Proteomic data analysis using the TPP

Transcription

1 Proteomic data analysis using the TPP 2013 ASMS short course. Instructor: Alexey Nesvizhskii, University of Michigan, PART A: Tutorial on running the TPP This tutorial was written for the TPP running on the Amazon cloud. The original full TPP tutorial was written for the TPP version that is installed on a local computer. Additional information and description of data used in this tutorial can be found in the following manuscript: 1. Deutsch EW, Mendoza L, Shteynberg D, Farrah T, Lam H, Tasman N, Sun Z, Nilsson E, Pratt B, Prazen B, Eng JK, Martin DB, Nesvizhskii AI, Aebersold R. A guided tour of the Trans-Proteomic Pipeline. Proteomics Mar;10(6): Additional reading: 2. Nesvizhskii AI. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J Proteomics Oct 10;73(11): Nesvizhskii AI, Aebersold R. Interpretation of shotgun proteomic data: the protein inference problem. Mol Cell Proteomics Oct;4(10): Technical help for installing/running the TPP: Wiki: TPP support google group: During the conference: ISB booth in the exhibit hall. 1. Access the TPP sever No software installation is needed for the ASMS short course tutorial using the TPP running on the Amazon cloud. Instead, open a browser window into the URL provided by the instructor. The URL for the TPP will look like this (this is just an example) ec us-west-2.compute.amazonaws.com/tpp/cgi-bin/tpp_gui.pl Log into Petunia (the web interface for the TPP) You can use the credentials guest and guest as user name and password to log in. Once you are in the Home page, please select Tandem as the analysis pipeline, just below the Welcome message. This refers to X! Tandem MS/MS database search tool that is provided as a default tool with the TPP. 2. View input data For this demo, we will be using a SILAC-labeled Yeast dataset comprised of 1 run on a high mass-accuracy Orbitrap instrument, along with a Yeast database appended with decoys (the full TPP tutorial uses 2 runs). The X! Tandem search parameters file is also included in the tutorial. To view the input files you will work with, do the following: Mouse-over on the Utilities portion in the navigation links near the top of the Petunia webpage; a popup menu should appear. Select the Browse files item in this menu. Alternatively, instead of mouse over, you can click on the larger Utilities portion of the navigation bars located below the navigation links.

2 Click on the tppdata directory link on the right portion of the page, then on the local link. You should see subfolders named demo1, demo2, Please go to the subfolder that was assigned to you by the instructor (all folders have identical content; the course participants are asked to use different folders for technical reasons). Click on Set as current working directory The directory contains several files. The main files you will work with are OR _S_SILAC-LH_1-1_01.mzML - Mass spectrometry data file tandem.xml - X! Tandem input file yeast_orfs_all_rev short.fasta - Protein sequence database Additional comment: The first step in the pipeline is the conversion of the vendor's raw data format to the open mzml format (for Orbitrap mass spectrometers, the files are in RAW format). To view RAW files one will need to install the free Thermo MS File Reader. For this tutorial we already converted the files into an open mzml file format. The TPP server already has all data prepared for you. However, if you would like to download RAW data or mzxml data, and the converters/raw data readers, you can find the links to data on the full TPP tutorial website mentioned above. 3. Search data with X! Tandem A custom version of the popular open-source search engine X! Tandem is bundled and installed with the TPP. It has been modified from the original distribution by adding the K-Score scoring function. Click the Database Search menu under Analysis Pipeline to access the X! Tandem search interface. You can see the Database Search menu when you mouse over the Analysis Pipeline portion of navigation links at the top of the page (or use the navigation bars instead) Under Specify mzxml Input Files, click Add Files and select the mzml files present in the tppdata/local/demon directory (again, the demo directory assigned to you) as input files for database searching. Similarly, under Specify Tandem Parameters File choose the X! Tandem parameters file called tandem.xml located in the same directory. This file defines the database search parameters that override the full set of default settings referenced in the file isb_default_input (also present in the same directory). In this analysis, the parent ion mass tolerance is set to 25ppm (monoisotopic mass). Note that the full tutorial (and the description in the input.xml file) suggests using a much wider mass tolerance. However, using 25ppm is recommended because of much faster database search time (the search results may also be slightly better). Also, in this analysis only fully tryptic peptides will be allowed.refinement mode is turned off. The residue modification mass is set to @C.In addition, SILAC modifications are specified as variable modifications @k, @r. Methionine oxidation is specified as variable modification as well, @M. For more information, please go to Select a sequence database to search against: yeast_orfs_all_rev short.fasta. Select Convert to PepXML option. Since each search engine provides results in different ways, the TPP requires that they be converted to a common format for downstream processing (PepXML format).

3 Start the search by clicking on Run Tandem Search. The search needs about 3 minutes. While you are waiting, you can periodically refresh the screen to see the output from X! Tandem printed on the screen. To refresh the screen, first click on Output :View and then Output: Refresh. When the search is finished, the folder will contain several more files, the main one is the X! Tandem output file (in PepXML format): OR _S_SILAC-LH_1-1_11.tandem.pep.xml 4. Validation of Peptide-Spectrum matches with PeptideProphet PeptideProphet provides statistical validation of search engine results by assigning a probability to each peptide-spectrum match. Click on the Analyze Peptides tab under the Analysis Pipeline section in Petunia to access the xinteract interface. xinteract is a general utility that is able to launch several components of the TPP, including PeptideProphet. Select the OR _S_SILAC-LH_1-1_11.tandem.pep.xml files in the directory tppdata/local/demon. Make sure that there is only one file selected for analysis; you can edit the selections using the checkboxes and Remove button on the right-hand side. Under PeptideProphet Options, find and select the option to Use accurate mass binning since this is a high-accuracy data. Also, find and select Only use Expect Score as the discriminant. This is also recommended for high mass accuracy data searched using narrow mass tolerance like 25ppm. Also, find Enter additional options to pass directly to the command-line (expert use only!). Enter -PPM (a dash character followed by PPM in upper case, no space in between). Since we switched to ppm (instead of Da), it is recommended to use ppm scale in modeling. Please also find and select Run ProteinProphet afterwards. With this option, ProteinProphet will start automatically after PeptideProphet (you can also run it separately, see below). Leave all other options set to their defaults, and click on Run XInteract at the bottom of the page to run PeptideProphet (and ProteinProphet if you selected this option as suggested above). Once the command finishes running, you can click on the link that appears in the Command Status box to view and analyze the results. Alternatively, mouse over the Utilities navigation link, select the Browse files item, and go to tppdata local demon. These are the main TPP files: interact.pep.shtml interact.prot.shtml Link to open PeptideProphet output file Link to open ProteinProphet output file View PeptideProphet results, interact.pep.shtml file. On the PeptideProphet page, sort the list in descending order based on Probabilities. The identifications at the top of the resulting list are most likely to be correct. Click on the hypertext link for any probability. This brings up a details page which shows graphically how successful the modeling was. In the upper pane, it is desirable for the red curve (sensitivity) to hug the upper right corner, and for the green curve (error) to hug the lower left corner.

4 The lower pane shows how well the data (black line) follows the PeptideProphet modeling for each charge state. The blue curve describes the modeling of the negative results, and the purple one, the positive results. If these two curves are well separated and fit the black line well, then the analysis for that charge state was successful. 5. Protein-level validation with ProteinProphet ProteinProphet is a protein inference tool that takes as input the list if identified peptides (output from PeptideProphet), groups peptides into proteins, and computes a probability of correct identification at the protein level. If you already selected Run ProteinProphet afterwards above, you do not need to run ProteinProphet again (unless you want to use advanced user options). To run ProteinProphet, mouse over on the Analysis Pipeline section of the navigation links at the top of the page and click on Analyze Proteins. Select the interact.pep.xml file in the directory tppdata/local/demon. Make sure that there is only one file selected for analysis; you can edit the selections using the checkboxes and Remove button on the right-hand side. Leave all other options set to their defaults, and click on Run ProteinProphet at the bottom of the page to run ProteinProphet. Once the command finishes running, you can click on the link that appears in the Command Status box to view and analyze the results. Alternatively, use the Utilities, Browse files, tppdata local demon routine to view all files in the folder, and click on View interact.prot.shtml file. Protein groups are sorted in descending order by Probability so that the groups at the top of the page are the most confident identifications. The protein probabilities are the red numbers listed next to each protein group. Part B: Exercises/homework 1. Database search with X! Tandem Open PeptideProphet results file, interact.pep.shtml. Using the filters in the Summary tab of the pepxml Viewer, sort the results by Probability from high to low. To sort, select probability and desc (descending order), and click on Update Page. You can also click on the Display Options tab and select all for rows per page and click on Update Page. This allows you to view all the data in one page. View MS/MS spectrum for one of the high scoring peptide assignments at the top of the sorted list (click on the Ions link, which has xx/yy format). i) Do you see a continuous series of y and b-ions? ii) On average, do y-ions or b-ions have higher intensity? iii) View several more MS/MS spectra assigned peptides with high probability, and then compare with lower scoring spectra in range, and also with those that have very low probability (spectra at the end of the list). 2. Validation of Peptide-Spectrum matches with PeptideProphet a) In the PeptideProphet results file, click on any probability link (left column). What is the total number of correct results (peptide to spectrum matches or PSMs) predicted by the model? PeptideProphet models the discriminant scores (which is essentially -10* log transformed X! Tandem s expect score in this case) of correct

5 results (referred to as pos ) and incorrect results (referred to as neg ) from the data. Do the learned discriminant score distributions among correct and incorrect results look reasonable, given the total distributions for the dataset? [Yes, the correct and incorrect distributions are well separated, the correct having a greater mean value than the incorrect] b) What is the model estimated FDR corresponding to the minimum probability threshold of 0.7 (Hint: on the same page that shows the learned discriminant score distributions, see (a) above, check the table on the right. FDR is referred to as error in this table). Note that the FDR is estimated here from the computed probabilities, NOT using decoys. Also note that there are two FDR tables on this page. The top table is for the overall model, while the lower one contains estimates computed separately for peptide ions of different charge states, 2+, 3+, etc. The top table is what you should be looking at. [0.022, or 2.2%]. c) Now scroll down the page. What distributions of missed enzymatic cleavages (NMC) did the model learn for the correct, and for the incorrect, peptide identifications? (These distributions are summarized under the heading no. missed enz. cleavages [nmc] ). Are the distributions identical for peptides identified from 2+ and 3+ charge state ions? Is it more or less likely to observe a correct peptide identification containing a missed cleavage when the charge state is 2+ as compared to 3+? Why? [For peptides identified from 2+ charge state ions, 96.6% of correct had NMC=0, 3.4% NMC 1-2, while 48% of incorrect results had NMC=0, 52% NMC 1-2. For peptides identified from 3+ charge state ions, 83.6% of correct had NMC=0, 16.4% NMC 1-2, while 36.3% of incorrect results had NMC=0, 63.7% NMC 1-2. Peptides containing 1 or more missed cleavages tend to be longer. Longer peptides, especially containing internal K or R residues, can hold more charges when they get ionized. So, the fraction of peptides containing a missed cleavage is higher among correct identifications in 3+ ions than 2+ ions] d) What distributions of mass accuracy parameter (massd =M exp -M calc, i.e. the difference between the measured and calculated peptide mass) did the model learn for the correct (positive), and for the incorrect (negative) peptide identifications? To see these learned distributions, open the file interact.pep_accmass_2.png (this file is another output from PeptideProphet and is located in the same folder as the rest of the files). We used 25 ppm as mass tolerance in the X! Tandem search. What is the mass accuracy of this instrument? What is the massd threshold above which peptide identifications having high massd values get penalized (instead of being rewarded for having massd value close to 0)? Cases with log(p/n) less than zero can be considered as penalized in the model. [The majority of the correct identifications have abs(massd) of less than 10ppm; peptides with massd above ~+ 6ppm or below ~-4ppm are penalized by the model]. e) The database in this search was appended with a set of decoy ( reversed ) sequences (protein names beginning with REV) generated from the forward database by retaining the position of all potential cleavage targets (and specific non-cut targets) and reversing the sequence of the peptides. Because of the similar sizes of the decoy and non-decoy parts of the database we make the assumption that the matches to decoy sequences represent roughly half of the total number of incorrect hits. Estimate FDR for the same probability threshold as used in 1b above (0.7) based decoy counts. Go to Summary tab of the pepxml Viewer and filter the data using minimum probability of 0.7. Write down the number of identifications passing this filter (which is the xx number in the line at the top: displaying xx of the total yy spectra ) [2863]. Go to Filtering Options tab of the Viewer. Find required protein text (regex allowed) box and enter REV (strictly upper case). Click on Update Page below. Manually count the number of peptide identifications from decoy proteins [23]. Estimate decoy-based FDR [FDR = 23*2/2863 = 0.016, or 1.6%]. Do model-based and decoy count-based FDR estimates agree with each other? [They agree reasonably well, the model-based estimate of 2.2% FDR is slightly conservative compared to decoy-based estimate of 1.6%].

6 3. Protein-level validation with ProteinProphet View the ProteinProphet results file, interact.prot.shtml a) Familiarize yourself with ProteinProphet output. Find entry #15 YBR189W i) What is the probability assigned to this protein? [1.0] ii) Are there any peptides identified multiple times (from multiple MS/MS spectra)? [Yes, peptide KAEASGEAAEEAEDEE] ii) Are there any shared peptides, i.e. peptides present not only in YBR189W but also in some other protein(s)? Suggestion: You can find proteins corresponding to a peptide by clicking on the hyperlink in the weight column (leftmost column). [Yes, peptides that do not have * ] b) Check FDR estimates Compare the probability-based FDR estimate with that obtained by using decoys (but remember that this is a very small dataset, so such comparisons are not very meaningful). Click on the Sensitivity/Error Info link at the top. How many false interactions does the model predict for the 0.5 minimum probability threshold? What is the model-estimated FDR? [Estimated FDR is 2.6%, expected number of false protein identifications is 6]. Next, manually count the number of decoys having protein probability above 0.5 (In the browser, search for REV, case sensitive). Are the numbers close? Suggestion: If you are using a browser that doesn't allow case sensitive searching, such as Google chrome, try searching for 'REV1_' instead. [There are 4 decoys, so the estimated total number of incorrect protein identifications is 8. Close enough.] c) Protein inference problem. Yeast is not a particularly good organism to illustrate this problem, but we can still find representative cases. The purpose of this exercise is to get familiar with the difficulty of inferring what proteins are present in the sample given the list of identified peptides. Go through the following examples: i) Distinguishable proteins. Consider again entry #15 YBR189W. It shares peptides with what other protein? [YPL081W] What is the probability for the other protein? [1.0] These two proteins can be called distinguishable because, despite the fact that they share some peptides, each is identified conclusively by one or more unique peptides. ii) Indistinguishable proteins. Find entry #72, YGR085C YPR102C. This is a typical example of multiple proteins that cannot be distinguished on the basis of identified peptides. In this case, the two proteins are two ribosomal proteins RPL11B and RPL11A. What can you conclude about the presence of these two proteins in the sample? [At least one of them is present, cannot say which one, or perhaps both]. Compare the sequence of these two proteins. Is there any hope, in a typical experiment using trypsin digestion, of identifying a peptide(s) that could distinguish between these two proteins? [The difference is just 1 amino acid in the N- terminal region. There is no good tryptic peptide in that region. So distinguishing between them using conventional shotgun with trypsin digestion proteomics would be hard]. iii) Subsumed proteins. Find entry 185, Protein Group 3. This group contains two proteins, 185a: YBR191W and 185b:YPL079W. Which protein is identified conclusively because it has at least one unique peptide? [The first one, YBR191W]. Which protein is identified only by shared peptides? [the second protein, YPL079W]. What probabilities did ProteinProphet assign to these two proteins as a group? [1.0] Individually within the group [1.0 for the first, 0 for the second]? What can be concluded about the presence of YPL079W in the sample? [There is no conclusive evidence for its presence. However, we cannot say that it is not in the sample either].