Detection of co-eluted peptides using database search methods

Finding haplotypic signatures in proteins

BACKGROUND

The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown.

FINDINGS

Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches.

CONCLUSIONS

As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

Mass Spectrometric (MS) Analysis of Proteins and Peptides

The human genome is sequenced and comprised of ~30,000 genes, making humans just a little bit more complicated than worms or flies. However, complexity of humans is given by proteins that these genes code for because one gene can produce many proteins mostly through alternative splicing and tissue-dependent expression of particular proteins. In addition, post-translational modifications (PTMs) in proteins greatly increase the number of gene products or protein isoforms. Furthermore, stable and transient interactions between proteins, protein isoforms/proteoforms and PTM-ed proteins (protein-protein interactions, PPI) add yet another level of complexity in humans and other organisms. In the past, all of these proteins were analyzed one at the time. Currently, they are analyzed by a less tedious method: mass spectrometry (MS) for two reasons: 1) because of the complexity of proteins, protein PTMs and PPIs and 2) because MS is the only method that can keep up with such a complex array of features. Here, we discuss the applications of mass spectrometry in protein analysis.

Multiple solvent elution, a method to counter the effects of coelution and ion suppression in LC-MS analysis in bottom up proteomics

On average a human cell type expresses around 10,000 different protein coding genes synthesizing all the different molecular forms of the protein product (proteoforms) found in a cell. In a typical shotgun bottom up proteomic approach, the proteins are enzymatically cleaved, producing several 100,000 s of different peptides that are analyzed with liquid chromatography-tandem mass spectrometry (LC-MSMS). One of the major consequences of this high sample complexity is that coelution of peptides cannot be avoided. Moreover, low abundant peptides are difficult to identify as they have a lower chance of being selected for fragmentation due to ion-suppression effects and the semi-stochastic nature of the precursor selection in data-dependent shotgun proteomic analysis where peptides are selected for fragmentation analysis one-by-one as they elute from the column. In the current study we explore a simple novel approach that has the potential to counter some of the effect of coelution of peptides and improves the number of peptide identifications in a bottom-up proteomic analysis. In this method, peptides from a HeLa cell digest were eluted from the reverse phase column using three different elution solvents (acetonitrile, methanol and acetone) in three replicate reversed phase LC-MS/MS shotgun proteomic analysis. Results were compared with three technical replicates using the same solvent, which is common practice in proteomic analysis. In total, we see an increase of up to 10% in unique protein and up to 30% in unique peptide identifications from the combined analysis using different elution solvents when compared to the combined identifications from the three replicates of the same solvent. In addition, the overlap of unique peptide identifications common in all three LC-MS analyses in our approach is only 23% compared to 50% in the replicates using the same solvent. The method presented here thus provides an easy to implement method to significantly reduce the effects of coelution and ion suppression of peptides and improve protein coverage in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD011908.

CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction

Coeluting peptides are still a major challenge for the identification and validation of MS/MS spectra, but carry great potential. To tackle these problems, we have developed the here presented CharmeRT workflow, combining a chimeric spectra identification strategy implemented as part of the MS Amanda algorithm with the validation system Elutator, which incorporates a highly accurate retention time prediction algorithm. For high-resolution data sets this workflow identifies 38-64% chimeric spectra, which results in up to 63% more unique peptides compared to a conventional single search strategy.

An Approach for Peptide Identification by De Novo Sequencing of Mixture Spectra

Mixture spectra occur quite frequently in a typical wet-lab mass spectrometry experiment, which result from the concurrent fragmentation of multiple precursors. The ability to efficiently and confidently identify mixture spectra is essential to alleviate the existent bottleneck of low mass spectra identification rate. However, most of the traditional computational methods are not suitable for interpreting mixture spectra, because they still take the assumption that the acquired spectra come from the fragmentation of a single precursor. In this manuscript, we formulate the mixture spectra de novo sequencing problem mathematically, and propose a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra data sets to verify the merits of the proposed algorithm.

Multiplexed and data-independent tandem mass spectrometry for global proteome profiling

One of the most important early developments in the field of proteomics was the advent of automated data acquisition routines that allowed high-throughput unattended data acquisition during HPLC introduction of peptide mixtures to a tandem mass spectrometer. Prior to this, data acquisition was orders of magnitude less efficient being based entirely on lists of predetermined ions generated in a prior HPLC-MS experiment. This process, known generically as data-dependent analysis, empowered the development of shotgun proteomics where hundreds to thousands of peptide sequences are matched per experiment. In their most popular implementation, the most abundant ionized species from every precursor ion scan at each moment in chromatographic time are successively selected for isolation, activation and tandem mass analysis. While extremely powerful, this strategy has one primary limitation in that detectable dynamic range is restricted (in a top-down manner) to the peptides that ionize the best. To circumvent the serial nature of the data-dependent process and increase detectable dynamic range, the concepts of multiplexed and data-independent acquisition (DIA) have emerged. Multiplexed-data acquisition is based on more efficient co-selection and co-dissociation of multiple precursor ions in parallel, the data from which is subsequently de-convoluted to provide polypeptide sequences for each individual precursor ion. DIA has similar goals, but there is no real-time ion selection based on prior precursor ion scans. Instead, predefined m/z ranges are interrogated either by fragmenting all ions entering the mass spectrometer at every single point in chromatographic time; or by dividing the m/z range into smaller m/z ranges for isolation and fragmentation. These approaches aim to fully utilize the capabilities of mass spectrometers to maximize tandem MS acquisition time and to address the need to expand the detectable dynamic range, lower the limit of detection, and improve the overall confidence of peptide identifications and relative protein quantification measurements. This review covers all aspects of multiplexed- and data-independent tandem mass spectrometry in proteomics, from experimental implementations to advances in software for data interpretation.

Mass spectrometry-based protein identification with accurate statistical significance assignment

MOTIVATION

Assigning statistical significance accurately has become increasingly important as metadata of many types, often assembled in hierarchies, are constructed and combined for further biological analyses. Statistical inaccuracy of metadata at any level may propagate to downstream analyses, undermining the validity of scientific conclusions thus drawn. From the perspective of mass spectrometry-based proteomics, even though accurate statistics for peptide identification can now be achieved, accurate protein level statistics remain challenging.

RESULTS

We have constructed a protein ID method that combines peptide evidences of a candidate protein based on a rigorous formula derived earlier; in this formula the database P-value of every peptide is weighted, prior to the final combination, according to the number of proteins it maps to. We have also shown that this protein ID method provides accurate protein level E-value, eliminating the need of using empirical post-processing methods for type-I error control. Using a known protein mixture, we find that this protein ID method, when combined with the Sorić formula, yields accurate values for the proportion of false discoveries. In terms of retrieval efficacy, the results from our method are comparable with other methods tested.

AVAILABILITY AND IMPLEMENTATION

The source code, implemented in C++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit.

MixGF: spectral probabilities for mixture spectra from more than one peptide

In large-scale proteomic experiments, multiple peptide precursors are often cofragmented simultaneously in the same mixture tandem mass (MS/MS) spectrum. These spectra tend to elude current computational tools because of the ubiquitous assumption that each spectrum is generated from only one peptide. Therefore, tools that consider multiple peptide matches to each MS/MS spectrum can potentially improve the relatively low spectrum identification rate often observed in proteomics experiments. More importantly, data independent acquisition protocols promoting the cofragmentation of multiple precursors are emerging as alternative methods that can greatly improve the throughput of peptide identifications but their success also depends on the availability of algorithms to identify multiple peptides from each MS/MS spectrum. Here we address a fundamental question in the identification of mixture MS/MS spectra: determining the statistical significance of multiple peptides matched to a given MS/MS spectrum. We propose the MixGF generating function model to rigorously compute the statistical significance of peptide identifications for mixture spectra and show that this approach improves the sensitivity of current mixture spectra database search tools by a ≈30-390%. Analysis of multiple data sets with MixGF reveals that in complex biological samples the number of identified mixture spectra can be as high as 20% of all the identified spectra and the number of unique peptides identified only in mixture spectra can be up to 35.4% of those identified in single-peptide spectra.

Angiotensin I-converting enzyme (ACE) inhibitory activity and ACE inhibitory peptides of salmon (Salmo salar) protein hydrolysates obtained by human and porcine gastrointestinal enzymes

The objectives of the present study were two-fold: first, to detect whether salmon protein fractions possess angiotensin I-converting enzyme (ACE) inhibitory properties and whether salmon proteins can release ACE inhibitory peptides during a sequential in vitro hydrolysis (with commercial porcine enzymes) and ex vivo digestion (with human gastrointestinal enzymes). Secondly, to evaluate the ACE inhibitory activity of generated hydrolysates. A two-step ex vivo and in vitro model digestion was performed to simulate the human digestion process. Salmon proteins were degraded more efficiently by porcine enzymes than by human gastrointestinal juices and sarcoplasmic proteins were digested/hydrolyzed more easily than myofibrillar proteins. The ex vivo digested myofibrillar and sarcoplasmic duodenal samples showed IC50 values (concentration required to decrease the ACE activity by 50%) of 1.06 and 2.16 mg/mL, respectively. The in vitro hydrolyzed myofibrillar and sarcoplasmic samples showed IC50 values of 0.91 and 1.04 mg/mL, respectively. Based on the results of in silico studies, it was possible to identify 9 peptides of the ex vivo hydrolysates and 7 peptides of the in vitro hydrolysates of salmon proteins of 11 selected peptides. In both types of salmon hydrolysates, ACE-inhibitory peptides IW, IY, TVY and VW were identified. In the in vitro salmon protein hydrolysates an ACE-inhibitory peptides VPW and VY were also detected, while ACE-inhibitory peptides ALPHA, IVY and IWHHT were identified in the hydrolysates generated with ex vivo digestion. In our studies, we documented ACE inhibitory in vitro effects of salmon protein hydrolysates obtained by human and as well as porcine gastrointestinal enzymes.

Recent advances in mass spectrometry: data independent analysis and hyper reaction monitoring

New mass spectrometry (MS) methods, collectively known as data independent analysis and hyper reaction monitoring, have recently emerged. These methods hold promises to address the shortcomings of data-dependent analysis and selected reaction monitoring (SRM) employed in shotgun and targeted proteomics, respectively. They allow MS analyses of all species in a complex sample indiscriminately, or permit SRM-like experiments conducted with full high-resolution product ion spectra, potentially leading to higher sequence coverage or analytical selectivity. These methods include MS(E), all-ion fragmentation, Fourier transform-all reaction monitoring, SWATH Acquisition, multiplexed MS/MS, pseudo-SRM (pSRM) and parallel reaction monitoring (PRM). In this review, the strengths and pitfalls of these methods are discussed and illustrated with examples. In essence, the suitability of the use of each method is contingent on the biological questions posed. Although these methods do not fundamentally change the shape of proteomics, they are useful additional tools that should expedite biological discoveries.

Proteomics and the analysis of proteomic data: 2013 overview of current protein-profiling technologies

Mass spectrometry has become a major tool in the study of proteomes. The analysis of proteolytic peptides and their fragment ions by this technique enables the identification and quantitation of the precursor proteins in a mixture. However, deducing chemical structures and then protein sequences from mass-to-charge ratios is a challenging computational task. Software tools incorporating powerful algorithms and statistical methods improved our ability to process the large quantities of proteomics data. Repositories of spectral data make both data analysis and experimental design more efficient. New approaches in quantitative and statistical proteomics make possible a greater coverage of the proteome, the identification of more post-translational modifications, and a greater sensitivity in the quantitation of targeted proteins.

Peptide identification by database search of mixture tandem mass spectra

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.

DtaRefinery, a software tool for elimination of systematic errors from parent ion mass measurements in tandem mass spectra data sets

Hybrid two-stage mass spectrometers capable of both highly accurate mass measurement and high throughput MS/MS fragmentation have become widely available in recent years, allowing for significantly better discrimination between true and false MS/MS peptide identifications by the application of a relatively narrow window for maximum allowable deviations of measured parent ion masses. To fully gain the advantage of highly accurate parent ion mass measurements, it is important to limit systematic mass measurement errors. Based on our previous studies of systematic biases in mass measurement errors, here, we have designed an algorithm and software tool that eliminates the systematic errors from the peptide ion masses in MS/MS data. We demonstrate that the elimination of the systematic mass measurement errors allows for the use of tighter criteria on the deviation of measured mass from theoretical monoisotopic peptide mass, resulting in a reduction of both false discovery and false negative rates of peptide identification. A software implementation of this algorithm called DtaRefinery reads a set of fragmentation spectra, searches for MS/MS peptide identifications using a FASTA file containing expected protein sequences, fits a regression model that can estimate systematic errors, and then corrects the parent ion mass entries by removing the estimated systematic error components. The output is a new file with fragmentation spectra with updated parent ion masses. The software is freely available.