MassSorter: a tool for administrating and analyzing data from mass spectrometry experiments on proteins with known amino acid sequences
© Barsnes et al; licensee BioMed Central Ltd. 2006
Received: 17 October 2005
Accepted: 26 January 2006
Published: 26 January 2006
Proteomics is the study of the proteome, and is critical to the understanding of cellular processes. Two central and related tasks of proteomics are protein identification and protein characterization. Many small laboratories are interested in the characterization of a small number of proteins, e.g., how posttranslational modifications change under different conditions.
We have developed a software tool called MassSorter for administrating and analyzing data from peptide mass fingerprinting experiments on proteins with known amino acid sequences. It is meant for small scale mass spectrometry laboratories that are interested in posttranslational modifications of known proteins. Several experiments can be compared simultaneously, and the matched and unmatched peak values are clearly indicated. The hits can be sorted according to m/z values (default) or according to the sequence of the protein. Filters defined by the user can mark autolytic protease peaks and other contaminating peaks (keratins, proteins co-migrating with the protein of interest, etc.). Unmatched peaks can be further analyzed for unexpected modifications by searches against a local version of the UniMod database. They can also be analyzed for unexpected cleavages, a highly useful feature for proteins that undergo maturation by proteolytic cleavage, creating new N- or C-terminals. Additional tools exist for visualization of the results, like sequence coverage, accuracy plots, different types of statistics, 3D models, etc. The program and a tutorial are freely available for academic users at http://www.bioinfo.no/software/massSorter.
MassSorter has a number of useful features that can promote the analysis and administration of MS-data.
Proteomics is the study of the proteome, the protein complement of the genome, and is critical for the understanding of cellular biological processes. Two central and related tasks of proteomics are protein identification and protein characterization. Identification is commonly done by peptide mass fingerprinting (PMF), where the masses of a set of peptides from the protein(s) are determined by mass spectrometry (MS), followed by a search in a sequence database. Alternatively, single peptides can after MS be selected for fragmentation followed by another MS experiment (tandem MS or MS/MS). The resulting MS/MS spectra are used for searching in a sequence database. For reviews of mass spectrometry and bioinformatics in proteomics, see for example [1–4].
Although there has been an enormous increase in large-scale proteomics, there is still a need for tools for researchers concentrating on the characterization of single or a small number of proteins. One of the most important tasks for characterization of a known protein (known sequence) is the determination of posttranslational modifications, which can be done both by MS- and MS/MS experiments. Typically an MS experiment can discover that a modification has occurred, but not the position in the peptide (if there are several alternatives). The exact position can be determined by MS/MS.
There are a number of programs intended for the identification of proteins by PMF, e.g., MS-Fit, a program in ProteinProspector , Mascot , Profound , Aldente , Phenyx , GPMAW , etc. As a part of the search parameters, the user can choose different modifications believed to be present in the proteins analyzed, but other modifications are not considered. Thereby a partial characterization is also achieved. A program like MS-Screener  may promote the identification of proteins by removing common contaminating peaks from different samples. Other programs are directed towards further characterization of PMF data from the identified proteins. For example FindMod  can suggest modifications present in peptides, and FindPept  can suggest whether unexpected cleavages have occurred.
Among all these programs, only Phenyx has an administrative unit that collects and analyzes data from several experiments. Phenyx is mostly directed toward protein identification, and not the detailed and repeated analysis of single proteins. Furthermore, it is intended for large-scale, high-throughput MS and MS/MS, and it is machine-demanding. A software application for small-scale proteomics should include an administrative unit that can function as a database of results, and it should be possible to directly compare several experiments in a table or a spreadsheet. The analytical tools should be integrated around this administrative unit. All the analytical tools should have a uniform and user-friendly style, such that the data flow between the tools becomes easy. Furthermore, it should be platform and server independent, and optimized for small scale analysis.
We have developed a set of tools, MassSorter, that satisfies these requirements. MassSorter sorts, systematizes and analyzes data obtained from MS experiments where a known protein is analyzed for sequence coverage, posttranslational modifications, modifications occurring during sample handling, and induced chemical modifications. MassSorter functions as a database for all the peptides detected in the experiments and at the same time sorts the data according to given parameters by comparing obtained data with theoretical data. Data not recognized by this first comparison can go through a second round of analysis where other tools can suggest the origin of the still unidentified data. MassSorter is intended as a tool for small mass spectrometry laboratories that are interested in characterization of known proteins.
Modifications, enzymes, terminals and amino acid data
A given set of default modifications, enzymes and terminals are included in MassSorter, along with information about the 20 standard amino acids. The user may add his/her own alternatives and/or edit the existing ones. In this way, MassSorter can be tailored to the individual user's needs. For a user-specified modification, the following data are needed: i) an abbreviation, ii) the amino acid(s) affected, iii) the mass modification, iv) a comment explaining the details of the modification, and v) a set of rules to determine where the modification can occur. For enzymes, a name and the cleavage rules must be specified. The user can also specify his/her own amino acids, as well as N- and C-terminals.
MassSorter contains three main data tables.
Theoretical background table (TBT)
The TBT contains the theoretical peptide peak list of the protein digested by a specific protease. It also contains the parameters used for theoretical digestion, including considered modifications.
The experimental data table (EDT)
An EDT contains four main features for each experiment: i) information about the experiment (protein, enzyme and date), ii) experimental comments, iii) a list of expected posttranslational modifications, and iv) the peak list. Additional information about each peak (e.g., intensity and manually added comments) is also included. During the import of the peak list, it is possible to manually edit the data, e.g., remove a peak that the user identifies as noise, or add a peak that the instrumental processing software has not recognized.
The data sheet table (DST)
After importing the experimental peak lists into MassSorter, each list is compared to the theoretical peak list in the TBT. Each peptide mass from an experimental peak list is compared to all the theoretical peptide masses, and matches within the selected accuracy limit are detected. If an experimental peptide mass is unmatched, i.e. there is no matching theoretical peptide mass, the given peptide mass is compared to the peptide masses from the other MS experiments, if any. The results from all of these comparisons are visualized in a spreadsheet called the data sheet table (DST).
A primary match between an experimental and a theoretical peptide mass, colored light green. Optionally, shadings of green can be used to indicate the peak intensity.
A secondary match between an experimental and a theoretical peptide mass, colored dark green.
An unmatched experimental peptide mass, colored yellow.
The terms primary and secondary matches are used to separate two types of matches between an experimental and a theoretical peptide mass. When comparing an experimental peptide mass to the theoretical peptide masses, more than one theoretical peptide may match within the given accuracy limit. Without further analysis of the alternatives, it is not possible to determine which is the correct match.
Although the closest match might be the correct match, this is not always the case. It is even possible that all the found matches are correct. The user of the program must therefore decide which of the matches to keep and use in the subsequent analysis. The accepted matches are labeled primary matches and the others are labeled secondary matches. As a default, the match with the least distance in ppm (part per million) or Dalton is labeled primary match and the others are labeled secondary matches.
When the comparison of an experimental mass and a theoretical mass results in a match, the theoretical peptide data are inserted into the DST on the same row as the experimental mass. The first six columns of the DST are therefore reserved for data about the theoretical peptides: the theoretical peptide mass, the start and end position of the peptide, added modifications, number of missed cleavages and the peptide sequence. For the unmatched masses, these columns are left empty. The following columns contain the information about the experiments, three columns per experiment.
There are two kinds of theoretical peptides; modified and unmodified. If a match with a modified theoretical peptide occurs, all the modifications have to be possible on the experimental peptide for the match to be allowed.
The analytical tools included in MassSorter are centrered around the DST. Several experiments can be displayed in one DST, and the analytical tools process all experiments shown in the DST.
Detecting additional matches
- 1.Masses belonging to additional proteins in the mixture, for example the protease used for digestion. There could also be contaminating proteins, like keratin or proteins comigrating with the protein of interest during gel electrophoresis. It is possible to use such proteins as filters, which are combined with the TBT. Filters can be defined in three different ways:
As experimental masses, for example the autolytic peptide peaks from the protease
As a protein sequence, which then has to be theoretically digested
As masses defined by the user, either directly (the mass), or indirectly as a peptide sequence, from which the mass has to be calculated
Masses from peptides resulting from unexpected cleavage sites. This is handled by the tool SequenceSuggester, which compares an unmatched mass to any linear amino acid sequence (with and without given modifications) occurring in the protein.
Masses resulting from unexpected modifications. This is handled by the tool UniModSearch which searches a local version of the UniMod database [14, 15] for unexpected modifications, or by the tool ChangeModifications which searches for other user-specified modifications.
The two last tools take an unmatched peptide mass as input and return a list of possible explanations. It is up to the user to accept or discard the suggested explanation(s). If an explanation is accepted, the match is inserted into the DST and is colored blue.
ProteinDigester is the tool MassSorter uses for the theoretical digestion. The basic input is an amino acid sequence along with a set of parameters that characterizes the digestion and the resulting mass calculation: i) the enzyme, ii) the peptide N- and C-terminals, iii) possible charges, iv) a list of possible modifications, v) limits for the properties of the peptides created (the minimum peptide mass, the maximum peptide mass, the minimum peptide length, the maximum number of missed cleavages), and vi) use of monoisotopic or average amino acid residue masses.
There already exist several programs for theoretical digestion, either as separate programs or as part of database search programs, see for example MS-Digest, a tool in ProteinProspector [5, 16], and PeptideCutter . However, we decided to develop our own to make the connection to the other tools faster and simpler.
The results are presented and viewed in several ways.
The Data Sheet Table is displayed in a spreadsheet (Figure 2).
Report is a tool on top of the DST where all the matched and unmatched masses per experiment are grouped and counted. In addition, the sequence coverage is calculated and visualized.
The experimental masses can be grouped according to the following matching types: i) matches with unmodified theoretical peptides, ii) matches with modified theoretical peptides, iii) unexpected matches, iv) matches with filter(s), v) unmatched masses, and vi) secondary matches. The number of experimental peptide masses and the resulting match percentage are also shown.
The second part of the Report visualizes the sequence coverage, both per experiment and combined for all experiments. The entire amino acid sequence is shown and the detected parts of the sequence are colored. The possible cleavage sites of the enzyme used are marked, and also the modified residues. The Report is connected to the DST, which means that if the user wants to know which of the detected peptides include a certain residue in the protein, the user can right click on the residue and a window will appear containing the peptide information from the DST. The Report also includes an option to view the amino acid sequence as a 3D model.
Protein Viewer – 3D modeling
After using the MassSorter tools for locating the origin of the unmatched masses, a complete sequence coverage is still unlikely to have been obtained. Our experience shows that a sequence coverage of 20–40% might be realistic in single experiments. Even if all the experimental peptide masses in an MS experiment are matched to theoretical peptide masses, a sequence coverage of 100% is still unlikely. The properties that may effect the sequence coverage can be divided into three categories: i) peptide properties, ii) instrument properties, and iii) protocol properties. Several papers have analyzed such properties, see for example [21–23]. The current version of MassSorter includes a tool, PeptideStatistics, that considers and makes statistics for the following peptide properties: hydropathy, peptide length, amino acid frequencies, cleavage site frequency and peptide mass. It also includes a tool, Accuracy Statistics, for statistical analysis of the spread of the matching accuracy to see if there are accuracy values that differ substantially from the rest. Analysing the details of the spread of the ppm (or Dalton) values might be a way of eliminating some of the less likely matches. This is because after calibration the spectrum is transformed into a spectrum where the peptide's accuracy values have a close to uniform spread. It is assumed that the correct matches will lie within a certain area of the range of accepted accuracy values. If there are any matches with a ppm (or Da) value far from the common range, this might be an indicator of an incorrect match. MassSorter therefore contains a tool for plotting the experimental m/z values against the accuracy values. Another way of detecting correct matches is to perform MS/MS experiments on the peptide mass in question and compare the results to the sequence of the proposed matching theoretical peptide.
Fractional masses (also known as the half decimal point rule ) is dependent on the ratio of the different atoms in the peptides. As several types of modifications will change this ratio, fractional mass may help to predict peptides with such modifications. Therefore, MassSorter can display a plot of fractional masses for the peaks obtained in the experiment. The constants and borders (four standard deviations) are calculated as in .
The user interface was developed following the eight golden rules given in . This has resulted in a uniform and easy to use interface, which can also easily be changed to accommodate specific user preferences.
Diagram of MassSorter
Results and discussion
MMP-2 is a 72 kDa protease that may autoactivate by cleavage into a 62 kDa form. Both forms were studied by MALDI-TOF-MS . The tryptic peak lists were imported into MassSorter and the two MMP-2 forms were assembled into one DST. The standard sorting of the data in the DST is according to increasing m/z ratio, but the data can also be sorted according to the amino acid sequence of the protein. This made it immediately evident that the 62 kDa form lacked all peaks N-terminal to position 115 (numbering according to GenBank accession number NP_004521). A peak at m/z 843.4 was present from the 62 kD form, but absent from the 72 kDa form. SequenceSuggester gave several alternatives for this m/z value, but only one contained a C-terminal arginine (or lysine). This peptide corresponded to positions 110–115. This is likely to be the correct N-terminus of the 62 kDa form . This example is included in the tutorial.
It is considered an advantage to perform internal calibration in PMF experiments. If possible, autolysis peaks from the protease are used. Chymotrypsin is rarely used for identification purposes, but can be useful for characterization purposes. Bovine chymotrypsin (Sigma C-6423) gave a number of autolysis peaks that fitted with chymotryptic autodigestion. However, the most intense peak at m/z 1523.8 did not fit with standard rules of chymotryptic cleavage (C-terminal to F, Y, W, L). SequenceSuggester gave three alternative peptides within the 50 ppm accuracy limit. The results of subsequent experiments (tryptic cleavage of the chymotryptic peptide, C-terminal sequencing with carboxypeptidase Y, and partial post-source decay sequencing) were consistent with the one of the three suggested peptides, 149-ANTPDRLQQASLPL-162 (cleaved C-terminal to an N, with m/z 1523.8182). As a result, this peptide could be used as an internal calibrant in later experiments.
A protein was purified from cultured cells and tryptic peptides were analyzed by MALDI-TOF-MS. While most of the peptides showed a distribution of accuracies more or less around the theoretical values, one peptide consistently showed accuracies around -30 to -60 ppm, as shown in Figure 5. Its aberrant behavior was very obvious in the accuracy plot. If the peptide is indeed generated from this protein, it is unlikely to be the indicated peptide, but rather another peptide that contains an unknown modification. No reasonable suggestions were obtained by using UniModSearch. We therefore excluded this particular peptide from subsequent analyses.
MALDI-TOF-MS instruments are becoming more abundant, and they are very useful for both identification and characterization purposes. Many laboratories are interested in detailed characterizations of a low number of proteins. Such characterizations can be performed by manual analysis of the spectra and peak lists, but this is very time consuming. A simple and basic question valid both for identification and characterization purposes is "how reproducible are our results?". In a manual analysis, this would need lengthy comparisons of the peak lists. With MassSorter, the alignment of multiple experiments can be done in seconds after the import of the experimental data, and their reproducibilities and accuracies can be viewed in several ways. Furthermore, reproducible peaks that do not fit with the theoretical digestion of the protein, are immediately evident. Such peaks can be further analyzed by the two tools, SequenceSuggester and UniModSearch. The suggestions from these tools may be used as basis for subsequent experiments as indicated in example 2 above. If the structure of the protein is known and available as a PDB file, the detected peptides can be indicated in the 3D model of the protein. Specific peptides or amino acids can be highlighted in this model and this can be used for further interpretation of the results. For example a phosphorylation or glycosylation is unlikely to be hidden in the internal structures of a protein. There is also the possibility to manually add modification definitions, e.g., modifications not available in the UniMod database. It is therefore also easy to reanalyze old data if the user becomes aware of a modification that has not been considered before.
We have developed MassSorter as a software tool for small-scale mass spectrometry laboratories performing PMF experiments. It is independent of the producer of the instruments and only needs the peak lists to be exported (or copied) as a text format. It has a number of features that facilitates administration and analysis of the data.
Availability and requirements
Project name: MassSorter
Project home page: http://www.bioinfo.no/software/massSorter
Operating system(s): Platform independent
Programming language: Java, Netbeans  used for the user interface
Other requirements: Java 5.0 or higher, Java 3D 1.3.1
License: The program is freely available for academic users after registration at the project's home page
Prof. Per Seglen is thanked for making the MS instrument available to us. Anders Øverbye and Mads Haugen are thanked for participating in testing the program. Dr. Véronique Cruciani is thanked for useful discussions. MS experiments were supported by the Norwegian Cancer Society (through VC and prof. Jan-Oluf Winberg). Thanks also to Saywan Pasha for letting us include his Protein Viewer program, and to T. Can et al. for making their program FPV freely available, on which Protein Viewer is based. Harald Barsnes was supported by the FUGE program in The Research Council of Norway (western regional funds).
- Aebersold R, Mann M: Mass spectrometry-based proteomics. Nature 2003, 422: 198–207. 10.1038/nature01511View ArticlePubMedGoogle Scholar
- Cristoni S, Bernardi L: Bioinformatics in mass spectrometry data analysis for proteomics studies. Exp Rev Prot 2004, 1: 469–483. 10.1586/147894188.8.131.529View ArticleGoogle Scholar
- Steen H, Mann M: The abc's (and xyz's) of peptide sequencing. Nat Rev Mol Cell Biol 2004, 5: 699–711. 10.1038/nrm1468View ArticlePubMedGoogle Scholar
- Reinders J, Lewandrowski U, Moebius J, Wagner Y, Sickmann A: Challenges in mass spectrometry-based proteomics. Proteomics 2004, 4: 3686–3703. 10.1002/pmic.200400869View ArticlePubMedGoogle Scholar
- Perkins D, Pappin D, Creasy D, Cottrell J: Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20: 3551–3567. 10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2View ArticlePubMedGoogle Scholar
- Zhang W, Chait B: ProFound – an expert system for protein identification using mass spectrometric peptide mapping information. Anal Chem 2000, 72: 2482–2489. 10.1021/ac991363oView ArticlePubMedGoogle Scholar
- Tuloup M, Hernandez C, Coro I, Hoogland C, Binz PA, Appel R: Aldente and BioGraph: An improved peptide mass fingerprinting protein identification environment. In Understanding Biological Systems through Proteomics, Basel, Switzerland. Edited by: FontisMedia. Swiss Proteomics Society; 2003:174–176.Google Scholar
- Peri S, Steen H, Pandey A: GPMAW – a software tool for analyzing proteins and peptides. Trends Biochem Sci 2001, 11: 687–689. 10.1016/S0968-0004(01)01954-5View ArticleGoogle Scholar
- Schmidt F, Schmid M, Jungblut P, Mattow J, Facius A, Pleissner KP: Iterative data analysis is the key for exhaustive analysis of peptide mass fingerprints from proteins separated by two-dimensional electrophoresis. J Am Soc Mass Spectrom 2003, 14: 943–956. 10.1016/S1044-0305(03)00345-3View ArticlePubMedGoogle Scholar
- Gattiker A, Bienvenut WV, Bairoch A, Gasteiger E: FindPept, a tool to identify unmatched masses in peptide mass fingerprinting protein identification. Proteomics 2002, 2: 1435–1444. 10.1002/1615-9861(200210)2:10<1435::AID-PROT1435>3.0.CO;2-9View ArticlePubMedGoogle Scholar
- Ceasy DM, Cottrell JS: UniMod: Protein modifications for mass spectrometry. Proteomics 2004, 4: 1534–1536. 10.1002/pmic.200300744View ArticleGoogle Scholar
- Clauser KR, Baker PR, Burlingame AL: Role of accurate mass measurement (+/- 10 ppm) in protein identification strategies employing MS or MS/MS and database searching. Anal Chem 1999, 71: 2871–2882. 10.1021/ac9810516View ArticlePubMedGoogle Scholar
- Deshpande N, Addess KJ, Bluhm WF, Merino-Ott JC, Townsend-Merino W, Zhang Q, Knezevich C, Xie L, Chen L, Feng F, Green RK, Flippen-Anderson JL, Westbrook J, Berman HM, Bourne PE: The RCSB Protein Data bank: a redesigned query system and relational database based on the mmCIF schema. Nucleic Acids Res 2005, 33: D233-D237. 10.1093/nar/gki057PubMed CentralView ArticlePubMedGoogle Scholar
- Pasha S: Visualization Tools for Bioinformatics, Master thesis, University of Bergen, Department of informatics. 2004.Google Scholar
- Can T, Wang Y, Wang YF, Su J: FPV: fast protein visualization using Java 3D™. Bioinformatics 2003, 19: 913–922. 10.1093/bioinformatics/btg104View ArticlePubMedGoogle Scholar
- Valero ML, Giralt E, Andreu D: An investigation of residue-specific contributions to peptide desorption in MALDI-TOF mass spectrometry. Lett Pept Sci 1999, 6: 109–115. 10.1023/A:1008819923662Google Scholar
- Gay S, Binz PA, Hochstrasser DF, Appel RD: Peptide mass fingerprinting peak intensity prediction: Extracting knowledge from spectra. Proteomics 2002, 2: 1374–1391. 10.1002/1615-9861(200210)2:10<1374::AID-PROT1374>3.0.CO;2-DView ArticlePubMedGoogle Scholar
- Baumgart S, Lindner Y, Kühne R, Oberemm A, Wenschuh H, Krause E: The contributions of specific amino acid side chains to signal intensities of peptides in matrix-assisted laser desorption/ionization mass spectrometry. Rapid Commun Mass Spectrom 2004, 18: 863–868. 10.1002/rcm.1416View ArticlePubMedGoogle Scholar
- Wool A, Smilansky Z: Precalibration of matrix-assisted laser desorption/ionization-time of flight spectra for peptide mass fingerprinting. Proteomics 2002, 2: 1365–1373. 10.1002/1615-9861(200210)2:10<1365::AID-PROT1365>3.0.CO;2-9View ArticlePubMedGoogle Scholar
- Shneiderman B: Designing the User Interface Addison Wesley. 1998.Google Scholar
- Lindstad RI, Sylte I, Mikalsen SO, Seglen PO, Berg E, Winberg JO: Pancreatic trypsin activates human promatrix metalloproteinase-2. J Mol Biol 2005, 350: 682–698. 10.1016/j.jmb.2005.05.018View ArticlePubMedGoogle Scholar
- Stetler-Stevenson WG, Krutzsch HC, Wacher MP, Margulies IM, Liotta LA: The activation of human type IV collagenase proenzyme. Sequence identification of the major conversion product following organomercurial activation. J Biol Chem 1989, 264: 1353–1356.PubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.