Reducing the haystack to find the needle: improved protein identification after fast elimination of non-interpretable peptide MS/MS spectra and noise reduction

Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) is the method of choice for the identification of proteins extracted from biological samples. The standard procedure of post-MS/MS data processing involves computer-aided interpretation of the measured spectra with MASCOT [1], SEQUEST [2] or some other software for comparing theoretical spectra calculated for database sequences with the experimental ones. But modern instruments generate extremely large sets of MS/MS spectra (in the order of 10000 per sample), which are heavily contaminated with different types of background and noise. In addition to b-, y- and their derivative ions from peptides, spectra contain repeated shifted signals due to the natural isotope distribution (isotope clusters), multiply charged replicas, peaks from unknown fragmentation pathways, sample-specific or systematic chemical contaminations and random noise from the electronic detection system.

Thus, the spectra consist mostly of background; typically, only a few percent of the spectra recorded have signals from target protein fragments and just about 10% of the peaks in such a spectrum contribute to the peptide identification. Thus, computer resources in mass spectrometry departments all over the world are mostly spent on analyzing non-relevant data if the identification of the protein with significance is possible within the background at all. This strategy clashes with limitations in compute server capacity in proteomics laboratories and seriously limits the access of less generously equipped teams to the field.

With the broad availability of accurate MS/MS instruments with resolution in the order of tenths of a Dalton, automatic background removal procedures before interpretation software application became possible [3–5]. Various spectrum pre-processing rules, deconvolution of multiply charged peaks and deisotoping procedures have been described [6–15]. It should be noted that many spectra do not contain peaks from peptide fragmentations or are extremely noisy and, therefore, are non-interpretable into peptide sequences reliably. Thus, the exclusion of non-interpretable spectra is a valid strategy for reducing the computational load. For a well performing method, one would desire it to remove clearly more than half or three quarters of the experimental MS/MS spectra and, essentially, to keep all interpretable ones. At the same time, computation time for this task should be negligible or, at least, small compared to the processing time used by an interpretation program such as MSACOT that is saved by unselecting a large spectra subset.

Published approaches to this problem differ in the criterion for spectrum selection, either with empirically defined score functions or with a classifier generated by automated learning approaches [16–23]. Although many of these methods apply quite sophisticated criteria, they either are not efficient filters or suffer from a substantial fraction of unselected but nevertheless interpretable MS/MS spectra (e.g., loss of ~10% of the interpretable spectra for removing ~75% of the total number spectra in Figures 2 and 3 of Bern et al. [18]). Thus, substantial computational load reduction is traded in for the risk not to find the desired peptide hit. Consequently, none of the published techniques has routinely entered the laboratories so far.

In the attempt to develop an alternative methodical approach, we propose to return to ideas from the beginning of mass spectrometry of proteins. Originally, interpretation of an MS/MS spectrum meant experts trying to manually find sequence ladders (i.e., sets of peaks with amino acid mass spacing between them) among the high-intensity peaks. The concept of searching mainly among the higher intensity peaks is still reminiscent in the formulas for evaluating the significance of a peptide hit as used in MASCOT [1]. Indeed, a peptide the theoretical fragmentation spectrum of which matches exclusively low intensity peaks cannot serve as convincing explanation of the experimental data.

In this work, we explore the idea that at least some short oligopeptide segment of a significant peptide hit should be fully matched by the higher intensity peaks in the spectrum. In an efficient implementation, the computational costs are low if one tries just to check whether small peptide ladders of predefined length do occur in a MS/MS spectrum at all among the top fraction of most intense peaks. The identity of the oligopepetide is not important in this context; it is rather questioned whether such an amino acid chain theoretically exists at all. It is reasonable to suggest that the spectrum is probably not interpretable into a peptide sequence with statistical significance if not even a short oligopeptide sequence is matched by this criterion.

After this unselecting procedure, the remaining spectra still contain considerable background in the typical case. In a previous publication [24], we developed an approach based on techniques from electrical signal processing. Periodical band-reject and high-frequency filters as well as correlation analyses with etalons of multiply charged clusters can successfully be used for background suppression. In this work, we describe a workflow involving sequence ladder and improved signal processing criteria on a large MS/MS dataset exemplified in the MS Cleaner version 2.0 that efficiently reduces the number and the size of spectra and, subsequently, dramatically shrinks the computing time used by the interpretation software. To emphasize, the approach described in this work is thought to increase the efficiency of protein identification. It is not considered to process MS/MS data that is intended to be screened for protein posttranslational modifications.

For the initial determination of optimal parameter ranges (sequence ladder length n and peak intensity threshold s), we used the datasets for bovine serum albumin (BSA), yeast alcohol dehydrogenase (ADH) and human transferrin (TRF) from our previous work [24] since they are quite small (less than 3000 .dta-files per set). We checked the influence of the preprocessing procedures on the spectrum interpretation with the MASCOT tool. A systematic analysis was performed; sequence ladder length was tested with values n between 2 and 6 and the high-intensity threshold s was varied from 5% to 35% (the sequence ladder was searched for only among the 5%, 10%, 15%, ..., or 35% of most intense peaks). The goal is to have as many unselected "bad" spectra as possible (the savings in computing time are about proportional to the fraction of spectra that is not handed over to the spectrum interpretation program) without losses of (i) MASCOT score, (ii) spectra giving peptide matches and (iii) sequence coverage.

The sequence coverage is more sensitive to the pre-processing parameters. For a sequence ladder length of n= 5 residues, we see a trend that sequence coverage is slightly decreased with respect to that of unprocessed data (41-54% instead of 55% for BSA, 21-31% instead of 39% for ADH, 45-48% instead of 47% for TRF). Sequence coverage is about the same or even slightly higher as for non-preprocessed data for sequence ladder lengths n= 3 and n= 4 and intensity thresholds s at and above 20%. With regard to the number of spectra that lead to a significant peptide match in the MASCOT search, the settings n= 3, s= 20%; n= 3, s= 25%; n= 4, s= 20% and n= 4, s= 25% are close to reproduce the result achieved with the unprocessed data for the BSA and TRF cases. Surprisingly, the number of peptide matches is slightly higher for s= 100% (all peaks are included in the sequence ladder search) than for the datasets without preprocessing. Thus, the number of falsely rejected spectra by the sequence-ladder algorithm is essentially zero in these two cases. For ADH, the number of spectra matching peptides is always somewhat lower if the tandem MS/MS data is pre-processed, although MASCOT score and sequence coverage do not suffer from choices of n= 3, or n= 4 and the higher values of s.

To detect a considerable fraction of the bad spectra and to reduce the time for interpretation by MASCOT, these results support the selection of a sequence ladder length equal to n= 4 and an intensity threshold of s= 20%. If the sequence coverage is more important than computational time savings, softer parameters can be chosen, for example with an intensity threshold of s= 25%. With these parameters, it is possible to eliminate more than 80% of all spectra in the datasets BSA, ADH and TRF by declaring them non-interpretable in oligopeptides (see Table 1). Minor sequence coverage loss, if at all observed, does not affect the interpretation result. Yet, the total computing time required for interpretation narrows up to only 20% of the original value. The computing time consumption for MS Cleaner alone in such a setting is ~2% of MASCOT time for non-preprocessed data (see Table 1); i.e., it is essentially negligible.

To summarize, the results support that testing spectra for interpretability in oligopeptides is a useful criterion for dataset reduction in protein mass spectrometry if a sequence ladder of a tetrapeptide segment is searched for among the 20% (or 25%) most intense peaks. This preprocessing is accompanied by an increase in MASCOT score and more significant top protein hits and it does not significantly affect sequence coverage. Running MS Cleaner 2.0 as a standard preprocessing step in peptide tandem MS data analysis for protein identification is recommended.

The idea of using short series of sequence ions (peptide sequence tags) as a specific identifier that speeds up searches for matches between spectra and sequences in databases (either by searching the database with the tag or by creating sequence tag database filters in order to reduce the size of a database via a preprocessing step) is extensively explored in the literature [25–27]. It is interesting to see that this simple idea applied to the problem of recognizing spectra non-interpretable in oligopeptides greatly reduces the complexity of analyzing protein mass spectrometry data.