The Mass Distance Fingerprint: A statistical framework for de novo detection of predominant modifications using high-accuracy mass spectrometry

A Metabolomics-Inspired Strategy for the Identification of Protein Covalent Modifications

Identification of protein covalent modifications (adducts) is a challenging task mainly due to the lack of data processing approaches for adductomics studies. Despite the huge technological advances in mass spectrometry (MS) instrumentation and bioinformatics tools for proteomics studies, these methodologies have very limited success on the identification of low abundant protein adducts. Herein we report a novel strategy inspired on the metabolomics workflows for the identification of covalently-modified peptides that consists on LC-MS data preprocessing followed by statistical analysis. The usefulness of this strategy was evaluated using experimental LC-MS data of histones isolated from HepG2 and THLE2 cells exposed to the chemical carcinogen glycidamide. LC-MS data was preprocessed using the open-source software MZmine and potential adducts were selected based on the m/z increments corresponding to glycidamide incorporation. Then, statistical analysis was applied to reveal the potential adducts as those ions are differently present in cells exposed and not exposed to glycidamide. The results were compared with the ones obtained upon the standard proteomics methodology, which relies on producing comprehensive MS/MS data by data dependent acquisition and analysis with proteomics data search engines. Our novel strategy was able to differentiate HepG2 and THLE2 and to identify adducts that were not detected by the standard methodology of adductomics. Thus, this metabolomics driven approach in adductomics will not only open new opportunities for the identification of protein epigenetic modifications, but also adducts formed by endogenous and exogenous exposure to chemical agents.

A Cautionary Tale on the Inclusion of Variable Posttranslational Modifications in Database-Dependent Searches of Mass Spectrometry Data

Mass spectrometry-based proteomics allows in principle the identification of unknown target proteins of posttranslational modifications and the sites of attachment. Including a variety of posttranslational modifications in database-dependent searches of high-throughput mass spectrometry data holds the promise to gain spectrum assignments to modified peptides, thereby increasing the number of assigned spectra, and to identify potentially interesting modification events. However, these potential benefits come for the price of an increased search space, which can lead to reduced scores, increased score thresholds, and erroneous peptide spectrum matches. We have assessed here the advantages and disadvantages of including the variable posttranslational modifications methionine oxidation, protein N-terminal acetylation, cysteine carbamidomethylation, transformation of N-terminal glutamine to pyroglutamic acid (Gln→pyro-Glu), and deamidation of asparagine and glutamine. Based on calculations of local false discovery rates and comparisons to known features of the respective modifications, we recommend for searches of samples that were not enriched for specific posttranslational modifications to only include methionine oxidation, protein N-terminal acetylation, and peptide N-terminal Gln→pyro-Glu as variable modifications. The principle of the validation strategy adopted here can also be applied for assessing the inclusion of posttranslational modifications for differently prepared samples, or for additional modifications. In addition, we have reassessed the special properties of the ubiquitin footprint, which is the remainder of ubiquitin moieties attached to lysines after tryptic digest. We show here that the ubiquitin footprint often breaks off as neutral loss and that it can be distinguished from dicarbamidomethylation events.

Detection of busulfan adducts on proteins

RATIONALE

Busulfan is a bifunctional alkyl sulfonate antineoplastic drug. This alkylating agent was described as forming covalent adducts on proteins. However, only limited data are available regarding the interaction of busulfan with proteins. Mass spectrometry and bioinformatics were used to identify busulfan adducts on human serum albumin and hemoglobin.

METHODS

Albumin and hemoglobin were incubated with busulfan or control compounds, digested with trypsin and analyzed by liquid chromatography/tandem mass spectrometry (LC/MS/MS) on a Thermo Fisher LTQ Orbitrap Velos Pro. MS data were used to generate spectral libraries of non-modified peptides and an open modification search was performed to identify potential adduct mass shifts and possible modification sites. Results were confirmed by a second database search including identified mass shifts and by visual inspection of annotated tandem mass spectra of adduct-carrying peptides.

RESULTS

Five structures of busulfan adducts were detected and a chemical structure could be attributed to four of them. Two were primary adducts corresponding to busulfan monoalkylation and alkylation of two amino acid residues by a single busulfan molecule. Two others corresponded to secondary adducts generated during sample processing. Adducts were mainly detected on Asp, Glu, and His residues. These findings were confirmed by subsequent database searches and experiments with synthetic peptides.

CONCLUSIONS

The combination of in vitro incubation of proteins with the drug of interest or control compounds, high-resolution mass spectrometry, and open modification search allowed confirmation of the direct interaction of busulfan with proteins and characterization of the resulting adducts. Our results also showed that careful analysis of the data is required to detect experimental artifacts. Copyright © 2016 John Wiley & Sons, Ltd.

Software eyes for protein post-translational modifications

Post-translational modifications (PTMs) are critical to almost all aspects of complex processes of the cell. Identification of PTMs is one of the biggest challenges for proteomics, and there have been many computational studies for the analysis of PTMs from tandem mass spectrometry (MS/MS). Most early PTM identification studies have been performed by matching MS/MS data to protein databases, using database search tools, but they are prohibitively slow when a large number of PTMs is given as a search parameter. In this article, we present recent developments to search for more types of PTMs and to speed up the search, and discuss many computational issues and solutions in terms of identifying multiply modified peptides or searching for all possible modifications at once in unrestrictive mode. Apart from the most common type of PTMs involving covalent addition of functional groups to proteins, PTMs such as disulfide linkage require dedicated software for the analysis because they may involve cross-linking between two different parts of proteins. Finally, methods for identification of protein disulfide bonds are presented.

A mass spectrometry-based method to screen for α-amidated peptides

Amidation is a post-translational modification found at the C-terminus of ~50% of all neuropeptide hormones. Cleavage of the C(α)-N bond of a C-terminal glycine yields the α-amidated peptide in a reaction catalyzed by peptidylglycine α-amidating monooxygenase (PAM). The mass of an α-amidated peptide decreases by 58 Da relative to its precursor. The amino acid sequences of an α-amidated peptide and its precursor differ only by the C-terminal glycine meaning that the peptides exhibit similar RP-HPLC properties and tandem mass spectral (MS/MS) fragmentation patterns. Growth of cultured cells in the presence of a PAM inhibitor ensured the coexistence of α-amidated peptides and their precursors. A strategy was developed for precursor and α-amidated peptide pairing (PAPP): LC-MS/MS data of peptide extracts were scanned for peptide pairs that differed by 58 Da in mass, but had similar RP-HPLC retention times. The resulting peptide pairs were validated by checking for similar fragmentation patterns in their MS/MS data prior to identification by database searching or manual interpretation. This approach significantly reduced the number of spectra requiring interpretation, decreasing the computing time required for database searching and enabling manual interpretation of unidentified spectra. Reported here are the α-amidated peptides identified from AtT-20 cells using the PAPP method.

DeltAMT: a statistical algorithm for fast detection of protein modifications from LC-MS/MS data

Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified.

Bioinformatics for LC-MS/MS-based proteomics

Mass spectrometry instrumentation has continued to develop rapidly in the last two decades, enabled in part by advances in microelectronic hardware controllers and computerized control and data acquisition systems. The wealth and complexity of data produced by a modern instrument is such that the data can no longer be analyzed manually. Computerized data analysis has become de rigueur and the bioinformatics field has expanded to provide software applications for all aspects of the data analysis needed by LC-MS/MS. The bioinformatics field is evolving rapidly and software applications are continually being improved or replaced for existing applications as well as developed to support new types of experiments and analysis enabled by modern instrumentation. Entire books have been written on MS data analysis in proteomics but this review will be necessarily brief. In this chapter we will review the bioinformatics software applications available for different LC-MS/MS analysis tasks.

Rapid validation of Mascot search results via stable isotope labeling, pair picking, and deconvolution of fragmentation patterns

Conventional LC-MS/MS data analysis matches each precursor ion and fragmentation pattern to their best fit within databases of theoretical spectra, yielding a peptide identification. Confidence is estimated by a score but can be validated by statistics, false discovery rates, and/or manual validation. A weakness is that each ion is evaluated independently, discarding potentially useful cross-correlations. In a classical approach to de novo sequence analysis, mixtures of peptides differing only in a carboxyl-terminal isotopic label yield fragmentation spectra with single, unlabeled b-type ions but pairs of isotope-labeled y-type ions, facilitating confident assignments. To apply this principle to identification by fragmentation pattern matching, we developed Validator, software that recognizes isotopic peptide pairs and compares their identifications and fragmentation patterns. Testing Validator 1 on a Mascot results file from FT-ICR LC-MS/MS of (16)O/(18)O-labeled yeast cell lysate peptides yielded 2,775 peptide pairs sharing a common identification but differing in carboxyl-terminal label. Comparing observed b- and y-ions with the predicted fragmentation pattern improved the threshold Mascot score for 5% false discovery from 36 to 22, significantly increasing both sensitivity and specificity. Validator 2, which identifies pairs by precursor mass difference alone before comparing observed fragmentation with that predicted by Mascot, found 2,021 isotopic pairs, similarly achieving improved sensitivity and specificity. Finally Validator 3, which finds pairs based on mass difference alone and then deconvolutes fragmentation patterns independently of Mascot, found 964 predicted peptides. Validator 3 allowed raw mass spectrometry data to be mined not only to validate Mascot results but also to discover peptides missed by Mascot. Using standard desktop hardware, the Validator 1-3 software processed the 11,536 spectra in the 93-MB Mascot .DAT file in less than 6 min (32 spectra/s), revealing high confidence peptide identifications without regard to Mascot score, far faster than manual or other independent validation methods.

Efficient discovery of abundant post-translational modifications and spectral pairs using peptide mass and retention time differences

Background

Peptide identification via tandem mass spectrometry is the basic task of current proteomics research. Due to the complexity of mass spectra, the majority of mass spectra cannot be interpreted at present. The existence of unexpected or unknown protein post-translational modifications is a major reason.

Results

This paper describes an efficient and sequence database-independent approach to detecting abundant post-translational modifications in high-accuracy peptide mass spectra. The approach is based on the observation that the spectra of a modified peptide and its unmodified counterpart are correlated with each other in their peptide masses and retention time. Frequently occurring peptide mass differences in a data set imply possible modifications, while small and consistent retention time differences provide orthogonal supporting evidence. We propose to use a bivariate Gaussian mixture model to discriminate modification-related spectral pairs from random ones. Due to the use of two-dimensional information, accurate modification masses and confident spectral pairs can be determined as well as the quantitative influences of modifications on peptide retention time.

Conclusion

Experiments on two glycoprotein data sets demonstrate that our method can effectively detect abundant modifications and spectral pairs. By including the discovered modifications into database search or by propagating peptide assignments between paired spectra, an average of 10% more spectra are interpreted.