inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction

Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.     

Improving MHC‐I Ligand Identifications from LC‐MS/MS Data by Incorporating Allelic Peptide Motifs

MHC class I (MHC‐I)‐bound ligands play a pivotal role in CD8 T cell immunity and are hence of major interest in understanding and designing immunotherapies. One of the most commonly utilized approaches for detecting MHC ligands is LC‐MS/MS. Unfortunately, the effectiveness of current algorithms to identify MHC ligands from LC‐MS/MS data is limited because the search algorithms used were originally developed for proteomics approaches detecting tryptic peptides. Consequently, the analysis often results in inflated false discovery rate (FDR) statistics and an overall decrease in the number of peptides that pass FDR filters. Andreatta et al. describe a new scoring tool (MS‐rescue) for peptides from MHC‐I immunopeptidome datasets. MS‐rescue incorporates the existence of MHC‐I peptide motifs to rescore peptides from ligandome data. The tool is demonstrated here using peptides assigned from LC‐MS/MS data with PEAKs software but can be deployed on data from any search algorithm. This new approach increased the number of peptides identified by up to 20–30% and promises to aid the discovery of novel MHC‐I ligands with immunotherapeutic potential.     

A computational pipeline to generate MHC binding motifs

BACKGROUND: Major histocompatibility complex (MHC) class I molecules play key roles in host immunity against pathogens by presenting peptide antigens to CD8+ T-cells. Many variants of MHC molecules exist, and each has a unique preference for certain peptide ligands. Both experimental approaches and computational algorithms have been utilized to analyze these peptide MHC binding characteristics. Traditionally, MHC binding specificities have been described in terms of binding motifs. Such motifs classify certain peptide positions as primary and secondary anchors according to their impact on binding, and they list the preferred and deleterious residues at these positions. This provides a concise and easily communicatable summary of MHC binding specificities. However, so far there has been no algorithm to generate such binding motifs in an automated and uniform fashion. In this paper, we present a computational pipeline that takes peptide MHC binding data as input and produces a concise MHC binding motif. We tested our pipeline on a set of 18 MHC class I molecules and showed that the derived motifs are consistent with historic expert assignments. We have implemented a pipeline that formally codifies rules to generate MHC binding motifs. The pipeline has been incorporated into the immune epitope database and analysis resource (IEDB) and motifs can be visualized while browsing MHC alleles in the IEDB.     

High‐sensitivity HLA class I peptidome analysis enables a precise definition of peptide motifs and the identification of peptides from cell lines and patients’ sera

The characterization of peptides bound to human leukocyte antigen (HLA) class I is of fundamental importance for understanding CD8+ T cell‐driven immunological processes and for the development of immunomodulatory therapeutic strategies. However, until now, the mass spectrometric analysis of HLA‐bound peptides has typically required billions of cells, still resulting in relatively few high‐confidence peptide identifications. Capitalizing on the recent developments in mass spectrometry and bioinformatics, we have implemented a methodology for the efficient recovery of acid‐eluted HLA peptides after purification with the pan‐reactive antibody W6/32 and have identified a total of 27 862 unique peptides with high confidence (1% false discovery rate) from five human cancer cell lines. More than 93% of the identified peptides were eight to 11 amino acids in length and contained signatures that were in excellent agreement with published HLA binding motifs. Furthermore, by purifying soluble HLA class I complexes (sHLA) from sera of melanoma patients, up to 972 high‐confidence peptides could be identified, including melanoma‐associated antigens already described in the literature. Knowledge of the HLA class I peptidome should facilitate multiplex tetramer technology‐based characterization of T cells, and allow the development of patient selection, stratification and immunomodulatory therapeutic strategies.     

Cutting edge: HLA-B27 acquires many N-terminal dibasic peptides: coupling cytosolic peptide stability to antigen presentation

Ag presentation by MHC class I is a highly inefficient process because cytosolic peptidases destroy most peptides after proteasomal generation. Various mechanisms shape the MHC class I peptidome. We define a new one: intracellular peptide stability. Peptides with two N-terminal basic amino acids are more stable than other peptides. Such peptides should be overrepresented in the peptidome of MHC class I-associated peptides. HLA-B27 binding peptides use anchor residue R at P2 and, although most amino acids are allowed, particular amino acids are overrepresented at P1, including R and K. We show that such N-terminal dibasic peptides are indeed more efficiently presented by HLA-B27. This suggests that HLA-B27 can present peptides from Ags present in fewer copies than required for successful peptide generation for other MHC class I molecules.     

Purification of soluble HLA class I complexes from human serum or plasma deliver high quality immuno peptidomes required for biomarker discovery

Soluble human leukocyte antigen class I (sHLA)‐peptide complexes have been suggested to play a role in the modulation of immune responses and in immune evasion of cancer cells. The set of peptides eluted from sHLA molecules could serve as biomarker for the monitoring of patients with cancer or other conditions. Here, we describe an improved sHLA peptidomics methodology resulting in the identification of 1816 to 2761 unique peptide sequences from triplicate analyses of serum or plasma taken from three healthy donors. More than 90% of the identified peptides were 8–11mers and 74% of these sequences were predicted to bind to cognate HLA alleles, confirming the quality of the resulting immunopeptidomes. In comparison to the HLA peptidome of cultured cells, the plasma‐derived peptides were predicted to have a higher stability in complex with the cognate HLA molecules and mainly derived from proteins of the plasma membrane or from the extracellular space. The sHLA peptidomes can efficiently be characterized by using the new methodology, thus serving as potential source of biomarkers in various pathological conditions.     

Diversity of Natural Self-Derived Ligands Presented by Different HLA Class I Molecules in Transporter Antigen Processing-Deficient Cells

The transporter associated with antigen processing (TAP) translocates the cytosol-derived proteolytic peptides to the endoplasmic reticulum lumen where they complex with nascent human leukocyte antigen (HLA) class I molecules. Non-functional TAP complexes and viral or tumoral blocking of these transporters leads to reduced HLA class I surface expression and a drastic change in the available peptide repertoire. Using mass spectrometry to analyze complex human leukocyte antigen HLA-bound peptide pools isolated from large numbers of TAP-deficient cells, we identified 334 TAP-independent ligands naturally presented by four different HLA-A, -B, and -C class I molecules with very different TAP dependency from the same cell line. The repertoire of TAP-independent peptides examined favored increased peptide lengths and a lack of strict binding motifs for all four HLA class I molecules studied. The TAP-independent peptidome arose from 182 parental proteins, the majority of which yielded one HLA ligand. In contrast, TAP-independent antigen processing of very few cellular proteins generated multiple HLA ligands. Comparison between TAP-independent peptidome and proteome of several subcellular locations suggests that the secretory vesicle-like organelles could be a relevant source of parental proteins for TAP-independent HLA ligands. Finally, a predominant endoproteolytic peptidase specificity for Arg/Lys or Leu/Phe residues in the P₁ position of the scissile bond was found for the TAP-independent ligands. These data draw a new and intricate picture of TAP-independent pathways.     

Extensive characterization of peptides from Panax ginseng C. A. Meyer using mass spectrometric approach

Panax ginseng is an important herb that has clear effects on the treatment of diverse diseases. Until now, the natural peptide constitution of this herb remains unclear. Here, we conduct an extensive characterization of Ginseng peptidome using MS‐based data mining and sequencing. The screen on the charge states of precursor ions indicated that Ginseng is a peptide‐rich herb in comparison of a number of commonly used herbs. The Ginseng peptides were then extracted and submitted to nano‐LC‐MS/MS analysis using different fragmentation modes, including CID, high‐energy collisional dissociation, and electron transfer dissociation. Further database search and de novo sequencing allowed the identification of total 308 peptides, some of which might have important biological activities. This study illustrates the abundance and sequences of endogenous Ginseng peptides, thus providing the information of more candidates for the screening of active compounds for future biological research and drug discovery studies.     

Rapid and sensitive identification of major histocompatibility complex class I-associated tumor peptides by Nano-LC MALDI MS/MS

Identification of major histocompatibility complex (MHC)-associated peptides recognized by T-lymphocytes is a crucial prerequisite for the detection and manipulation of specific immune responses in cancer, viral infections, and autoimmune diseases. Unfortunately immunogenic peptides are less abundant species present in highly complex mixtures of MHC-extracted material. Most peptide identification strategies use microcapillary LC coupled to nano-ESI MS/MS in a challenging on-line approach. Alternatively MALDI PSD analysis has been applied for this purpose. We report here on the first off-line combination of nanoscale (nano) LC and MALDI TOF/TOF MS/MS for the identification of naturally processed MHC peptide ligands. These peptides were acid-eluted from human leukocyte antigen (HLA)-A2, HLA-A3, and HLA-B/-C complexes separately isolated from a renal cell carcinoma cell lysate using HLA allele-specific antibodies. After reversed-phase HPLC, peptides were further fractionated via nano-LC. This additional separation step provided a substantial increase in the number of detectable candidate species within the complex peptide pools. MALDI MS/MS analysis on nano-LC-separated material was then sufficiently sensitive to rapidly identify more than 30 novel HLA-presented peptide ligands. Peptide sequences contained perfect anchor amino acid residues described previously for HLA-A2, HLA-A3, and HLA-B7. The most promising candidate for a T-cell epitope is an HLA-B7-binding nonamer peptide derived from the tumor-associated gene NY-BR-16. To demonstrate the sensitivity of our approach we characterized peptides binding to HLA-C molecules that are usually expressed at the cell surface at approximately only 10% the levels of HLA-A or HLA-B. In fact, multiple renal cell carcinoma peptides were identified that contained anchor amino acid residues of HLA-Cw5 and HLA-Cw7. We conclude that the nano-LC MALDI MS/MS approach is a sensitive tool for the rapid and automated identification of MHC-associated tumor peptides.     

A structure-based approach for prediction of MHC-binding peptides

Identification of immunodominant peptides is the first step in the rational design of peptide vaccines aimed at T-cell immunity. The advances in sequencing techniques and the accumulation of many protein sequences without the purified protein challenge the development of computer algorithms to identify dominant T-cell epitopes based on sequence data alone. Here, we focus on antigenic peptides recognized by cytotoxic T cells. The selection of T-cell epitopes along a protein sequence is influenced by the specificity of each of the processing stages that precede antigen presentation. The most selective of these processing stages is the binding of the peptides to the major histocompatibility complex molecules, and therefore many of the predictive algorithms focus on this stage. Most of these algorithms are based on known binding peptides whose sequences have been used for the characterization of binding motifs or profiles. Here, we describe a structure-based algorithm that does not rely on previous binding data. It is based on observations from crystal structures that many of the bound peptides adopt similar conformations and placements within the MHC groove. The algorithm uses a structural template of the peptide in the MHC groove upon which peptide candidates are threaded and their fit to the MHC groove is evaluated by statistical pairwise potentials. It can rank all possible peptides along a protein sequence or within a suspected group of peptides, directing the experimental efforts towards the most promising peptides. This approach is especially useful when no previous peptide binding data are available.     

Integrated analysis of the cerebrospinal fluid peptidome and proteome

Cerebrospinal fluid (CSF) is the only body fluid in direct contact with the brain and thus is a potential source of biomarkers. Furthermore, CSF serves as a medium of endocrine signaling and contains a multitude of regulatory peptides. A combined study of the peptidome and proteome of CSF or any other body fluid has not been reported previously. We report confident identification in CSF of 563 peptide products derived from 91 precursor proteins as well as a high confidence CSF proteome of 798 proteins. For the CSF peptidome, we use high accuracy mass spectrometry (MS) for MS and MS/MS modes, allowing unambiguous identification of neuropeptides. Combination of the peptidome and proteome data suggests that enzymatic processing of membrane proteins causes release of their extracellular parts into CSF. The CSF proteome has only partial overlap with the plasma proteome, thus it is produced locally rather than deriving from plasma. Our work offers insights into CSF composition and origin.     

Randomized sequence databases for tandem mass spectrometry peptide and protein identification

Tandem mass spectrometry (MS/MS) combined with database searching is currently the most widely used method for high-throughput peptide and protein identification. Many different algorithms, scoring criteria, and statistical models have been used to identify peptides and proteins in complex biological samples, and many studies, including our own, describe the accuracy of these identifications, using at best generic terms such as "high confidence." False positive identification rates for these criteria can vary substantially with changing organisms under study, growth conditions, sequence databases, experimental protocols, and instrumentation; therefore, study-specific methods are needed to estimate the accuracy (false positive rates) of these peptide and protein identifications. We present and evaluate methods for estimating false positive identification rates based on searches of randomized databases (reversed and reshuffled). We examine the use of separate searches of a forward then a randomized database and combined searches of a randomized database appended to a forward sequence database. Estimated error rates from randomized database searches are first compared against actual error rates from MS/MS runs of known protein standards. These methods are then applied to biological samples of the model microorganism Shewanella oneidensis strain MR-1. Based on the results obtained in this study, we recommend the use of use of combined searches of a reshuffled database appended to a forward sequence database as a means providing quantitative estimates of false positive identification rates of peptides and proteins. This will allow researchers to set criteria and thresholds to achieve a desired error rate and provide the scientific community with direct and quantifiable measures of peptide and protein identification accuracy as opposed to vague assessments such as "high confidence."     

SYFPEITHI: database for MHC ligands and peptide motifs

 The first version of the major histocompatibility complex (MHC) databank SYFPEITHI: database for MHC ligands and peptide motifs, is now available to the general public. It contains a collection of MHC class I and class II ligands and peptide motifs of humans and other species, such as apes, cattle, chicken, and mouse, for example, and is continuously updated. All motifs currently available are accessible as individual entries. Searches for MHC alleles, MHC motifs, natural ligands, T-cell epitopes, source proteins/organisms and references are possible. Hyperlinks to the EMBL and PubMed databases are included. In addition, ligand predictions are available for a number of MHC allelic products. The database content is restricted to published data only.     

Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes

Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines – that is, Mascot, Mascot+Percolator, and PEAKS DB – as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by MS to benchmark methods’ performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.     

The dominantly expressed class II molecule from a resistant MHC haplotype presents only a few Marek’s disease virus peptides by using an unprecedented binding motif

Viral diseases pose major threats to humans and other animals, including the billions of chickens that are an important food source as well as a public health concern due to zoonotic pathogens. Unlike humans and other typical mammals, the major histocompatibility complex (MHC) of chickens can confer decisive resistance or susceptibility to many viral diseases. An iconic example is Marek’s disease, caused by an oncogenic herpesvirus with over 100 genes. Classical MHC class I and class II molecules present antigenic peptides to T lymphocytes, and it has been hard to understand how such MHC molecules could be involved in susceptibility to Marek’s disease, given the potential number of peptides from over 100 genes. We used a new in vitro infection system and immunopeptidomics to determine peptide motifs for the 2 class II molecules expressed by the MHC haplotype B2, which is known to confer resistance to Marek’s disease. Surprisingly, we found that the vast majority of viral peptide epitopes presented by chicken class II molecules arise from only 4 viral genes, nearly all having the peptide motif for BL2*02, the dominantly expressed class II molecule in chickens. We expressed BL2*02 linked to several Marek’s disease virus (MDV) peptides and determined one X-ray crystal structure, showing how a single small amino acid in the binding site causes a crinkle in the peptide, leading to a core binding peptide of 10 amino acids, compared to the 9 amino acids in all other reported class II molecules. The limited number of potential T cell epitopes from such a complex virus can explain the differential MHC-determined resistance to MDV, but raises questions of mechanism and opportunities for vaccine targets in this important food species, as well as providing a basis for understanding class II molecules in other species including humans.

This study shows that chicken MHC class II molecules present peptides from only a handful of the more than 100 genes of the oncogenic herpesvirus Marek’s disease virus, explaining the strong genetic association of chicken MHC with resistance and susceptibility to this and other economically-important pathogens.     

Integrated data management and validation platform for phosphorylated tandem mass spectrometry data

MS/MS is a widely used method for proteome‐wide analysis of protein expression and PTMs. The thousands of MS/MS spectra produced from a single experiment pose a major challenge for downstream analysis. Standard programs, such as MASCOT, provide peptide assignments for many of the spectra, including identification of PTM sites, but these results are plagued by false‐positive identifications. In phosphoproteomic experiments, only a single peptide assignment is typically available to support identification of each phosphorylation site, and hence minimizing false positives is critical. Thus, tedious manual validation is often required to increase confidence in the spectral assignments. We have developed phoMSVal, an open‐source platform for managing MS/MS data and automatically validating identified phosphopeptides. We tested five classification algorithms with 17 extracted features to separate correct peptide assignments from incorrect ones using over 2600 manually curated spectra. The naïve Bayes algorithm was among the best classifiers with an AUC value of 97% and PPV of 97% for phosphotyrosine data. This classifier required only three features to achieve a 76% decrease in false positives as compared with MASCOT while retaining 97% of true positives. This algorithm was able to classify an independent phosphoserine/threonine data set with AUC value of 93% and PPV of 91%, demonstrating the applicability of this method for all types of phospho‐MS/MS data. PhoMSVal is available at http://csbi.ltdk.helsinki.fi/phomsval .     

Changes at the floor of the peptide-binding groove induce a strong preference for proline at position 3 of the bound peptide: molecular dynamics simulations of HLA-A*0217

We report on molecular dynamics simulations of major histocompatibility complex (MHC)-peptide complexes. Class I MHC molecules play an important role in cellular immunity by presenting antigenic peptides to cytotoxic T cells. Pockets in the peptide-binding groove of MHC molecules accommodate anchor side chains of the bound peptide. Amino acid substitutions in MHC affect differences in the peptide-anchor motifs. HLA-A*0217, human MHC class I molecule, differs from HLA-A*0201 only by three amino acid residues substitutions (positions 95, 97, and 99) at the floor of the peptide-binding groove. A*0217 showed a strong preference for Pro at position 3 (p3) and accepted Phe at p9 of its peptide ligands, but these preferences have not been found in other HLA-A2 ligands. To reveal the structural mechanism of these observations, the A*0217-peptide complexes were simulated by 1000 ps molecular dynamics at 300 K with explicit solvent molecules and compared with those of the A*0201-peptide complexes. We examined the distances between the anchor side chain of the bound peptide and the pocket, and the rms fluctuations of the bound peptides and the HLA molecules. On the basis of the results from our simulations, we propose that Pro at p3 serves as an optimum residue to lock the dominant anchor residue (p9) tightly into pocket F and to hold the peptide in the binding groove, rather than a secondary anchor residue fitting optimally the complementary pocket. We also found that Phe at p9 is used to occupy the space created by replacements of three amino acid residues at the floor within the groove. These findings would provide a novel understanding in the peptide-binding motifs of class I MHC molecules.     

Comprehensive peptidome analysis of mouse livers by size exclusion chromatography prefractionation and nanoLC-MS/MS identification

Peptidome analysis has received increasing attention in recent years. Cancer diagnosis by serum peptidome has also been reported by peptides' profiling for discovery of peptide biomarkers. Tissue, which may have a higher biomarker concentration than blood, has not been investigated extensively by means of peptidome analysis. Here, a method for the peptidome analysis of mouse liver was developed by the combination of size exclusion chromatography (SEC) prefractionation with nano-liquid chromatography-tamdem mass spectrometry (nanoLC-MS/MS) analysis. The extracted peptides from mouse liver were separated according to their molecular weight using a size exclusion column. MALDI-TOF MS was used to characterize the molecular weight distribution of the peptides in fractions eluted from the SEC column. The low molecular weight (LMW) (MW < 3000 Da) peptides in the collected fractions were directly analyzed by LC-MS/MS which resulted in the identification of 1181 unique peptides (from 371 proteins). The high molecular weight (HMW) (MW > 3000 Da) peptides in the early two fractions from the SEC column were first digested with trypsin, and the resulted digests were then analyzed by LC-MS/MS, which led to the identification of 123 and 127 progenitor proteins of the HMW peptides in fractions 1 and 2, respectively. Analysis of the peptides' cleavage sites showed that the peptides are cleaved in regulation, which may reflect the protease activity and distribution in body, and also represent the biological state of the tissue and provide a fresh source for biomarker discovery.     

Optimization of Conditions for Blood Plasma Peptidome Analysis

The conditions for peptidome analysis of blood plasma were optimized and the efficacy of the proposed approach was compared with the methods described in the literature. The method implies solution of two main problems: inactivation of blood plasma proteases and dissociation of peptides from major blood plasma proteins, which they are quantitatively associated with. To solve these problems, we proposed a new method of sample preparation. The essence of the method is simultaneous denaturation of plasma proteins plus reduction and alkylation of thiol groups of Cys, which is achieved by heating a blood plasma sample (95°C) in the presence of sodium deoxycholate, tris(2-carboxyethyl)phosphine, and 2-chloroacetamide. After separation of peptides from proteins by ultrafiltration on microcentrifuge filters and removal of sodium deoxycholate, the peptides are identified by LC-MS/MS using a Q Exactive HF (Thermo Scientific) mass spectrometer. As a result of one LC-MS/MS run of the peptide mixture obtained from ~15 μL of blood plasma, 2257 peptide fragments of 867 proteins were identified, which is 1.5 times higher than the values achieved by using the generally accepted method of differential solubilization. Our immediate plans include the use of our approach for cataloguing human blood plasma peptides, as well as establishing the magnitude of individual variability and the features of the peptidome that are related to gender and age.