Spectral dictionaries: Integrating de novo peptide sequencing with database search of tandem mass spectra

Database search tools identify peptides by matching tandem mass spectra against a protein database. We study an alternative approach when all plausible de novo interpretations of a spectrum (spectral dictionary) are generated and then quickly matched against the database. We present a new MS-Dictionary algorithm for efficiently generating spectral dictionaries and demonstrate that MS-Dictionary can identify spectra that are missed in the database search. We argue that MS-Dictionary enables proteogenomics searches in six-frame translation of genomic sequences that may be prohibitively time-consuming for existing database search approaches. We show that such searches allow one to correct sequencing errors and find programmed frameshifts.     

Clostridiolysin S,a Post-translationally Modified Biotoxin from Clostridium botulinum

Through elaboration of its botulinum toxins, Clostridium botulinum produces clinical syndromes of infant botulism, wound botulism, and other invasive infections. Using comparative genomic analysis, an orphan nine-gene cluster was identified in C. botulinum and the related foodborne pathogen Clostridium sporogenes that resembled the biosynthetic machinery for streptolysin S, a key virulence factor from group A Streptococcus responsible for its hallmark β-hemolytic phenotype. Genetic complementation, in vitro reconstitution, mass spectral analysis, and plasmid intergrational mutagenesis demonstrate that the streptolysin S-like gene cluster from Clostridium sp. is responsible for the biogenesis of a novel post-translationally modified hemolytic toxin, clostridiolysin S.     

Fish infections associated with the genus Aeromonas: a review of the effects on oxidative status

The aim of this review was to summarize the current knowledge regarding the effects of aeromonosis on fish oxidative status. The bibliographic survey was carried out on the research platforms: Scopus and Science Direct. The keywords ‘Aeromonas’, ‘fish’ and ‘oxidative status’ (or ‘oxidative stress’, ‘oxidative damage’ and similar terms) were used. Scientific papers and short communications were considered. Studies involving fish aeromonosis and enzymatic or non-enzymatic markers of oxidative status were selected. The results of antioxidant enzymes activities/expressions after infection lack consistency, suggesting that these findings should be interpreted with caution. Most of the analysed studies pointed to an increase in reactive oxygen species, malondialdehyde and protein carbonylation levels, indicating possible oxidative damage caused by the infection. Thus, these three biomarkers are excellent indicators of oxidative stress during infection. Regarding respiratory burst activity, several studies have indicated increased activity, but other studies have indicated unchanged activity after infection. Nitric oxide levels also increased after infection in most studies. Therefore, it is suggested that the fish’s immune system tries to fight a bacterial infection by releasing reactive oxygen and nitrogen species.     

Modelling foraging ants in a dynamic and confined environment

In social insects, the superposition of simple individual behavioral rules leads to the emergence of complex collective patterns and helps solve difficult problems inherent to surviving in hostile habitats. Modelling ant colony foraging reveals strategies arising from the insects’ self-organization and helps develop of new computational strategies in order to solve complex problems. This paper presents advances in modelling ants’ behavior when foraging in a confined and dynamic environment, based on experiments with the Argentine ant Linepithema humile in a relatively complex artificial network. We propose a model which overcomes the problem of stagnation observed in earlier models by taking into account additional biological aspects, by using non-linear functions for the deposit, perception and evaporation of pheromone, and by introducing new mechanisms to represent randomness and the exploratory behavior of the ants.     

Shotgun Protein Sequencing with Meta-contig Assembly

Full-length de novo sequencing from tandem mass (MS/MS) spectra of unknown proteins such as antibodies or proteins from organisms with unsequenced genomes remains a challenging open problem. Conventional algorithms designed to individually sequence each MS/MS spectrum are limited by incomplete peptide fragmentation or low signal to noise ratios and tend to result in short de novo sequences at low sequencing accuracy. Our shotgun protein sequencing (SPS) approach was developed to ameliorate these limitations by first finding groups of unidentified spectra from the same peptides (contigs) and then deriving a consensus de novo sequence for each assembled set of spectra (contig sequences). But whereas SPS enables much more accurate reconstruction of de novo sequences longer than can be recovered from individual MS/MS spectra, it still requires error-tolerant matching to homologous proteins to group smaller contig sequences into full-length protein sequences, thus limiting its effectiveness on sequences from poorly annotated proteins. Using low and high resolution CID and high resolution HCD MS/MS spectra, we address this limitation with a Meta-SPS algorithm designed to overlap and further assemble SPS contigs into Meta-SPS de novo contig sequences extending as long as 100 amino acids at over 97% accuracy without requiring any knowledge of homologous protein sequences. We demonstrate Meta-SPS using distinct MS/MS data sets obtained with separate enzymatic digestions and discuss how the remaining de novo sequencing limitations relate to MS/MS acquisition settings.Database search tools, such as Sequest (3), Mascot (4), and InsPecT (5), are the most frequently used methods for reliable protein identification in tandem mass (MS/MS) spectrometry based proteomics. These operate by separately matching each MS/MS spectrum to peptide sequences from reference protein databases where all proteins of interest are presumably contained. But this assumption often does not hold true as many important proteins, such as monoclonal antibodies, are not contained in any database because mechanisms of antibody variation (including genetic recombination and somatic hyper-mutation (6)) constantly create new proteins with novel unique sequences. These mechanisms of variation are the foundation of adaptive immune systems and have enabled highly successful antibody-based therapeutic strategies (7, 8). Nevertheless, such variation also means that antibody MS/MS spectra are typically impossible to identify via standard database search techniques whenever the corresponding sequences are not known in advance. An inherent drawback of database search strategies is that they are only as good as the database(s) being searched and incomplete databases often result in proteins being misidentified or left unidentified (9).Despite the importance of novel protein identification, few high-throughput methods have been developed for de novo sequencing of unknown proteins. Low-throughput Edman degradation is a well-known de novo sequencing approach that can accurately call amino acid sequences in N/C-terminal regions of unknown proteins but has drawbacks that make it unsuitable for sequencing proteins longer than 50 amino acids or proteins with post-translational modifications (10, 11). Many have recognized the potential of tandem mass spectrometry for protein sequencing. For example, in 1987 Johnson and Biemann (12) manually sequenced a complete protein from rabbit bone marrow. Meanwhile, automated de novo sequencing methods that rely on interpretations of individual MS/MS spectra are limited in that they typically cannot reconstruct long (8+ AA) sequences without mis-predicting 1 in 5 AA on average for low accuracy collision-induced dissociation (CID) spectra (13, 14). Recent advances in de novo peptide sequencing have improved sequencing accuracy to over 95% for high resolution higher energy collisional dissociation (HCD)¹ spectra (15), but at limited sequence coverage (Chi H et al. report only 55% sequence coverage of peptides identified by database search). In fact, all current per-spectrum de novo sequencing strategies face a significant tradeoff between sequencing accuracy and coverage as spectra exhibiting complete peptide fragmentation rarely cover entire target proteins, yet are required to accurately reconstruct full-length peptide sequences. An alternative approach to separately sequencing individual spectra is to simultaneously interpret multiple MS/MS spectra from overlapping peptides. This Shotgun Protein Sequencing (SPS) paradigm differs from traditional algorithms by deriving consensus sequences from contigs - sets of multiple MS/MS spectra from distinct peptides with overlapping sequences (1, 16). Because SPS aggregates multiple spectra from overlapping peptides, protein sequences extending beyond the length of enzymatically digested peptides can be extracted from spectra with incomplete peptide fragmentation. Furthermore, SPS has been found to generate sequences that frequently cover 90–95+% of the target protein sequence(s) whereas mis-predicting only 1 out of every 20 amino acids on high resolution MS/MS spectra (2). But a remaining limitation of SPS is that it still generates fragmented sequences that do not singularly cover large regions of the target protein sequences, much less complete proteins: SPS sequences have an average length of 10–15 amino acids (depending on input data) and the longest recovered SPS de novo sequence is less than 45 amino acids long (1).The considerable limitations of de novo sequencing strategies have typically been addressed by attempting to circumvent them using error-tolerant matching to known protein sequences. One such strategy (17) is to generate short de novo sequence tags and then match them exactly to protein databases without requiring matching the N/C-term flanking masses (to allow for unexpected polymorphisms or post-translational modifications). Short sequence tags are usually derived from parts of the spectrum with high signal-to-noise ratios and typically have higher sequencing accuracy than full-length de novo sequences (18). This approach was later extended in MS-Shotgun (19) and continues to be a popular technique for speeding up database search tools (5, 20–22). Homology matching of full length de novo sequences was first explored in CIDentify (23) and later in MS-BLAST (24) by searching de novo sequences using FASTA and WU-BLAST2 (respectively) to find homologous matches to sequences of related proteins; FASTS (25) also approached the problem using a modified version of FASTA. However, common de novo sequencing errors tend to produce sequences that are heavily penalized in pure sequence homology searches. For example, missing peaks in MS/MS spectra may easily cause GA subsequences to be reconstructed as Q or AG (same-mass sequences), thus making subsequent BLAST searches unlikely to succeed. This issue was partially considered in CIDentify and more thoroughly addressed in SPIDER (26) by explicitly modeling de novo sequencing errors together with BLOSUM scores in MS/MS-based sequence homology searches. In addition, OpenSea (27) further explored database matching of de novo sequences for analysis of unexpected post-translational modifications (PTMs). Finally, Shen et al. (28) used short unique de novo sequence tags, called UStags, to discover protein-localized PTMs.Recent approaches to homology matching of de novo sequences have built on genome assembly and sequencing techniques to achieve database-assisted full-length sequencing of unknown proteins. Comparative Shotgun Protein Sequencing (cSPS) complemented SPS assembly techniques with usage of error tolerant matching of de novo sequences to find overlapping SPS de novo sequences that are then further assembled into full-length protein sequences (2). cSPS was designed to support the sequencing of highly divergent proteins that have regions close enough in homology to transfer matches from a reference. cSPS was shown to enable de novo sequencing of monoclonal antibodies at 95+% sequencing accuracy, while simultaneously tolerating and identifying unexpected PTMs (29). In difference from cSPS, Champs (30) de novo sequences individual spectra to obtain putative peptide sequences, which are then mapped to homologous proteins to correct sequencing errors and reconstruct protein sequences with 100% accuracy and 99% coverage. However, Champs is designed to only map peptides that differ from the reference sequence by one or two amino acids and does not handle PTMs. As such, its sequencing accuracy is not directly comparable to that of cSPS as Champs was not designed to sequence highly divergent proteins (such as monoclonal antibodies) with multiple PTMs, insertions, deletions, and/or recombinations. GenoMS (31) extended the approaches in cSPS/Champs by explicitly modeling protein splice variants as paths in splice graphs where nodes represent translated exon regions (32). MS/MS spectra are first searched for exact sequence matches against all possible protein isoforms. The remaining unidentified MS/MS spectra are then aligned to the matched peptides and de novo sequenced to extend the matched sequences into novel regions. Reported sequences are 97–99% accurate and cover 96–99% of target proteins depending on sequence similarity between the novel and reference sequences (31). However, GenoMS de novo sequences are usually extended less than 3 amino acids beyond matched peptides because sequencing accuracy degrades as sequences are extended, thus preventing the consistent extension of long (10+ AA) sequences. Altogether, the use of homology matching approaches for full-length de novo protein sequencing continues to be limited by 1) requiring the previous knowledge of closely related protein sequences and 2) the inherent difficulties in statistically significant homology-tolerant matching of error-prone short de novo sequences.The Meta-SPS approach proposed here seeks to de novo sequence complete proteins, or long protein regions, without any use of a database. Meta-SPS builds upon SPS by treating SPS de novo sequences (contig sequences) as input spectra and further assembling them into longer de novo sequences (meta-contig sequences). We show that Meta-SPS extends de novo sequences to lengths over 100 AA while boosting sequencing accuracy to only 1 mistake per 40 amino acid predictions, thus enabling database-free de novo sequencing of completely novel proteins while also allowing error-tolerant matching approaches to support higher-divergence homologies (by searching longer, more accurate de novo sequences). Meta-SPS algorithms are demonstrated on CID and HCD MS/MS spectra and its limitations are discussed in relation to the underlying limitations of bottom-up tandem mass spectrometry.     

Accurate mass spectrometry based protein quantification via shared peptides

In mass spectrometry-based protein quantification, peptides that are shared across different protein sequences are often discarded as being uninformative with respect to each of the parent proteins. We investigate the use of shared peptides which are ubiquitous (~50% of peptides) in mass spectrometric data-sets for accurate protein identification and quantification. Different from existing approaches, we show how shared peptides can help compute the relative amounts of the proteins that contain them. Also, proteins with no unique peptide in the sample can still be analyzed for relative abundance. Our article uses shared peptides in protein quantification and makes use of combinatorial optimization to reduce the error in relative abundance measurements. We describe the topological and numerical properties required for robust estimates, and use them to improve our estimates for ill-conditioned systems. Extensive simulations validate our approach even in the presence of experimental error. We apply our method to a model of Arabidopsis thaliana root knot nematode infection, and investigate the differential role of several protein family members in mediating host response to the pathogen.