Searching sequence databases via de novo peptide sequencing by tandem mass spectrometry

There are many computer programs that can match tandem mass spectra of peptides to database-derived sequences; however, situations can arise where mass spectral data cannot be correlated with any database sequence. In such cases, sequences can be automatically deduced de novo, without recourse to sequence databases, and the resulting peptide sequences can be used to perform homologous nonexact searches of sequence databases. This article describes details on how to implement both a de novo sequencing program called “Lutefisk,” and a version of FASTA that has been modified to account for sequence ambiguities inherent in tandem mass spectrometry data.     

Assign-MALDI – A free software for assignment of MALDI-TOF MS spectra of glycans derivatized using common and novel labeling strategies

Matrix Assisted Laser Desorption/Ionization Time-of-flight (MALDI-ToF) MS is a popular method to analyze glycans released from proteins, cell lines, and tissue samples. Chemical modification of glycans (derivatization) can enhance ionization, enable semi-quantitation, and assist in linkage identification. However, the mass changes incurred by novel and more recently developed derivatizations are not accommodated by most spectral assignment programs, necessitating manual assignment which increases both the difficultly and the likelihood of error. AssignMALDI is a software tool designed to create glycan databases with customized derivatizations (labels) and automatically assign glycan masses in MALDI-TOF spectra using the new database. It can also average peak intensities across multiple spectra and prepare publication-ready assignment tables. To make it easy to use with different platforms, all input files and most output files are in text format. An interactive display enables users to inspect and edit peak assignments prior to producing charts and tables for publication. The program is freely available through GitHUB and Python-savvy users can add or adjust features as needed.     

Protein identification from product ion spectra of peptides validated by correlation between measured and predicted elution times in liquid chromatography/mass spectrometry

Reversed-phase liquid chromatography (LC) directly coupled with electrospray-tandem mass spectrometry (MS/MS) is a successful choice to obtain a large number of product ion spectra from a complex peptide mixture. We describe a search validation program, ScoreRidge, developed for analysis of LC-MS/MS data. The program validates peptide assignments to product ion spectra resulting from usual probability-based searches against primary structure databases. The validation is based only on correlation between the measured LC elution time of each peptide and the deduced elution time from the amino acid sequence assigned to product ion spectra obtained from the MS/MS analysis of the peptide. Sufficient numbers of probable assignments gave a highly correlative curve. Any peptide assignments within a certain tolerance from the correlation curve were accepted for the following arrangement step to list identified proteins. Using this data validation program, host protein candidates responsible for interaction with human hepatitis B virus core protein were identified from a partially purified protein mixture. The present simple and practical program complements protein identification from usual product ion search algorithms and reduces manual interpretation of the search result data. It will lead to more explicit protein identification from complex peptide mixtures such as whole proteome digests from tissue samples.     

BioParser

The widely used programs BLAST (in this article, 'BLAST' includes both the National Center for Biotechnology Information [NCBI] BLAST and the Washington University version WU BLAST) and FASTA for similarity searches in nucleotide and protein databases usually result in copious output. However, when large query sets are used, human inspection rapidly becomes impractical. BioParser is a Perl program for parsing BLAST and FASTA reports. Making extensive use of the BioPerl toolkit, the program filters, stores and returns components of these reports in either ASCII or HTML format. BioParser is also capable of automatically feeding a local MySQL database with the parsed information, allowing subsequent filtering of hits and/or alignments with specific attributes. For this reason, BioParser is a valuable tool for large-scale similarity analyses by improving the access to the information present in BLAST or FASTA reports, facilitating extraction of useful information of large sets of sequence alignments, and allowing for easy handling and processing of the data. AVAILABILITY: BioParser is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 2.0 license terms (http://creativecommons.org/licenses/by-nc-nd/2.0/) and is available upon request. Additional information can be found at the BioParser website (http://www.dbbm.fiocruz.br/BioParser.html).     

CHOMPER: a bioinformatic tool for rapid validation of tandem mass spectrometry search results associated with high-throughput proteomic strategies

Current efforts aimed at developing high-throughput proteomics focus on increasing the speed of protein identification. Although improvements in sample separation, enrichment, automated handling, mass spectrometric analysis, as well as data reduction and database interrogation strategies have done much to increase the quality, quantity and efficiency of data collection, significant bottlenecks still exist. Various separation techniques have been coupled with tandem mass spectrometric (MS/MS) approaches to allow a quicker analysis of complex mixtures of proteins, especially where a high number of unambiguous protein identifications are the exception, rather than the rule. MS/MS is required to provide structural / amino acid sequence information on a peptide and thus allow protein identity to be inferred from individual peptides. Currently these spectra need to be manually validated because: (a) the potential of false positive matches i.e., protein not in database, and (b) observed fragmentation trends may not be incorporated into current MS/MS search algorithms. This validation represents a significant bottleneck associated with high-throughput proteomic strategies. We have developed CHOMPER, a software program which reduces the time required to both visualize and confirm MS/MS search results and generate post-analysis reports and protein summary tables. CHOMPER extracts the identification information from SEQUEST MS/MS search result files, reproduces both the peptide and protein identification summaries, provides a more interactive visualization of the MS/MS spectra and facilitates the direct submission of manually validated identifications to a database.     

MASCOT HTML and XML parser: an implementation of a novel object model for protein identification data

Protein identification using MS is an important technique in proteomics as well as a major generator of proteomics data. We have designed the protein identification data object model (PDOM) and developed a parser based on this model to facilitate the analysis and storage of these data. The parser works with HTML or XML files saved or exported from MASCOT MS/MS ions search in peptide summary report or MASCOT PMF search in protein summary report. The program creates PDOM objects, eliminates redundancy in the input file, and has the capability to output any PDOM object to a relational database. This program facilitates additional analysis of MASCOT search results and aids the storage of protein identification information. The implementation is extensible and can serve as a template to develop parsers for other search engines. The parser can be used as a stand-alone application or can be driven by other Java programs. It is currently being used as the front end for a system that loads HTML and XML result files of MASCOT searches into a relational database. The source code is freely available at http://www.ccbm.jhu.edu and the program uses only free and open-source Java libraries.     

Matrix-assisted laser desorption/ionization-tandem mass spectrometry with high resolution and sensitivity for identification and characterization of proteins

Although peptide mass fingerprinting is currently the method of choice to identify proteins, the number of proteins available in databases is increasing constantly, and hence, the advantage of having sequence data on a selected peptide, in order to increase the effectiveness of database searching, is more crucial. Until recently, the ability to identify proteins based on the peptide sequence was essentially limited to the use of electrospray ionization tandem mass spectrometry (MS) methods. The recent development of new instruments with matrix-assisted laser desorption/ionization (MALDI) sources and true tandem mass spectrometry (MS/MS) capabilities creates the capacity to obtain high quality tandem mass spectra of peptides. In this work, using the new high resolution tandem time of flight MALDI-(TOF/TOF) mass spectrometer from Applied Biosystems, examples of successful identification and characterization of bovine heart proteins (SWISS-PROT entries: P02192, Q9XSC6, P13620) separated by two-dimensional electrophoresis and blotted onto polyvinylidene difluoride membrane are described. Tryptic protein digests were analyzed by MALDI-TOF to identify peptide masses afterward used for MS/MS. Subsequent high energy MALDI-TOF/TOF collision-induced dissociation spectra were recorded on selected ions. All data, both MS and MS/MS, were recorded on the same instrument. Tandem mass spectra were submitted to database searching using MS-Tag or were manually de novo sequenced. An interesting modification of a tryptophan residue, a "double oxidation", came to light during these analyses.     

Database-independent, database-dependent, and extended interpretation of peptide mass spectra in VEMS V2.0

The Virtual Expert Mass Spectrometrist (VEMS) program package was developed for flexible, automated, and manual de novo tandem mass spectrometry (MS/MS) protein sequencing, and includes accessory programs for matrix-assisted laser desorption/ionization-mass spectrometry (MS) interpretation, and generation of protein and peptide databases. VEMS V2.0 has been developed into a fast tool for combining database-independent and -dependent protein assignments in an extended analysis of MS/MS-peptide data. MS or MS/MS data can be directly recalibrated after the first search by fitting the data to the best search result using polynomial equations. The score function is an improvement of known scoring algorithms and can be adapted for any MS instrument type. In addition, VEMS offers a novel statistical model for evaluating the significance of the protein assignment. The novel features are illustrated by the analysis of the fragmentation spectra obtained by liquid chromatrography-MS/MS analysis of peptides from an anionic peroxidase enriched protein fraction from potato root tissue. The extended analysis mode resulted in the additional assignment of spectra for nine modified tryptic peptides and nine miscleaved peptides, in addition to the 45 spectra from regular tryptic peptides. Of the nine modified peptides, three were glycosylated.     

The power and the limitations of cross-species protein identification by mass spectrometry-driven sequence similarity searches

Mass spectrometry-driven BLAST (MS BLAST) is a database search protocol for identifying unknown proteins by sequence similarity to homologous proteins available in a database. MS BLAST utilizes redundant, degenerate, and partially inaccurate peptide sequence data obtained by de novo interpretation of tandem mass spectra and has become a powerful tool in functional proteomic research. Using computational modeling, we evaluated the potential of MS BLAST for proteome-wide identification of unknown proteins. We determined how the success rate of protein identification depends on the full-length sequence identity between the queried protein and its closest homologue in a database. We also estimated phylogenetic distances between organisms under study and related reference organisms with completely sequenced genomes that allow substantial coverage of unknown proteomes.     

GFPE: gene-finding program evaluation

Gene-finding program evaluation (GFPE) is a set of Java classes for evaluating gene-finding programs. A command-line interface is also provided. Inputs to the program include the sequence data (in FASTA format), annotations of "actual" sequence features, and annotations of "predicted" sequence features. Annotation files are in the General Feature Format promoted by the Sanger center. GFPE calculates a number of metrics of accuracy of predictions at three levels:the coding level, the exon level, and the protein level.     

Popitam: towards new heuristic strategies to improve protein identification from tandem mass spectrometry data

In recent years, proteomics research has gained importance due to increasingly powerful techniques in protein purification, mass spectrometry and identification, and due to the development of extensive protein and DNA databases from various organisms. Nevertheless, current identification methods from spectrometric data have difficulties in handling modifications or mutations in the source peptide. Moreover, they have low performance when run on large databases (such as genomic databases), or with low quality data, for example due to bad calibration or low fragmentation of the source peptide. We present a new algorithm dedicated to automated protein identification from tandem mass spectrometry (MS/MS) data by searching a peptide sequence database. Our identification approach shows promising properties for solving the specific difficulties enumerated above. It consists of matching theoretical peptide sequences issued from a database with a structured representation of the source MS/MS spectrum. The representation is similar to the spectrum graphs commonly used by de novo sequencing software. The identification process involves the parsing of the graph in order to emphasize relevant sections for each theoretical sequence, and leads to a list of peptides ranked by a correlation score. The parsing of the graph, which can be a highly combinatorial task, is performed by a bio-inspired algorithm called Ant Colony Optimization algorithm.     

COMPASS: a suite of pre- and post-search proteomics software tools for OMSSA

Here we present the Coon OMSSA Proteomic Analysis Software Suite (COMPASS): a free and open-source software pipeline for high-throughput analysis of proteomics data, designed around the Open Mass Spectrometry Search Algorithm. We detail a synergistic set of tools for protein database generation, spectral reduction, peptide false discovery rate analysis, peptide quantitation via isobaric labeling, protein parsimony and protein false discovery rate analysis, and protein quantitation. We strive for maximum ease of use, utilizing graphical user interfaces and working with data files in the original instrument vendor format. Results are stored in plain text comma-separated value files, which are easy to view and manipulate with a text editor or spreadsheet program. We illustrate the operation and efficacy of COMPASS through the use of two LC-MS/MS data sets. The first is a data set of a highly annotated mixture of standard proteins and manually validated contaminants that exhibits the identification workflow. The second is a data set of yeast peptides, labeled with isobaric stable isotope tags and mixed in known ratios, to demonstrate the quantitative workflow. For these two data sets, COMPASS performs equivalently or better than the current de facto standard, the Trans-Proteomic Pipeline.     

Local database and the search program for proteomic analysis of sperm proteins in the ascidian Ciona intestinalis

Separation of proteins by two-dimensional electrophoresis and following mass spectrometry (MS) is now a conventional technique for proteomic analysis. For proteomic analysis of a certain tissue with a limited information of primary structures of proteins, we have developed an analytical system for peptide mass fingerprinting in gene products in the testis of the ascidian Ciona intestinalis. Ciona sperm proteins were separated by two-dimensional gel electrophoresis and the tryptic fragments were subjected to MALDI-TOF/MS. The mass pattern was searched against on-line databases but resulted in less identification of these proteins. We have constructed a MS database from Ciona testis ESTs and the genome draft sequence, along with a newly devised, perl-based search program PerMS for peptide mass fingerprinting. This system could identify more than 80% of Ciona sperm proteins, suggesting that it could be widely applied for proteomic analysis for a limited tissue with less genomic information.     

Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms.

The sensitivity and selectivity of the FASTA and the Smith-Waterman protein sequence comparison algorithms were evaluated using the superfamily classification provided in the National Biomedical Research Foundation/Protein Identification Resource (PIR) protein sequence database. Sequences from each of the 34 superfamilies in the PIR database with 20 or more members were compared against the protein sequence database. The similarity scores of the related and unrelated sequences were determined using either the FASTA program or the Smith-Waterman local similarity algorithm. These two sets of similarity scores were used to evaluate the ability of the two comparison algorithms to identify distantly related protein sequences. The FASTA program using the ktup = 2 sensitivity setting performed as well as the Smith-Waterman algorithm for 19 of the 34 superfamilies. Increasing the sensitivity by setting ktup = 1 allowed FASTA to perform as well as Smith-Waterman on an additional 7 superfamilies. The rigorous Smith-Waterman method performed better than FASTA with ktup = 1 on 8 superfamilies, including the globins, immunoglobulin variable regions, calmodulins, and plastocyanins. Several strategies for improving the sensitivity of FASTA were examined. The greatest improvement in sensitivity was achieved by optimizing a band around the best initial region found for every library sequence. For every superfamily except the globins and immunoglobulin variable regions, this strategy was as sensitive as a full Smith-Waterman. For some sequences, additional sensitivity was achieved by including conserved but nonidentical residues in the lookup table used to identify the initial region.     

SPLICE, a computer program for automated extraction of information from GenBank sequence entries

SPLICE, a software tool for the extraction of sequences fromfiles in GenBank tape format, has been developed. The programcan analyze the features table in this format and use any ofthe information provided to write the corresponding sequencesinto a standard sequence file format suitable for use with sequenceanalysis programs. Sequences that are present as several subsequentfragments in a single GenBank file, such as those encoding apeptide, can be spliced together by the program. Further, sequencesthat are present in more than one Genbank file, such as an exonwhich spans several different files, can also be spliced intoone sequence. SPLICE runs under the MS/DOS and Unix operatingsystems, can be called as a sub-process by other programs andcan process batches of files. Received on December 26, 1989; accepted on May 30, 1990     

GWFASTA: server for FASTA search in eukaryotic and microbial genomes

Similarity searches are a powerful method for solving important biological problems such as database scanning, evolutionary studies, gene prediction, and protein structure prediction. FASTA is a widely used sequence comparison tool for rapid database scanning. Here we describe the GWFASTA server that was developed to assist the FASTA user in similarity searches against partially and/or completely sequenced genomes. GWFASTA consists of more than 60 microbial genomes, eight eukaryote genomes, and proteomes of annotatedgenomes. Infact, it provides the maximum number of databases for similarity searching from a single platform. GWFASTA allows the submission of more than one sequence as a single query for a FASTA search. It also provides integrated post-processing of FASTA output, including compositional analysis of proteins, multiple sequences alignment, and phylogenetic analysis. Furthermore, it summarizes the search results organism-wise for prokaryotes and chromosome-wise for eukaryotes. Thus, the integration of different tools for sequence analyses makes GWFASTA a powerful toolfor biologists.     

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.     

A platform for biological sequence comparison on parallel computers

We have written two programs for searching biological sequencedatabases that run on Intel hypercube computers. PSCANLJB comparesa single sequence against a sequence library, and PCOMPLIB comparesall the entries in one sequence library against a second library.The programs provide a general framework for similarity searching;they include functions for reading in query sequences, searchparameters and library entries, and reporting the results ofa search. We have isolated the code for the specific functionthat calculates the similarity score between the query and librarysequence; alternative searching algorithms can be implementedby editing two files. We have implemented the rapid FASTA sequencecomparison algorithm and the more rigorous Smith — Watermanalgorithm within this framework. The PSCANLIB program on a 16node iPSC/2 80386-based hypercube can compare a 229 amino acidprotein sequence with a 3.4 million residue sequence libraryin {small tilde}16s with the FASTA algorithm. Using the Smith— Waterman algorithm, the same search takes 35 min. ThePCOMPUB program can compare a 0.8 millon amino acid proteinsequence library with itself in 5.3 min with FASTA on a third-generation32 node Intel iPSC/860 hypercube. Received on September 8, 1990; accepted on December 15, 1990     

Analysing proteomic data

The rapid growth of proteomics has been made possible by the development of reproducible 2D gels and biological mass spectrometry. However, despite technical improvements 2D gels are still less than perfectly reproducible and gels have to be aligned so spots for identical proteins appear in the same place. Gels can be warped by a variety of techniques to make them concordant. When gels are manipulated to improve registration, information is lost, so direct methods for gel registration which make use of all available data for spot matching are preferable to indirect ones. In order to identify proteins from gel spots a property or combination of properties that are unique to that protein are required. These can then be used to search databases for possible matches. Molecular mass, pI, amino acid composition and short sequence tags can all be used in database searches. Currently the method of choice for protein identification is mass spectrometry. Proteins are eluted from the gels and cleaved with specific endoproteases to produce a series of peptides of different molecular mass. In peptide mass fingerprinting, the peptide profile of the unknown protein is compared with theoretical peptide libraries generated from sequences in the different databases. Tandem mass spectroscopy (MS/MS) generates short amino acid sequence tags for the individual peptides. These partial sequences combined with the original peptide masses are then used for database searching, greatly improving specificity. Increasingly protein identification from MS/MS data is being fully or partially automated. When working with organisms, which do not have sequenced genomes (the case with most helminths), protein identification by database searching becomes problematical. A number of approaches to cross species protein identification have been suggested, but if the organism being studied is only distantly related to any organism with a sequenced genome then the likelihood of protein identification remains small. The dynamic nature of the proteome means that there really is no such thing as a single representative proteome and a complete set of metadata (data about the data) is going to be required if the full potential of database mining is to be realised in the future.