Package: proteosushi
ProteoSushi GUI should be working again.ProteoSushiProteoSushi transforms peptide-centric, PTM-enriched peptide data into condensed and annotated PTM site output that is easy to understand and analyze.ProteoSushi is designed to be simple to use and easy to access.RequirementsPrior to using ProteoSushi, there are some common, minor requirements so it will work correctly.The machine running ProteoSushi must have:stable internet accessat least 8GB of RAM, 12GB for Windows 1016GB or higher is recommended, but ultimately depends on the user's memory usage (having other programs open will decrease the memory available for ProteoSushi). More memory allows for more flexibility in the number and scope of programs running at once.With only 8GB of RAM, the user will likely need to free up memory by closing other open programsPython version 3.8 or higher installedearlier versions of python may work, but nothing as early as 2.7 will workInstallationDownload python fromhereand install if needed.Those who have python 2.7 installed along with python 3.x will need to replace the python and pip commands with python3 and pip3 respectivelyThere are two ways to install ProteoSushi:ThroughPIPThis is (likely) the easier way to install.In Windows:Open the command prompt by typingcmdinto the search bar (probably at the bottom of the screeen) and clicking on "Command Prompt" when it pops up. Run the command:py -m pip install proteosushiWhile in the command prompt, run ProteoSushi with the command:py -m proteosushiTip: If the py command doesn't work, try replacing py with pythonIn MacOS/Linux:Open the terminal in MacOS by either searching for it in spotlight or manually finding it in the application list.In linux, the terminal is among one of the installed applications. If you use linux, you most likely already know how to use the terminal.Once the terminal is open, run the command:python -m pip install proteosushiWhile the terminal is open, run ProteoSushi with the command:python -m proteosushiThroughGithubNOTE:If you have installed ProteoSushi via pip, it will always use that version first. Even if you download the files and runpython run_proteoSushi.py, it will STILL use your installed version through pip and not use the files you downloaded from GitHub. In this case, you will need to first uninstall the pip version of ProteoSushi with the commandpip uninstall proteosushito be able to use the downloaded files.Download the files directly fromGitHub. Click on the Code button and then on Download ZIP.Once unpacked (unzipped), we will need to use the terminal (command prompt in Windows) to run ProteoSushi.Before running ProteoSushi, you will need to install some dependencies. In the terminal/command prompt, run the following commands:pip install PyQt5

pip install requests

pip install pandasIf that doesn't install correctly, use the following commands:python -m pip install PyQt5

python -m pip install requests

python -m pip install pandasIn Windows, run these commands instead:py -m pip install PyQt5

py -m pip install requests

py -m pip install pandasNow we will run ProteoSushi itself.ForMacOS, use Finder to navigate to the 'proteosushi' folder in the downloaded files. Right click (or command click) on the 'proteosushi' folder -> Services -> New Terminal at Folder to open a new terminal in the 'proteosushi' folder. Run the commandpython run_proteoSushi.pyto start the GUI.Alternatively, inMacOSorLinux, you can open the terminal and navigate within the terminal to the 'proteosushi' folder in the downloaded files. Use the cd command to change the current folder as incd Downloads/ProteoSushi-master/proteosushiand use the ls command to list the contents of the current folder as inls, then use the commandpython run_proteoSushi.pyto run ProteoSushi.Finally, inWindows, first try to open the "run_proteosushi.py" file in the "proteosushi" folder by double-clicking on it.In case it doesn't start, open the command prompt by clicking on the search bar in the toolbar at the bottom (usually) of the screen. Type in 'cmd' and click on Command Prompt when it pops up. Once Command Prompt pops up, use the command cd to change to the 'proteosushi' folder in the downloaded files, as incd Downloads/ProteoSushi-master/proteosushiand the dir command to list the contents of the current folder, as indir, then use the commandpy run_proteoSushi.pyto run ProteoSushi.Again, if the command isn't working, try replacing py with pythonFiles NeededIn order to run ProteoSushi, there are some required files in specific formats. Example files are included in the 'examples' folder in the downloaded files:The CSV file output fromMascotThe file must have the header lines with the information from the search. In order to make sure that the file will be processed correctly, be sure to specify the following settings:The protease used with the sampleThe maximum number of missed cleavages (usually 1 or 2)The variable modifications present in the sample(s)Or the txt output folder fromMaxQuantThis folder must have thesummary.txtandevidence.txtfiles. Other files from the output are not used.NOTE: It is recommended that you use the newest version of MaxQuantOr the output from any other search engineThis file must have a column for peptide sequence named “peptide sequence”Also a column for peptide modified sequence (with PTMs included in parenthesis () or brackets []) named “peptide modified sequence”If you elect to use the quantitation values in the analysis, there must be a column with numbers (presumably positive) with "intensity" or "intensities" in the nameAdd these columns in if neededA FASTA Uniprot Proteome fileIdeally this is the same proteome file used in the peptide searchThe following files are optional:Alist of gene namesin a TXT file, one gene name per lineThis file can be used to prioritize the provided genes whenever there are multiple matches once ProteoSushi performs a search. For example, this can be used to highlight mitochondrial genes if the sample was from the mitochondriaUsing ProteoSushiRun ProteoSushi and the GUI should pop up and look like this:First, choose thesearch engineused and select the output to use with the window that pops up. For a given search engine, they will need to be one of the following:MaxQuantChoose the MaxQuant output folder with the evidence.txt and summary.txt files inside (any extra files will not be used)In the example files fromGitHub, the maxquant folder is located inProteosushi-master/proteosushi/examplesand named "txt".MascotChoose the annotated Mascot output file, should be a CSV fileThe example file fromGitHubis located inProteosushi-master/proteosushi/examplesand named "MascotEGFR.csv"GenericChoose the output from any search engine, however, it must have a peptide sequence and peptide modified sequence columns (along with a quantitation column labeled “Intensity” if you choose that option)The example file fromGitHubis located inProteosushi-master/proteosushi/examplesand named "EGFR_Skyline_data.csv"ProteoSushi will then parse the file to autofill some information (Species ID, Max missed cleavages, protease) and add the option to choose the PTM(s) to use in the analysisChoose the PTM(s)that will be used in the analysis. The options available are dynamic and will change based on the file provided. (Note: If there are many different PTMs in the file, you may need to move the ProteoSushi window horizontally as they will all be in the same line)For the example files, the PTMs that appear are differentFor Maxquant, choose the "Carbamidomethyl (C)" PTMFor Mascot, choose the "carbamidomethyl (c)" PTMFor Generic, choose the "C[+57]" PTMNext, choose the FASTA Uniprot proteome to use in ProteoSushi.The example files are based on the "Uni-Hum-Ref-20141022.fasta" file located in "Proteosushi-master/proteosushi/examples/fastas", so this is the file that should be used.If not already filled in, specify the number ofmaximum allowed missed cleavagesfor a given peptide (usually about 3).A higher number will cause the analysis to take longer, but can allow for more matches (possibly).It is usually best to stay consistent with what was chosen for the search engine originally.The Generic search engine option will not fill in the missed cleavages, so for the example, use 3.If not correctly filled in, specify theproteaseused in the sample digestion step.Possible proteases are specified if you hover the cursor over the text here.These include:trypsin/pCleaves after Lysine (K) or Arginine (R)trypsin!pCleaves after Lysine (K) or Arginine (R), but not before Proline (P)lys-cCleaves after Lysine (K)asp-nCleaves before Aspartate (D)asp-neCleaves before Aspartate (D) and Glutamate (E)lys-nCleaves before Lysine (K)The examples should use trypsin/pAfter that, choose the ProteoSushi output file name and location. Click the "Output name and location" button and a window will pop up to choose the location (folder/directory) and type in the name. The output will be saved as a CSV file that can be easily opened in any spreadsheet program.Following this is theOptionssection with some settings that can be added or ignored based on your analysis. IfFirst, if using MaxQuant, alocalization thresholdcan be set as a number between 0 and 1.In the maxquantevidence.txtfile, the localization probability is in the columns "PTMProbabilities".The localization probability indicates the likelihood that the PTM referenced in the header is at the indicated site.Any PTM sites with a localization probability below the provided threshold will not be rolled up.If this is not provided, ProteoSushi will use the localization determination by MaxQuant.Second, choose whether to use a prioritizedgene list. If so, choose the file to be used.These genes will be used if there is a tie of annotation score between multiple matches for a PTM site of a peptide.If one of the PTM sites is part of a gene from this list, it will be chosen.This may be helpful if the user is specifically interested in proteins from a specific organelle or pathway.Third, choose whether to use thequantitation values.You will need to specify whether to sum or average values that will be combined.The columns used for quantitation must have "intensity" or "intensities" in the header.Fourth, choose whether ProteoSushi shouldannotatedthe rolled-up sites using data from UniprotFifth, if using Mascot or Maxquant, you can optionally specify aFalse Discovery Rate (FDR) thresholdbetween 0 and 1.In Maxquant, this is taken from the "PEP" column and in Mascot, this is taken from the "pep_expect" column.More specifically, the PEP column in maxquant means Posterior Error Probability and is the likelihood that the peptide is wrongly assigned when using a target decoy database.The pep_expect column in Mascot is theExpectation value of the protein match (PMF only).Once all of the necessary options are included, click on theRollup!button to start the analysis.After the analysis finishes, you can compare your results to the example files provided in theoutput folder.Running ExamplesA quick overview to run the examples in ProteoSushi.Example files must be downloaded fromGitHub.Install and run ProteoSushi following the instructions listed inInstallationandUsing ProteoSushi.Select any of the 3 options for search engine and click on the button that appears on the right.For Maxquant, choose the txt folder listed in the examples folderFor Mascot, choose the MascotEGFR.csv fileFor Generic, choose the EGFR_Skyline_data.csv fileAt this point, the "PTMs for Current Analysis" section will pop up and show checkboxes where you can choose any or all of the PTMs as listed. I personally recommend clicking on the carbamidomethyl or c[+57] PTMClick on the "Uniprot Proteome FASTA" button and choose the "Uni-Hum-Ref-20141022.fasta" file.The next 3 lines should autofill unless you chose the Generic option earlier. In that case, put 3 in the "Max Missed Cleavages" box.Choose the output location and filename. Click on the "Output Name and Location" button to pop open the window and type in the filename once it is in the folder you want. Click save.Anything in theOptionssection can be ignored unless you want to use one of the specific functions. If so, please refer to the appropriate section inUsing ProteoSushiClick the "Rollup!" button once you are ready.Updates as ProteoSushi is running are displayed in the Terminal/Command Prompt.Once you have the results, you can compare to the example files in theoutput folderResultsResults will be returned as a CSV spreadsheet with the name and location based on what the user chose earlier.The resulting file will include information for each modified residue of interest including:Gene NameSiteThis is the site of the PTM referenced in this lineProtein_NameShared_GenesThe other gene names that also match up with this site. They also have a separate entry in the output.Target_GenesIf the user provided a list of genes to prioritize matching and at least one of those genes matched for this site, it is displayed here.Note that the annotation score rule is overidden if a target gene is present.Peptide_SequenceNote that each cleaved peptide displayed must have a minimum length of 6 and a maximum length of 55. If the peptide is too short, the shortest cleaved version 6 amino acids or longer will be shown.Peptide_Modified_SequenceThis is the peptide sequence with the PTM(s) inserted in parentheses()or brackets[]after the amino acid it is attached toAnnotation_ScoreThe score provided by Uniprot for this Uniprot entry out of 5, with 5 being the highest score (or highest quality annotation), and 1 being the lowest score (or lowest quality annotation)Uniprot_Accession_IDThe unique identifier by Uniprot for a specific protein entry. Information about the protein entry can be accessed by with the URLwww.uniprot.org/uniprot/[Uniprot_Accession_ID]Intensit(y|ies) ... (sum|average)Column(s) showing either the summed or averaged quantification value for the site, if provided and chosen by the userLength_Of_SequenceThe length of the proteinRange_of_InterestThe number range(s) of relevant peptide sequence(s) for this PTM siteRegion_of_InterestThe actual sequence(s) of the same relevant peptide sequence(s) in the last columnSubcellular_LocationThe location(s) within the cell where this protein is typically found. For example, mitochondria, nucleus, endoplasmic reticulum, etc.Enzyme_ClassThe type of enzyme that this protein functions as (if applicable)rheaHyperlinks to theRHEA databaseSecondary_StructureThe protein secondary structure that this PTM site is a part of, if applicable. For example, (alpha) Helix, Beta_Strand, or (beta) Turn.Active_Site_AnnotationAnnotation if the PTM site is an active siteAlternative_Sequence_AnnotationAnnotation related to protein isoformsChain_AnnotationAnnotation for the chain in the mature protein after processing at the PTM siteCompositional_Bias_AnnotationAnnotation indicating overrepresentation of certain amino acidsDisulfide_Bond_AnnotationAnnotation if the PTM site part of a disulfide bondDomain_Extent_AnnotationDescription of the domainLipidation_AnnotationAnnotation if the residue is known to be lipidatedMetal_Binding_AnnotationType of metal binding at the PTM siteModified_Residue_AnnotationKnown type of PTM at siteMotif_AnnotationDescription of a short, conserved sequence motif of biological significanceMutagenesis_AnnotationMutations and known effect on protein function at the siteNatural_Variant_AnnotationKnown natural variant at the PTM site (if there is one)NP_Binding_AnnotationIf the site is a known nucleotide binding siteOtherIf there is an annotation different than the other _Annotation columnsRegion_AnnotationAnnotation related to the ‘Region_of_Interest’ columnRepeat_AnnotationThe types of repeated sequence motifs or repeated domainsTopological_Domain_AnnotationOrientation in the plasma membrane (cytosolic or extracellular)Zinc_Finger_AnnotationIf the modified site is within a zinc finger domainMore detailed information on any of the above annotations is available on the help section of theUniProt website.