A systematic investigation into the nature of tryptic HCD spectra

Experimentally Determined Diagnostic Ions for Identification of Peptide Glycotopes

The analysis of the structures of glycans present on glycoproteins is an essential component for determining glycoprotein function; however, detailed glycan structural assignment on glycopeptides from proteomics mass spectrometric data remains challenging. Glycoproteomic analysis by mass spectrometry currently can provide significant, yet incomplete, information about the glycans present, including the glycan monosaccharide composition and in some circumstances the site(s) of glycosylation. Advancements in mass spectrometric resolution, using high-mass accuracy instrumentation and tailored MS/MS fragmentation parameters, coupled with a dedicated definition of diagnostic fragmentation ions have enabled the determination of some glycan structural features, or glycotopes, expressed on glycopeptides. Here we present a collation of diagnostic glycan fragments produced by traditional positive-ion-mode reversed-phase LC-ESI MS/MS proteomic workflows and describe the specific fragmentation energy settings required to identify specific glycotopes presented on N- or O-linked glycopeptides in a typical proteomics MS/MS experiment.

Internal Fragments Enhance Middle-Down Mass Spectrometry Structural Characterization of Monoclonal Antibodies and Antibody-Drug Conjugates

Monoclonal antibodies (mAbs) and antibody-drug conjugates (ADCs) are important large biotherapeutics (∼150 kDa) and high structural complexity that require extensive sequence and structure characterization. Middle-down mass spectrometry (MD-MS) is an emerging technique that sequences and maps subunits larger than those released by trypsinolysis. It avoids potentially introducing artifactual modifications that may occur in bottom-up MS while achieving higher sequence coverage compared to top-down MS. However, returning complete sequence information by MD-MS is still challenging. Here, we show that assigning internal fragments in direct infusion MD-MS of a mAb and an ADC substantially improves their structural characterization. For MD-MS of the reduced NIST mAb, including internal fragments recovers nearly 100% of the sequence by accessing the middle sequence region that is inaccessible by terminal fragments. The identification of important glycosylations can also be improved after the inclusion of internal fragments. For the reduced lysine-linked IgG1-DM1 ADC, we show that considering internal fragments increases the DM1 conjugation sites coverage to 80%, comparable to the reported 83% coverage achieved by peptide mapping on the same ADC (Luo et al. Anal. Chem. 2016, 88, 695-702). This study expands our work on the application of internal fragment assignments in top-down MS of mAbs and ADCs and can be extended to other heterogeneous therapeutic molecules such as multispecifics and fusion proteins for more widespread applications.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Diagnostic utility of N-terminal TMPP labels for unambiguous identification of clipped sites in therapeutic proteins

Protein therapeutics are susceptible to clipping via enzymatic and nonenzymatic mechanisms that create neo-N-termini. Typically, neo-N-termini are identified by chemical derivatization of the N-terminal amine with (N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) followed by proteolysis and mass spectrometric analysis. Detection of the TMPP-labeled peptide is achieved by mapping the peptide sequence to the product ion spectrum derived from collisional activation. The site-specific localization of the TMPP tag enables unambiguous determination of the true N-terminus or neo-N-termini. In addition to backbone product ions, TMPP reporter ions at m/z 573, formed via collision-induced dissociation, can be diagnostic for the presence of a processed N-termini. However, reporter ions generated by collision-induced dissociation may be uninformative because of their low abundance. We demonstrate a novel high-throughput LC-MS method for the facile generation of the TMPP reporter ion at m/z 533 and, in some instances m/z 590, upon electron transfer dissociation. We further demonstrate the diagnostic utility of TMPP labeled peptides derived from a total cell lysate shows high degree of specificity towards selective N-terminal labeling over labeling of lysine and tyrosine and highly-diagnostic Receiver Operating Characteristic's (ROC) of TMPP reporter ions of m/z 533 and m/z 590. The abundant generation of these reporters enables subsequent MS/MS by intensity and m/z-dependent triggering of complementary ion activation modes such as collision-induced dissociation, high-energy collision dissociation, or ultraviolet photo dissociation for subsequent peptide sequencing.

AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence

The unbounded permutations of biological molecules, including proteins and their constituent peptides, present a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides.

Collision energies: Optimization strategies for bottom-up proteomics

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

A Highly Efficient Workflow for Detecting and Identifying Sequence Variants in Therapeutic Proteins with a High Resolution LC-MS/MS Method

Large molecule protein therapeutics have steadily grown and now represent a significant portion of the overall pharmaceutical market. These complex therapies are commonly manufactured using cell culture technology. Sequence variants (SVs) are undesired minor variants that may arise from the cell culture biomanufacturing process that can potentially affect the safety and efficacy of a protein therapeutic. SVs have unintended amino acid substitutions and can come from genetic mutations or translation errors. These SVs can either be detected using genetic screening methods or by mass spectrometry (MS). Recent advances in Next-generation Sequencing (NGS) technology have made genetic testing cheaper, faster, and more convenient compared to time-consuming low-resolution tandem MS and Mascot Error Tolerant Search (ETS)-based workflows which often require ~6 to 8 weeks data turnaround time. However, NGS still cannot detect non-genetic derived SVs while MS analysis can do both. Here, we report a highly efficient Sequence Variant Analysis (SVA) workflow using high-resolution MS and tandem mass spectrometry combined with improved software to greatly reduce the time and resource cost associated with MS SVA workflows. Method development was performed to optimize the high-resolution tandem MS and software score cutoff for both SV identification and quantitation. We discovered that a feature of the Fusion Lumos caused significant relative under-quantitation of low-level peptides and turned it off. A comparison of common Orbitrap platforms showed that similar quantitation values were obtained on a spiked-in sample. With this new workflow, the amount of false positive SVs was decreased by up to 93%, and SVA turnaround time by LC-MS/MS was shortened to 2 weeks, comparable to NGS analysis speed and making LC-MS/MS the top choice for SVA workflow.

RDD-HCD Provides Variable Fragmentation Routes Dictated by Radical Stability

Radical-directed dissociation (RDD) is a fragmentation technique in which a radical created by selective 213/266 nm photodissociation of a carbon-iodine bond is reisolated and collisionally activated. In previous RDD experiments, collisional activation was effected by ion-trap collision-induced dissociation (CID). Higher-energy collisional dissociation (HCD) differs from CID both in terms of how ions are excited and in the number, type, or abundance of fragments that are observed. In this paper, we explore the use of HCD for activation in RDD experiments. While RDD-CID favors fragments produced from radical-directed pathways such as a/z-ions and side chain losses regardless of the activation energy employed, RDD-HCD spectra vary considerably as a function of activation energy, with lower energies favoring RDD while higher energies favor products resulting from cleavage directed by mobile protons (b/y-ions). RDD-HCD therefore affords more tunable fragmentation based on the HCD energy provided. Importantly, the abundance of radical products decreases as a function of increasing HCD energy, confirming that RDD generally proceeds via lower-energy barriers relative to mobile-proton-driven dissociation. The dominance of b/y-ions at higher energies for RDD-HCD can therefore be explained by the higher survivability of fragments not containing the radical after the initial or subsequent dissociation events. Furthermore, these results confirm previous suspicions that HCD spectra differ from CID spectra due to multiple dissociation events.

MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search

Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.

Critical evaluation of the use of artificial data for machine learning based de novo peptide identification

Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art de novo peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of de novo peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of de novo algorithms as well as alleviating the reliance on database searches for training data.

Top-down mass spectrometry and assigning internal fragments for determining disulfide bond positions in proteins

Disulfide bonds in proteins have a substantial impact on protein structure, stability, and biological activity. Localizing disulfide bonds is critical for understanding protein folding and higher-order structure. Conventional top-down mass spectrometry (TD-MS), where only terminal fragments are assigned for disulfide-intact proteins, can access disulfide information, but suffers from low fragmentation efficiency, thereby limiting sequence coverage. Here, we show that assigning internal fragments generated from TD-MS enhances the sequence coverage of disulfide-intact proteins by 20-60% by returning information from the interior of the protein sequence, which cannot be obtained by terminal fragments alone. The inclusion of internal fragments can extend the sequence information of disulfide-intact proteins to near complete sequence coverage. Importantly, the enhanced sequence information that arise from the assignment of internal fragments can be used to determine the relative position of disulfide bonds and the exact disulfide connectivity between cysteines. The data presented here demonstrates the benefits of incorporating internal fragment analysis into the TD-MS workflow for analyzing disulfide-intact proteins, which would be valuable for characterizing biotherapeutic proteins such as monoclonal antibodies and antibody-drug conjugates.

Reversed-Phase Liquid Chromatography of Peptides for Bottom-Up Proteomics: A Tutorial

The performance of the current bottom-up liquid chromatography hyphenated with mass spectrometry (LC-MS) analyses has undoubtedly been fueled by spectacular progress in mass spectrometry. It is thus not surprising that the MS instrument attracts the most attention during LC-MS method development, whereas optimizing conditions for peptide separation using reversed-phase liquid chromatography (RPLC) remains somewhat in its shadow. Consequently, the wisdom of the fundaments of chromatography is slowly vanishing from some laboratories. However, the full potential of advanced MS instruments cannot be achieved without highly efficient RPLC. This is impossible to attain without understanding fundamental processes in the chromatographic system and the properties of peptides important for their chromatographic behavior. We wrote this tutorial intending to give practitioners an overview of critical aspects of peptide separation using RPLC to facilitate setting the LC parameters so that they can leverage the full capabilities of their MS instruments. After briefly introducing the gradient separation of peptides, we discuss their properties that affect the quality of LC-MS chromatograms the most. Next, we address the in-column and extra-column broadening. The last section is devoted to key parameters of LC-MS methods. We also extracted trends in practice from recent bottom-up proteomics studies and correlated them with the current knowledge on peptide RPLC separation.

Experimental validation that human microbiome phages use alternative genetic coding

Previous bioinformatic analyses of metagenomic data have indicated that bacteriophages can use genetic codes different from those of their host bacteria. In particular, reassignment of stop codon TAG to glutamine (a variation known as 'genetic code 15') has been predicted. Here, we use LC-MS/MS-based metaproteomics of human fecal samples to provide experimental evidence of the use of genetic code 15 in two crAss-like phages. Furthermore, the proteomic data from several phage structural proteins supports the reassignment of the TAG stop codon to glutamine late in the phage infection cycle. Thus, our work experimentally validates the expression of genetic code 15 in human microbiome phages.

Discovery of marker peptides of spirulina microalga proteins for allergen detection in processed foodstuffs

Spirulina (Arthrospira platensis) proteins were extracted, digested, and analyzed by LC-ESI-FTMS/MS to find highly conserved peptides as markers of the microalga occurrence in foodstuffs. Putative markers were firstly chosen after in silico digestion of allergenic proteins, according to the FAO and WHO criteria, after assuring their presence in food supplements and in (un)processed foodsuffs. Parameters such as sensitivity, sequence size, and uniqueness for spirulina proteins were also evaluated. Three peptides belonging to C-phycocyanin beta subunit (P72508) were designated as qualifiers (ETYLALGTPGSSVAVGVGK and YVTYAVFAGDASVLEDR) and quantifier (ITSNASTIVSNAAR) marker peptides and used to validate the method for linearity, recovery, reproducibility, matrix effects, processing effects, LOD, and LOQ. The main aim was to determine spirulina in commercial foodstuffs like pasta, crackers, and homemade bread incurred with the microalga. The possible inclusion of the designated peptides in a standardized method, based on multiple reaction monitoring using a linear ion trap MS, was also demonstrated.

A new paradigm to search for allergenic proteins in novel foods by integrating proteomics analysis and in silico sequence homology prediction: Focus on spirulina and chlorella microalgae

Since novel nutrient sources with high protein content, such as yeast, fungi, bacteria, algae, and insects, are increasingly introduced in the consumer market, safety evaluation studies on their potentially allergenic proteins are required. A pipeline for in silico establishing the sequence-based homology between proteins of spirulina (Arthrospira platensis) and chlorella (Chlorella vulgaris) micro-algae and those included in the AllergenOnline (AO) database (AllergenOnline.org) is described. The extracted proteins were first identified through tryptic peptides analysis by reversed-phase liquid chromatography and high resolution/accuracy Fourier-transform tandem mass spectrometry (RPLC-ESI-FTMS/MS), followed by a quest on the UniProt database. The AO database was subsequently interrogated to assess sequence similarity between identified microalgal proteins and known allergens, based on criteria established by the World Health Organization (WHO) and Food and Agriculture Organization (FAO). A direct search for microalgal proteins already included in allergen databases was also performed using the Allergome database. Six proteins exhibiting a significant homology with food allergens were identified in spirulina extracts. Five of them, i.e., two thioredoxins (D4ZSU6, K1VP15), a superoxide dismutase (C3V3P3), a glyceraldehyde-3-phosphate dehydrogenase (K1W168), and a triosephosphate isomerase (D5A635), resulted from the search on AO. The sixth protein, C-phycocyanin beta subunit (P72508), was directly obtained after examining the Allergome database. Two proteins exhibiting significant sequence homology with food allergens were retrieved in chlorella extracts, viz. calmodulin (A0A2P6TFR8), which is related to troponin c (D7F1Q2), and fructose-bisphosphate aldolase (A0A2P6TDD0). Specific serum screenings based on immunochemical tests should be undertaken to confirm or rule out the allergenicity of the identified proteins.

Towards understanding the formation of internal fragments generated by collisionally activated dissociation for top-down mass spectrometry

Top-down mass spectrometry (TD-MS) generates fragment ions that returns information on the polypeptide amino acid sequence. In addition to terminal fragments, internal fragments that result from multiple cleavage events can also be formed. Traditionally, internal fragments are largely ignored due to a lack of available software to reliably assign them, mainly caused by a poor understanding of their formation mechanism. To accurately assign internal fragments, their formation process needs to be better understood. Here, we applied a statistical method to compare fragmentation patterns of internal and terminal fragments of peptides and proteins generated by collisionally activated dissociation (CAD). Internal fragments share similar fragmentation propensities with terminal fragments (e.g., enhanced cleavages N-terminal to proline and C-terminal to acidic residues), suggesting that their formation follows conventional CAD pathways. Internal fragments should be generated by subsequent cleavages of terminal fragments and their formation can be explained by the well-known mobile proton model. In addition, internal fragments can be coupled with terminal fragments to form complementary product ions that span the entire protein sequence. These enhance our understanding of internal fragment formation and can help improve sequencing algorithms to accurately assign internal fragments, which will ultimately lead to more efficient and comprehensive TD-MS analysis of proteins and proteoforms.

Internal Fragment Ions Disambiguate and Increase Identifications in Top-Down Proteomics

A large fraction of observed fragment ion intensity remains unidentified in top-down proteomics. The elucidation of these unknown fragment ions could enable researchers to identify additional proteoforms and reduce proteoform ambiguity in their analyses. Internal fragment ions have received considerable attention as a major source of these unidentified fragment ions. Internal fragments are product ions that contain neither protein terminus, in contrast with terminal ions that contain a single terminus. There are many more possible internal fragments than terminal fragments, and the resulting computational complexity has historically limited the application of internal fragment ions to low-complexity samples containing only one or a few proteins of interest. We implemented internal fragment ion functionality in MetaMorpheus to allow the proteome-wide annotation of internal fragment ions. MetaMorpheus first uses terminal fragment ions to identify putative proteoforms and then employs internal fragment ions to disambiguate similar proteoforms. In the analysis of mammalian cell lysates, we found that MetaMorpheus could disambiguate over half of its previously ambiguous proteoforms while also providing up to a 7% increase in proteoform-spectrum matches identified at a 1% false discovery rate.

Improved Sequence Analysis of Intact Proteins by Parallel Ion Parking during Electron Transfer Dissociation

Electron transfer dissociation (ETD) is an analytically useful tool for primary structure interrogation of intact proteins, but its utility is limited by higher-order reactions with the products. To inhibit these higher-order reactions, first-generation fragment ions are kinetically excited by applying an experimentally tailored parallel ion parking waveform during ETD (ETD-PIP). In combination with subsequent ion/ion proton transfer reactions, precursor-to-product conversion was maximized as evidenced by the consumption of more than 90% of the 21 kDa Protein G precursor to form ETD product ions. The employment of ETD-PIP increased sequence coverage to 90% from 80% with standard ETD. Additionally, the inhibition of sequential electron transfers was reflected in the high number of complementary ion pairs from ETD-PIP (90%) compared to standard ETD (39%).

Site-Specific N- and O-Glycosylation Analysis of Human Plasma Fibronectin

Human plasma fibronectin is an adhesive protein that plays a crucial role in wound healing. Many studies had indicated that glycans might mediate the expression and functions of fibronectin, yet a comprehensive understanding of its glycosylation is still missing. Here, we performed a comprehensive N- and O-glycosylation mapping of human plasma fibronectin and quantified the occurrence of each glycoform in a site-specific manner. Intact N-glycopeptides were enriched by zwitterionic hydrophilic interaction chromatography, and N-glycosite sites were localized by the ¹⁸O-labeling method. O-glycopeptide enrichment and O-glycosite identification were achieved by an enzyme-assisted site-specific extraction method. An RP–LC–MS/MS system functionalized with collision-induced dissociation and stepped normalized collision energy (sNCE)-HCD tandem mass was applied to analyze the glycoforms of fibronectin. A total of 6 N-glycosites and 53 O-glycosites were identified, which were occupied by 38 N-glycoforms and 16 O-glycoforms, respectively. Furthermore, 77.31% of N-glycans were sialylated, and O-glycosylation was dominated by the sialyl-T antigen. These site-specific glycosylation patterns on human fibronectin can facilitate functional analyses of fibronectin and therapeutics development.

ClipsMS: An Algorithm for Analyzing Internal Fragments Resulting from Top-Down Mass Spectrometry

Top-down mass spectrometry (TD-MS) of peptides and proteins results in product ions that can be correlated to polypeptide sequence. Fragments can either be terminal fragments, which contain either the N- or the C-terminus, or internal fragments that contain neither termini. Normally, only terminal fragments are assigned due to the computational difficulties of assigning internal fragments. Here we describe ClipsMS, an algorithm that can assign both terminal and internal fragments generated by top-down MS fragmentation. Further, ClipsMS can be used to locate various modifications on the protein sequence. Using ClipsMS to assign TD-MS generated product ions, we demonstrate that for apo-myoglobin, the inclusion of internal fragments increases the sequence coverage up to 78%. Interestingly, many internal fragments cover complementary regions to the terminal fragments that enhance the information that is extracted from a single top-down mass spectrum. Analysis of oxidized apo-myoglobin using terminal and internal fragment matching by ClipsMS confirmed the locations of oxidation sites on the two methionine residues. Internal fragments can be beneficial for top-down protein fragmentation analysis, and ClipsMS can be a valuable tool for assigning both terminal and internal fragments present in a top-down mass spectrum. Data are available via the MassIVE community resource with the identifiers MSV000086788 and MSV000086789.

Collision energies on QTof and Orbitrap instruments: How to make proteomics measurements comparable?

Quadrupole time-of-flight (QTof) collision-induced dissociation (CID) and Orbitrap higher-energy collisional dissociation (HCD) are the most commonly used fragmentation techniques in mass spectrometry-based proteomics workflows. The information content of the MS/MS spectra is first and foremost determined by the applied collision energy. How can we set up the two instrument types to achieve maximum transferability? To answer this question, we compared MS/MS spectra obtained on a Bruker QTof CID and a Thermo Q-Exactive Focus Orbitrap HCD instrument as a function of collision energy using the similarity index. Results show that with a few eV lower collision energy setting on HCD (Orbitrap-specific CID) than on QTof CID, nearly identical MS/MS spectra can be obtained for leucine enkephalin pentapeptide standard, for selected +2 and +3 enolase tryptic peptides and for a large number of peptides in a HeLa protein digest. The Bruker QTof was able to produce colder ions, which may be significant to study inherently labile compounds. Further, we examined energy dependence of peptide identification confidence, as characterized by Mascot scores, on the HeLa peptides. In line with earlier QTof results, this dependence shows one or two maxima (unimodal or bimodal behavior) on Orbitrap. The fraction of bimodal peptides is lower on Orbitrap. Optimal energies as a function of m/z show a similar linear trend on both instruments, which suggests that with appropriate collision energy adjustment, matching conditions for proteomics can be achieved. Data have been deposited in the MassIVE repository (MSV000086434).

Deep Learning in Proteomics

Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.

Internal Fragments Generated by Electron Ionization Dissociation Enhance Protein Top-Down Mass Spectrometry

Top-down proteomics by mass spectrometry (MS) involves the mass measurement of an intact protein followed by subsequent activation of the protein to generate product ions. Electron-based fragmentation methods like electron capture dissociation and electron transfer dissociation are widely used for these types of analyses. Recently, electron ionization dissociation (EID), which utilizes higher energy electrons (>20 eV) has been suggested to be more efficient for top-down protein fragmentation compared to other electron-based dissociation methods. Here, we demonstrate that the use of EID enhances protein fragmentation and subsequent detection of protein fragments. Protein product ions can form by either single cleavage events, resulting in terminal fragments containing the C-terminus or N-terminus of the protein, or by multiple cleavage events to give rise to internal fragments that include neither the C-terminus nor the N-terminus of the protein. Conventionally, internal fragments have been disregarded, as reliable assignments of these fragments were limited. Here, we demonstrate that internal fragments generated by EID can account for ∼20-40% of the mass spectral signals detected by top-down EID-MS experiments. By including internal fragments, the extent of the protein sequence that can be explained from a single tandem mass spectrum increases from ∼50 to ∼99% for 29 kDa carbonic anhydrase II and 8.6 kDa ubiquitin. When searching for internal fragments during data analysis, previously unassigned peaks can be readily and accurately assigned to confirm a given protein sequence and to enhance the utility of top-down protein sequencing experiments.

Using 10,000 Fragment Ions to Inform Scoring in Native Top-down Proteomics

Protein fragmentation is a critical component of top-down proteomics, enabling gene-specific protein identification and full proteoform characterization. The factors that influence protein fragmentation include precursor charge, structure, and primary sequence, which have been explored extensively for collision-induced dissociation (CID). Recently, noticeable differences in CID-based fragmentation were reported for native versus denatured proteins, motivating the need for scoring metrics that are tailored specifically to native top-down mass spectrometry (nTDMS). To this end, position and intensity were tracked for 10,252 fragment ions produced by higher-energy collisional dissociation (HCD) of 159 native monomers and 70 complexes. We used published structural data to explore the relationship between fragmentation and protein topology and revealed that fragmentation events occur at a large range of relative residue solvent accessibility. Additionally, our analysis found that fragment ions at sites with an N-terminal aspartic acid or a C-terminal proline make up on average 40 and 27%, respectively, of the total matched fragment ion intensity in nTDMS. Percent intensity contributed by each amino acid was determined and converted into weights to (1) update the previously published C-score and (2) construct a native Fragmentation Propensity Score. Both scoring systems showed an improvement in protein identification or characterization in comparison to traditional methods and overall increased confidence in results with fewer matched fragment ions but with high probability nTDMS fragmentation patterns. Given the rise of nTDMS as a tool for structural mass spectrometry, we forward these scoring metrics as new methods to enhance analysis of nTDMS data.

Ultraviolet Photodissociation of Tryptic Peptide Backbones at 213 nm

We analyzed the backbone fragmentation behavior of tryptic peptides of a four-protein mixture and of E. coli lysate subjected to ultraviolet photodissociation (UVPD) at 213 nm on a commercially available UVPD-equipped tribrid mass spectrometer. We obtained 15 178 unique high-confidence peptide UVPD spectrum matches by recording a reference beam-type collision-induced dissociation (HCD) spectrum of each precursor, ensuring that our investigation includes a broad selection of peptides, including those that fragmented poorly by UVPD. Type a, b, and y ions were most prominent in UVPD spectra, and median sequence coverage ranged from 5.8% (at 5 ms laser excitation time) to 45.0% (at 100 ms). Overall, the sequence fragment intensity remained relatively low (median: 0.4% (5 ms) to 16.8% (100 ms) of total intensity), and the remaining precursor intensity, high. The sequence coverage and sequence fragment intensity ratio correlated with the precursor charge density, suggesting that UVPD at 213 nm may suffer from newly formed fragments sticking together due to noncovalent interactions. The UVPD fragmentation efficiency therefore might benefit from supplemental activation, as was shown for ETD. Aromatic amino acids, most prominently tryptophan, facilitated UVPD. This points to aromatic tags as possible enhancers of UVPD. Data are available via ProteomeXchange with identifier PXD018176 and on spectrumviewer.org/db/UVPD-213nm-trypPep.

Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network

The ability to predict tandem mass (MS/MS) spectra from peptide sequences can significantly enhance our understanding of the peptide fragmentation process and could improve peptide identification in proteomics. However, current approaches for predicting high-energy collisional dissociation (HCD) spectra are limited to predict the intensities of expected ion types, that is, the a/b/c/x/y/z ions and their neutral loss derivatives (referred to as backbone ions). In practice, backbone ions only account for <70% of total ion intensities in HCD spectra, indicating many intense ions are ignored by current predictors. In this paper, we present a deep learning approach that can predict the complete spectra (both backbone and nonbackbone ions) directly from peptide sequences. We made no assumptions or expectations on which kind of ions to predict but instead predicting the intensities for all possible m/z. Training this model needs no annotations of fragment ion nor any prior knowledge of the fragmentation rules. Our analyses show that the predicted 2+ and 3+ HCD spectra are highly similar to the experimental spectra, with average full-spectrum cosine similarities of 0.820 (±0.088) and 0.786 (±0.085), respectively, very close to the similarities between the experimental replicated spectra. In contrast, the best-performed backbone only models can only achieve an average similarity below 0.75 and 0.70 for 2+ and 3+ spectra, respectively. Furthermore, we developed a multitask learning (MTL) approach for predicting spectra of insufficient training samples, which allows our model to make accurate predictions for electron transfer dissociation (ETD) spectra and HCD spectra of less abundant charges (1+ and 4+).

Thorough Performance Evaluation of 213 nm Ultraviolet Photodissociation for Top-down Proteomics

Top-down proteomics studies intact proteoform mixtures and offers important advantages over more common bottom-up proteomics technologies, as it avoids the protein inference problem. However, achieving complete molecular characterization of investigated proteoforms using existing technologies remains a fundamental challenge for top-down proteomics. Here, we benchmark the performance of ultraviolet photodissociation (UVPD) using 213 nm photons generated by a solid-state laser applied to the study of intact proteoforms from three organisms. Notably, the described UVPD setup applies multiple laser pulses to induce ion dissociation, and this feature can be used to optimize the fragmentation outcome based on the molecular weight of the analyzed biomolecule. When applied to complex proteoform mixtures in high-throughput top-down proteomics, 213 nm UVPD demonstrated a high degree of complementarity with the most employed fragmentation method in proteomics studies, higher-energy collisional dissociation (HCD). UVPD at 213 nm offered higher average proteoform sequence coverage and degree of proteoform characterization (including localization of post-translational modifications) than HCD. However, previous studies have shown limitations in applying database search strategies developed for HCD fragmentation to UVPD spectra which contains up to nine fragment ion types. We therefore performed an analysis of the different UVPD product ion type frequencies. From these data, we developed an ad hoc fragment matching strategy and determined the influence of each possible ion type on search outcomes. By paring down the number of ion types considered in high-throughput UVPD searches from all types down to the four most abundant, we were ultimately able to achieve deeper proteome characterization with UVPD. Lastly, our detailed product ion analysis also revealed UVPD cleavage propensities and determined the presence of a product ion produced specifically by 213 nm photons. All together, these observations could be used to better elucidate UVPD dissociation mechanisms and improve the utility of the technique for proteomic applications.

Critical review on proteotypic peptide marker tracing for six allergenic ingredients in incurred foods by mass spectrometry

Peptide marker identification is one of the most important steps in the development of a mass spectrometry (MS) based method for allergen detection, since the robustness and sensitivity of the overall analytical method will strictly depend on the reliability of the proteotypic peptides tracing for each allergen. The European legislation in place issues the mandatory labelling of fourteen allergenic ingredients whenever used in different food formulations. Among these, six allergenic ingredients, namely milk, egg, peanut, soybean, hazelnut and almond, can be prioritized in light of their higher occurrence in food recalls for undeclared presence with serious risk decision. In this work, we described the results of a comprehensive evaluation of the current literature on MS-based allergen detection aiming at collecting all available information about proteins and peptide markers validated in independent studies for the six allergenic ingredients of interest. The main features of the targeted proteins were commented reviewing all details available about known isoforms and sequence homology particularly in plant-derived allergens. Several critical aspects affecting peptide markers reliability were discussed and according to this evaluation a final short-list of candidate markers was compiled likely to be standardized and implemented in MS methods for allergen analysis.

LysargiNase and Chemical Derivatization Based Strategy for Facilitating In-Depth Profiling of C-Terminome

Global identification of protein C-termini is highly challenging due to their low abundance in conventional shotgun proteomics. Several enrichment strategies have been developed to facilitate the detection of C-terminal peptides. One major issue of previous approaches is the limited C-terminome coverage. Herein, we integrated LysargiNase digestion, chemical acetylation on neo-N-terminus, and a-ion-aided peptide matching into poly(allylamine)-based C-terminomics (termed as LAACTer). In this strategy, we leveraged LysargiNase, a protease with cleavage specificity N-terminal to Lys and Arg residues, to cover previously unidentifiable C-terminome and employed chemical acetylation and a-ion-aided peptide matching to efficiently boost peptide identifications. Triplicates of LAACTer identified a total of 834 C-termini from proteome of 293T cell, which expanded the coverage by 164% (643 more unique C-termini) compared with the parallel experiments using the original workflow. Compared with the largest human C-terminome data sets (containing 800-900 C-termini), LAACTer not only achieved comparable profiling depth but also yielded 465 previously unidentified C-termini. In a SILAC (stable isotope labeling with amino acids in cell culture)-based quantitative study for identification of GluC-cleaved products, LAACTer quantified 300% more C-terminal peptides than the original workflow. Using LAACTer and the original workflow, we performed global analysis for the C-terminal sequences of 293T cell. The original and processed C-termini displayed distinct sequence patterns, implying the "C-end rules" that regulates protein stability could be more complex than just amino acid motifs. In conclusion, we reason LAACTer could be a powerful proteomic tool for in-depth C-terminomics and would benefit better functional understanding of protein C-termini.

Sialic acid mediated mechanical activation of β2 adrenergic receptors by bacterial pili

Meningococcus utilizes β-arrestin selective activation of endothelial cell β₂ adrenergic receptor (β₂AR) to cause meningitis in humans. Molecular mechanisms of receptor activation by the pathogen and of its species selectivity remained elusive. We report that β₂AR activation requires two asparagine-branched glycan chains with terminally exposed N-acetyl-neuraminic acid (sialic acid, Neu5Ac) residues located at a specific distance in its N-terminus, while being independent of surrounding amino-acid residues. Meningococcus triggers receptor signaling by exerting direct and hemodynamic-promoted traction forces on β₂AR glycans. Similar activation is recapitulated with beads coated with Neu5Ac-binding lectins, submitted to mechanical stimulation. This previously unknown glycan-dependent mode of allosteric mechanical activation of a G protein-coupled receptor contributes to meningococcal species selectivity, since Neu5Ac is only abundant in humans due to the loss of CMAH, the enzyme converting Neu5Ac into N-glycolyl-neuraminic acid in other mammals. It represents an additional mechanism of evolutionary adaptation of a pathogen to its host.

Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models

Tandem mass spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. Peptide spectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments. Using millions of MS/MS spectra we show that there is high reproducibility across different fragmentation spectra given the precursor peptide and charge state, implying that there is a pattern to fragmentation. To capture this pattern we propose a novel prediction algorithm based on hidden Markov models with an efficient training process. We investigated the performance of our interpolated-HMM model, trained on millions of MS2 spectra, and found that our model picks up meaningful patterns in peptide fragmentation. Second, looking at the variability of the prediction performance by varying the train/test data split, we observed that our model performs well independent of the specific peptides that are present in the training data. Furthermore, we propose that the real value of this model is as a preprocessing step in the peptide identification process. The model can discern fragment ions that are unlikely to be intense for a given candidate peptide rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra will leverage better statistics.

Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?

While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies. The main objective of this work is to provide a detailed evaluation on the performance of de novo sequencing algorithms on high-resolution data. For this purpose, we processed four experimental data sets acquired from different instrument types from collision-induced dissociation and higher energy collisional dissociation (HCD) fragmentation mode using the software packages Novor, PEAKS and PepNovo. Moreover, the accuracy of these algorithms is also tested on ground truth data based on simulated spectra generated from peak intensity prediction software. We found that Novor shows the overall best performance compared with PEAKS and PepNovo with respect to the accuracy of correct full peptide, tag-based and single-residue predictions. In addition, the same tool outpaced the commercial competitor PEAKS in terms of running time speedup by factors of around 12-17. Despite around 35% prediction accuracy for complete peptide sequences on HCD data sets, taken as a whole, the evaluated algorithms perform moderately on experimental data but show a significantly better performance on simulated data (up to 84% accuracy). Further, we describe the most frequently occurring de novo sequencing errors and evaluate the influence of missing fragment ion peaks and spectral noise on the accuracy. Finally, we discuss the potential of de novo sequencing for now becoming more widely used in the field.

Toward an efficient workflow for the analysis of the human milk peptidome

There is a growing interest for investigating endogenous peptides from human biofluids which may provide yet unknown functional benefits or provide an early indication of disease states as potential biomarkers. A major technical bottleneck in the investigation of endogenous peptides from body fluids, e.g., serum, urine, saliva, and milk, is that each of these fluids seems to require unique workflows for peptide extraction and analysis. Thus, protocols optimized for serum cannot be directly translated to milk. One biofluid that is readily available, but which has not been extensively explored, is human milk, whose analysis could contribute to our understanding of the immune development of the newborn infant. Due to the occurrence of highly abundant lipids, proteins, and saccharides, milk peptidomics requires dedicated sample preparation steps. The aim of this study was to develop a time and cost-efficient workflow for the analysis of the human milk peptidome, for which we compared peptide extraction methodologies and peptide fragmentation methods. A method using strong acid protein precipitation and analysis by collision-induced dissociation fragmentation was found to be superior to all other test methods, allowing us qualitative and quantitative detection of about 4000 endogenous human milk peptides in a total analysis time of just 18 h.

ProteomeTools: Systematic Characterization of 21 Post-translational Protein Modifications by Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) Using Synthetic Peptides

The analysis of the post-translational modification (PTM) state of proteins using mass spectrometry-based bottom-up proteomic workflows has evolved into a powerful tool for the study of cellular regulatory events that are not directly encoded at the genome level. Besides frequently detected modifications such as phosphorylation, acetylation and ubiquitination, many low abundant or less frequently detected PTMs are known or postulated to serve important regulatory functions. To more broadly understand the LC-MS/MS characteristics of PTMs, we synthesized and analyzed ∼5,000 peptides representing 21 different naturally occurring modifications of lysine, arginine, proline and tyrosine side chains and their unmodified counterparts. The analysis identified changes in retention times, shifts of precursor charge states and differences in search engine scores between modifications. PTM-dependent changes in the fragmentation behavior were evaluated using eleven different fragmentation modes or collision energies. We also systematically investigated the formation of diagnostic ions or neutral losses for all PTMs, confirming 10 known and identifying 5 novel diagnostic ions for lysine modifications. To demonstrate the value of including diagnostic ions in database searching, we reprocessed a public data set of lysine crotonylation and showed that considering the diagnostic ions increases confidence in the identification of the modified peptides. To our knowledge, this constitutes the first broad and systematic analysis of the LC-MS/MS properties of common and rare PTMs using synthetic peptides, leading to direct applicable utility for bottom-up proteomic experiments.

Fragmentation patterns of chromophore-tagged peptides in visible laser induced dissociation

RATIONALE

Tandem mass spectrometry (MS/MS) is the pivotal tool for protein structural characterization and quantification. Identification relies on the fragmentation step of tryptic peptides in bottom-up strategy. Specificity of fragmentation can be obtained using laser-induced dissociation (LID) in the visible range, after tagging of the targeted peptides with an adequate chromophore. Backbone fragmentation is required to obtain specific fragments and confident identification. We present herein a study of fragmentation patterns of chromophore-tagged peptides in LID, showing the potential of LID methodology to provide the maximum number of fragments for further identification and quantification.

METHODS

A total of 401 cysteine-containing tryptic peptides originating from the human proteome were derivatizated on the thiol group of cysteine with a Dabcyl maleimide chromophore, which has a high photo-absorption cross section at 473 nm. The derivatized peptides were then analyzed by LID at 473 nm on a Q Exactive instrument.

RESULTS

LID spectra present a characteristic fragment at m/z 252.112 for all precursors. This product ion arises from the internal dissociation of the Dabcyl chromophore. Several peptide-backbone fragment ions are also detected. Results show the quasi absence of fragmentation at the cysteine site. This indicates that part of the energy must be redistributed across the entire system despite excitation initially localized at the chromophore. Indeed, the fragmentation mainly occurs at 3 to 5 amino acids from the derivatized cysteine residue.

CONCLUSIONS

LID of derivatized cysteine-containing peptides displays the initial fragmentation of the chromophore. As energy is redistributed all along the peptide sequence, fragmentation of the peptide backbone is also observed. Thus, LID of chromophore-tagged peptides produces adequate fragment ions, allowing both good sequence coverage for a greater confidence of identification, and a large choice of transitions for specific quantification.

High-Throughput Analysis of Intact Human Proteins Using UVPD and HCD on an Orbitrap Mass Spectrometer

The analysis of intact proteins (top-down strategy) by mass spectrometry has great potential to elucidate proteoform variation, including patterns of post-translational modifications (PTMs), which may not be discernible by analysis of peptides alone (bottom-up approach). To maximize sequence coverage and localization of PTMs, various fragmentation modes have been developed to produce fragment ions from deep within intact proteins. Ultraviolet photodissociation (UVPD) has recently been shown to produce high sequence coverage and PTM retention on a variety of proteins, with increasing evidence of efficacy on a chromatographic time scale. However, utilization of UVPD for high-throughput top-down analysis to date has been limited by bioinformatics. Here we detected 153 proteins and 489 proteoforms using UVPD and 271 proteins and 982 proteoforms using higher energy collisional dissociation (HCD) in a comparative analysis of HeLa whole-cell lysate by qualitative top-down proteomics. Of the total detected proteoforms, 286 overlapped between the UVPD and HCD data sets, with 68% of proteoforms having C scores greater than 40 for UVPD and 63% for HCD. The average sequence coverage (28 ± 20% for UVPD versus 17 ± 8% for HCD, p < 0.0001) was found to be higher for UVPD than HCD and with a trend toward improvement in q value for the UVPD data set. This study demonstrates the complementarity of UVPD and HCD for more extensive protein profiling and proteoform characterization.

SILAC-Based Quantitative Strategies for Accurate Histone Posttranslational Modification Profiling Across Multiple Biological Samples

Histone posttranslational modifications (hPTMs) play a key role in regulating chromatin dynamics and fine-tuning DNA-based processes. Mass spectrometry (MS) has emerged as a versatile technology for the analysis of histones, contributing to the dissection of hPTMs, with special strength in the identification of novel marks and in the assessment of modification cross talks. Stable isotope labeling by amino acid in cell culture (SILAC), when adapted to histones, permits the accurate quantification of PTM changes among distinct functional states; however, its application has been mainly confined to actively dividing cell lines. A spike-in strategy based on SILAC can be used to overcome this limitation and profile hPTMs across multiple samples. We describe here the adaptation of SILAC to the analysis of histones, in both standard and spike-in setups. We also illustrate its coupling to an implemented "shotgun" workflow, by which heavy arginine-labeled histone peptides, produced upon Arg-C digestion, are qualitatively and quantitatively analyzed in an LC-MS/MS system that combines ultrahigh-pressure liquid chromatography (UHPLC) with new-generation Orbitrap high-resolution instrument.

Top-down protein identification using isotopic envelope fingerprinting

For top-down protein database search and identification from tandem mass spectra, our isotopic envelope fingerprinting search algorithm and ProteinGoggle search engine have demonstrated their strength of efficiently resolving heavily overlapping data as well separating non-ideal data with non-ideal isotopic envelopes from ideal ones with ideal isotopic envelopes. Here we report our updated ProteinGoggle 2.0 for intact protein database search with full-capacity. The indispensable updates include users' optional definition of dynamic post-translational modifications and static chemical labeling during database creation, comprehensive dissociation methods and ion series, as well as a Proteoform Score for each proteoform. ProteinGoggle has previously been benchmarked with both collision-based dissociation (CID, HCD) and electron-based dissociation (ETD) data of either intact proteins or intact proteomes. Here we report our further benchmarking of the new version of ProteinGoggle with publically available photon-based dissociation (UVPD) data (http://hdl.handle.net/2022/17316) of intact E. coli ribosomal proteins.

BIOLOGICAL SIGNIFICANCE

Protein species (aka proteoforms) function at their molecular level, and diverse structures and biological roles of every proteoform come from often co-occurring proteolysis, amino acid variation and post-translational modifications. Complete and high-throughput capture of this combinatorial information of proteoforms has become possible in evolving top-down proteomics; yet, various methods and technologies, especially database search and bioinformatics identification tools, in the top-down pipeline are still in their infancy stages and demand intensive research and development.

The MaxQuant computational platform for mass spectrometry-based shotgun proteomics

MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.

Xilmass: A New Approach toward the Identification of Cross-Linked Peptides

Chemical cross-linking coupled with mass spectrometry plays an important role in unravelling protein interactions, especially weak and transient ones. Moreover, cross-linking complements several structural determination approaches such as cryo-EM. Although several computational approaches are available for the annotation of spectra obtained from cross-linked peptides, there remains room for improvement. Here, we present Xilmass, a novel algorithm to identify cross-linked peptides that introduces two new concepts: (i) the cross-linked peptides are represented in the search database such that the cross-linking sites are explicitly encoded, and (ii) the scoring function derived from the Andromeda algorithm was adapted to score against a theoretical tandem mass spectrometry (MS/MS) spectrum that contains the peaks from all possible fragment ions of a cross-linked peptide pair. The performance of Xilmass was evaluated against the recently published Kojak and the popular pLink algorithms on a calmodulin-plectin complex data set, as well as three additional, published data sets. The results show that Xilmass typically had the highest number of identified distinct cross-linked sites and also the highest number of predicted cross-linked sites.

Statistical Examination of the a and a + 1 Fragment Ions from 193 nm Ultraviolet Photodissociation Reveals Local Hydrogen Bonding Interactions

Dissociation of proteins and peptides by 193 nm ultraviolet photodissociation (UVPD) has gained momentum in proteomic studies because of the diversity of backbone fragments that are produced and subsequent unrivaled sequence coverage obtained by the approach. The pathways that form the basis for the production of particular ion types are not completely understood. In this study, a statistical approach is used to probe hydrogen atom elimination from a + 1 radical ions, and different extents of elimination are found to vary as a function of the identity of the C-terminal residue of the a product ions and the presence or absence of hydrogen bonds to the cleaved residue. Graphical Abstract ᅟ.