HSPVdb—the Human Short Peptide Variation Database for improved mass spectrometry-based detection of polymorphic HLA-ligands

T cell epitopes derived from polymorphic proteins or from proteins encoded by alternative reading frames (ARFs) play an important role in (tumor) immunology. Identification of these peptides is successfully performed with mass spectrometry. In a mass spectrometry-based approach, the recorded tandem mass spectra are matched against hypothetical spectra generated from known protein sequence databases. Commonly used protein databases contain a minimal level of redundancy, and thus, are not suitable data sources for searching polymorphic T cell epitopes, either in normal or ARFs. At the same time, however, these databases contain much non-polymorphic sequence information, thereby complicating the matching of recorded and theoretical spectra, and increasing the potential for finding false positives. Therefore, we created a database with peptides from ARFs and peptide variation arising from single nucleotide polymorphisms (SNPs). It is based on the human mRNA sequences from the well-annotated reference sequence (RefSeq) database and associated variation information derived from the Single Nucleotide Polymorphism Database (dbSNP). In this process, we removed all non-polymorphic information. Investigation of the frequency of SNPs in the dbSNP revealed that many SNPs are non-polymorphic “SNPs”. Therefore, we removed those from our dedicated database, and this resulted in a comprehensive high quality database, which we coined the Human Short Peptide Variation Database (HSPVdb). The value of our HSPVdb is shown by identification of the majority of published polymorphic SNP- and/or ARF-derived epitopes from a mass spectrometry-based proteomics workflow, and by a large variety of polymorphic peptides identified as potential T cell epitopes in the HLA-ligandome presented by the Epstein–Barr virus cells.     

Characterization of the human salivary proteome by capillary isoelectric focusing/nanoreversed-phase liquid chromatography coupled with ESI-tandem MS

Saliva is a readily available body fluid with great diagnostic potential. The foundation for saliva-based diagnostics, however, is the development of a complete catalog of secreted and "leaked" proteins detectable in saliva. By employing a capillary isoelectric focusing-based multidimensional separation platform coupled with electrospray ionization tandem mass spectrometry (MS), a total of 5338 distinct peptides were sequenced, leading to the identification of 1381 distinct proteins. A search of bacterial protein sequences also identified many peptides unique to several organisms and unique to the NCBI nonredundant database. To the best of our knowledge, this proteome study represents the largest catalog of proteins measured from a single saliva sample to date. Data analysis was performed on individual MS/MS spectra using the highly specific peptide identification algorithm, OMSSA. Searches were conducted against a decoyed SwissProt human database to control the false-positive rate at 1%. Furthermore, the well-curated SwissProt sequences represent perhaps the least redundant human protein sequence database (12,484 records versus the 50,009 records found in the International Protein Index human database), therefore minimizing multiple protein inferences from single peptides. This combined bioanalytical and bioinformatic approach has established a solid foundation for building up the human salivary proteome for the realization of the diagnostic potential of saliva.     

Mass Spectral Analysis of Synthetic Peptides: Implications in Proteomics

Sequence determination of peptides is a crucial step in mass spectrometry–based proteomics. Peptide sequences are determined either by database search or by de novo sequencing using tandem mass spectrometry. Determination of all the theoretical expected peptide fragments and eliminating false discoveries remains a challenge in proteomics. Developing standards for evaluating the performance of mass spectrometers and algorithms used for identification of proteins is important for proteomics studies. The current study is focused on these aspects by using synthetic peptides. A total of 599 peptides were designed from in silico tryptic digest with 1 or 2 missed cleavages from 199 human proteins, and synthetic peptides corresponding to these sequences were obtained. The peptides were mixed together, and analysis was carried out using liquid chromatography–electrospray ionization tandem mass spectrometry on a Q-Exactive HF mass spectrometer. The peptides and proteins were identified with SEQUEST program. The analysis was carried out using the proteomics workflows. A total of 573 peptides representing 196 proteins could be identified, and a spectral library was created for these peptides. Analysis parameters such as “no enzyme selection” gave the maximum number of detected peptides as compared with trypsin in the selection. False discoveries could be identified. This study highlights the limitations of peptide detection and the need for developing powerful algorithms along with tools to evaluate mass spectrometers and algorithms. It also shows the limitations of peptide detection even with high-end mass spectrometers. The mass spectral data are available in ProteomeXchange with accession no. PXD017992.     

Accurate mass tag retention time database for urine proteome analysis by chromatography-mass spectrometry

Information about peptides and proteins in urine can be used to search for biomarkers of early stages of various diseases. The main technology currently used for identification of peptides and proteins is tandem mass spectrometry, in which peptides are identified by mass spectra of their fragmentation products. However, the presence of the fragmentation stage decreases sensitivity of analysis and increases its duration. We have developed a method for identification of human urinary proteins and peptides. This method based on the accurate mass and time tag (AMT) method does not use tandem mass spectrometry. The database of AMT tags containing more than 1381 AMT tags of peptides has been constructed. The software for database filling with AMT tags, normalizing the chromatograms, database application for identification of proteins and peptides, and their quantitative estimation has been developed. The new procedures for peptide identification by tandem mass spectra and the AMT tag database are proposed. The paper also lists novel proteins that have been identified in human urine for the first time.     

Quantitative proteome analysis using differential stable isotopic labeling and microbore LC-MALDI MS and MS/MS

We demonstrate an approach for global quantitative analysis of protein mixtures using differential stable isotopic labeling of the enzyme-digested peptides combined with microbore liquid chromatography (LC) matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS). Microbore LC provides higher sample loading, compared to capillary LC, which facilitates the quantification of low abundance proteins in protein mixtures. In this work, microbore LC is combined with MALDI MS via a heated droplet interface. The compatibilities of two global peptide labeling methods (i.e., esterification to carboxylic groups and dimethylation to amine groups of peptides) with this LC-MALDI technique are evaluated. Using a quadrupole-time-of-flight mass spectrometer, MALDI spectra of the peptides in individual sample spots are obtained to determine the abundance ratio among pairs of differential isotopically labeled peptides. MS/MS spectra are subsequently obtained from the peptide pairs showing significant abundance differences to determine the sequences of selected peptides for protein identification. The peptide sequences determined from MS/MS database search are confirmed by using the overlaid fragment ion spectra generated from a pair of differentially labeled peptides. The effectiveness of this microbore LC-MALDI approach is demonstrated in the quantification and identification of peptides from a mixture of standard proteins as well as E. coli whole cell extract of known relative concentrations. It is shown that this approach provides a facile and economical means of comparing relative protein abundances from two proteome samples.     

Peptidomic analysis reveals multiple protection of human breast milk on infants during different stages

It has been shown that human breast milk (HBM) is an important nutrient for the growth and development of newborns. Currently, peptide drugs provide promising regimes in neonatal disease treatment, especially peptides from HBM that exhibit multiple functions within cells. To explore the potential biological function peptides among the colostrum, transition and mature milk from mother of extremely low birth weight children (the samples were collected from Women's Hospital of Nanjing Medical University from December 2016 to February 2017). A total of 3,182 nonredundant peptides were identified and compared among colostrum, transitional and mature milk using liquid chromatography/mass spectrometry technology, and the numbers and fragments of peptides were various. The isoelectric point and molecular weight analysis of the differentially expressed peptides basically accord with the range of mass spectrometry identification (<3 kDa). Gene Ontology analysis and Pathway analysis, restriction sites analysis, as well as bioinformatics analysis showed that these differentially expressed peptides enriched a variety of biological processes. We identified several putative peptides that might have bioactive effects in diseases and development of newborns, which will inform further functional investigations. Our preliminary research provided a better understanding of the function of peptides during the newborn periods. Furthermore, it laid a foundation for discovering new peptide drugs in neonatal disease treatment.     

High-throughput functional affinity purification of mannose binding proteins from Oryza sativa

We have used affinity chromatography in combination with mass spectrometry to isolate, identify, and assign a preliminary functional annotation to a large number of both known and novel proteins from rice. Rice (Oryza sativa) leaf, root, and seed tissue extracts were fractionated by column affinity chromatography using alpha-D-mannose as the ligand. Bound fractions were eluted and subjected to one-dimensional electrophoresis, followed by high-performance liquid chromatography-tandem mass spectrometric analysis of separated proteins. This multiplexed technology resulted in the isolation and identification of 136 distinct mannose binding proteins from rice. A comparative analysis demonstrates very little overlap of identified proteins between the respective tissues, and confirms the correctly compartmentalized presence of a significant number of proteins from largely tissue-specific biochemical pathways. Over 30% of the identified proteins with a previously annotated function are directly involved in sugar metabolism, including several highly expressed known rice lectins. Direct comparison of the peptide sequences identified in this study to those peptides identified in the most comprehensive survey of the rice proteome to date indicates that our current data represents a significant enrichment of proteins unique to this dataset. Nearly 15% of the identified proteins, identified on the basis of exact peptide matching to sequences in the rice genomic database, represent proteins without a previously known functional annotation, indicating the potential of this combined chromatographic approach to assign a preliminary function to novel proteins in a high-throughput fashion.     

A hybrid method for peptide identification using integer linear optimization, local database search, and quadrupole time-of-flight or OrbiTrap tandem mass spectrometry

A novel hybrid methodology for the automated identification of peptides via de novo integer linear optimization, local database search, and tandem mass spectrometry is presented in this article. A modified version of the de novo identification algorithm PILOT, is utilized to construct accurate de novo peptide sequences. A modified version of the local database search tool FASTA is used to query these de novo predictions against the nonredundant protein database to resolve any low-confidence amino acids in the candidate sequences. The computational burden associated with performing several alignments is alleviated with the use of distributive computing. Extensive computational studies are presented for this new hybrid methodology, as well as comparisons with MASCOT for a set of 38 quadrupole time-of-flight (QTOF) and 380 OrbiTrap tandem mass spectra. The results for our proposed hybrid method for the OrbiTrap spectra are also compared with a modified version of PepNovo, which was trained for use on high-precision tandem mass spectra, and the tag-based method InsPecT. The de novo sequences of PILOT and PepNovo are also searched against the nonredundant protein database using CIDentify to compare with the alignments achieved by our modifications of FASTA. The comparative studies demonstrate the excellent peptide identification accuracy gained from combining the strengths of our de novo method, which is based on integer linear optimization, and database driven search methods.     

Algorithm for identification of fusion proteins via mass spectrometry

Identification of fusion proteins has contributed significantly to our understanding of cancer progression, yielding important predictive markers and therapeutic targets. While fusion proteins can be potentially identified by mass spectrometry, all previously found fusion proteins were identified using genomic (rather than mass spectrometry) technologies. This lack of MS/MS applications in studies of fusion proteins is caused by the lack of computational tools that are able to interpret mass spectra from peptides covering unknown fusion breakpoints (fusion peptides). Indeed, the number of potential fusion peptides is so large that the existing MS/MS database search tools become impractical even in the case of small genomes. We explore computational approaches to identifying fusion peptides, propose an algorithm for solving the fusion peptide identification problem, and analyze the performance of this algorithm on simulated data. We further illustrate how this approach can be modified for human exons prediction.     

Discovery of neuropeptides in the nematode Ascaris suum by database mining and tandem mass spectrometry

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was used to discover peptides in extracts of the large parasitic nematode Ascaris suum. This required the assembly of a new database of known and predicted peptides. In addition to those already sequenced, peptides were either previously predicted to be processed from precursor proteins identified in an A. suum library of expressed sequence tags (ESTs) or newly predicted from a library of A. suum genome survey sequences (GSSs). The predicted MS/MS fragmentation patterns of this collection of real and putative peptides were compared with the actual fragmentation patterns found in the MS/MS spectra of peptides fractionated by MS; this enabled individual peptides to be sequenced. Many previously identified peptides were found, and 21 novel peptides were discovered. Thus, this approach is very useful, despite the fact that the available GSS database is still preliminary, having only 1× coverage.     

Visualization of neuropeptides in paraffin-embedded tissue sections of the central nervous system in the decapod crustacean, Penaeus monodon, by imaging mass spectrometry

The distributions of neuropeptides in paraffin-embedded tissue sections (PETS) of the eyestalk, brain, and thoracic ganglia of the shrimp Penaeus monodon were visualized by imaging mass spectrometry (IMS). Peptide signals were obtained from PETS without affecting morphological features. Twenty-nine neuropeptides comprising members of FMRFamide, SIFamides, crustacean hyperglycaemic hormone, orcokinin-related peptides, tachykinin-related peptides, and allatostatin A were detected and visualized. Among these findings we first identified tachykinin-related peptide as a novel neuropeptide in this shrimp species. We found that these neuropeptides were distributed at specific areas in the three neural organs. In addition, 28 peptide sequences derived from 4 types of constitutive proteins, including actin, histones, arginine kinase, and cyclophilin A were also detected. All peptide sequences were verified by liquid chromatography-tandem mass spectrometry. The use of IMS on acetic acid-treated PETS enabled us to identify peptides and obtain their specific localizations in correlation with the undisturbed histological structure of the tissue samples.     

Mass-spectrometric determination of the amino acid sequences in peptides isolated from protein silk fibroin of Bombyx mori

Several peptides were isolated from the protein silk fibroin of Bombyx mori by means of ion-exchange chromatography of a chymotryptic digest. The sequences of three of the peptides, Gly-Ala-Gly-Tyr, Gly-Val-Gly-Tyr and Gly-Ala-Gly-Ala-Gly-Ala-Gly-Tyr, were known from previous chemical work, but the sequence of the fourth, Gly-Ala-Gly-Val-Gly-Ala-Gly-Tyr, was previously only partially known. The necessary volatility for mass-spectrometric examination of the peptides was achieved by permethylation of the N-acetyl-peptide methyl ester derivatives. From the mass spectra it was possible to confirm the known sequences and to establish that of the partially known one. In one instance it was possible to deduce from the same mass spectrum the sequence of a main peptide component and that of a small amount of contaminating peptide. These results demonstrate for the first time the use of mass spectrometry in the determination of the amino acid sequences in peptides from a protein hydrolysate.     

Mammalian homologues of the Drosophila slit protein are ligands of the heparan sulfate proteoglycan glypican-1 in brain.

Using an affinity matrix in which a recombinant glypican-Fc fusion protein expressed in 293 cells was coupled to protein A-Sepharose, we have isolated from rat brain at least two proteins that were detected by SDS-polyacrylamide gel electrophoresis as a single 200-kDa silver-stained band, from which 16 partial peptide sequences were obtained by nano-electrospray tandem mass spectrometry. Mouse expressed sequence tags containing two of these peptides were employed for oligonucleotide design and synthesis of probes by polymerase chain reaction and enabled us to isolate from a rat brain cDNA library a 4.1-kilobase clone that encoded two of our peptide sequences and represented the N-terminal portion of a protein containing a signal peptide and three leucine-rich repeats. Comparisons with recently published sequences also showed that our peptides were derived from proteins that are members of the Slit/MEGF protein family, which share a number of structural features such as N-terminal leucine-rich repeats and C-terminal epidermal growth factor-like motifs, and in Drosophila Slit is necessary for the development of midline glia and commissural axon pathways. All of the five known rat and human Slit proteins contain 1523-1534 amino acids, and our peptide sequences correspond best to those present in human Slit-1 and Slit-2. Binding of these ligands to the glypican-Fc fusion protein requires the presence of the heparan sulfate chains, but the interaction appears to be relatively specific for glypican-1 insofar as no other identified heparin-binding proteins were isolated using our affinity matrix. Northern analysis demonstrated the presence of two mRNA species of 8. 6 and 7.5 kilobase pairs using probes based on both N- and C-terminal sequences, and in situ hybridization histochemistry showed that these glypican-1 ligands are synthesized by neurons, such as hippocampal pyramidal cells and cerebellar granule cells, where we have previously also demonstrated glypican-1 mRNA and immunoreactivity. Our results therefore indicate that Slit family proteins are functional ligands of glypican-1 in nervous tissue and suggest that their interactions may be critical for certain stages of central nervous system histogenesis.     

The mixed xylem sap proteome of Fusarium oxysporum-infected tomato plants

Secreted proteins are known to play decisive roles in plant–fungus interactions. To study the molecular details of the interaction between the xylem-colonizing, plant-pathogenic fungus Fusarium oxysporum and tomato, the composition of the xylem sap proteome of infected tomato plants was investigated and compared with that of healthy plants. Two-dimensional gel separation and mass spectrometry yielded peptide masses and peptide sequences of 33 different proteins. Despite the absence of complete genome sequences of either tomato or F. oxysporum , 21 proteins were identified as tomato proteins and seven as fungal proteins. Thirteen of the tomato proteins were specific for infected plants. Sixteen tomato proteins were found in xylem sap for the first time, four of which were identified based on matches to expressed sequences only. Coding sequences for new proteins from F. oxysporum were identified through either direct matching to a database sequence, matching of peptide sequences to genome or expressed sequence tag databases of other Fusarium species, or PCR with degenerate primers on cDNA derived from infected plants followed by screening of a F. oxysporum BAC library. Together, these findings provide an excellent basis for further exploration of the interaction between xylem-colonizing pathogens and their hosts.     

Classification and identification of bacteria using mass spectrometry-based proteomics

Timely classification and identification of bacteria is of vital importance in many areas of public health. Mass spectrometry-based methods provide an attractive alternative to well-established microbiologic procedures. Mass spectrometry methods can be characterized by the relatively high speed of acquiring taxonomically relevant information. Gel-free mass spectrometry proteomics techniques allow for rapid fingerprinting of bacterial proteins using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or, for high-throughput sequencing of peptides from protease-digested cellular proteins, using mass analysis of fragments from collision-induced dissociation of peptide ions. The latter technique uses database searching of product ion mass spectra. A database contains a comprehensive list of protein sequences translated from protein-encoding open reading frames found in bacterial genomes. The results of such searches allow the assignment of experimental peptide sequences to matching theoretical bacterial proteomes. Phylogenetic profiles of sequenced peptides are then used to create a matrix of sequence-to-bacterium assignments, which are analyzed using numerical taxonomy tools. The results thereof reveal the relatedness between bacteria, and allow the taxonomic position of an investigated strain to be inferred.     

A Heuristic method for assigning a false-discovery rate for protein identifications from Mascot database search results

MS/MS and database searching has emerged as a valuable technology for rapidly analyzing protein expression, localization, and post-translational modifications. The probability-based search engine Mascot has found widespread use as a tool to correlate tandem mass spectra with peptides in a sequence database. Although the Mascot scoring algorithm provides a probability-based model for peptide identification, the independent peptide scores do not correlate with the significance of the proteins to which they match. Herein, we describe a heuristic method for organizing proteins identified at a specified false-discovery rate using Mascot-matched peptides. We call this method PROVALT, and it uses peptide matches from a random database to calculate false-discovery rates for protein identifications and reduces a complex list of peptide matches to a nonredundant list of homologous protein groups. This method was evaluated using Mascot-identified peptides from a Trypanosoma cruzi epimastigote whole-cell lysate, which was separated by multidimensional LC and analyzed by MS/MS. PROVALT was then compared with the two traditional methods of protein identification when using Mascot, the single peptide score and cumulative protein score methods, and was shown to be superior to both in regards to the number of proteins identified and the inclusion of lower scoring nonrandom peptide matches.     

Classification and identification of bacteria using mass spectrometry-based proteomics

Timely classification and identification of bacteria is of vital importance in many areas of public health. Mass spectrometry-based methods provide an attractive alternative to well-established microbiologic procedures. Mass spectrometry methods can be characterized by the relatively high speed of acquiring taxonomically relevant information. Gel-free mass spectrometry proteomics techniques allow for rapid fingerprinting of bacterial proteins using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or, for high-throughput sequencing of peptides from protease-digested cellular proteins, using mass analysis of fragments from collision-induced dissociation of peptide ions. The latter technique uses database searching of product ion mass spectra. A database contains a comprehensive list of protein sequences translated from protein-encoding open reading frames found in bacterial genomes. The results of such searches allow the assignment of experimental peptide sequences to matching theoretical bacterial proteomes. Phylogenetic profiles of sequenced peptides are then used to create a matrix of sequence-to-bacterium assignments, which are analyzed using numerical taxonomy tools. The results thereof reveal the relatedness between bacteria, and allow the taxonomic position of an investigated strain to be inferred.     

Identification of four proteins from the small subunit of the mammalian mitochondrial ribosome using a proteomics approach

Proteins in the small subunit of the mammalian mitochondrial ribosome were separated by two-dimensional polyacrylamide gel electrophoresis. Four individual proteins were subjected to in-gel Endoprotease Lys-C digestion. The sequences of selected proteolytic peptides were obtained by electrospray tandem mass spectrometry. Peptide sequences obtained from in-gel digestion of individual spots were used to screen human, mouse, and rat expressed sequence tag databases, and complete consensus cDNAs for these species were deduced in silico. The corresponding protein sequences were characterized by comparison to known ribosomal proteins in protein databases. Four different classes of mammalian mitochondrial small subunit ribosomal proteins were identified. Only two of these proteins have significant sequence similarities to ribosomal proteins from prokaryotes. These proteins are homologs to Escherichia coli S9 and S5 proteins. The presence of these newly identified mitochondrial ribosomal proteins are also investigated in the Drosophila melanogaster, Caenorhabditis elegans, and in the genomes of several fungi.