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>
	<li>Matsuyama, K., Natori, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Purification+of+Three+Antibacterial+Proteins+from+the+Culture+Medium+of+NIH-Sape-4,+an+Embryonic+Cell+Line+of+Sarcophaga+peregrina.">Purification of Three Antibacterial Proteins from the Culture Medium of NIH-Sape-4, an Embryonic Cell Line of Sarcophaga peregrina.</a> J Biol Chem. 263 (32), 17112-17116 (1988).</li><li>Lambert, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Insect+immunity:+Isolation+from+immune+blood+of+the+dipteran+Phormia+terranovae.+of+two+insect+antibacterial+peptides+with+sequence+homology+to+rabbit+lung+macrophage+bactericidal+peptides.">Insect immunity: Isolation from immune blood of the dipteran Phormia terranovae. of two insect antibacterial peptides with sequence homology to rabbit lung macrophage bactericidal peptides.</a> PNAS. 86 (262-266), (1989).</li><li>Dimarcq, J. -. L., Bulet, P., Hetru, C., Hoffmann, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cysteine-rich+antimicrobial+peptides+in+invertebrates.">Cysteine-rich antimicrobial peptides in invertebrates.</a> Biopolymers. 47, 465-477 (1998).</li><li>Shafee, T. M. A., Lay, F. T., Hulett, M. D., Anderson, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Defensins+Consist+of+Two+Independent,+Convergent+Protein+Superfamilies.">The Defensins Consist of Two Independent, Convergent Protein Superfamilies.</a> Mol Biol Evol. 33 (9), 2345-2356 (2016).</li><li>Zhu, S., Gao, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nematode-derived+drosomycin-type+antifungal+peptdies+provide+evidence+for+plant-to-ecdysozoan+horizontal+transfer+of+a+disease+resistance+gene.">Nematode-derived drosomycin-type antifungal peptdies provide evidence for plant-to-ecdysozoan horizontal transfer of a disease resistance gene.</a> Nat Commun. 5, (2014).</li><li>Zhu, S., Gao, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evolutionary+origin+of+b-defensins.">Evolutionary origin of b-defensins.</a> Dev. Comp. Immunol. 39, 79-84 (2013).</li><li>Bonmatin, J. -. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Two-dimensional+1H+NMR+study+of+recombinant+insect+defensin+A+in+water:+Resonance+assignments,+secondary+structure+and+global+folding.">Two-dimensional 1H NMR study of recombinant insect defensin A in water: Resonance assignments, secondary structure and global folding.</a> J Biomol NMR. 2 (3), 235-256 (1992).</li><li>Cornet, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Refined+three-dimensional+solution+structure+of+insect+defensin+A.">Refined three-dimensional solution structure of insect defensin A.</a> Structure. 3 (5), 435-448 (1995).</li><li>Murzin, A. G., Brenner, S. E., Hubbard, T., Chothia, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SCOP:+a+structural+classification+of+proteins+database+for+the+investigations+of+sequences+and+structures.">SCOP: a structural classification of proteins database for the investigations of sequences and structures.</a> J Mol Biol. 247, 536-540 (1995).</li><li>Sillitoe, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CATH:+comprehensive+structural+and+functional+annotations+for+genome+sequences.">CATH: comprehensive structural and functional annotations for genome sequences.</a> Nucleic Acids Res. 43, 376-381 (2015).</li><li>Lam, S. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene3D:+expanding+the+utility+of+domain+assignments.">Gene3D: expanding the utility of domain assignments.</a> Nucleic Acids Res. 44, 404-409 (2016).</li><li>Tarr, D. E. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Establishing+a+reference+array+for+the+CS-ab+superfamily+of+defensive+peptides.">Establishing a reference array for the CS-ab superfamily of defensive peptides.</a> BMC Res Notes. 9, 490 (2016).</li><li>Shafee, T. M. A., Robinson, A. J., van der Weerden, N., Anderson, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+homology+guided+alignment+of+cysteine+rich+proteins.">Structural homology guided alignment of cysteine rich proteins.</a> SpringerPlus. 5 (27), (2016).</li><li>Altschul, S. F., Gish, W., Miller, W., Myers, E. W., Lipman, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Basic+Local+Alignment+Search+Tool.">Basic Local Alignment Search Tool.</a> J Mol Biol. 215 (3), 403-410 (1990).</li><li>Duckert, P., Brunak, S., Blom, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+proprotein+convertase+cleavage+sites.">Prediction of proprotein convertase cleavage sites.</a> Protein Eng Des Sel. 17 (1), 107-112 (2004).</li><li>Petersen, T. N., Brunak, S., von Heijne, G., Nielsen, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SignalP+4.0:discriminating+signal+peptides+from+transmembrane+regions.">SignalP 4.0:discriminating signal peptides from transmembrane regions.</a> Nat Methods. 8, 785-786 (2011).</li><li>Kobayashi, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cysteine-stabilized+a-helix:+A+common+structural+motif+of+ion-channel+blocking+neurotoxic+peptides.">The cysteine-stabilized a-helix: A common structural motif of ion-channel blocking neurotoxic peptides.</a> Biopolymers. 31, 1213-1220 (1991).</li><li>Gao, B., del Carmen Rodriguez, M., Lanz-Mendoza, H., Zhu, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AdDLP,+a+bacterial+defensin-like+peptide,+exhibits+anti-Plasmodium.+activity.">AdDLP, a bacterial defensin-like peptide, exhibits anti-Plasmodium. activity.</a> Biochem Biophys Res Commun. 387, 393-398 (2009).</li><li>Tamura, K., Stecher, G., Peterson, D., Filipski, A., Kumar, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MEGA6:+Molecular+Evolutionary+Genetics+Analysis.">MEGA6: Molecular Evolutionary Genetics Analysis.</a> Mol Biol Evol. 30 (12), 2725-2729 (2013).</li><li>Edgar, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MUSCLE:+multiple+sequence+alignment+with+high+accuracy+and+high+throughput.">MUSCLE: multiple sequence alignment with high accuracy and high throughput.</a> Nucleic Acids Res. 32 (5), 1792-1797 (2004).</li><li>Ronquist, F., Huelsenbeck, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MrBayes+3:+Bayesian+phylogenetic+inference+under+mixed+models.">MrBayes 3: Bayesian phylogenetic inference under mixed models.</a> Bioinformatics. 19 (12), 1572-1574 (2003).</li><li>Altschul, S. 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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

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly being extended to integrative studies to enhance the understanding of molecular dynamics within cells. Current tools for the visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies. Furthermore, they only capture basic sequence identify, discarding post-translational modifications and quantitation. To address these issues, we developed PoGo to map peptides with associated post-translational modifications and quantification to reference genome annotation. In addition, the tool was developed to enable the mapping of peptides identified from customized sequence databases incorporating single amino acid variants. While PoGo is a command line tool, the graphical interface PoGoGUI enables non-bioinformatics researchers to easily map peptides to 25 species supported by Ensembl genome annotation. The generated output borrows file formats from the genomics field and, therefore, visualization is supported in most genome browsers. For large-scale studies, PoGo is supported by TrackHubGenerator to create web-accessible repositories of data mapped to genomes that also enable an easy sharing of proteogenomics data. With little effort, this tool can map millions of peptides to reference genomes within only a few minutes, outperforming other available sequence-identity based tools. This protocol demonstrates the best approaches for proteogenomics mapping through PoGo with publicly available datasets of quantitative and phosphoproteomics, as well as large-scale studies.

Here we present the proteogenomic tool PoGo and protocols for fast, quantitative, post-translational modification and variant enabled mapping of peptides identified through mass spectrometry onto reference genomes. This tool is of use to integrate and visualize proteogenomic and personal proteomic studies interfacing with orthogonal genomics data.

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly ...

Genetics

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 ...

Identification of Functional Protein Regions Through Chimeric Protein Construction

The goal of this protocol encompasses the design of chimeric proteins in which distinct regions of a protein are replaced by their corresponding sequences in a structurally similar protein, in order to determine the functional importance of these regions. Such chimeras are generated by means of a nested PCR protocol using overlapping DNA fragments and adequately designed primers, followed by their expression within a mammalian system to ensure native secondary structure and post-translational modifications. 
The functional role of a distinct region is then indicated by a loss of activity of the chimera in an appropriate readout assay. In consequence, regions harboring a set of critical amino acids are identified, which can be further screened by complementary techniques (e.g. site-directed mutagenesis) to increase molecular resolution. Although limited to cases in which a structurally related protein with differing functions can be found, chimeric proteins have been successfully employed to identify critical binding regions in proteins such as cytokines and cytokine receptors. This method is particularly suitable in cases in which the protein's functional regions are not well defined, and constitutes a valuable first step in directed evolution approaches to narrow down the regions of interest and reduce the screening effort involved.

Structurally related proteins frequently exert distinct biological functions. The exchange of equivalent regions of these proteins in order to create chimeric proteins constitutes an innovative approach to identify critical protein regions that are responsible for their functional divergence.

The goal of this protocol encompasses the design of chimeric proteins in which distinct regions of a protein are replaced by their corresponding sequences ...

Biochemistry

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 ...

Predicting Amino Acid Preferences of Protein-Protein Binding Interfaces

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the recognition of linear motifs with variable conservation. The combination of highly conserved and more variable regions in the same ligands often contributes to the multispecificity of binding, a common property of enzymes and cell signaling proteins. Characterization of amino acid preferences of peptide-binding domains is important for the design of mediators of protein-protein interactions (PPIs). Computational methods are an efficient alternative to the often costly and cumbersome experimental techniques, enabling the design of potential mediators that can be later validated in downstream experiments. Here, we described a methodology using the Pepspec application of the Rosetta molecular modeling package to predict the amino acid preferences of peptide-binding domains. This methodology is useful when the structure of the receptor protein and the nature of the peptide ligand are both known or can be inferred. The methodology starts with a well-characterized anchor from the ligand, which is extended by randomly adding amino acid residues. The binding affinity of peptides generated this way is then evaluated by flexible-backbone peptide docking in order to select the peptides with the best predicted binding scores. These peptides are then used to calculate amino acid preferences and to optionally compute a position-weight matrix (PWM) that can be used in further studies. To illustrate the application of this methodology, we used the interaction between subunits of human interferon regulatory factor 5 (IRF5), previously known to be multispecific but globally guided by a short conserved motif called pLxIS. The estimated amino acid preferences were consistent with previous knowledge about the IRF5 binding surface. Positions occupied by phosphorylatable serine residues exhibited a high frequency of aspartate and glutamate, likely because their negatively charged side chains are similar to phosphoserine.

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

We describe a methodology based on sequence diversification to estimate the amino acid preferences of multispecific binding sites in protein-protein interactions (PPIs). In this strategy, thousands of potential peptide ligands are generated and screened in silico, thus overcoming some limitations of available experimental methods.

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the ...

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

Cell-Type Specific Protein Purification and Identification from Complex Tissues Using a Mutant Methionine tRNA Synthetase Mouse Line

Understanding protein homeostasis in vivo is key to knowing how the cells work in both physiological and disease conditions. The present protocol describes in vivo labeling and subsequent purification of newly synthesized proteins using an engineered mouse line to direct protein labeling to specific cellular populations. It is an inducible line by Cre recombinase expression of L274G-Methionine tRNA synthetase (MetRS*), enabling azidonorleucine (ANL) incorporation to the proteins, which otherwise will not occur. Using the method described here, it is possible to purify cell-type-specific proteomes labeled in vivo and detect subtle changes in protein content due to sample complexity reduction.

This protocol describes how to perform cell-type-specific protein labeling with azidonorleucine (ANL) using a mouse line expressing a mutant L274G-Methionine tRNA synthetase (MetRS*) and the necessary steps for labeled cell-type-specific proteins isolation. We outline two possible ANL administration routes in live mice by (1) drinking water and (2) intraperitoneal injections.

Understanding protein homeostasis in vivo is key to knowing how the cells work in both physiological and disease conditions. The present protocol ...

Neuroscience

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This protocol provides step-by-step instructions for the development and application of quantitative assays using selected reaction monitoring (SRM) mass spectrometry (MS). First, likely quantotypic target peptides are identified based on numerous criteria. This includes identifying proteotypic peptides, avoiding sites of posttranslational modification, and analyzing the uniqueness of the target peptide to the target protein. Next, crude external peptide standards are synthesized and used to develop SRM assays, and the resulting assays are used to perform qualitative analyses of the biological samples. Finally, purified, quantified, heavy isotope labeled internal peptide standards are prepared and used to perform isotope dilution series SRM assays. Analysis of all of the resulting MS data is presented. This protocol was used to accurately assay the absolute abundance of proteins of the chemotaxis signaling pathway within RAW 264.7 cells (a mouse monocyte/macrophage cell line). The quantification of Gi2 (a heterotrimeric G-protein &#945;-subunit) is described in detail.

This protocol describes how to perform absolute quantification assays of target proteins within complex biological samples using selected reaction monitoring. It was used to accurately quantify proteins of the mouse macrophage chemotaxis signaling pathway. Target peptide selection, assay development, and qualitative and quantitative assays are described in detail.

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This ...

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 ...

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.&#160;&#160; Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.&#160;&#160; If these new proteins contain similar amino acids in key locations, protein domains, and possibly the overall three-dimensional structure, can remain similar.&#160;&#160; Proteins within a family can have as low as 30% amino acid sequence homology but still perform related functions.
Protein Superfamilies
Protein superfamilies are larger groups of proteins that have evolved from a more distant ancestor. They generally have lower sequence homology as compared to a protein family but still have significant structural features in common. Each superfamily can contain several protein families with more closely related structures and functions.&#160; Some larger families are even further divided into sub-families.&#160; The exact distinction as to whether proteins belong to a superfamily, family, or subfamily can vary between classification systems and is still changing as the amount of protein sequence and structural data continues to grow.
The immunoglobulin protein superfamily (IgSF) is one of the largest protein superfamilies; over 700 superfamily members are found in the human genome.&#160; All members of the superfamily contain one or more immunoglobulin (Ig) domains. This domain has a unique three-dimensional structure composed of a sandwich of two anti-parallel beta-sheets, and most are involved in cell adhesion or ligand binding. The IgSF contains many families including antigen receptors, cell adhesion molecules (CAMs), cytoskeletal proteins, and several growth-factor and cytokine receptor groups. Several of the larger families are further divided into subfamilies.&#160;&#160; The antigen receptor family can be further divided into subfamilies: the antibody or immunoglobulin family and the T- cell receptor family; the CAMs can be divided into the NCAM, ICAM, and CD2 related protein families.
Classification Databases
Protein family classifications allow scientists to understand functional and evolutionary relationships between proteins. Several online resources can be used to search for known protein families or classify newly discovered proteins.&#160; Pfam is one of several online databases where a scientist can search for known proteins and their family members.&#160; A researcher can also enter the amino acid sequence of a newly discovered protein to see if it might belong to a known family of proteins due to sequence similarity.&#160; This can provide a testable hypothesis as to the possible role of the novel protein as&#160; family members often have similar structures and functions.

Protein Families and Superfamilies &amp; Classification Database

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein ...