Name: creating and applying a reference to facilitate the discussion and classification of proteins in a diverse group
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Description: Watch this Scientific Journal Video about Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group at JoVE.com

Ongoing tardigrade antimicrobial peptide research is supported by intramural funding from the Midwestern University Office of Research and Sponsored Programs (ORSP). The ORSP had no role in study design, data collection, analysis, interpretation, or manuscript preparation.

1. Determine the Defining Features of the Protein Group of Interest
<ol>
	<li>Consult previous publications to determine if there is a consensus regarding the features that are necessary to be considered part of the group. Take note of any inconsistencies or differences in opinion between research groups, and include characteristics that may serve to differentiate one subgroup from another.</li>
	<li>If previous literature does not address defining characteristics, use sequences that are considered representative of the group as a starting point to identify conserved features.</li>
</ol>
2. Collect Relevant Sequences
<ol>
	<li>If reviews have been written that include analyses of sequences that are representing the group, include these sequences in the raw dataset. Retrieve sequences using accession numbers referenced in the literature and save in a standard sequence editing program (e.g., EditSeq in the Lasergene suite or one of many available for free online).</li>
	<li>If the group in question has been defined in one of the structural databases, include the sequences the database lists as being part of the group.&#160;Retrieve sequences using accession numbers provided in the database and save in a standard sequence editing program, as above. 
	NOTE: For example, the sequences categorized in the CS-&#945;&#946; (scorpion toxin-like) superfamily in the SCOP database can be found here: http://scop.mrc-lmb.cam.ac.uk/scop/data/scop.b.h.c.h.html.&#160;</li>
	<li>Perform Basic Local Alignment Search Tool (BLAST)14 searches of public, online databases available through the National Center for Biotechnology Information (NCBI) to find sequences that may have not been included in the literature or structural databases. For the most complete results, use both the protein BLAST (blastp) and translated blast with protein query (tblastn) programs; these are both available at: https://blast.ncbi.nlm.nih.gov/Blast.cgi.
	<ol>
		<li>Use sequences known to be part of the group of interest as query sequences. Copy and paste the sequence into the search box at the top, or provide a GenBank accession number or gi identifier, if available.</li>
		<li>Choose the database from the dropdown menu. Choose non-redundant protein sequences (nr) for blastp and expressed sequence tags for tblastn.</li>
		<li>Search for results in specific taxa in the organism setting by typing the organism or taxon name and choosing from the list that appears while typing. To add additional organisms or taxa to exclude, click the &#34;+&#34; button and another field will appear. Exclude any unwanted taxa in the organism box by typing the organism or taxon name, choosing from the list that appears while typing, and checking the &#34;Exclude&#34; box on the right.</li>
		<li>Access additional parameters by clicking on &#34;Algorithm parameters&#34; near the bottom of the page. Leave at default unless there is a rationale for changing a parameter.</li>
		<li>Click the &#34;BLAST&#34; button to run the analysis; it may take some time for the results to appear. In general, retrieve hits with an expect value (or e-value) of &#34;-05&#34; or better and save in a standard sequence editing program.
		<ol>
			<li>If all hits are above this threshold, rerun the search with an increased number of target sequences (in the algorithm parameters section) to obtain all relevant sequences.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>If necessary, trim the sequences to exclude irrelevant information (e.g., the CS-&#945;&#946; fold only applies to the mature peptide). Identify signal peptides and pro-peptides for removal using ProP15 (available online), or SignalP for more sophisticated signal peptide prediction16 (available online).</li>
</ol>
3. Generate a Reference in a Spreadsheet Based on the Important Features That Were Identified
<ol>
	<li>Identify the defining characteristics of the group of interest. For example, use the CS-&#945;&#946; fold definitively established by the solution structure of insect defensin A from Phormia terraenovae (Figure 1)8.
	<ol>
		<li>This fold includes a smaller motif called the cysteine-stabilized helix (CSH)17; identify this motif by a CXXXC (where X is any amino acid) upstream of a CXC that form two disulfide bonds (Figure 1, solid pink lines). 
		NOTE: To complete the CS-&#945;&#946; motif, a third disulfide bond is formed from additional cysteines placed before each half of the CSH motif (Figure 1, dotted pink lines).</li>
	</ol>
	</li>
	<li>Enter these defining features into a spreadsheet. See Figure 2.
	<ol>
		<li>Use columns for the conserved features and to represent the spaces between these features. Keep the columns wide enough to fit numbers and ensure that they have a consistent width. Set the width using the &#34;Format| Column Width&#34; function (Figure 2, pink arrow).</li>
		<li>Use the rows for the sequence names.</li>
		<li>When a sequence has the feature, fill in the box using the fill function (Figure 2, pink square). For spacing between features, enter the number of amino acids in the box between and leave it unfilled. For example, using the insect defensin sequence gives a reference that includes six cysteines, with defined spacings between C2 and C3 and between C5 and C6.</li>
	</ol>
	</li>
	<li>Add representative sequences that have been previously established as members of the group based on the structural databases and literature. 
	NOTE: For example, previous literature and the SCOP database identify several groups for inclusion: insect defensins, short-chain scorpion toxins, long-chain scorpion toxins, MGD-1, plant defensins, nematode ABFs, drosomycins from Drosophila, and macins. The literature also identifies a bacterial sequence with only four cysteines that might represent the ancestor of this superfamily18. Adding these sequences increases the number of cysteines in the reference from six to ten but maintains the alignment of the important structural features (Figure 3).
	<ol>
		<li>To add a feature that is likely to define a subgroup of sequences (for example, an extra cysteine), use the &#34;Insert&#34; function (Figure 3, pink arrow).</li>
		<li>If there are features missing from a given sequence, leave the box unfilled and combine it with boxes representing intervening amino acids. If necessary, merge the cells using the merge and center feature (Figure 3, pink box).</li>
	</ol>
	</li>
	<li>Continue adding sequences to the groups to gain a better picture of the variation in each group of the larger superfamily. Summarize the group characteristics to facilitate comparisons (Figure 4).
	<ol>
		<li>When the number of amino acids between major features varies, use a hyphen to indicate a range, such as 6 - 12 (6 to 12 amino acids), and a slash to indicate either/or, such as 7/10 (7 or 10 amino acids).</li>
		<li>Choose a way to annotate features of sequences that may be relevant but do not occur often enough to include in the reference. For example, since cysteines are important in this superfamily, label additional cysteines (Figure 4, pink boxes).</li>
	</ol>
	</li>
	<li>Add newly-identified sequences to the spreadsheet using the established sequences as a guide. For example, adding sequences from tardigrades (yellow) shows that the tardigrade sequences fall into several different groups of the superfamily (Figure 5 shows summaries instead of a row per sequence for space purposes).</li>
	<li>Show variability within a taxonomic group by rearranging the rows (Figure 6).</li>
</ol>
4. Use the Reference to Refine Amino Acid Alignments
NOTE: There are many programs that can be used for multiple sequence alignments, but this demonstration will use Molecular Evolutionary Genetics Analysis (MEGA6)19 because it is available to download for free.
<ol>
	<li>Download and install the software.</li>
	<li>Begin a new alignment in MEGA by selecting &#34;Edit/Build Alignment&#34; under the Align tab. Select &#34;Create a new alignment&#34; in the box that appears and click &#34;OK.&#34; Then select &#34;Protein.&#34;</li>
	<li>Select &#34;Insert Sequence from File&#34; in the &#34;Edit&#34; menu to import the sequences. 
	NOTE: Sequences will need to be in FASTA format for import into MEGA. Background colors that reflect different amino acid types are used by default, but this option can be turned off under the &#34;Display&#34; menu.</li>
	<li>Once all sequences are entered, click the flexing arm icon and then &#34;Align Protein&#34; to align the sequences using the MUSCLE algorithm20. 
	NOTE: ClustalW is also available.
	<ol>
		<li>If a message saying that nothing has been selected pops up and asks to select all, click &#34;OK.&#34;</li>
		<li>NOTE: This opens a window that allows one to change some parameters, but they should only be changed there is reason to do so. This analysis uses a subset of the sequences analyzed in a previous paper12.</li>
	</ol>
	</li>
	<li>Check the alignment based on the important features; note that the top bar above the sequences will show any columns where the amino acid is completely conserved (*). See Figure 7. See that the initial alignment shows only three of the four conserved cysteines (Figure 7, pink boxes); looking down the column, the AlCRP sequence is clearly misaligned (Figure 7, pink arrow).</li>
	<li>To get rid of the large gap between the I and the conserved C, highlight the dashes and press the &#34;delete&#34; key. Do not highlight any amino acids, or they will be deleted as well.</li>
	<li>To move amino acids to the right, highlight and press the space bar.
	<ol>
		<li>Note that the AlCRP now has the structural cysteines aligned and that the last C of the CXXXC motif is conserved throughout the alignment (Figure 8). Adjust the alignment as necessary to prioritize the most important features of the sequences.</li>
	</ol>
	</li>
</ol>
5. Compare the Groups Identified Using the Reference with Results from Phylogenetic Analyses
<ol>
	<li>From preliminary alignments, determine which sequences should be included in a phylogenetic analysis; for a small number of sequences, this step may be unnecessary.
	<ol>
		<li>Keep an alignment file that includes all sequences, but for a phylogenetic analysis, remove redundant sequences (Figure 9, pink boxes show pairs of redundant sequences).</li>
		<li>If the data set includes a large number of sequences, run a preliminary analysis and select representatives from groups that always form a clade.</li>
	</ol>
	</li>
	<li>Determine the best amino acid substitution model.
	<ol>
		<li>Export the alignment in MEGA format (under the data tab).</li>
		<li>Go to the Models menu and select &#34;Find Best DNA/Protein Model.&#34; Choose the file just saved and open it; this will open a window that has some parameters that can be changed.</li>
		<li>Use the default parameters unless there is a reason to change them. Click &#34;Compute&#34; to begin the analysis.</li>
	</ol>
	</li>
	<li>Run a maximum likelihood (ML) analysis in MEGA.
	<ol>
		<li>Choose &#34;Construct/Test Maximum Likelihood Tree&#34; from the Phylogeny menu.</li>
		<li>Choose the model determined to be the best fit for the data from step 5.2 (the output will give the substitution model as well as the best &#34;rates among sites&#34; parameter).</li>
		<li>Choose 1,000 bootstrap replicates to obtain the measures of support for the tree.</li>
		<li>Click &#34;Compute&#34; to run the analysis; MEGA has a &#34;Tree Explorer&#34; to visualize the tree.</li>
	</ol>
	</li>
	<li>Run a Bayesian analysis in MrBayes open-source software21. 
	NOTE: A MrBayes manual is also available from this site. This is intended to provide basic steps and is not a comprehensive guide to conducting Bayesian phylogenetic analysis.
	<ol>
		<li>Export the MEGA alignment in PAUP (Nexus) format to the same folder as the MrBayes program.</li>
		<li>Open MrBayes and type &#34;exe Filename&#34; (e.g., &#34;exe Alignment.nex&#34;).</li>
		<li>Specify the model and analysis parameters. Choose either the model specified in step 5.2 or choose the &#34;mixed&#34; setting that will try various models and report the frequency of the model in the trees with the best posterior probabilities (prset aamodelpr=mixed). Type &#34;showmodel&#34; to report the current model settings and &#34;help mcmc&#34; to show current parameter settings, with a brief explanation of each.</li>
		<li>Set the number of generations using the &#34;mcmcp ngen=&#34; command (1 million is typical).</li>
		<li>Type &#34;mcmc&#34; to begin the analysis.</li>
		<li>When the number of generations has completed, the program will ask to add more generations. If the average standard deviation of split frequencies is less than 0.1, type no. If it is above 0.1, the analysis should be allowed to continue, or some parameters should be changed (see the manual).</li>
		<li>Use the &#34;sumt&#34; command to generate the tree files.</li>
		<li>After the analysis is complete and a consensus tree is generated, the tree can be viewed in FigTree (available online).</li>
	</ol>
	</li>
	<li>Compare the trees to see if the methods generate consistent results. 
	NOTE: Some sequences do not provide a lot of information: the trees may not be well resolved and the branches may have minimal support (Figure 10).</li>
	<li>Compare trees to the groups identified using the reference to see if the phylogenetic analyses support these groups.</li>
</ol>

<ol>
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The author has nothing to disclose.

The criteria for naming a protein within a group should be clear, but this is not always the case. Sequences that have the CS-&#945;&#946; fold have been studied in many labs using a variety of organisms, resulting in different systems of nomenclature, as well as varying levels of characterization. Attempting to impose a completely new nomenclature is not reasonable and would result in a great deal of confusion when consulting previous literature. A reference numbering system can be used in addition to the name of a prot...

A protein&#39;s name should reflect is characteristics and relationship to other proteins. Unfortunately, names are generally assigned at the time of discovery and, as research continues, the understanding of the larger context may change. This can lead to multiple names if a protein was independently identified by more than one lab, to changes in nomenclature or in the characteristics thought to be definitive when assigning the name, and to the name no longer sufficiently differentiating the protein from others.
Invertebrate defensins provide a good example of degeneration in nomenclature and classification. The first invertebrate defensins were reported from insects, and the name &#34;insect defensin&#34; was proposed based on the perceived homology to mammalian defensins1,2. The term defensin is still used, even though it is now clear that invertebrate and mammalian defensins do not share a common ancestor3,4. Depending on the species, an invertebrate &#34;defensin&#34; may have six or eight cysteines (that form three or four disulfide bonds) and a variety of antimicrobial activities. To complicate the situation, proteins with the same characteristics as defensins are not always called &#34;defensins,&#34; such as the recently identified cremycins from Caenorhabditis remanei5. In addition, invertebrate big defensins are more likely to be evolutionarily related to vertebrate &#946;-defensins than to other invertebrate defensins6. Despite this, researchers sometimes rely on the name &#34;defensin&#34; when determining which sequences should be included in analyses.
Structural studies revealed the similarity between insect defensins and scorpion toxins7, and the CS-&#945;&#946; fold was subsequently established as the defining structural characteristic of insect defensins8. This fold defines the scorpion toxin-like (CS-&#945;&#946;) superfamily in the Structural Classification of Proteins (SCOP) database9, which currently includes five families: insect defensins, short-chain scorpion toxins, long-chain scorpion toxins, MGD-1 (from a mollusk), and plant defensins. This superfamily is synonymous with the recently described cis-defensins4 and Superfamily 3.30.30.10 in the CATH/Gene 3D database10,11. Studies from a variety of invertebrate taxa, plants, and fungi show that the names of proteins that contain this fold are not clearly related to cysteine number or bonding pattern, antimicrobial activity, or evolutionary history12.
The lack of consistency and clear criteria make it challenging to name and classify newly-identified sequences in this superfamily. A major obstacle to comparing proteins in this superfamily is that cysteines are numbered with respect to each individual sequence (the first cysteine in each sequence is C1), with no way to account for the structural role. This means that only sequences with the same number of cysteines can be compared. There is little sequence conservation other than the cysteines forming the CS-&#945;&#946; fold, which makes alignments and phylogenetic analyses difficult. By developing a numbering system that prioritizes structural features, superfamily sequences can be more easily compared and aligned. Conserved features, as well as those defining subgroups, can be visualized quickly, and new sequences can be more easily placed into the appropriate context.
This paper uses a spreadsheet software (e.g., Excel) to generate a reference numbering system for the CS-&#945;&#946; superfamily. It shows how this clarifies comparisons between sequences and applies it to new CS-&#945;&#946; sequences identified from tardigrades. Using the CS-&#945;&#946; superfamily as an example, the protocol was written to provide guidance when using sequences of interest; however, it is not intended to be specific to this superfamily or to cysteine-rich sequences. This method will likely be most useful for groups of proteins that have been researched independently in divergent taxa and/or have little overall sequence homology, with discrete characteristics that may not be easily recognized by molecular analysis software. This method requires some a priori decisions regarding important features, so it will be of limited utility if no important features have been identified. The primary goal is to show how a simple visualization of the sequence relationships can be achieved. This can then be used to inform sequence alignment and analysis, but if alignment and analysis are the primary goals, a barcode method would be a suitable alternative that has more capacity for automation13. The current method displays the features of each peptide in a linear form, so it will not be helpful for the direct visualization of 3D structure.

<table><tbody><tr><td>BLAST webpage</td><td></td><td></td><td>https://blast.ncbi.nlm.nih.gov/Blast.cgi</td></tr><tr><td>EditSeq (Lasergene suite)</td><td>DNASTAR</td><td></td><td>https://www.dnastar.com/t-allproducts.aspx</td></tr><tr><td>Excel 2013</td><td>Microsoft</td><td></td><td></td></tr><tr><td>FigTree</td><td></td><td></td><td>&amp;nbsp;http://tree.bio.ed.ac.uk/software/figtree/</td></tr><tr><td>MEGA</td><td></td><td></td><td>www.megasoftware.net</td></tr><tr><td>MrBayes</td><td></td><td></td><td>http://mrbayes.sourceforge.net/</td></tr><tr><td>SCOP database</td><td></td><td></td><td>http://scop.mrc-lmb.cam.ac.uk/scop/</td></tr></tbody></table>

creating and applying a reference to facilitate the discussion and classification of proteins in a diverse group

Related proteins that have been studied in different labs using varying organisms may lack a uniform system of nomenclature and classification, making it difficult to discuss the group as a whole and to place new sequences into the appropriate context. Developing a reference that prioritizes important sequence features related to structure and/or activity can be used in addition to established names to add some coherency to a diverse group of proteins. This paper uses the cysteine-stabilized alpha-helix (CS-&#945;&#946;) superfamily as an example to show how a reference generated in&#160;spreadsheet software can clarify relationships between existing proteins in the superfamily, as well as facilitate the addition of new sequences. It also shows how the reference can help to refine sequence alignments generated in commonly used software, which impacts the validity of phylogenetic analyses. The use of a reference will likely be most helpful for protein groups that include highly divergent sequences from a broad spectrum of taxa, with features that are not adequately captured by molecular analyses.

Groups of sequences in the CS-&#945;&#946; superfamily reported in the literature are shown in Figure 4. The cysteine pairings based on the numbering for each sequence suggest five basic groups (Table 1, middle column). Group 1 has six cysteines that from three disulfide bonds and includes sequences from insects, arachnids, mollusks, nematodes, and fungi. Groups 2, 3, and 4 have 8 cysteines that form four disulfide bonds. Group 2 includes ins...

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

The goal of this protocol is to develop a reference for divergent proteins in a group that lacks coherent criteria for nomenclature and classification. This reference will facilitate analyses and discussion of the group as a whole and can be used in addition to established names.

Related proteins that have been studied in different labs using varying organisms may lack a uniform system of nomenclature and classification, making it ...

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7.0K Views.  Midwestern University. The goal of this protocol is to develop a reference for divergent proteins in a group that lacks coherent criteria for nomenclature and classification. This reference will facilitate analyses and discussion of the group as a whole and can be used in addition to established names.

Video: Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality for: (1) The identification of related protein structure sets to user specified thresholds of similarity; (2) Their multiple alignment and structure superposition; (3) Sequence and structure conservation analysis; (4) Inter-conformer relationship mapping with principal component analysis, and (5) comparison of predicted internal dynamics via ensemble normal mode analysis. This integrated functionality provides a complete online workflow for investigating sequence-structure-dynamic relationships within protein families and superfamilies.

A protocol for the online investigation of protein sequence-structure-dynamics relationships using Bio3D-web is presented.

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality ...

Biochemistry

mRNA Interactome Capture from Plant Protoplasts

RNA-binding proteins (RBPs) determine the fates of RNAs. They participate in all RNA biogenesis pathways and especially contribute to post-transcriptional gene regulation (PTGR) of messenger RNAs (mRNAs). In the past few years, a number of mRNA-bound proteomes from yeast and mammalian cell lines have been successfully isolated through the use of a novel method called &#34;mRNA interactome capture,&#34; which allows for the identification of mRNA-binding proteins (mRBPs) directly from a physiological environment. The method is composed of in vivo ultraviolet (UV) crosslinking, pull-down and purification of messenger ribonucleoprotein complexes (mRNPs) by oligo(dT) beads, and the subsequent identification of the crosslinked proteins by mass spectrometry (MS). Very recently, by applying the same method, several plant mRNA-bound proteomes have been reported simultaneously from different Arabidopsis tissue sources: etiolated seedlings, leaf tissue, leaf mesophyll protoplasts, and cultured root cells. Here, we present the optimized mRNA interactome capture method for Arabidopsis thaliana leaf mesophyll protoplasts, a cell type that serves as a versatile tool for experiments that include various cellular assays. The conditions for optimal protein yield include the amount of starting tissue and the duration of UV irradiation. In the mRNA-bound proteome obtained from a medium-scale experiment (107 cells), RBPs noted to have RNA-binding capacity were found to be overrepresented, and many novel RBPs were identified. The experiment can be scaled up (109 cells), and the optimized method can be applied to other plant cell types and species to broadly isolate, catalog, and compare mRNA-bound proteomes in plants.

Here, we present an interactome capture protocol applied to Arabidopsis thaliana leaf mesophyll protoplasts. This method critically relies on in vivo UV crosslinking and allows for the isolation and identification of plant mRNA-binding proteins from a physiological environment.

RNA-binding proteins (RBPs) determine the fates of RNAs. They participate in all RNA biogenesis pathways and especially contribute to post-transcriptional ...

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame (ORF) annotation impose two arbitrary criteria: a minimum length of 100 codons and a single ORF per transcript. However, a growing number of studies report expression of proteins from allegedly non-coding regions, challenging the accuracy of current genome annotations. These novel proteins were found encoded either within non-coding RNAs, 5' or 3' untranslated regions (UTRs) of mRNAs, or overlapping a known coding sequence (CDS) in an alternative ORF. OpenProt is the first database that enforces a polycistronic model for eukaryotic genomes, allowing annotation of multiple ORFs per transcript. OpenProt is freely accessible and offers custom downloads of protein sequences across 10 species. Using OpenProt database for proteomic experiments enables novel proteins discovery and highlights the polycistronic nature of eukaryotic genes. The size of OpenProt database (all predicted proteins) is substantial and need be taken in account for the analysis. However, with appropriate false discovery rate (FDR) settings or the use of a restricted OpenProt database, users will gain a more realistic view of the proteomic landscape. Overall, OpenProt is a freely available tool that will foster proteomic discoveries.

OpenProt is a freely accessible database that enforces a polycistronic model of eukaryotic genomes. Here, we present a protocol for the use of OpenProt databases when interrogating mass spectrometry datasets. Using OpenProt database for analysis of proteomic experiments allows for discovery of novel and previously undetectable proteins.

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame ...

Genetics

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering (SEC-MALS)

Analytical size-exclusion chromatography (SEC), commonly used for the determination of the molecular weight of proteins and protein-protein complexes in solution, is a relative technique that relies on the elution volume of the analyte to estimate molecular weight. When the protein is not globular or undergoes non-ideal column interactions, the calibration curve based on protein standards is invalid, and the molecular weight determined from elution volume is incorrect. Multi-angle light scattering (MALS) is an absolute technique that determines the molecular weight of an analyte in solution from basic physical equations. The combination of SEC for separation with MALS for analysis constitutes a versatile, reliable means for characterizing solutions of one or more protein species including monomers, native oligomers or aggregates, and heterocomplexes. Since the measurement is performed at each elution volume, SEC-MALS can determine if an eluting peak is homogeneous or heterogeneous and distinguish between a fixed molecular weight distribution versus dynamic equilibrium. Analysis of modified proteins such as glycoproteins or lipoproteins, or conjugates such as detergent-solubilized membrane proteins, is also possible. Hence, SEC-MALS is a critical tool for the protein chemist who must confirm the biophysical properties and solution behavior of molecules produced for biological or biotechnological research. This protocol for SEC-MALS analyzes the molecular weight and size of pure protein monomers and aggregates. The data acquired serve as a foundation for further SEC-MALS analyses including those of complexes, glycoproteins and surfactant-bound membrane proteins.

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering SEC-MALS

This protocol describes the combination of size exclusion chromatography with multi-angle light scattering (SEC-MALS) for absolute characterization of proteins and complexes in solution. SEC-MALS determines the molecular weight and size of pure proteins, native oligomers, heterocomplexes and modified proteins such as glycoproteins.

Analytical size-exclusion chromatography (SEC), commonly used for the determination of the molecular weight of proteins and protein-protein complexes in ...

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view of the impact of a disease, treatment, or other condition on the proteome as a whole. This report provides a detailed protocol for the extraction, quantification, precipitation, digestion, labeling, and subsequent data analysis of protein samples. Our optimized TMT labeling protocol requires a lower tag-label concentration and achieves consistently reliable data. We have used this protocol to evaluate protein expression profiles in a variety of mouse tissues (i.e., heart, skeletal muscle, and brain) as well as cells cultured in vitro. In addition, we demonstrate how to evaluate thousands of proteins from the resulting dataset.

We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data analyses.

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view ...

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. ...

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

Protein-RNA interactions regulate gene expression and cellular functions at transcriptional and post-transcriptional levels. For this reason, identifying the binding partners of an RNA of interest remains of high importance to unveil the mechanisms behind many cellular processes. However, RNA molecules might interact transiently and dynamically with some RNA-binding proteins (RBPs), especially with non-canonical ones. Hence, improved methods to isolate and identify such RBPs are greatly needed.
To identify the protein partners of a known RNA sequence efficiently and quantitatively, we developed a method based on the pull-down and characterization of all interacting proteins, starting from cellular total protein extract. We optimized the protein pull-down using biotinylated RNA pre-loaded on streptavidin-coated beads. As a proof of concept, we employed a short RNA sequence known to bind the neurodegeneration-associated protein TDP-43 and a negative control of a different nucleotide composition but the same length. After blocking the beads with yeast tRNA, we loaded the biotinylated RNA sequences on the streptavidin beads and incubated them with the total protein extract from HEK 293T cells. After incubation and several washing steps to remove nonspecific binders, we eluted the interacting proteins with a high-salt solution, compatible with the most commonly used protein quantification reagents and with sample preparation for mass spectrometry. We quantified the enrichment of TDP-43 in the pull-down performed with the known RNA binder compared to the negative control by mass spectrometry. We used the same technique to verify the selective interactions of other proteins computationally predicted to be unique binders of our RNA of interest or of the control. Finally, we validated the protocol by western blot via the detection of TDP-43 with an appropriate antibody. 
This protocol will allow the study of the protein partners of an RNA of interest in near-to-physiological conditions, helping uncover unique and unpredicted protein-RNA interactions.

Here, we present an optimized in vitro method to uncover, quantify, and validate protein interactors of specific RNA sequences, using total protein extract from human cells, streptavidin beads coated with biotinylated RNA, and mass spectrometry analysis.

Protein-RNA interactions regulate gene expression and cellular functions at transcriptional and post-transcriptional levels. For this reason, identifying ...

A Protocol for Computer-Based Protein Structure and Function Prediction

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently &gt;20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.

Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.

Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the ...

Biology

Interactome-Seq: A Protocol for Domainome Library Construction, Validation and Selection by Phage Display and Next Generation Sequencing

Folding reporters are proteins with easily identifiable phenotypes, such as antibiotic resistance, whose folding and function is compromised when fused to poorly folding proteins or random open reading frames. We have developed a strategy where, by using TEM-1 &#946;-lactamase (the enzyme conferring ampicillin resistance) on a genomic scale, we can select collections of correctly folded protein domains from the coding portion of the DNA of any intronless genome. The protein fragments obtained by this approach, the so called &#34;domainome&#34;, will be well expressed and soluble, making them suitable for structural/functional studies.
By cloning and displaying the &#34;domainome&#34; directly in a phage display system, we have showed that it is possible to select specific protein domains with the desired binding properties (e.g., to other proteins or to antibodies), thus providing essential experimental information for gene annotation or antigen identification.
The identification of the most enriched clones in a selected polyclonal population can be achieved by using novel next-generation sequencing technologies (NGS). For these reasons, we introduce deep sequencing analysis of the library itself and the selection outputs to provide complete information on diversity, abundance and precise mapping of each of the selected fragment. The protocols presented here show the key steps for library construction, characterization, and validation.

The protocols described allow the construction, characterization and selection (against the target of choice) of a &#34;domainome&#34; library made from any DNA source. This is achieved by a research pipeline that combines different technologies: phage display, a folding reporter and next generation sequencing with a web tool for data analysis.

Folding reporters are proteins with easily identifiable phenotypes, such as antibiotic resistance, whose folding and function is compromised when fused to ...

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.

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 ...