New Data Base-independent, Sequence Tag-based Scoring of Peptide MS/MS Data Validates Mowse Scores, Recovers Below Threshold Data, Singles Out Modified Peptides, and Assesses the Quality of MS/MS Techniques

Proteomics Reveals Damaging Effect of Alpha-Cypermethrin Exposure in a Non-Target Freshwater Microalga Chlorella sp. NC-MKM

Alpha-cypermethrin, a pyrethroid pesticide, is frequently used on crops to prevent insect attacks. However, occasionally, due to drift, leaching, or with rainwater, it enters the aquatic environment and poses a serious threat to the growth of non-target aquatic organisms. In the current study, we were interested in investigating the damaging effect of alpha-cypermethrin on a local freshwater non-target green alga Chlorella sp. NC-MKM in terms of its protein levels. This was achieved by exposing Chlorella sp. NC-MKM to an EC₅₀ concentration of alpha-cypermethrin for 1 day, followed by the two-dimensional (2-D) gel electrophoresis and MALDI-TOF MS. Fifty-three proteins, which had showed significant differential accumulation (> 1.5 fold, P < 0.05) after exposure to alpha-cypermethrin, were considered as differentially accumulated proteins (DAPs). These DAPs were further divided into several functional categories, and the expressions of each in control and treatment samples were compared. Comparison revealed that alpha-cypermethrin exposure affects the accumulation of proteins related with photosynthesis, stress response, carbohydrate metabolism, signal transduction and transporters, translation, transcription, cell division, lipid metabolism, amino acid and nucleotide biosynthesis, secondary metabolites production, and post-translational modification, and thus rendered the tested algal isolate sensitive toward this pesticide. The overall findings of this research thus offer a fundamental understanding of the possible mechanism of action of the insecticide alpha-cypermethrin on the microalga Chlorella sp. NC-MKM and also suggest potential biomarkers for the investigation of pesticide exposed microalgae.

FT-MS in the de novo top-down sequencing of natural nontryptic peptides

The present review covers available results on the application of FT-MS for the de novo sequencing of natural peptides of various animals: cones, bees, snakes, amphibians, scorpions, and so forth. As these peptides are usually bioactive, the animals efficiently use them as a weapon against microorganisms or higher animals including predators. These peptides represent definite interest as drugs of future generations since the mechanism of their activity is completely different in comparison with that of the modern antibiotics. Utilization of those peptides as antibiotics can eliminate the problem of the bacterial resistance development. Sequence elucidation of these bioactive peptides becomes even more challenging when the species genome is not available and little is known about the protein origin and other properties of those peptides in the study. De novo sequencing may be the only option to obtain sequence information. The benefits of FT-MS for the top-down peptide sequencing, the general approaches of the de novxxo sequencing, the difficult cases involving sequence coverage, isobaric and isomeric amino acids, cyclization of short peptides, the presence of posttranslational modifications will be discussed in the review.

Validation of De Novo Peptide Sequences with Bottom-Up Tag Convolution

De novo sequencing is indispensable for the analysis of proteins from organisms with unknown genomes, novel splice variants, and antibodies. However, despite a variety of methods developed to this end, distinguishing between the correct interpretation of a mass spectrum and a number of incorrect alternatives often remains a challenge. Tag convolution is computed for a set of peptide sequence tags of a fixed length k generated from the input tandem mass spectra and can be viewed as a generalization of the well-known spectral convolution. We demonstrate its utility for validating de novo peptide sequences by using a set of those generated by the algorithm PepNovo+ from high-resolution bottom-up data sets for carbonic anhydrase 2 and the Fab region of alemtuzumab and indicate its further potential applications.

A Potential Golden Age to Come-Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics

In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community.

De Novo Sequencing of Peptides from High-Resolution Bottom-Up Tandem Mass Spectra using Top-Down Intended Methods

Despite high-resolution mass spectrometers are becoming accessible for more and more laboratories, tandem (MS/MS) mass spectra are still often collected at a low resolution. And even if acquired at a high resolution, software tools used for their processing do not tend to benefit from that in full, and an ability to specify a relative mass tolerance in this case often remains the only feature the respective algorithms take advantage of. We argue that a more efficient way to analyze high-resolution MS/MS spectra should be with methods more explicitly accounting for the precision level, and sustain this claim through demonstrating that a de novo sequencing framework originally developed for (high-resolution) top-down MS/MS data is perfectly suitable for processing high-resolution bottom-up datasets, even though a top-down like deconvolution performed as the first step will leave in many spectra at most a few peaks.

De Novo Sequencing of Top-Down Tandem Mass Spectra: A Next Step towards Retrieving a Complete Protein Sequence

De novo sequencing of tandem (MS/MS) mass spectra represents the only way to determine the sequence of proteins from organisms with unknown genomes, or the ones not directly inscribed in a genome-such as antibodies, or novel splice variants. Top-down mass spectrometry provides new opportunities for analyzing such proteins; however, retrieving a complete protein sequence from top-down MS/MS spectra still remains a distant goal. In this paper, we review the state-of-the-art on this subject, and enhance our previously developed Twister algorithm for de novo sequencing of peptides from top-down MS/MS spectra to derive longer sequence fragments of a target protein.

SpotLight Proteomics: uncovering the hidden blood proteome improves diagnostic power of proteomics

The human blood proteome is frequently assessed by protein abundance profiling using a combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS). In traditional sequence database search, many good-quality MS/MS data remain unassigned. Here we uncover the hidden part of the blood proteome via novel SpotLight approach. This method combines de novo MS/MS sequencing of enriched antibodies and co-extracted proteins with subsequent label-free quantification of new and known peptides in both enriched and unfractionated samples. In a pilot study on differentiating early stages of Alzheimer’s disease (AD) from Dementia with Lewy Bodies (DLB), on peptide level the hidden proteome contributed almost as much information to patient stratification as the apparent proteome. Intriguingly, many of the new peptide sequences are attributable to antibody variable regions, and are potentially indicative of disease etiology. When the hidden and apparent proteomes are combined, the accuracy of differentiating AD (n = 97) and DLB (n = 47) increased from ≈85% to ≈95%. The low added burden of SpotLight proteome analysis makes it attractive for use in clinical settings.

Ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry and tandem mass spectrometry for peptide de novo amino acid sequencing for a seven-protein mixture by paired single-residue transposed Lys-N and Lys-C digestion

RATIONALE

Bottom-up tandem mass spectrometry (MS/MS) is regularly used in proteomics to identify proteins from a sequence database. De novo sequencing is also available for sequencing peptides with relatively short sequence lengths. We recently showed that paired Lys-C and Lys-N proteases produce peptides of identical mass and similar retention time, but different tandem mass spectra. Such parallel experiments provide complementary information, and allow for up to 100% MS/MS sequence coverage.

METHODS

Here, we report digestion by paired Lys-C and Lys-N proteases of a seven-protein mixture: human hemoglobin alpha, bovine carbonic anhydrase 2, horse skeletal muscle myoglobin, hen egg white lysozyme, bovine pancreatic ribonuclease, bovine rhodanese, and bovine serum albumin, followed by reversed-phase nanoflow liquid chromatography, collision-induced dissociation, and 14.5 T Fourier transform ion cyclotron resonance mass spectrometry.

RESULTS

Matched pairs of product peptide ions of equal precursor mass and similar retention times from each digestion are compared, leveraging single-residue transposed information with independent interferences to confidently identify fragment ion types, residues, and peptides. Selected pairs of product ion mass spectra for de novo sequenced protein segments from each member of the mixture are presented.

CONCLUSIONS

Pairs of the transposed product ions as well as complementary information from the parallel experiments allow for both high MS/MS coverage for long peptide sequences and high confidence in the amino acid identification. Moreover, the parallel experiments in the de novo sequencing reduce false-positive matches of product ions from the single-residue transposed peptides from the same segment, and thereby further improve the confidence in protein identification. Copyright © 2016 John Wiley & Sons, Ltd.

Top-down analysis of protein samples by de novo sequencing techniques

MOTIVATION

Recent technological advances have made high-resolution mass spectrometers affordable to many laboratories, thus boosting rapid development of top-down mass spectrometry, and implying a need in efficient methods for analyzing this kind of data.

RESULTS

We describe a method for analysis of protein samples from top-down tandem mass spectrometry data, which capitalizes on de novo sequencing of fragments of the proteins present in the sample. Our algorithm takes as input a set of de novo amino acid strings derived from the given mass spectra using the recently proposed Twister approach, and combines them into aggregated strings endowed with offsets. The former typically constitute accurate sequence fragments of sufficiently well-represented proteins from the sample being analyzed, while the latter indicate their location in the protein sequence, and also bear information on post-translational modifications and fragmentation patterns.

AVAILABILITY AND IMPLEMENTATION

Freely available on the web at http://bioinf.spbau.ru/en/twister

CONTACT

vyatkina@spbau.ru or ppevzner@ucsd.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Quantitative LC-MS/MS Analysis of Proteins Involved in Metastasis of Breast Cancer

The purpose of this study was to develop quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods for the analysis of proteins involved in metastasis of breast cancer for diagnosis and determining disease prognosis, as well as to further our understand of metastatic mechanisms. We have previously demonstrated that the protein type XIV collagen may be specifically expressed in metastatic tissues by two dimensional LC-MS/MS. In this study, we developed quantitative LC-MS/MS methods for type XIV collagen. Type XIV collagen was quantified by analyzing 2 peptides generated by digesting type XIV collagen using stable isotope-labeled peptides. The individual concentrations were equivalent between 2 different peptides of type XIV collagen by evaluation of imprecise transitions and using the best transition for the peptide concentration. The results indicated that type XIV collagen is highly expressed in metastatic tissues of patients with massive lymph node involvement compared to non-metastatic tissues. These findings were validated by quantitative real-time RT-PCR. Further studies on type XIV collagen are desired to verify its role as a prognostic factor and diagnosis marker for metastasis.

Mining proteomic data to expose protein modifications in Methanosarcina mazei strain Gö1

Proteomic tools identify constituents of complex mixtures, often delivering long lists of identified proteins. The high-throughput methods excel at matching tandem mass spectrometry data to spectra predicted from sequence databases. Unassigned mass spectra are ignored, but could, in principle, provide valuable information on unanticipated modifications and improve protein annotations while consuming limited quantities of material. Strategies to "mine" information from these discards are presented, along with discussion of features that, when present, provide strong support for modifications. In this study we mined LC-MS/MS datasets of proteolytically-digested concanavalin A pull down fractions from Methanosarcina mazei Gö1 cell lysates. Analyses identified 154 proteins. Many of the observed proteins displayed post-translationally modified forms, including O-formylated and methyl-esterified segments that appear biologically relevant (i.e., not artifacts of sample handling). Interesting cleavages and modifications (e.g., S-cyanylation and trimethylation) were observed near catalytic sites of methanogenesis enzymes. Of 31 Methanosarcina protein N-termini recovered by concanavalin A binding or from a previous study, only M. mazei S-layer protein MM1976 and its M. acetivorans C2A orthologue, MA0829, underwent signal peptide excision. Experimental results contrast with predictions from algorithms SignalP 3.0 and Exprot, which were found to over-predict the presence of signal peptides. Proteins MM0002, MM0716, MM1364, and MM1976 were found to be glycosylated, and employing chromatography tailored specifically for glycopeptides will likely reveal more. This study supplements limited, existing experimental datasets of mature archaeal N-termini, including presence or absence of signal peptides, translation initiation sites, and other processing. Methanosarcina surface and membrane proteins are richly modified.

Screening for protein phosphorylation using nanoscale reactions on microdroplet arrays

We present a novel and straightforward screening method to detect protein phosphorylations in complex protein mixtures. A proteolytic digest is separated by a conventional nanoscale liquid chromatography (nano-LC) separation and the eluate is immediately compartmentalized into microdroplets, which are spotted on a microarray MALDI plate. Subsequently, the enzyme alkaline phosphatase is applied to every second microarray spot to remove the phosphate groups from phosphorylated peptides, which results in a mass shift of n×-80 Da. The MALDI-MS scan of the microarray is then evaluated by a software algorithm to automatically identify the phosphorylated peptides by exploiting the characteristic chromatographic peak profile induced by the phosphatase treatment. This screening method does not require extensive MS/MS experiments or peak list evaluation and can be easily extended to other enzymatic or chemical reactions.

N-terminal sequence tagging using reliably determined b2 ions: a useful approach to deconvolute tandem mass spectra of co-fragmented peptides in proteomics

With the recent introduction of higher-energy collisional dissociation (HCD) in Orbitrap mass spectrometry, the popularity of that technique has grown tremendously in the proteomics society. HCD spectra, however, are characterized by a limited distribution of bn-type ions, which permit the generation of reliable sequence tags based on complementary b,y pairs both for de novo sequencing and sequence tagging strategies. Instead, most peptide HCD spectra (~95%) are dominated with b2 ions. In this work, we analyzed positive predictive values of b2 ions in HCD, and found that b2 ions can be determined with >97% certainty in the presence of a2 and its complementary yn-2 ions. Analytically, b2 ions provide information on the composition of the first two N-terminal amino acids in peptides. Their utilization in N-terminal sequence tagging leads to a significant decrease in false discovery rate by filtering out false positives while retaining true positive identifications. As a consequence, the number of peptide spectrum matches (PSMs) increased by 4.8% at fixed FDR (1%). This approach allows for deconvolution of mixture spectra and increased the number of PSM to 9.2% in a complex human sample and to 24% in a complex sample of synthetic peptides at 1% FDR.

Condenser: a statistical aggregation tool for multi-sample quantitative proteomic data from Matrix Science Mascot Distiller™

We describe Condenser, a freely available, comprehensive open-source tool for merging multidimensional quantitative proteomics data from the Matrix Science Mascot Distiller Quantitation Toolbox into a common format ready for subsequent bioinformatic analysis. A number of different relative quantitation technologies, such as metabolic (15)N and amino acid stable isotope incorporation, label-free and chemical-label quantitation are supported. The program features multiple options for curative filtering of the quantified peptides, allowing the user to choose data quality thresholds appropriate for the current dataset, and ensure the quality of the calculated relative protein abundances. Condenser also features optional global normalization, peptide outlier removal, multiple testing and calculation of t-test statistics for highlighting and evaluating proteins with significantly altered relative protein abundances. Condenser provides an attractive addition to the gold-standard quantitative workflow of Mascot Distiller, allowing easy handling of larger multi-dimensional experiments. Source code, binaries, test data set and documentation are available at http://condenser.googlecode.com/.

Protein primary structure using orthogonal fragmentation techniques in Fourier transform mass spectrometry

Proteomics analysis using tandem mass spectrometry requires informative backbone fragmentation of peptide ions. Collision-activated dissociation (CAD) of cations alone is not sufficiently informative to satisfy all requirements. Thus, there is a need to supplement CAD with a complementary fragmentation technique. Electron capture dissociation (ECD) is complementary to collisional excitation in terms of the cleavage of a different bond (N-Calpha versus C-N bond) and other properties. CAD-ECD combination improves protein identification and enables high-throughput de novo sequencing of peptides. ECD and its variants are also useful in mapping labile post-translational modifications in proteins and isomer differentiation; for example, distinguishing Ile from Leu, iso-Asp from Asp and even D- from L-amino acid residues.

Understanding protein trafficking in plant cells through proteomics

The functions of approximately one-third of the proteins encoded by the Arabidopsis thaliana genome are completely unknown. Moreover, many annotations of the remainder of the genome supply tentative functions, at best. Knowing the ultimate localization of these proteins, as well as the pathways used for getting there, may provide clues as to their functions. The putative localization of most proteins currently relies on in silico-based bioinformatics approaches, which, unfortunately, often result in erroneous predictions. Emerging proteomics techniques coupled with other systems biology approaches now provide researchers with a plethora of methods for elucidating the final location of these proteins on a large scale, as well as the ability to dissect protein-sorting pathways in plants.

Electron capture dissociation in the analysis of protein phosphorylation

Protein phosphorylation is a widespread and important post-translational modification. Despite recent advances in phosphoproteomic methods, phosphopeptide identification and site localization remain challenging. Electron capture dissociation has inherent advantages for phosphorylation analysis. The use of electron capture dissociation in this area to date is reviewed and future prospects are outlined.

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.

Proteomics of nonmodel plant species

Until recently, large scale proteomic investigations in the plant field have only been possible for a few model species for which the whole genome sequence had been fully determined. In contrast, for many other species with a strong economic interest as sources of human food and animal feed, as well as industrial and pharmacological molecules, little was known about their genome sequence and identifying the proteome in these species was still considered challenging. However, progress has been made as a result of several recent advances in proteomics tools, e.g. in MS technology and data search programs, and the increasing availability of genomic and cDNA sequences from various species. Moreover, next-generation sequencing technologies now make it possible to rapidly determine, at a reasonable cost, the genome or RNA sequence of species not currently considered as models, thus considerably expanding the plant sequence databases. This review will show how these advances make it possible to identify a large set of proteins, even for species for which few sequences are currently available.

Nitrogen under- and over-supply induces distinct protein responses in maize xylem sap

Xylem sap primarily transports water and mineral nutrients such as nitrogen (N) from roots to shoots in vascular plants. However, it remains largely unknown how nitrogenous compounds, especially proteins in xylem sap, respond to N under- or over-supply. We found that reducing N supply increased amino-N percentage of total N in maize (Zea mays L.) xylem sap. Proteomic analysis showed that 23 proteins in the xylem sap of maize plants, including 12 newly identified ones, differentially accumulated in response to various N supplies. Fifteen of these 23 proteins were primarily involved in general abiotic or biotic stress responses, whereas the other five proteins appeared to respond largely to N under- or over-supply, suggesting distinct protein responses in maize xylem upon N under- and over-supply. Furthermore, one putative xylanase inhibitor and two putative O-glycosyl hydrolases had preferential gene expression in shoots.

Protein identification by spectral networks analysis

While advances in tandem mass spectrometry (MS/MS) steadily increase the rate of generation of MS/MS spectra, standard algorithmic approaches for peptide identification recently seemed to be reaching the limit on the amount of information that could be extracted from MS/MS spectra. However, a closer look reveals that a common limiting procedure is to analyze each spectrum in isolation, even though high throughput mass spectrometry regularly generates many spectra from related peptides. By capitalizing on this redundancy we show that, similarly to the alignment of protein sequences, unidentified MS/MS spectra can also be aligned for the identification of modified and unmodified variants of the same peptide. Moreover, this alignment procedure can be iterated for the accurate grouping of multiple modification variants of the same peptides. Furthermore, the combination of shotgun proteomics with the alignment of spectra from overlapping peptides led to the development of Shotgun Protein Sequencing - similarly to the assembly of DNA reads into whole genomic sequences, we show that assembly of MS/MS spectra enables the highest ever de novo sequencing accuracy, while recovering nearly complete protein sequences. We further show that shotgun protein sequencing has the potential to overcome the limitations of -current protein sequencing approaches and thus catalyze the otherwise impractical applications of proteomics methodologies in studies of unknown proteins.

ATP enhances neuronal differentiation of PC12 cells by activating PKCα interactions with cytoskeletal proteins

PKCα is a key mediator of the neuronal differentiation controlled by NGF and ATP. However, its downstream signaling pathways remain to be elucidated. To identify the signaling partners of PKCα, we analyzed proteins coimmunoprecipitated with this enzyme in PC12 cells differentiated with NGF and ATP and compared them with those obtained with NGF alone or growing media. Mass spectrometry analysis (LC-MS/MS) identified plectin, peripherin, filamin A, fascin, and β-actin as potential interacting proteins. The colocalization of PKCα and its interacting proteins increased when PC12 cells were differentiated with NGF and ATP. Peripherin and plectin organization and the cortical remodeling of β-actin were dramatically affected when PKCα was down-regulated, suggesting that all three proteins might be functional targets of ATP-dependent PKCα signaling. Taken together, these data demonstrate that PKCα is essential for controlling the neuronal development induced by NGF and ATP and interacts with the cytoskeletal components at two levels: assembly of the intermediate filament peripherin and organization of cortical actin.

H-score, a mass accuracy driven rescoring approach for improved peptide identification in modification rich samples

Currently, scoring algorithms of many popular search engines for tandem mass spectrometry (MS/MS) data only partially utilize the information content of high mass accuracy MS/MS data. We have developed a new rescoring scheme, H-score, that employs high mass accuracy matching of all detected fragment ions to candidate peptide sequences in an abundance independent fashion. Peptides for which b or y ions are found for all or almost all backbone fragmentation sites are rewarded. For peptide hits generated by Mascot, rescoring proved to be particularly beneficial when applied on samples containing many different potential modifications. For a histone sample acquired on an Orbitrap Velos using HCD for peptide fragmentation, the H-score identified 24% more spectra at 0.01 false positive rate than Mascot scoring of spectra processed according to state-of-the-art methods and 61% better than Mascot scoring of unprocessed MS/MS spectra. For a low-abundance sample, where many weak spectra were detected, these numbers went up to 53 and 190%, respectively. When applied on a kinase-enriched sample containing only a few modifications, a smaller but still significant gain of 5% was observed.

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.

Unrestricted identification of modified proteins using MS/MS

Proteins undergo PTM, which modulates their structure and regulates their function. Estimates of the PTM occurrence vary but it is safe to assume that there is an important gap between what is currently known and what remains to be discovered. The highest throughput and most comprehensive efforts to catalogue protein mixtures have so far been using MS-based shotgun proteomics. The standard approach to analyse MS/MS data is to use Peptide Fragment Fingerprinting tools such as Sequest, MASCOT or Phenyx. These tools commonly identify 5-30% of the spectra in an MS/MS data set while only a limited list of predefined protein modifications can be screened. An important part of the unidentified spectra is likely to be spectra of peptides carrying modifications not considered in the search. Bioinformatics for PTM discovery is an active area of research. In this review we focus on software solutions developed for unrestricted identification of modifications in MS/MS data, here referred to as open modification search tools. We give an overview of the conceptually different algorithmic solutions to evaluate the large number of candidate peptides per spectrum when accounting for modifications of unrestricted size and demonstrate the value of results of large-scale open modification search studies. Efficient and easy-to-use tools for protein modification discovery should prove valuable in the quest for mapping the dynamics of proteomes.

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

Background

Tandem mass spectrometry (MS/MS) has become a standard method for identification of proteins extracted from biological samples but the huge number and the noise contamination of MS/MS spectra obstruct swift and reliable computer-aided interpretation. Typically, a minor fraction of the spectra per sample (most often, only a few %) and about 10% of the peaks per spectrum contribute to the final result if protein identification is not prevented by the noise at all.

Results

Two fast preprocessing screens can substantially reduce the haystack of MS/MS data. (1) Simple sequence ladder rules remove spectra non-interpretable in peptide sequences. (2) Modified Fourier-transform-based criteria clear background in the remaining data. In average, only a remainder of 35% of the MS/MS spectra (each reduced in size by about one quarter) has to be handed over to the interpretation software for reliable protein identification essentially without loss of information, with a trend to improved sequence coverage and with proportional decrease of computer resource consumption.

Conclusions

The search for sequence ladders in tandem MS/MS spectra with subsequent noise suppression is a promising strategy to reduce the number of MS/MS spectra from electro-spray instruments and to enhance the reliability of protein matches. Supplementary material and the software are available from an accompanying WWW-site with the URL http://mendel.bii.a-star.edu.sg/mass-spectrometry/MSCleaner-2.0/.

De novo sequencing methods in proteomics

The review describes methods of de novo sequencing of peptides by mass spectrometry. De novo methods utilize computational approaches to deduce the sequence or partial sequence of peptides directly from the experimental MS/MS spectra. The concepts behind a number of de novo sequencing methods are discussed. The other approach to identify peptides by tandem mass spectrometry is to match the fragment ions with virtual peptide ions generated from a genomic or protein database. De novo methods are essential to identify proteins when the genomes are not known but they are also extremely useful even when the genomes are known since they are not affected by errors in a search database. Another advantage of de novo methods is that the partial sequence can be used to search for posttranslation modifications or for the identification of mutations by homology based software.

Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins

The number of protein and peptide structures included in Protein Data Bank (PDB) and Gen Bank without functional annotation has increased. Consequently, there is a high demand for theoretical models to predict these functions. Here, we trained and validated, with an external set, a Markov Chain Model (MCM) that classifies proteins by their possible mechanism of action according to Enzyme Classification (EC) number. The methodology proposed is essentially new, and enables prediction of all EC classes with a single equation without the need for an equation for each class or nonlinear models with multiple outputs. In addition, the model may be used to predict whether one peptide presents a positive or negative contribution of the activity of the same EC class. The model predicts the first EC number for 106 out of 151 (70.2%) oxidoreductases, 178/178 (100%) transferases, 223/223 (100%) hydrolases, 64/85 (75.3%) lyases, 74/74 (100%) isomerases, and 100/100 (100%) ligases, as well as 745/811 (91.9%) nonenzymes. It is important to underline that this method may help us predict new enzyme proteins or select peptide candidates that improve enzyme activity, which may be of interest for the prediction of new drugs or drug targets. To illustrate the model's application, we report the 2D-Electrophoresis (2DE) isolation from Leishmania infantum as well as MADLI TOF Mass Spectra characterization and theoretical study of the Peptide Mass Fingerprints (PMFs) of a new protein sequence. The theoretical study focused on MASCOT, BLAST alignment, and alignment-free QSAR prediction of the contribution of 29 peptides found in the PMF of the new protein to specific enzyme action. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification.

Filtering strategies for improving protein identification in high-throughput MS/MS studies

Despite the recent advances in streamlining high-throughput proteomic pipelines using tandem mass spectrometry (MS/MS), reliable identification of peptides and proteins on a larger scale has remained a challenging task, still involving a considerable degree of user interaction. Recently, a number of papers have proposed computational strategies both for distinguishing poor MS/MS spectra prior to database search (pre-filtering) as well as for verifying the peptide identifications made by the search programs (post-filtering). Both of these filtering approaches can be very beneficial to the overall protein identification pipeline, since they can remove a substantial part of the time consuming manual validation work and convert large sets of MS/MS spectra into more reliable and interpretable proteome information. The choice of the filtering method depends both on the properties of the data and on the goals of the experiment. This review discusses the different pre- and post-filtering strategies available to the researchers, together with their relative merits and potential pitfalls. We also highlight some additional research topics, such as spectral denoising and statistical assessment of the identification results, which aim at further improving the coverage and accuracy of high-throughput protein identification studies.

Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks

Since the advent of Molecular Dynamics (MD) in biopolymers science with the study by Karplus et al. on protein dynamics, MD has become the by foremost well established, computational technique to investigate structure and function of biomolecules and their respective complexes and interactions. The analysis of the MD trajectories (MDTs) remains, however, the greatest challenge and requires a great deal of insight, experience, and effort. Here, we introduce a new class of invariants for MDTs based on the spatial distribution of Mean-Energy values ξ_k (L) on a 2D Euclidean space representation of the MDTs. The procedure forces one MD trajectory to fold into a 2D Cartesian coordinates system using a step-by-step procedure driven by simple rules. The ξ_k (L) values are invariants of a Markov matrix (¹ Π), which describes the probabilities of transition between two states in the new 2D space; which is associated to a graph representation of MDTs similar to the lattice networks (LNs) of DNA and protein sequences. We also introduce a new algorithm to perform phylogenetic analysis of peptides based on MDTs instead of the sequence of the polypeptide. In a first experiment, we illustrate this algorithm for 35 peptides present on the Peptide Mass Fingerprint (PMF) of a new protein of Leishmania infantum studied in this work. We report, by the first time, 2D Electrophoresis isolation, MALDI TOF Mass Spectroscopy characterization, and MASCOT search results for this PMF. In a second experiment, we construct the LNs for 422 MDTs obtained in DNA-Drug Docking simulations of the interaction of 57 anticancer furocoumarins with a DNA oligonucleotide. We calculated the respective ξ_k (L) values for all these LNs and used them as inputs to train a new classifier with Accuracy = 85.44% and 84.91% in training and validation respectively. The new model can be used as scoring function to guide DNA-Drug Docking studies in drug design of new coumarins for PUVA therapy. The new phylogenetics analysis algorithms encode information different from sequence similarity and may be used to analyze MDTs obtained in Docking or modeling experiments for any classes of biopolymers. The work opens new perspective on the analysis and applications of MD in polymer sciences.

QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein

The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

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.

Tools for exploring the proteomosphere

Homology-driven proteomics aims at exploring the proteomes of organisms with unsequenced genomes that, despite rapid genomic sequencing progress, still represent the overwhelming majority of species in the biosphere. Methodologies have been developed to enable automated LC-MS/MS identifications of unknown proteins, which rely on the sequence similarity between the fragmented peptides and reference database sequences from phylogenetically related species. However, because full sequences of matched proteins are not available and matching specificity is reduced, estimating protein abundances should become the obligatory element of homology-driven proteomics pipelines to circumvent the interpretation bias towards proteins from evolutionary conserved families.

Bifurcating fragmentation behavior of gas-phase tryptic peptide dications in collisional activation

Collision-activated dissociation (CAD) of tryptic peptides is a cornerstone of mass spectrometry-based proteomics research. Principal component analysis of a database containing 15,000 high-resolution CAD mass spectra of gas-phase tryptic peptide dications revealed that they fall into two classes with a good separation between the classes. The main factor determining the class identity is the relative abundance of the peptide bond cleavage after the first two N-terminal residues. A possible scenario explaining this bifurcation involves trans- to cis-isomerization of the N-terminal peptide bond, which facilitates solvation of the N-terminal charge on the second backbone amide and formation of stable b(2) ions in the form of protonated diketopiperazines. Evidence supporting this scenario is derived from statistical analysis of the high-resolution CAD MS/MS database. It includes the observation of the strong deficit of a(3) ions and anomalous amino acid preferences for b(2) ion formation.

Analytical utility of small neutral losses from reduced species in electron capture dissociation studied using SwedECD database

Small neutral losses from charge-reduced species [M + nH] (( n-1)+* ) is one of the most abundant fragmentation channels in both electron capture dissociation, ECD, and electron transfer dissociation, ETD. Several groups have previously studied these losses on particular examples. Now, the availability of a large (11 491 entries) SwedECD database ( http://www.bmms.uu.se/CAD/indexECD.html) of high-resolution ECD data sets on doubly charged tryptic peptides has made possible a systematic study involving statistical evaluation of neutral losses from [M + 2H] (+ * ) ions. Several new types of losses are discovered, and 16 specific (>94%) losses are characterized according to their specificity and sensitivity, as well as occurrence for peptides of different lengths. On average, there is more than one specific loss per ECD mass spectrum, and two-thirds of all MS/MS data sets in SwedECD contain at least one specific loss. Therefore, specific neutral losses are analytically useful for improved database searching and de novo sequencing. In particular, N and GG isomeric sequences can be distinguished. The pattern of neutral losses was found to be remarkably dissimilar with the losses from radical z* fragment ions: e.g., there is no direct formation of w ions from the reduced species. This finding emphasizes the difference in fragmentation behaviors of hydrogen-abundant and hydrogen-deficient species.

Improved sequence tag generation method for peptide identification in tandem mass spectrometry

The sequence tag-based peptide identification methods are a promising alternative to the traditional database search approach. However, a more comprehensive analysis, optimization, and comparison with established methods are necessary before these methods can gain widespread use in the proteomics community. Using the InsPecT open source code base ( Tanner et al., Anal. Chem. 2005, 77, 4626- 39 ), we present an improved sequence tag generation method that directly incorporates multicharged fragment ion peaks present in many tandem mass spectra of higher charge states. We also investigate the performance of sequence tagging under different settings using control data sets generated on five different types of mass spectrometers, as well as using a complex phosphopeptide-enriched sample. We also demonstrate that additional modeling of InsPecT search scores using a semiparametric approach incorporating the accuracy of the precursor ion mass measurement provides additional improvement in the ability to discriminate between correct and incorrect peptide identifications. The overall superior performance of the sequence tag-based peptide identification method is demonstrated by comparison with a commonly used SEQUEST/PeptideProphet approach.

The effect of phosphorylation on the electron capture dissociation of peptide ions

The effect of site and frequency of phosphorylation on the electron capture dissociation of peptide ions has been investigated. The ECD of a suite of synthetic peptides (APLSFRGSLPKSYVK; one unmodified, three singly-phosphorylated, three-doubly phosphorylated, and one triply-phosphorylated); two tryptic phosphopeptides (YKVPQLEIVPN(p)SAEER, alpha-casein and FQ(p)SEEQQQTEDELQDK, beta-casein) and their unmodified counterparts, were determined over a range of ECD cathode potentials. The results show that, for doubly-charged precursor ions, the presence of phosphorylation has a deleterious effect on ECD sequence coverage. The fragmentation patterns observed suggest that for peptides with multiple basic residues, the phospho-groups exist in their deprotonated form and form salt-bridges with protonated amino acid side chains. The fragmentation observed for the acidic tryptic peptides suggested the presence of noncovalent interactions, which were perturbed on phosphorylation. Increasing the ECD electron energy significantly improves sequence coverage. Alternatively, improved sequence coverage can be achieved by performing ECD on triply-charged precursor ions. The findings are important for the understanding of gas-phase fragmentation of phosphopeptides.

Peptizer, a tool for assessing false positive peptide identifications and manually validating selected results

False positive peptide identifications are a major concern in the field of peptidecentric, mass spectrometry-driven gel-free proteomics. They occur in regions where the score distributions of true positives and true negatives overlap. Removal of these false positive identifications necessarily involves a trade-off between sensitivity and specificity. Existing postprocessing tools typically rely on a fixed or semifixed set of assumptions in their attempts to optimize both the sensitivity and the specificity of peptide and protein identification using MS/MS spectra. Because of the expanding diversity in available proteomics technologies, however, these postprocessing tools often struggle to adapt to emerging technology-specific peculiarity. Here we present a novel tool named Peptizer that solves this adaptability issue by making use of pluggable assumptions. This research-oriented postprocessing tool also includes a graphical user interface to perform efficient manual validation of suspect identifications for optimal sensitivity recovery. Peptizer is open source software under the Apache2 license and is written in Java.

Separating the wheat from the chaff: unbiased filtering of background tandem mass spectra improves protein identification

Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.

Immunoaffinity enrichments followed by mass spectrometric detection for studying global protein tyrosine phosphorylation

Phosphorylation of protein tyrosine residues regulates important cell functions and is, when dysregulated, often crucially involved in oncogenesis. It is therefore important to develop and evaluate methods for identifying and studying tyrosine phosphorylated (P-Tyr) proteins. P-Tyr proteins are present at very low concentrations within cells, requiring highly selective enrichment methods to be detected. In this study, we applied immunoaffinity as enrichment step for P-Tyr proteins. Five selected anti-phosphotyrosine antibodies (monoclonal antibodies 4G10, PY100, PYKD1, 13F9 and one polyclonal antiserum) were evaluated with respect to their capability to enrich P-Tyr proteins from cell extracts of the K562 leukemia cell line. The enrichment resulted in the detection of a group of proteins that potentially were tyrosine-phosphorylated (putative P-Tyr proteins). High accuracy identification of actual P-Tyr sites were performed using a highly selective and sensitive liquid chromatography Fourier transform mass spectrometer (LC-FTMS) setup with complementary collision activated dissociation (CAD) and electron capture dissociation (ECD) fragmentations. 4G10 and PY100 antibodies recognized the greatest number of putative P-Tyr proteins in initial screening experiments and were therefore further evaluated and compared in immunoaffinity enrichment of both P-Tyr proteins and peptides. Using the 4G10 antibody for enrichment of proteins, we identified 459 putative P-Tyr proteins by MS. Out of these proteins, 12 were directly verified as P-Tyr proteins by MS analysis of the actual site. Using the PY100 antibody for enrichment of peptides, we detected 67 P-Tyr peptides (sites) and 89 putative P-Tyr proteins. Generally, enrichment at the peptide level made it difficult to reliably determine the identity of the proteins. In contrast, protein identification following immunoaffinity enrichment at the protein level gave greater sequence coverage and thus a higher confidence in the protein identification. By combining all available information, 40 proteins were identified as true P-Tyr proteins from the K562 cell line. In conclusion, this study showed that a combination of immunoaffinity enrichment using multiple antibodies of both intact and digested proteins in parallel experiments is required for best possible coverage of all possible P-Tyr proteins in a sample.

Electron capture/transfer versus collisionally activated/induced dissociations: solo or duet?

New ion fragmentation technologies--electron capture dissociation (ECD) and electron-transfer dissociation (ETD)--are based on interaction of multiply charged polypeptides with either free electrons (ECD) or anionic species (ETD). After initial difficulties, these ECD/ETD (ExD) technologies are now being increasingly implemented in high-throughput proteomics work. This critical analysis presents arguments for the combined use of ExD with the conventional low-energy collisional excitation CID/CAD (CxD). It is argued that the database search, a key technology in MS/MS-based proteomics, is vulnerable with respect to the incomplete sequence information obtainable with either of the techniques, peptide MS/MS homology being a major complicating factor. De novo sequencing is viewed as the only adequate answer to this challenge and it can be achieved only with combined use of ExD and CxD. The payoff in the form of additional sequence information is projected to exceed the costs of such implementation. The greatest impact of combining ExD and CxD is expected in high-resolution instruments.

Comparison of Mascot and X!Tandem performance for low and high accuracy mass spectrometry and the development of an adjusted Mascot threshold

It is a major challenge to develop effective sequence database search algorithms to translate molecular weight and fragment mass information obtained from tandem mass spectrometry into high quality peptide and protein assignments. We investigated the peptide identification performance of Mascot and X!Tandem for mass tolerance settings common for low and high accuracy mass spectrometry. We demonstrated that sensitivity and specificity of peptide identification can vary substantially for different mass tolerance settings, but this effect was more significant for Mascot. We present an adjusted Mascot threshold, which allows the user to freely select the best trade-off between sensitivity and specificity. The adjusted Mascot threshold was compared with the default Mascot and X!Tandem scoring thresholds and shown to be more sensitive at the same false discovery rates for both low and high accuracy mass spectrometry data.