Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data

MOTIVATION: Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches. RESULTS: We present the implementation of a Genetic Algorithm (GA) in the reconstruction of amino acid sequences using only spectral features, discuss some of the problems associated with this approach and compare its performance to a de novo sequencing method. The GA can potentially overcome some of the most problematic aspects associated with de novo analysis of real MS/MS data such as missing or unclearly defined peaks and may prove to be a valuable tool in the proteomics field. We assess the performance of our algorithm under conditions of perfect spectral information, in situations where key spectral features are missing, and using real MS/MS spectral data.     

AUDENS: a tool for automated peptide de novo sequencing

We present AUDENS, a new platform-independent open source tool for automated de novo sequencing of peptides from MS/MS data. We implemented a dynamic programming algorithm and combined it with a flexible preprocessing module which is designed to distinguish between signal and other peaks. By applying a user-defined set of heuristics, AUDENS screens through the spectrum and assigns high relevance values to putative signal peaks. The algorithm constructs a sequence path through the MS/MS spectrum using the peak relevances to score each suggested sequence path, i.e., the corresponding amino acid sequence. At present, we consider AUDENS a prototype that unfolds its biggest potential if used in parallel with other de novo sequencing tools. AUDENS is available open source and can be downloaded with further documentation at http://www.ti.inf.ethz.ch/pw/software/audens/ .     

ADEPTS: advanced peptide de novo sequencing with a pair of tandem mass spectra

De novo sequencing is an important task in proteomics to identify novel peptide sequences. Traditionally, only one MS/MS spectrum is used for the sequencing of a peptide; however, the use of multiple spectra of the same peptide with different types of fragmentation has the potential to significantly increase the accuracy and practicality of de novo sequencing. Research into the use of multiple spectra is in a nascent stage. We propose a general framework to combine the two different types of MS/MS data. Experiments demonstrate that our method significantly improves the de novo sequencing of existing software.     

SPIDER: software for protein identification from sequence tags with de novo sequencing error

For the identification of novel proteins using MS/MS, de novo sequencing software computes one or several possible amino acid sequences (called sequence tags) for each MS/MS spectrum. Those tags are then used to match, accounting amino acid mutations, the sequences in a protein database. If the de novo sequencing gives correct tags, the homologs of the proteins can be identified by this approach and software such as MS-BLAST is available for the matching. However, de novo sequencing very often gives only partially correct tags. The most common error is that a segment of amino acids is replaced by another segment with approximately the same masses. We developed a new efficient algorithm to match sequence tags with errors to database sequences for the purpose of protein and peptide identification. A software package, SPIDER, was developed and made available on Internet for free public use. This paper describes the algorithms and features of the SPIDER software.     

Multi-spectra peptide sequencing and its applications to multistage mass spectrometry

Despite a recent surge of interest in database-independent peptide identifications, accurate de novo peptide sequencing remains an elusive goal. While the recently introduced spectral network approach resulted in accurate peptide sequencing in low-complexity samples, its success depends on the chance of presence of spectra from overlapping peptides. On the other hand, while multistage mass spectrometry (collecting multiple MS 3 spectra from each MS 2 spectrum) can be applied to all spectra in a complex sample, there are currently no software tools for de novo peptide sequencing by multistage mass spectrometry. We describe a rigorous probabilistic framework for analyzing spectra of overlapping peptides and show how to apply it for multistage mass spectrometry. Our software results in both accurate de novo peptide sequencing from multistage mass spectra (despite the inferior quality of MS 3 spectra) and improved interpretation of spectral networks. We further study the problem of de novo peptide sequencing with accurate parent mass (but inaccurate fragment masses), the protocol that may soon become the dominant mode of spectral acquisition. Most existing peptide sequencing algorithms (based on the spectrum graph approach) do not track the accurate parent mass and are thus not equipped for solving this problem. We describe a de novo peptide sequencing algorithm aimed at this experimental protocol and show that it improves the sequencing accuracy on both tandem and multistage mass spectrometry.     

Determination of glycan structure from tandem mass spectra

Glycans are molecules made from simple sugars that form complex tree structures. Glycans constitute one of the most important protein modifications and identification of glycans remains a pressing problem in biology. Unfortunately, the structure of glycans is hard to predict from the genome sequence of an organism. In this paper, we consider the problem of deriving the topology of a glycan solely from tandem mass spectrometry (MS) data. We study, how to generate glycan tree candidates that sufficiently match the sample mass spectrum, avoiding the combinatorial explosion of glycan structures. Unfortunately, the resulting problem is known to be computationally hard. We present an efficient exact algorithm for this problem based on fixed-parameter algorithmics that can process a spectrum in a matter of seconds. We also report some preliminary results of our method on experimental data, combining it with a preliminary candidate evaluation scheme. We show that our approach is fast in applications, and that we can reach very well de novo identification results. Finally, we show how to count the number of glycan topologies for a fixed size or a fixed mass. We generalize this result to count the number of (labeled) trees with bounded out degree, improving on results obtained using Pólya's enumeration theorem.     

Proteomics-grade de novo sequencing approach

The conventional approach in modern proteomics to identify proteins from limited information provided by molecular and fragment masses of their enzymatic degradation products carries an inherent risk of both false positive and false negative identifications. For reliable identification of even known proteins, complete de novo sequencing of their peptides is desired. The main problems of conventional sequencing based on tandem mass spectrometry are incomplete backbone fragmentation and the frequent overlap of fragment masses. In this work, the first proteomics-grade de novo approach is presented, where the above problems are alleviated by the use of complementary fragmentation techniques CAD and ECD. Implementation of a high-current, large-area dispenser cathode as a source of low-energy electrons provided efficient ECD of doubly charged peptides, the most abundant species (65-80%), in a typical trypsin-based proteomics experiment. A new linear de novo algorithm is developed combining efficiency and speed, processing on a conventional 3 GHz PC, 1000 MS/MS data sets in 60 s. More than 6% of all MS/MS data for doubly charged peptides yielded complete sequences, and another 13% gave nearly complete sequences with a maximum gap of two amino acid residues. These figures are comparable with the typical success rates (5-15%) of database identification. For peptides reliably found in the database (Mowse score > or = 34), the agreement with de novo-derived full sequences was >95%. Full sequences were derived in 67% of the cases when full sequence information was present in MS/MS spectra. Thus the new de novo sequencing approach reached the same level of efficiency and reliability as conventional database-identification strategies.     

De novo peptide sequencing via tandem mass spectrometry.

Peptide sequencing via tandem mass spectrometry (MS/MS) is one of the most powerful tools in proteomics for identifying proteins. Because complete genome sequences are accumulating rapidly, the recent trend in interpretation of MS/MS spectra has been database search. However, de novo MS/MS spectral interpretation remains an open problem typically involving manual interpretation by expert mass spectrometrists. We have developed a new algorithm, SHERENGA, for de novo interpretation that automatically learns fragment ion types and intensity thresholds from a collection of test spectra generated from any type of mass spectrometer. The test data are used to construct optimal path scoring in the graph representations of MS/MS spectra. A ranked list of high scoring paths corresponds to potential peptide sequences. SHERENGA is most useful for interpreting sequences of peptides resulting from unknown proteins and for validating the results of database search algorithms in fully automated, high-throughput peptide sequencing.     

Constrained de novo sequencing of conotoxins

De novo peptide sequencing by mass spectrometry (MS) can determine the amino acid sequence of an unknown peptide without reference to a protein database. MS-based de novo sequencing assumes special importance in focused studies of families of biologically active peptides and proteins, such as hormones, toxins, and antibodies, for which amino acid sequences may be difficult to obtain through genomic methods. These protein families often exhibit sequence homology or characteristic amino acid content; yet, current de novo sequencing approaches do not take advantage of this prior knowledge and, hence, search an unnecessarily large space of possible sequences. Here, we describe an algorithm for de novo sequencing that incorporates sequence constraints into the core graph algorithm and thereby reduces the search space by many orders of magnitude. We demonstrate our algorithm in a study of cysteine-rich toxins from two cone snail species (Conus textile and Conus stercusmuscarum) and report 13 de novo and about 60 total toxins.     

Database independent detection of isotopically labeled MS/MS spectrum peptide pairs

Mass spectrometry data generated in differential profiling of complex protein samples are classically exploited using database searches. In addition, quantitative profiling is performed by various methods, one of them using isotopically coded affinity tags, where one typically uses a light and a heavy tag. Here, we present a new algorithm, ICATcher, which detects pairs of light/heavy peptide MS/MS spectra independent of sequence databases. The method can be used for de novo sequencing and detection of posttranslational modifications. ICATcher is distributed as open source software.     

Induction and partial characterization of California halibut (Paralichthys californicus) vitellogenin

The egg yolk precursor protein, vitellogenin (Vg), was isolated by size exclusion and ion exchange chromatography from plasma of California halibut (Paralichthys californicus) treated with estrogen. MALDI TOF mass spectrometry (MS) analysis resulted in a molecular mass of 188 kDa. MS/MS de novo sequencing identified the protein as Vg by matching sequences of tryptic peptides to the known sequences of several other species. Matches were also made to two different forms of Vg in haddock, medaka, and mummichog, providing evidence that California halibut has more than one form of Vg. Native PAGE and Western blot with an antibody to turbot (Scophthalmus maximus) Vg confirmed the identity of the protein. Protein resolved on the SDS PAGE as a double band of approximately the same mass as determined with MALDI TOF, and two lower mass bands that were also immunoreactive. MALDI TOF and MS/MS de novo sequencing were useful for determining the molecular mass, identification, and exploring the multiplicity of Vg. The potential of using other MS methods to understand the structure and function of Vg is discussed.     

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.     

Mass spectrometric genomic data mining: Novel insights into bioenergetic pathways in Chlamydomonas reinhardtii

A new high-throughput computational strategy was established that improves genomic data mining from MS experiments. The MS/MS data were analyzed by the SEQUEST search algorithm and a combination of de novo amino acid sequencing in conjunction with an error-tolerant database search tool, operating on a 256 processor computer cluster. The error-tolerant search tool, previously established as GenomicPeptideFinder (GPF), enables detection of intron-split and/or alternatively spliced peptides from MS/MS data when deduced from genomic DNA. Isolated thylakoid membranes from the eukaryotic green alga Chlamydomonas reinhardtii were separated by 1-D SDS gel electrophoresis, protein bands were excised from the gel, digested in-gel with trypsin and analyzed by coupling nano-flow LC with MS/MS. The concerted action of SEQUEST and GPF allowed identification of 2622 distinct peptides. In total 448 peptides were identified by GPF analysis alone, including 98 intron-split peptides, resulting in the identification of novel proteins, improved annotation of gene models, and evidence of alternative splicing.     

Shotgun protein sequencing: assembly of peptide tandem mass spectra from mixtures of modified proteins

Despite significant advances in the identification of known proteins, the analysis of unknown proteins by MS/MS still remains a challenging open problem. Although Klaus Biemann recognized the potential of MS/MS for sequencing of unknown proteins in the 1980s, low throughput Edman degradation followed by cloning still remains the main method to sequence unknown proteins. The automated interpretation of MS/MS spectra has been limited by a focus on individual spectra and has not capitalized on the information contained in spectra of overlapping peptides. Indeed the powerful shotgun DNA sequencing strategies have not been extended to automated protein sequencing. We demonstrate, for the first time, the feasibility of automated shotgun protein sequencing of protein mixtures by utilizing MS/MS spectra of overlapping and possibly modified peptides generated via multiple proteases of different specificities. We validate this approach by generating highly accurate de novo reconstructions of multiple regions of various proteins in western diamondback rattlesnake venom. We further argue that shotgun protein sequencing has the potential to overcome the limitations of current protein sequencing approaches and thus catalyze the otherwise impractical applications of proteomics methodologies in studies of unknown proteins.     

Sweet Substitute: a software tool for in silico fragmentation of peptide-linked N-glycans

A software tool, Sweet Substitute, is described, which assists tandem mass spectrometry (MS/MS)-based glycosylation characterization from within a tryptic digest. The algorithm creates a virtual nanoelectrospray-quadrupole time-of-flight style-MS/MS spectrum of any user-defined N-linked glycan structure. An empirical peak height modeling routine is implemented in the program. By comparing the theoretical MS/MS data with the deconvoluted and deisotoped experimental MS/MS data, the user is able to quickly assess whether a proposed candidate oligosaccharide structure is a plausible one.     

Two-dimensional mass spectra generated from the analysis of 15N-labeled and unlabeled peptides for efficient protein identification and de novo peptide sequencing

Protein identification has been greatly facilitated by database searches against protein sequences derived from product ion spectra of peptides. This approach is primarily based on the use of fragment ion mass information contained in a MS/MS spectrum. Unambiguous protein identification from a spectrum with low sequence coverage or poor spectral quality can be a major challenge. We present a two-dimensional (2D) mass spectrometric method in which the numbers of nitrogen atoms in the molecular ion and the fragment ions are used to provide additional discriminating power for much improved protein identification and de novo peptide sequencing. The nitrogen number is determined by analyzing the mass difference of corresponding peak pairs in overlaid spectra of (15)N-labeled and unlabeled peptides. These peptides are produced by enzymatic or chemical cleavage of proteins from cells grown in (15)N-enriched and normal media, respectively. It is demonstrated that, using 2D information, i.e., m/z and its associated nitrogen number, this method can, not only confirm protein identification results generated by MS/MS database searching, but also identify peptides that are not possible to identify by database searching alone. Examples are presented of analyzing Escherichia coli K12 extracts that yielded relatively poor MS/MS spectra, presumably from the digests of low abundance proteins, which can still give positive protein identification using this method. Additionally, this 2D MS method can facilitate spectral interpretation for de novo peptide sequencing and identification of posttranslational or other chemical modifications. We envision that this method should be particularly useful for proteome expression profiling of organelles or cells that can be grown in (15)N-enriched media.     

Peptide sequence tags for fast database search in mass-spectrometry

Filtration techniques in the form of rapid elimination of candidate sequences while retaining the true one are key ingredients of database searches in genomics. Although SEQUEST and Mascot perform a conceptually similar task to the tool BLAST, the key algorithmic idea of BLAST (filtration) was never implemented in these tools. As a result MS/MS protein identification tools are becoming too time-consuming for many applications including search for post-translationally modified peptides. Moreover, matching millions of spectra against all known proteins will soon make these tools too slow in the same way that "genome vs genome" comparisons instantly made BLAST too slow. We describe the development of filters for MS/MS database searches that dramatically reduce the running time and effectively remove the bottlenecks in searching the huge space of protein modifications. Our approach, based on a probability model for determining the accuracy of sequence tags, achieves superior results compared to GutenTag, a popular tag generation algorithm. Our tag generating algorithm along with our de novo sequencing algorithm PepNovo can be accessed via the URL http://peptide.ucsd.edu/.     

Inferring haplotypes from genotypes on a pedigree with mutations, genotyping errors and missing alleles

Inferring the haplotypes of the members of a pedigree from their genotypes has been extensively studied. However, most studies do not consider genotyping errors and de novo mutations. In this paper, we study how to infer haplotypes from genotype data that may contain genotyping errors, de novo mutations, and missing alleles. We assume that there are no recombinants in the genotype data, which is usually true for tightly linked markers. We introduce a combinatorial optimization problem, called haplotype configuration with mutations and errors (HCME), which calls for haplotype configurations consistent with the given genotypes that incur no recombinants and require the minimum number of mutations and errors. HCME is NP-hard. To solve the problem, we propose a heuristic algorithm, the core of which is an integer linear program (ILP) using the system of linear equations over Galois field GF(2). Our algorithm can detect and locate genotyping errors that cannot be detected by simply checking the Mendelian law of inheritance. The algorithm also offers error correction in genotypes/haplotypes rather than just detecting inconsistencies and deleting the involved loci. Our experimental results show that the algorithm can infer haplotypes with a very high accuracy and recover 65%-94% of genotyping errors depending on the pedigree topology.     

Proteome informatics I: bioinformatics tools for processing experimental data

Bioinformatics tools for proteomics, also called proteome informatics tools, span today a large panel of very diverse applications ranging from simple tools to compare protein amino acid compositions to sophisticated software for large-scale protein structure determination. This review considers the available and ready to use tools that can help end-users to interpret, validate and generate biological information from their experimental data. It concentrates on bioinformatics tools for 2-DE analysis, for LC followed by MS analysis, for protein identification by PMF, by peptide fragment fingerprinting and by de novo sequencing and for data quantitation with MS data. It also discloses initiatives that propose to automate the processes of MS analysis and enhance the quality of the obtained results.     

Proteomic profiling and protein identification by MALDI-TOF mass spectrometry in unsequenced parasitic nematodes

Lack of genomic sequence data and the relatively high cost of tandem mass spectrometry have hampered proteomic investigations into helminths, such as resolving the mechanism underpinning globally reported anthelmintic resistance. Whilst detailed mechanisms of resistance remain unknown for the majority of drug-parasite interactions, gene mutations and changes in gene and protein expression are proposed key aspects of resistance. Comparative proteomic analysis of drug-resistant and -susceptible nematodes may reveal protein profiles reflecting drug-related phenotypes. Using the gastro-intestinal nematode, Haemonchus contortus as case study, we report the application of freely available expressed sequence tag (EST) datasets to support proteomic studies in unsequenced nematodes. EST datasets were translated to theoretical protein sequences to generate a searchable database. In conjunction with matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF-MS), Peptide Mass Fingerprint (PMF) searching of databases enabled a cost-effective protein identification strategy. The effectiveness of this approach was verified in comparison with MS/MS de novo sequencing with searching of the same EST protein database and subsequent searches of the NCBInr protein database using the Basic Local Alignment Search Tool (BLAST) to provide protein annotation. Of 100 proteins from 2-DE gel spots, 62 were identified by MALDI-TOF-MS and PMF searching of the EST database. Twenty randomly selected spots were analysed by electrospray MS/MS and MASCOT Ion Searches of the same database. The resulting sequences were subjected to BLAST searches of the NCBI protein database to provide annotation of the proteins and confirm concordance in protein identity from both approaches. Further confirmation of protein identifications from the MS/MS data were obtained by de novo sequencing of peptides, followed by FASTS algorithm searches of the EST putative protein database. This study demonstrates the cost-effective use of available EST databases and inexpensive, accessible MALDI-TOF MS in conjunction with PMF for reliable protein identification in unsequenced organisms.