search for start/stop codons. To zoom in manually, hold the shift key and click and hold the mouse button while dragging along the region of interest. Alternatively, use the zoom in and zoom out buttons at the top of the page to navigate (1.5x, 3x, 10x, or base zoom options are available). 
	NOTE: Before using the zoom in/zoom out buttons, it is necessary to reposition the gene so that the region of interest is in the middle of the screen. To perform this action, click on the image and drag it left or right to move the genomic region horizontally as desired or use the move arrows at the top of the page.</li>
	<li>Zoom in until the nucleotide (base) sequence is visible. 
	NOTE: The nucleotide sequence will appear directly above the +1 Smoothed PhyloCSF score.</li>
	<li>Visually scan the nucleotide sequence near the beginning and end of the positively scoring PhyloCSF regions to identify putative start (ATG) and stop (TGA/TAA/TAG) codons. 
	NOTE: If the gene of interest is on the minus strand of DNA, the start and stop codons will be the reverse complement (i.e., CAT for the start codon and TCA/TTA/CTA for the stop codon).</li>
</ol>
5. Viewing homologous regions in other genomes
<ol>
	<li>Hover the mouse over the View heading at the top of the page and click on the In Other Genomes (Convert) option.</li>
	<li>Define the genome of interest using the dropdown menu below the New Genome heading.</li>
	<li>Select the genomic assembly of interest using the dropdown menu under the New Assembly heading, then click the Submit button.</li>
	<li>Once the browser returns a list of regions in the new assembly with similarity, click on the chromosome position link to navigate to the homologous region of interest. 
	NOTE: The percentage of total bases (nucleotides) and the span that are covered by the region will be defined for each region listed. The higher the percentage of matching bases, the higher the conservation is for the region of interest.</li>
	<li>Follow the same navigational strategies detailed in Section 4 to analyze the sequence.</li>
</ol>
6. Generating multi-species sequence alignments for microproteins of interest
<ol>
	<li>Click on the gene of interest in the GENCODE track on the UCSC Genome Browser (indicated in Figure 1A with a blue box) to navigate to the gene description page.</li>
	<li>Under the Sequence and Links to Tools and Databases heading, click on the link in the table that reads Other Species FASTA.</li>
	<li>Click on the boxes associated with the species of interest to select them. Click on Submit. Copy and paste the sequences appearing at the bottom of the page in FASTA format into a word processing document.</li>
	<li>Open a second browser window and navigate to the Clustal Omega Multiple Sequence Alignment tool52 on the European Bioinformatics Institute (EMBL-EBI) website53,54: <a href="https://www.ebi.ac.uk/Tools/msa/clustalo/">https://www.ebi.ac.uk/Tools/msa/clustalo/</a>.</li>
	<li>Paste the sequence files that are still on the clipboard into the box in STEP 1 that reads sequences in any supported format. Scroll to the bottom of the page and click on Submit. Look below the aligned results (in black font) for symbols that indicate the degree of conservation of each amino acid (symbols are defined in Table 1). 
	NOTE: It may take several minutes to generate the alignment.</li>
	<li>To view the amino acid properties in color, click on the Show Colors link directly above the sequences to color the amino acids according to their properties (defined in Table 2).</li>
	<li>Copy and paste the sequence alignment into a word processing or slideshow program to generate a figure or illustration file (e.g., Figure 2). 
	NOTE: Use a monospaced font for the alignment such as Courier.</li>
	<li>To view other outputs from the Clustal Omega results page, click on the appropriate tabs (i.e., Guide Tree or Phylogenetic Tree).</li>
	<li>Click on the Results Viewers tab for options to view the sequence information using Jalview, a free program that specializes in multiple sequence alignment editing, visualization, and analysis55, or to access direct links to MView and Simple Phylogeny56.</li>
</ol>

The authors declare that they have no competing financial interests.

The protocol presented here provides detailed instructions on how to interrogate genomic regions of interest for microprotein-coding potential using PhyloCSF on the user-friendly UCSC Genome Browser48,49,50,51. As detailed above, PhyloCSF is a powerful comparative genomics algorithm that integrates phylogenetic models and codon substitution frequencies to identify evolutionary signatures that are typical of protein-coding genes48,49. PhyloCSF has been widely used to identify functional microproteins in genomic regions previously annotated as noncoding38,39,40,41,42,43,44,45,46,47, and this approach has been shown to outperform other comparative genomics methods for short sequences such as microproteins as small as 13 amino acids and for small exons of canonical proteins35,48,49. Notably, the utility of PhyloCSF as a robust method to identify functional protein-coding sequences via evolutionary conservation extends beyond that of vertebrate and invertebrate species and has even been recently applied to viral genomes to successfully interrogate the protein-coding capacity of the SARS-CoV-2 genome67.
In addition to identifying putative coding sequences within annotated noncoding RNAs, an advantage of PhyloCSF is that it can also reliably detect conserved microproteins encoded by ORFs within annotated untranslated regions (UTRs) of canonical protein coding genes, including both 5&#39; upstream and 3&#39; downstream ORFs (uORFs and dORFs, respectively)8,19,66,68. For example, the MIEF1 microprotein (MIEF1-MP) is encoded in the 5&#39; UTR of mitochondrial elongation factor 1 (MIEF1)66. In the case of MIEF1-MP, a discrete positive PhyloCSF score corresponding to the MIEF1-MP is observed upstream of the ORF that encodes MIEF1 (Figure 5). While some uORF encoded microproteins directly interact with the downstream canonical proteins on their shared mRNA, (ex. MIEF1-MP and MIEF1), others function independently of the protein encoded by the main CDS66,68. Therefore, when characterizing uORF-encoded microproteins, it should not be assumed that they function via direct regulation of their downstream protein product.
While PhyloCSF has many clear strengths as a tool for the identification of conserved microprotein-coding sequences, it is important to recognize several limitations of this method. First, while sequence conservation strongly suggests that a genomic region has undergone functional selection and is thus coding, a lack of robust conservation and a resultant negative PhyloCSF score does not definitively rule out coding potential for a given sequence. In other words, relying exclusively on PhyloCSF may result in the oversight of translated ORFs that are not strongly conserved but still produce functional microproteins. Notably, genomic regions with low conservation or negative conservation scores could correspond to species-specific coding regions or those of evolutionary &#34;young&#34; genes via sequence divergence or de novo gene birth46,69,70,71,72,73,74. For example, the microprotein ASAP, which is encoded by what was formerly thought to be the human noncoding RNA LINC00467, is not scored positively by PhyloCSF because the amino acid sequence is only conserved in higher mammals75. Additionally, recent studies identified several human-specific microproteins, including one encoded by the intergenic lncRNA RP3-527G5.1, that does not generate a positive PhyloCSF score68,72. In this regard, the absence of a positive PhyloCSF score cannot be interpreted as proof of a noncoding region and should be interpreted with caution.
A second consideration to keep in mind when using PhyloCSF is that even though a positive score is highly suggestive of functional selection and protein-coding capacity, this line of evidence cannot stand alone and must be experimentally validated. Examples of methods that can be used to generate supporting evidence for stable microprotein expression include the detection of the putative protein by mass spectrometry or western blotting using an antibody raised against the microprotein sequence of interest. Alternatively, since it can be challenging to generate reliable antibodies for microproteins due to the lack of sequence choices for optimal antigenicity, it is also possible to use CRISPR/Cas9 and the homology-directed repair (HDR) pathway to introduce an epitope tag into the endogenous locus in frame with the putative microprotein sequence, thereby facilitating the detection of the protein of interest using a high-affinity antibody (e.g., FLAG, HA, V5, Myc)18. A final limitation of PhyloCSF to acknowledge is that although it is currently integrated into many of the commonly used genomic assemblies, including Homo sapiens (human hg19, hg38), Mus musculus (mouse mm10, mm39), Gallus gallus (chicken, galGal4, galGal6), Drosophila melanogaster (fruit fly, dm6), Caenorhabditis elegans (nematodes, ce11), and SARS-CoV-2 (wuhCor1), there are still many species that cannot currently be queried directly on the UCSC Genome Browser.
The identification of conserved domains or sequence characteristics within identified microproteins can help increase confidence in their functional relevance and provide some insight into their putative function. Here we provide recommendations for specific tools and resources that can be used to analyze identified microprotein amino acid sequences in further detail to gain such insight. The specific tools listed below (and summarized in the Table of Materials) are freely available to the public, and we have found them to be particularly user-friendly and robust in microprotein studies18,38,39,40,41,47. Beyond the tools described here, there are a multitude of additional resources that can be found in bioinformatics resource portals such as Expasy (<a href="https://www.expasy.org">https://www.expasy.org</a>) and EMBL-EBI (<a href="https://www.ebi.ac.uk/services/all">https://www.ebi.ac.uk/services/all</a>). However, detailing the specifics for each of the tools within these repositories is beyond the scope of this article. Here we recommend the following resources.&#160;
First, TMHMM76 (<a href="https://services.healthtech.dtu.dk/service.php?TMHMM-2.0">https://services.healthtech.dtu.dk/service.php?TMHMM-2.0</a>)&#160;analyzes protein sequences of interest for the presence of transmembrane domains. Notably, a number of microproteins that have been functionally characterized thus far contain single-pass transmembrane domains, which facilitates their localization to membrane regions and enables their direct regulation of ion channels, exchangers, and membrane-associated enzymes30. Second, the National Center for Biotechnology Information (NCBI) Conserved Domain Search77 (<a href="https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi">https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi</a>) is a popular tool used to identify conserved domains within protein or coding nucleotide sequences. Third, Protein family (Pfam)78 database (<a href="http://pfam.xfam.org">http://pfam.xfam.org</a>)&#160;provides alignments and classifications of protein families and domains. Fourth, WoLF PSORT79 (<a href="https://wolfpsort.hgc.jp/">https://wolfpsort.hgc.jp/</a>)&#160;is a tool that can be employed to predict subcellular protein localization. Fifth, COXPRESdB80 is a gene co-expression database (<a href="https://coxpresdb.jp">https://coxpresdb.jp</a>)&#160;that provides co-regulated gene relationships to estimate gene functions. Finally, SignalP 6.081 is a widely used prediction program (<a href="https://services.healthtech.dtu.dk/service.php?SignalP">https://services.healthtech.dtu.dk/service.php?SignalP</a>) that recognizes the presence of a signal peptide sequence and predicts the location of the cleavage site.
In summary, the methods described here can be used to effectively analyze genomic regions of interest for protein-coding potential using PhyloCSF on the UCSC Genome Browser. These methods are highly accessible and can be easily learned and efficiently applied by individuals without prior training or expertise in bioinformatics or comparative genomics. As demonstrated here in detail, PhyloCSF is a powerful tool that can be applied as a first-pass analysis to help distinguish protein-coding versus noncoding genes in vertebrate, invertebrate, and viral genomes, and the strengths of this approach heavily outweigh the noted weaknesses.

The identification of the complete set of coding elements in the genome has been a major goal since the initiation of the Human Genome Project, and remains a central objective toward the understanding of biological systems and the etiology of genetic-based diseases1,2,3,4. Advances in NGS techniques have led to the production of whole genome sequences for an extensive number of organisms, including vertebrates, invertebrates, yeast, and plants5. Additionally, high-throughput transcriptional sequencing methods have further revealed the complexity of the cellular transcriptome, and identified thousands of novel RNA molecules with both protein-coding and noncoding functions6,7. Decoding this vast amount of sequence information is an ongoing process, and challenges remain with comprehensive gene annotation efforts8.
The recent development of translational profiling methods, including ribosome profiling9,10 and poly-ribosome sequencing11, have provided evidence indicating that hundreds of noncanonical translation events map to currently unannotated sORFs throughout the genome, with the potential to generate small proteins called microproteins or micropeptides12,13,14,15,16,17. Microproteins have emerged as a novel class of versatile proteins previously overlooked by standard gene annotation methods due to their small size (&#60;100 amino acids) and lack of classical protein-coding gene characteristics8,12,18,19,20. Microproteins have been described in virtually all organisms, including yeast21,22, flies17,23,24, and mammals25,26,27,28, and have been shown to play critical roles in diverse processes, including development, metabolism, and stress signaling19,20,29,30,31,32,33,34. Thus, it is imperative to continue to mine the genome for additional members of this long-overlooked class of functional small proteins.
Despite the widespread recognition of the biological importance of microproteins, this class of genes remains vastly underrepresented in genome annotations, and their accurate identification continues to be an ongoing challenge that has hindered progress in the field. Various computational tools and experimental methods have recently been developed to overcome the difficulties associated with identifying microprotein-coding sequences (discussed extensively in several comprehensive reviews8,35,36,37). Many recent microprotein identification studies38,39,40,41,42,43,44,45,46,47 have relied heavily on the use of one such algorithm called PhyloCSF48,49, a powerful comparative genomics approach that can be leveraged to distinguish conserved protein-coding regions of the genome from those that are noncoding.
PhyloCSF compares codon substitution frequencies (CSF) using multi-species nucleotide alignments and phylogenetic models to detect evolutionary signatures of protein-coding genes. This empirical model-based approach relies on the premise that proteins are primarily conserved at the amino acid level rather than the nucleotide sequence. Therefore, synonymous codon substitutions, which encode the same amino acid, or codon substitutions to amino acids with conserved properties (i.e., charge, hydrophobicity, polarity) are scored positively, while non-synonymous substitutions, including missense and nonsense substitutions, score negatively. PhyloCSF is trained on whole-genome data and has proven to be effective in scoring short portions of a coding sequence (CDS) in isolation from the full sequence, which is necessary when analyzing microproteins or individual exons of standard protein-coding genes48,49.
Notably, the recent integration of the PhyloCSF track hubs in the University of California Santa Cruz (UCSC) Genome Browser49,50,51 enables investigators of all backgrounds to easily access a user-friendly interface to query genomic regions of interest for protein-coding potential. The protocol outlined below provides detailed instruction on how to load the PhyloCSF track hubs on the UCSC Genome Browser and subsequently interrogate genomic regions of interest to probe for high-confidence protein-coding regions (or the lack thereof). Additionally, in the case where a positive PhyloCSF score is observed, steps are delineated to further analyze microprotein-coding potential and efficiently generate multiple species alignments of the identified amino acid sequences to illustrate cross-species sequence conservation. Lastly, several additional publicly available resources and tools are introduced in the discussion to survey identified microprotein characteristics, including predicted domain structures and insight into putative microprotein function.

<table><tbody><tr><td>&lt;strong&gt;Website&lt;/strong&gt;</td><td>&lt;strong&gt;Website Address&lt;/strong&gt;</td><td>&lt;strong&gt;Requirements&lt;/strong&gt;</td><td></td></tr><tr><td>Clustal Omega Multiple Sequence Alignment Tool</td><td>&lt;a href=&quot;https://www.ebi.ac.uk/Tools/msa/clustalo/&quot;&gt;https://www.ebi.ac.uk/Tools/msa/clustalo/&lt;/a&gt;</td><td>Web browser</td><td>Multiple sequence alignment program for the efficient alignment of FASTA sequences (i.e. for cross-species comparison of identified microproteins)</td></tr><tr><td>COXPRESSdb</td><td>&lt;a href=&quot;https://coxpresdb.jp&quot;&gt;https://coxpresdb.jp&lt;/a&gt;</td><td>Web browser</td><td>Provides co-regulated gene relationships to estimate gene functions</td></tr><tr><td>EMBL-EBI Bioinformatics Tools FAQs</td><td>&lt;a href=&quot;https://www.ebi.ac.uk/seqdb/confluence/display/JDSAT/Bioinformatics+Tools+FAQ&quot;&gt;https://www.ebi.ac.uk/seqdb/confluence/display/JDSAT/Bioinformatics+Tools+FAQ&lt;/a&gt;</td><td>Web browser</td><td>Frequently Asked Questions (FAQs) for EMBL-EBI tools. Includes the color coding key for protein sequence alignments</td></tr><tr><td>European Bioinformatics Institute (EMBL-EBI),&lt;br/&gt; Tools and Data Resources</td><td>&lt;a href=&quot;https://www.ebi.ac.uk/services/all&quot;&gt;https://www.ebi.ac.uk/services/all&lt;/a&gt;</td><td>Web browser</td><td>Comprehensive list of freely available websites, tools and data resources</td></tr><tr><td>Expasy - Swiss Bioinformatics Resource Portal</td><td>&lt;a href=&quot;https://www.expasy.org&quot;&gt;https://www.expasy.org&lt;/a&gt;</td><td>Web browser</td><td>Suite of bioinformatic tools and resources for protein sequence analysis that is maintained by the Swiss Institute of Bioinformatics (SIB)</td></tr><tr><td>National Center for Biotechnology Information (NCBI)&lt;br/&gt; Conserved Domain Search</td><td>&lt;a href=&quot;https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi&quot;&gt;https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi&lt;/a&gt;</td><td>Web browser</td><td>Search tool to identify conserved domains within protein or coding nucleotide sequences</td></tr><tr><td>Pfam 35</td><td>&lt;a href=&quot;http://pfam.xfam.org&quot;&gt;http://pfam.xfam.org&lt;/a&gt;</td><td>Web browser</td><td>Protein family (Pfam) database, provides alignments and classification of protein families and domains</td></tr><tr><td>PhyloCSF Track Hub Description</td><td>&lt;a href=&quot;https://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=1267045267_TEc99h2oW5QedaCd4ir8aZ65ryaD&amp;amp;db=mm10&amp;amp;c=chr2&amp;amp;g=hub_109801_PhyloCSF_smooth&quot;&gt; https://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=1267045267_TEc99h2oW5Q&lt;br/&gt;edaCd4ir8aZ65ryaD&amp;amp;db=mm10&lt;br/&gt;&amp;amp;c=chr2&amp;amp;g=hub_109801_&lt;br/&gt;PhyloCSF_smooth&lt;/a&gt;</td><td>Web browser</td><td>Detailed description of the Smoothed PhyloCSF tracks and PhyloCSF Track Hub&lt;br/&gt;&amp;nbsp; &amp;nbsp;&lt;br/&gt;&amp;nbsp; &amp;nbsp;&lt;br/&gt;&amp;nbsp; &amp;nbsp;&lt;br/&gt;&amp;nbsp; &amp;nbsp;&lt;br/&gt;&amp;nbsp; &amp;nbsp;&lt;br/&gt;</td></tr><tr><td>SignalP 6.0</td><td>&lt;a href=&quot;https://services.healthtech.dtu.dk/service.php?SignalP-6.0&quot;&gt;https://services.healthtech.dtu.dk/service.php?SignalP-6.0&lt;/a&gt;</td><td>Web browser</td><td>Predicts the presence of signal peptides and the location of their cleavage sites</td></tr><tr><td>TMHMM - 2.0</td><td>&lt;a href=&quot;https://services.healthtech.dtu.dk/service.php?TMHMM-2.0&quot;&gt;https://services.healthtech.dtu.dk/service.php?TMHMM-2.0&lt;/a&gt;</td><td>Web browser</td><td>Prediction of transmembrane helices in proteins</td></tr><tr><td>UCSC Genome Browser BLAT Search</td><td>&lt;a href=&quot;https://genome.ucsc.edu/cgi-bin/hgBlat&quot;&gt;https://genome.ucsc.edu/cgi-bin/hgBlat&lt;/a&gt;</td><td>Web browser</td><td>Tool used to find genomic regions using DNA or protein sequence information</td></tr><tr><td>UCSC Genome Browser Gateway</td><td>&lt;a href=&quot;https://genome.ucsc.edu/cgi-bin/hgGateway&quot;&gt;https://genome.ucsc.edu/cgi-bin/hgGateway&lt;/a&gt;</td><td>Web browser</td><td>Direct link to the UCSC Genome Browser Gateway</td></tr><tr><td>UCSC Genome Browser Home</td><td>&lt;a href=&quot;https://genome.ucsc.edu/&quot;&gt;https://genome.ucsc.edu/&lt;/a&gt;</td><td>Web browser</td><td>Home website for the UCSC Genome Browser</td></tr><tr><td>UCSC Genome Browser Track Data Hubs</td><td>&lt;a href=&quot;https://genome.ucsc.edu/cgi-bin/hgHubConnect#publicHubs&quot;&gt;https://genome.ucsc.edu/cgi-bin/hgHubConnect#publicHubs&lt;/a&gt;</td><td>Web browser</td><td>Direct link to Track Data Hubs/Public Hubs database to search for and load the PhyloCSF Tracks</td></tr><tr><td>UCSC Genome Browser User Guide</td><td>&lt;a href=&quot;https://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html&quot;&gt;https://genome.ucsc.edu/goldenPath/help/hgTracksHelp.html&lt;/a&gt;</td><td>Web browser</td><td>Comprehensive user guide detailing how to navigate the UCSC Genome Browser</td></tr><tr><td>WoLF PSORT</td><td>&lt;a href=&quot;https://wolfpsort.hgc.jp&quot;&gt;https://wolfpsort.hgc.jp&lt;/a&gt;</td><td>Web browser</td><td>Protein subcellular localization prediction tool</td></tr></tbody></table>

an integrated approach for microprotein identification and sequence analysis

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model organisms. However, despite this wealth of sequence information, comprehensive gene annotation efforts have proven challenging, especially for small proteins. Notably, conventional protein annotation methods were designed to intentionally exclude putative proteins encoded by short open reading frames (sORFs) less than 300 nucleotides in length to filter out the exponentially higher number of spurious noncoding sORFs throughout the genome. As a result, hundreds of functional small proteins called microproteins (&#60;100 amino acids in length) have been incorrectly classified as noncoding RNAs or overlooked entirely.
Here we provide a detailed protocol to leverage free, publicly available bioinformatic tools to query genomic regions for microprotein-coding potential based on evolutionary conservation. Specifically, we provide step-by-step instructions on how to examine sequence conservation and coding potential using Phylogenetic Codon Substitution Frequencies (PhyloCSF) on the user-friendly University of California Santa Cruz (UCSC) Genome Browser. Additionally, we detail steps to efficiently generate multiple species alignments of identified microprotein sequences to visualize amino acid sequence conservation and recommend resources to analyze microprotein characteristics, including predicted domain structures. These powerful tools can be used to help identify putative microprotein-coding sequences in noncanonical genomic regions or to rule out the presence of a conserved coding sequence with translational potential in a noncoding transcript of interest.

Here we will use the validated microprotein mitoregulin (Mtln) as an example to demonstrate how a conserved sORF will generate a positive PhyloCSF score that can be easily visualized and analyzed on the UCSC Genome Browser. Mitoregulin was previously annotated as a noncoding RNA (formerly human gene ID LINC00116 and mouse gene ID 1500011K16Rik). Comparative genomics and sequence conservation analysis methods played a critical role in its initial discovery40,57,58,59,60,61, highlighting the strength of these methods. For this example, the mouse GRCm38/mm10 (Dec. 2011) assembly will be used. The search can be performed using the gene identifiers (mitoregulin, Mtln) or the gene position (chr2:127,791,364-127,792,496) as described in protocol section 2. Alternatively, the amino acid sequence for mitoregulin (shown in Figure 2) can be searched using the BLAT tool (described in protocol section 3).
A screen similar to the one depicted in Figure 1A will appear with the PhyloCSF Track Hub visible at the top of the screen. The Smoothed PhyloCSF tracks (smoothed with a hidden Markov model defining a probability that each codon is coding) are depicted as six total tracks, with three tracks corresponding to the plus strand of DNA (depicted in green as PhyloCSF +1, +2 and +3) and three tracks corresponding to the minus strand of DNA (depicted in red as PhyloCSF -1, -2 and -3). These tracks represent the three potential reading frames for the gene of interest in each direction. On the browser window, exons are depicted as blue rectangles connected by thin blue horizontal lines, which represent the introns. The arrowheads on the intronic regions indicate which direction the gene is transcribed in (and thus, which strand to focus on for the PhyloCSF score). For the example of Mtln in Figure 1, the intronic arrowheads are pointing to the left. Therefore, the Mtln gene is transcribed from the minus strand of DNA, and the relevant PhyloCSF score is depicted in the -1, -2, and -3 tracks (in red).
Each PhyloCSF track is depicted as a thin black line with negative scoring regions depicted in light green/red below the line and positive scoring regions indicated in dark green/red above the line. As described in the introduction, a positive PhyloCSF score indicates a conserved region that is likely coding. Note that for protein-coding regions with particularly high sequence conservation, they often also score positively on the antisense strand; however, the PhyloCSF score is usually higher on the correct strand. For example, this can be seen in Figure 1 for Mtln where the correct coding sequence scores very highly in the PhyloCSF -1 track, and the antisense strand (PhyloCSF +2 track) also generates a positive score. As seen in Figure 1A (indicated with black box), there is a region in the first exon of Mtln that scores very highly on the PhyloCSF -1 track, suggesting this may correspond to a coding region. To examine this region in further detail, it is helpful to zoom in and magnify the region (Figure 1B). As shown in Figure 1C,D, the positively scoring region in the first exon of Mtln begins directly over a start codon (Figure 1C) and terminates at a stop codon (Figure 1D), which indicates this ORF is highly conserved and strongly suggests it is a coding ORF. As Mtln is on the minus strand of DNA, the start and stop codons are shown as the reverse complement of the codon (i.e., the ATG start codon is shown as CAT [Figure 1C] and the TGA stop codon is shown as TCA [Figure 1D]).
In addition to using PhyloCSF to search for conserved regions with microprotein-coding potential, this technique can also be applied as a first-pass analysis of putative noncoding RNAs to rule out the presence of a conserved ORF, thus providing support for a noncoding annotation. For example, analysis of the well-characterized lncRNA HOTAIR62,63 using PhyloCSF shows a negative score throughout the entire gene across all six tracks (Figure 3), strongly indicating a lack of sequence conservation and providing support that HOTAIR is correctly annotated as a noncoding RNA.
As clearly seen in Figure 1, the entire coding ORF for mitoregulin is located within a single exon, thereby producing a simple and straightforward readout by PhyloCSF with a single, uninterrupted, positively scoring region. However, PhyloCSF track hub data is not always as clear-cut and easy to interpret. For example, the mitolamban/Stmp1/Mm47 microprotein encoded by the mouse 1810058I24Rik gene47,64,65 depicts a conserved ORF that spans three exons (Figure 4A), and the positive PhyloCSF score jumps from the +2 track in exon 1 (Figure 4B) to the +3 track in exon 2 (Figure 4C), and then back to the +2 track in exon 3 (Figure 4D). While at first glance this looks confusing, the explanation is quite straightforward. PhyloCSF scores the six potential reading frames (three on the plus strand of DNA and three on the minus strand) of genomic regions without considering the specific exon/intron architecture for each gene. Therefore, it retains the intronic sequence information in the 3-nucleotide periodicity of the reading frames. Thus, if an intron contains a number of nucleotides that is not divisible by three (i.e., three nucleotides/codon), the PhyloCSF reading frame will jump from one track to another.
Lastly, PhyloCSF can also be effectively used to identify multiple distinct coding ORFs within a single RNA molecule. For example, the MIEF1 microprotein (MIEF1-MP) is encoded within the 5&#39; UTR of mitochondrial elongation factor 1 (MIEF1)66 (Figure 5). When the MIEF1 genomic region is analyzed by PhyloCSF, a discrete positive PhyloCSF score corresponding to the MIEF1-MP (Figure 5C) can be readily observed upstream of the main CDS for MIEF1 (Figure 5B). Further discussion on MIEF1 and its associated microprotein (MIEF1-MP) is provided below in the discussion along with a summary of the strengths and weaknesses of the methods and protocols outlined in this article.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63841/63841fig01.jpg" /> 
Figure 1: PhyloCSF analysis of the mitoregulin (Mtln) gene indicates a region of high sequence conservation corresponding to a validated microprotein. (A) Screenshots of the UCSC Genome Browser and PhyloCSF Tracks show that Mtln contains two exons and a single intron. The arrowheads within the intron point to the left, indicating the Mtln gene is transcribed from the minus strand of DNA, and the relevant PhyloCSF scores are therefore shown in the -1, -2, and -3 tracks (in red). The complete mitoregulin coding sequence is contained within Exon 1 and scores highly on the PhyloCSF -1 track (B). A conserved start codon can be clearly observed at the beginning of the positively scoring region in the PhyloCSF -1 track (C), which is highlighted with a green box (CAT, reverse complement ATG). Additionally, a conserved stop codon (TCA, reverse complement TGA) is indicated with a red box in panel (D), which aligns with the end of the positively scoring PhyloCSF region. Detailed information about the Mtln gene can be found by clicking on the Mtln gene identifier within the blue box (shown in panel A). Of note, highly conserved protein-coding regions often also score positively on the antisense strand (seen here in the PhyloCSF +2 track for Mtln). However, the PhyloCSF score is typically higher on the correct strand (the PhyloCSF -1 track in this example). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63841/63841fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63841/63841fig02.jpg" /> 
Figure 2: Multiple species sequence alignment of the microprotein mitoregulin generated using the Clustal Omega program. The mitoregulin amino acid sequences for the eight species indicated were extracted as detailed in protocol section 6 and aligned with the Clustal Omega multiple sequence alignment tool. The properties of the amino acids are indicated by color (red, small/hydrophobic; blue, acidic; magenta, basic; green, hydroxl/sulfhydryl/amine) (further defined in Table 2). The symbols below the amino acids indicate the degree of conservation (asterisks, fully-conserved residues; colons, amino acids with strongly similar properties; periods, conservation between groups of weakly similar properties) (detailed extensively in Table 1). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63841/63841fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63841/63841fig03.jpg" /> 
Figure 3: A screenshot of the PhyloCSF tracks for the validated long noncoding RNA Hotair shows a lack of sequence conservation throughout its genomic locus. The arrowheads in the intronic region of Hotair are pointing left, indicating that the lncRNA is transcribed from the negative strand of DNA, and therefore the PhyloCSF -1, -2, and -3 tracks should be the focus of analysis. Note that the PhyloCSF score is negative throughout the entire gene (for all six tracks), indicating a lack of sequence conservation, which supports its proper annotation as a noncoding RNA. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63841/63841fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63841/63841fig04.jpg" /> 
Figure 4: PhyloCSF analysis of the mouse 1810058I24Rik gene, which encodes the microprotein mitolamban/Stmp1/Mm47. (A) The mouse 1810058I24Rik gene is comprised of three exons, and the arrowheads in the intronic regions point right, indicating it is transcribed on the plus strand of DNA and therefore the PhyloCSF +1, +2, and +3 tracks should be analyzed. The conserved microprotein coding sequence spans all three exons, starting in exon 1 (B), reading through exon 2 (C), and ending in exon 3 (D). Note that the positive PhyloCSF score is found on the +2 track in exon 1, the +3 track in exon 2, and the +2 track in exon 1. The reason for the movement of the positive score from one track to the other is that PhyloCSF analyzes the six potential reading frames of the DNA sequence independent of the gene&#39;s exon/intron structure. Therefore, an intron containing a number of nucleotides that is not divisible by three (three nucleotides/codon) will cause a shift in the reading frame to a different track. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63841/63841fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63841/63841fig05.jpg" /> 
Figure 5: Analysis of the Mief1 genomic locus with PhyloCSF identifies a region with protein-coding potential in the 5&#39; UTR that is independent of the main Mief1 CDS on the shared RNA. This conserved upstream ORF (uORF) has been shown to encode a microprotein named Mief1-MP. (A) Overview of the Mief1 genomic locus. The arrowheads in the introns point to the right, indicating Mief1 is transcribed from the plus strand of DNA (focus on the PhyloCSF +1, +2, and +3 tracks to determine coding potential). The main Mief1 CDS encodes a 463 amino acid protein and is shown in panel (B). However, there is also a distinct conserved upstream ORF within the 5&#39; UTR of Mief1 that encodes a unique 70 amino acid microprotein called Mief1-MP (C). As seen in Panel C, the Mief1-MP has its own conserved start and stop codon within the Mief1 5&#39; UTR, and the ORF scores very highly on the PhyloCSF +1 track, providing strong evidence that it encodes a functional microprotein. Abbreviations: ORF = open reading frame; uORF = upstream ORF; UTR = untranslated region; CDS = coding sequence. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63841/63841fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Symbol</td>
			<td>Level of Amino Acid Conservation</td>
			<td>Grouped Amino Acids</td>
		</tr>
		<tr>
			<td>Asterisk (*)</td>
			<td>Fully-conserved residue</td>
			<td>Not applicable (single, fully-conserved residue)</td>
		</tr>
		<tr>
			<td>Colon (:)</td>
			<td>Groups with strongly similar properties</td>
			<td>STA; NEQK; NHQK; NDEQ; QHRK; MILV; MILF; HY; FYW</td>
		</tr>
		<tr>
			<td>Period (.)</td>
			<td>Groups with weakly similar properties</td>
			<td>CSA; ATV; SAG; STNK; STPA; SGND; SNDEQK; NDEQHK; NEQHRK; FVLIM; HFY</td>
		</tr>
		<tr>
			<td>Space (no symbol)</td>
			<td>No similarity</td>
			<td>Not applicable (no similarity)</td>
		</tr>
	</tbody>
</table>
Table 1: Definitions of consensus symbols for Multiple Sequence Alignments generated by Clustal Omega. The multiple species sequence alignment shown in Figure 2 was generated using Clustal Omega52. Abbreviations: serine (S), threonine (T), alanine (A), asparagine (N), glutamic acid (E), glutamine (Q), lysine (K), aspartic acid (D), arginine (R), methionine (M), isoleucine (I), leucine (L), phenylalanine (F), histidine (H), tyrosine (Y), tryptophan (W), cysteine (C), valine (V), glycine (G), proline (P).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Font Color</td>
			<td>Property</td>
			<td>Amino Acid Residue [Abbreviation]</td>
		</tr>
		<tr>
			<td>Red</td>
			<td>Small, hydrophobic</td>
			<td>alanine [A], valine [V], phenylalanine [F], proline [P], methionine [M], isoleucine [I], leucine [L], tryptophan [W]</td>
		</tr>
		<tr>
			<td>Blue</td>
			<td>Acidic</td>
			<td>aspartic acid [D], glutamic acid [E]</td>
		</tr>
		<tr>
			<td>Magenta</td>
			<td>Basic</td>
			<td>arginine [R], lysine [K]</td>
		</tr>
		<tr>
			<td>Green</td>
			<td>Hydroxl, sulfhydryl, amine, +G</td>
			<td>serine [S], threonine [T], tyrosine [Y], histidine [H], cysteine [C], asparagine [N], glycine [G], glutamine [Q]</td>
		</tr>
	</tbody>
</table>
Table 2: Properties of the amino acids depicted in Figure 2. Clustal Omega52 was used to generate the multiple sequence alignment shown in Figure 2.

Watch this Scientific Journal Video about An Integrated Approach for Microprotein Identification and Sequence Analysis at JoVE.com

An Integrated Approach for Microprotein Identification and Sequence Analysis

The protocol described here provides detailed instructions on how to analyze genomic regions of interest for microprotein-coding potential using PhyloCSF on the user-friendly UCSC Genome Browser. Additionally, several tools and resources are recommended to further investigate sequence characteristics of identified microproteins to gain insight into their putative functions.

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model ...

an-integrated-approach-for-microprotein-identification-sequence

Nanyang Technological University

Research

JoVE Journal

Biology

3.3K Views.  Cincinnati Children's Hospital Medical Center. Het hier beschreven protocol biedt gedetailleerde instructies voor het analyseren van genomische gebieden die van belang zijn voor eiwitcoderingspotentieel met behulp van phyloCSF op de gebruiksvriendelijke UCSC-genoombrowser. PhloCSF kan effectief geconserveerde korte open leesframes identificeren met micro-eiwitcoderingspotentieel in genomische regio's die momenteel worden geannoteerd als niet-coderend. De hier beschreven methoden zijn gemakkelijk te gebruiken en kunnen worden geïmplemente...

Video: An Integrated Approach for Microprotein Identification and Sequence Analysis

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological processes. Affinity purification coupled to mass spectrometry (AP-MS) has become the method of choice for identifying protein-protein interactions. However, conventional AP-MS studies provide information on protein interactions, but the organizational information is lost. To address this issue, we developed a strategy to unravel the distinct functional assemblies a protein might be involved in, by resolving affinity-purified protein complexes prior to their characterization by mass spectrometry. Protein complexes isolated through affinity purification of a bait protein using an epitope tag and competitive elution are separated through blue native electrophoresis. Comparison of protein migration profiles through correlation profiling using quantitative mass spectrometry allows assignment of interacting proteins to distinct molecular entities. This method is able to resolve protein complexes of close molecular weights that might not be resolved by traditional chromatographic techniques such as gel filtration. With little more work than conventional AP-geLC-MS/MS, we demonstrate this strategy may in many cases be adequate for obtaining protein complex topological information concomitantly to identifying protein interactions.

Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological ...

Biochemistry

Split-BioID &#8212; Proteomic Analysis of Context-specific Protein Complexes in Their Native Cellular Environment

To complement existing affinity purification (AP) approaches for the identification of protein-protein interactions (PPI), enzymes have been introduced that allow the proximity-dependent labeling of proteins in living cells. One such enzyme, BirA* (used in the BioID approach), mediates the biotinylation of proteins within a range of approximately 10 nm. Hence, when fused to a protein of interest and expressed in cells, it allows the labeling of proximal proteins in their native environment. As opposed to AP that relies on the purification of assembled protein complexes, BioID detects proteins that have been marked within cells no matter whether they are still interacting with the protein of interest when they are isolated. Since it biotinylates proximal proteins, one can moreover capitalize on the exceptional affinity of streptavidin for biotin to very efficiently isolate them. While BioID performs better than AP for identifying transient or weak interactions, both AP- and BioID-mass spectrometry approaches provide an overview of all possible interactions a given protein may have. However, they do not provide information on the context of each identified PPI. Indeed, most proteins are typically part of several complexes, corresponding to distinct maturation steps or different functional units. To address this common limitation of both methods, we have engineered a protein-fragments complementation assay based on the BirA* enzyme. In this assay, two inactive fragments of BirA* can reassemble into an active enzyme when brought in close proximity by two interacting proteins to which they are fused. The resulting split-BioID assay thus allows the labeling of proteins that assemble around a pair of interacting proteins. Provided these two only interact in a given context, split-BioID then allows the analysis of specific context-dependent functional units in their native cellular environment. Here, we provide a step-by-step protocol to test and apply split-BioID to a pair of interacting proteins.

We provide a step-by-step protocol for split-BioID, a protein fragments-complementation assay based on the proximity-labeling technique BioID. Activated on the interaction of two given proteins, it allows the proteomics analysis of context-dependent protein complexes in their native cellular environment. The method is simple, cost-effective and only requires standard laboratory equipment.

To complement existing affinity purification (AP) approaches for the identification of protein-protein interactions (PPI), enzymes have been introduced ...

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ubiquitinated proteins, peptides with a diglycine remnant conjugated to the epsilon amino group of lysine (&#39;K-&#949;-diglycine&#39; or simply &#39;diGly&#39;) can be used to track back the original modification site. Efficient immunopurification of diGly peptides combined with sensitive detection by mass spectrometry has resulted in a huge increase in the number of ubiquitination sites identified up to date. We have made several improvements to this workflow, including offline high pH reverse-phase fractionation of peptides prior to the enrichment procedure, and the inclusion of more advanced peptide fragmentation settings in the ion routing multipole. Also, more efficient cleanup of the sample using a filter-based plug in order to retain the antibody beads results in a greater specificity for diGly peptides. These improvements result in the routine detection of more than 23,000 diGly peptides from human cervical cancer cells (HeLa) cell lysates upon proteasome inhibition in the cell. We show the efficacy of this strategy for in-depth analysis of the ubiquitinome profiles of several different cell types and of in vivo samples, such as brain tissue. This study presents an original addition to the toolbox for protein ubiquitination analysis to uncover the deep cellular ubiquitinome.

We present a method for the purification, detection, and identification of diGly peptides that originate from ubiquitinated proteins from complex biological samples. The presented method is reproducible, robust, and outperforms published methods with respect to the level of depth of the ubiquitinome analysis.

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ...

Evaluation of Protein&#8211;Protein Interactions using an On-Membrane Digestion Technique

Numerous intracellular proteins physically interact in accordance with their intracellular and extracellular circumstances. Indeed, cellular functions largely depend on intracellular protein&#8211;protein interactions. Therefore, research regarding these interactions is indispensable to facilitating the understanding of physiologic processes. Co-precipitation of associated proteins, followed by mass spectrometry (MS) analysis, enables the identification of novel protein interactions. In this study, we have provided details of the novel technique of immunoprecipitation-liquid chromatography (LC)-MS/MS analysis combined with on-membrane digestion for the analysis of protein&#8211;protein interactions. This technique is suitable for crude immunoprecipitants and can improve the throughput of proteomic analyses. Tagged recombinant proteins were precipitated using specific antibodies; next, immunoprecipitants blotted onto polyvinylidene difluoride membrane pieces were subjected to reductive alkylation. Following trypsinization, the digested protein residues were analyzed using LC-MS/MS. Using this technique, we were able to identify several candidate associated proteins. Thus, this method is convenient and useful for the characterization of novel protein&#8211;protein interactions.

Here, we present a protocol for an on-membrane digestion technique for the preparation of samples for mass spectrometry. This technique facilitates the convenient analysis of protein&#8211;protein interactions.

Numerous intracellular proteins physically interact in accordance with their intracellular and extracellular circumstances. Indeed, cellular functions ...

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

The vast majority of mass spectrometry (MS)-based protein analysis methods involve an enzymatic digestion step prior to detection, typically with trypsin. This step is necessary for the generation of small molecular weight peptides, generally with MW &#60; 3,000-4,000 Da, that fall within the effective scan range of mass spectrometry instrumentation. Conventional protocols involve O/N enzymatic digestion at 37 &#186;C. Recent advances have led to the development of a variety of strategies, typically involving the use of a microreactor with immobilized enzymes or of a range of complementary physical processes that reduce the time necessary for proteolytic digestion to a few minutes (e.g., microwave or high-pressure). In this work, we describe a simple and cost-effective approach that can be implemented in any laboratory for achieving fast enzymatic digestion of a protein. The protein (or protein mixture) is adsorbed on C18-bonded reversed-phase high performance liquid chromatography (HPLC) silica particles preloaded in a capillary column, and trypsin in aqueous buffer is infused over the particles for a short period of time. To enable on-line MS detection, the tryptic peptides are eluted with a solvent system with increased organic content directly in the MS ion source. This approach avoids the use of high-priced immobilized enzyme particles and does not necessitate any aid for completing the process. Protein digestion and complete sample analysis can be accomplished in less than ~3 min and ~30 min, respectively.

A quick protocol for proteolytic digestion with an in-house built flow-through tryptic microreactor coupled to an electrospray ionization (ESI) mass spectrometer is presented. The fabrication of the microreactor, the experimental setup and the data acquisition process are described.

The vast majority of mass spectrometry (MS)-based protein analysis methods involve an enzymatic digestion step prior to detection, typically with trypsin. ...

Bioengineering

Digital Microfluidics for Automated Proteomic Processing

Clinical proteomics has emerged as an important new discipline, promising the discovery of biomarkers that will be useful for early diagnosis and prognosis of disease. While clinical proteomic methods vary widely, a common characteristic is the need for (i) extraction of proteins from extremely heterogeneous fluids (i.e. serum, whole blood, etc.) and (ii) extensive biochemical processing prior to analysis. Here, we report a new digital microfluidics (DMF) based method integrating several processing steps used in clinical proteomics. This includes protein extraction, resolubilization, reduction, alkylation and enzymatic digestion. Digital microfluidics is a microscale fluid-handling technique in which nanoliter-microliter sized droplets are manipulated on an open surface. Droplets are positioned on top of an array of electrodes that are coated by a dielectric layer - when an electrical potential is applied to the droplet, charges accumulate on either side of the dielectric. The charges serve as electrostatic handles that can be used to control droplet position, and by biasing a sequence of electrodes in series, droplets can be made to dispense, move, merge, mix, and split on the surface. Therefore, DMF is a natural fit for carrying rapid, sequential, multistep, miniaturized automated biochemical assays. This represents a significant advance over conventional methods (relying on manual pipetting or robots), and has the potential to be a useful new tool in clinical proteomics.
Mais J. Jebrail, Vivienne N. Luk, and Steve C. C. Shih contributed equally to this work.
Sergio L. S. Freire's current address is at the University of the Sciences in Philadelphia located at 600 South 43rd Street, Philadelphia, PA 19104.

Digital Microfluidics is a technique characterized by the manipulation of discrete droplets (~nL - mL) on an array of electrodes by the application of electrical fields. It is well-suited for carrying out rapid, sequential, miniaturized automated biochemical assays. Here, we report a platform capable of automating several proteomic processing steps.

Clinical proteomics has emerged as an important new discipline, promising the discovery of biomarkers that will be useful for early diagnosis and ...

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

This protocol identifies the immunity proteins of the bactericidal enzymes: colicin E3 and bacteriocin, produced by a pathogenic Escherichia coli strain using antibiotic induction, and identified by MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. The immunity protein of colicin E3 (Im3) and the immunity protein of bacteriocin (Im-Bac) were identified from prominent b- and/or y-type fragment ions generated by the polypeptide backbone cleavage (PBC) on the C-terminal side of aspartic acid, glutamic acid, and asparagine residues by the aspartic acid effect fragmentation mechanism. The software rapidly scans in silico protein sequences derived from the whole genome sequencing of the bacterial strain. The software also iteratively removes amino acid residues of a protein sequence in the event that the mature protein sequence is truncated. A single protein sequence possessed mass and fragment ions consistent with those detected for each immunity protein. The candidate sequence was then manually inspected to confirm that all detected fragment ions could be assigned. The N-terminal methionine of Im3 was post-translationally removed, whereas Im-Bac had the complete sequence. In addition, we found that only two or three non-complementary fragment ions formed by PBC are necessary to identify the correct protein sequence. Finally, a promoter (SOS box) was identified upstream of the antibacterial and immunity genes in a plasmid genome of the bacterial strain.

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

Here we present a protocol for the rapid identification of proteins produced by genomically sequenced pathogenic bacteria using MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. Metastable protein ions fragment because of the aspartic acid effect and this specificity is exploited for protein identification.

This protocol identifies the immunity proteins of the bactericidal enzymes: colicin E3 and bacteriocin, produced by a pathogenic Escherichia coli strain ...

Chemistry

Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry

Since most cellular processes are mediated by macromolecular assemblies, the systematic identification of protein-protein interactions (PPI) and the identification of the subunit composition of multi-protein complexes can provide insight into gene function and enhance understanding of biological systems1, 2. Physical interactions can be mapped with high confidence vialarge-scale isolation and characterization of endogenous protein complexes under near-physiological conditions based on affinity purification of chromosomally-tagged proteins in combination with mass spectrometry (APMS). This approach has been successfully applied in evolutionarily diverse organisms, including yeast, flies, worms, mammalian cells, and bacteria1-6. In particular, we have generated a carboxy-terminal Sequential Peptide Affinity (SPA) dual tagging system for affinity-purifying native protein complexes from cultured gram-negative Escherichia coli, using genetically-tractable host laboratory strains that are well-suited for genome-wide investigations of the fundamental biology and conserved processes of prokaryotes1, 2, 7. Our SPA-tagging system is analogous to the tandem affinity purification method developed originally for yeast8, 9, and consists of a calmodulin binding peptide (CBP) followed by the cleavage site for the highly specific tobacco etch virus (TEV) protease and three copies of the FLAG epitope (3X FLAG), allowing for two consecutive rounds of affinity enrichment. After cassette amplification, sequence-specific linear PCR products encoding the SPA-tag and a selectable marker are integrated and expressed in frame as carboxy-terminal fusions in a DY330 background that is induced to transiently express a highly efficient heterologous bacteriophage lambda recombination system10. Subsequent dual-step purification using calmodulin and anti-FLAG affinity beads enables the highly selective and efficient recovery of even low abundance protein complexes from large-scale cultures. Tandem mass spectrometry is then used to identify the stably co-purifying proteins with high sensitivity (low nanogram detection limits).
Here, we describe detailed step-by-step procedures we commonly use for systematic protein tagging, purification and mass spectrometry-based analysis of soluble protein complexes from E. coli, which can be scaled up and potentially tailored to other bacterial species, including certain opportunistic pathogens that are amenable to recombineering. The resulting physical interactions can often reveal interesting unexpected components and connections suggesting novel mechanistic links. Integration of the PPI data with alternate molecular association data such as genetic (gene-gene) interactions and genomic-context (GC) predictions can facilitate elucidation of the global molecular organization of multi-protein complexes within biological pathways. The networks generated for E. coli can be used to gain insight into the functional architecture of orthologous gene products in other microbes for which functional annotations are currently lacking.

Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry

Affinity purification of tagged proteins in combination with mass spectrometry (APMS) is a powerful method for the systematic mapping of protein interaction networks and for investigating the mechanistic basis of biological processes. Here, we describe an optimized sequential peptide affinity (SPA) APMS procedure developed for the bacterium Escherichia coli that can be used to isolate and characterize stable multi-protein complexes to near homogeneity even starting from low copy numbers per cell.

Since most cellular processes are mediated by macromolecular assemblies, the systematic identification of protein-protein interactions (PPI) and the ...

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

High-density functional protein microarrays containing ~4,200 recombinant yeast proteins are examined for kinase protein-protein interactions using an affinity purified yeast kinase fusion protein containing a V5-epitope tag for read-out. Purified kinase is obtained through culture of a yeast strain optimized for high copy protein production harboring a plasmid containing a Kinase-V5 fusion construct under a GAL inducible promoter. The yeast is grown in restrictive media with a neutral carbon source for 6 hr followed by induction with 2% galactose. Next, the culture is harvested and kinase is purified using standard affinity chromatographic techniques to obtain a highly purified protein kinase for use in the assay. The purified kinase is diluted with kinase buffer to an appropriate range for the assay and the protein microarrays are blocked prior to hybridization with the protein microarray. After the hybridization, the arrays are probed with monoclonal V5 antibody to identify proteins bound by the kinase-V5 protein. Finally, the arrays are scanned using a standard microarray scanner, and data is extracted for downstream informatics analysis1,2 to determine a high confidence set of protein interactions for downstream validation in vivo.

Using protein microarrays containing nearly the entire S. cerevisiae proteome is probed for rapid unbiased interrogation of thousands of protein-protein interactions in parallel. This method can be utilized for protein-small molecule, posttranslational modification, and other assays in high-throughput.

High-density functional protein microarrays containing ~4,200 recombinant yeast proteins are examined for kinase protein-protein interactions using an ...

Combining Chemical Cross-linking and Mass Spectrometry of Intact Protein Complexes to Study the Architecture of Multi-subunit Protein Assemblies

Proteins interact with their ligands to form active and dynamic assemblies which carry out various cellular functions. Elucidating these interactions is therefore fundamental for the understanding of cellular processes. However, many protein complexes are dynamic assemblies and are not accessible by conventional structural techniques. Mass spectrometry contributes to the structural investigation of these assemblies, and particularly the combination of various mass spectrometric techniques delivers valuable insights into their structural arrangement.
In this article, we describe the application and combination of two complementary mass spectrometric techniques, namely chemical cross-linking coupled with mass spectrometry and native mass spectrometry. Chemical cross-linking involves the covalent linkage of amino acids in close proximity by using chemical reagents. After digestion with proteases, cross-linked di-peptides are identified by mass spectrometry and protein interactions sites are uncovered. Native mass spectrometry on the other hand is the analysis of intact protein assemblies in the gas phase of a mass spectrometer. It reveals protein stoichiometries as well as protein and ligand interactions. Both techniques therefore deliver complementary information on the structure of protein-ligand assemblies and their combination proved powerful in previous studies.

The architecture of protein complexes is essential for their function. Combining various mass spectrometric techniques proved powerful to study their assembly. We provide protocols for chemical cross-linking and native mass spectrometry and show how these complementary techniques help to elucidate the architecture of multi-subunit protein assemblies.

Proteins interact with their ligands to form active and dynamic assemblies which carry out various cellular functions. Elucidating these interactions is ...

An Integrated Approach for Microprotein Identification and Sequence Analysis

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