PepExplorer: a similarity-driven tool for analyzing de novo sequencing results

A prospective study of the proteome of equine pre-implantation embryo

The present study was conducted to investigate the global proteome of 8-day-old equine blastocysts. Follicular dynamics of eight adult mares were monitored by ultrasonography and inseminated 24 h after the detection of a preovulatory follicle. Four expanded blastocysts were recovered, pooled, and subjected to protein extraction and mass spectrometry. Protein identification was conducted based on four database searches (PEAKS, Proteome Discoverer software, SearchGUI software, and PepExplorer). Enrichment analysis was performed using g:Profiler, Panther, and String platforms. After the elimination of identification redundancies among search tools (at three levels, based on identifiers, peptides, and cross-database mapping), 1977 proteins were reliably identified in the samples of equine embryos. Proteomic analysis unveiled robust metabolic activity in the 8-day equine embryo, highlighted by an abundance of proteins engaged in key metabolic pathways like the TCA cycle, ATP biosynthesis, and glycolysis. The prevalence of chaperones among highly abundant proteins suggests that regulation of protein folding, and degradation is a key process during embryo development. These findings pave the way for developing new strategies to improve equine embryo media and optimize in vitro fertilization techniques.

De novo sequencing and native mass spectrometry revealed hetero-association of dirigent protein homologs and potential interacting proteins in Forsythia × intermedia

The discovery of dirigent proteins (DPs) and their functions in plant phenol biochemistry was made over two decades ago with Forsythia × intermedia. Stereo-selective, DP-guided, monolignol-derived radical coupling in vitro was then reported to afford the optically active lignan, (+)-pinoresinol from coniferyl alcohol, provided one-electron oxidase/oxidant capacity was present. It later became evident that DPs have several distinct sub-families, presumably with different functions. Some known DPs require other essential enzymes/proteins (e.g. oxidases) for their functions. However, the lack of a fully sequenced genome for Forsythia × intermedia made it difficult to profile other components co-purified with the (+)-pinoresinol forming DP. Herein, we used an integrated bottom-up, top-down, and native mass spectrometry (MS) approach to de novo sequence the extracted proteins via adaptation of our initial report of DP solubilization and purification. Using publicly available transcriptome and genomic data from closely related species, we identified 14 proteins that were putatively associated with either DP function or the cell wall. Although their co-occurrence after extraction and chromatographic separation is suggestive for potential protein-protein interactions, none were found to form stable protein complexes with DPs in native MS under the specific experimental conditions we have explored. Interestingly, two new DP homologs were found and they formed hetero-trimers. Molecular dynamics simulations suggested that similar hetero-trimers were possible between Arabidopsis DP homologs with comparable sequence similarities. Nevertheless, our integrated mass spectrometry method development helped prepare for future investigations directed to the discovery of novel proteins and protein-protein interactions. These advantages can be highly beneficial for plant and microbial research where fully sequenced genomes may not be readily available.

Exploring the Sexual Dimorphism of Crustacean Neuropeptide Expression Using Callinectes sapidus as a Model Organism

The impact of numerous diseases has been linked to differences in sex between organisms, including various neurological diseases. As neuropeptides are known to be key players in the nervous system, studying the variation of neuropeptidomic profiles between males and females in a crustacean model organism is of interest. By using high-resolution mass spectrometry with two complementary ionization sources in conjunction with quantitative chemical labeling (isotopic reductive dimethylation), differences were observed in five key neural tissues and hemolymph. Interestingly, while males and females possess numerous neuropeptide isoforms that are unique to their sex, the represented families of each sex remain largely consistent. However, some differences in familial isoforms were also observed, such as the relative numbers of neuropeptides belonging to RFamide and allatostatin A-type families. Additionally, >100 neuropeptides detected across five neural tissues and hemolymph were found to have statistically significant differences in abundance between male and female blue crab samples. Also, hundreds of putative peptide sequences were identified by de novo sequencing that may be indicative of previously undiscovered neuropeptides, highlighting the power of using a multifaceted MS approach.

Mass Spectrometry Quantification, Localization, and Discovery of Feeding-Related Neuropeptides in Cancer borealis

The crab Cancer borealis nervous system is an important model for understanding neural circuit dynamics and modulation, but the identity of neuromodulatory substances and their influence on circuit dynamics in this system remains incomplete, particularly with respect to behavioral state-dependent modulation. Therefore, we used a multifaceted mass spectrometry (MS) method to identify neuropeptides that differentiate the unfed and fed states. Duplex stable isotope labeling revealed that the abundance of 80 of 278 identified neuropeptides was distinct in ganglia and/or neurohemal tissue from fed vs unfed animals. MS imaging revealed that an additional 7 and 11 neuropeptides exhibited altered spatial distributions in the brain and the neuroendocrine pericardial organs (POs), respectively, during these two feeding states. Furthermore, de novo sequencing yielded 69 newly identified putative neuropeptides that may influence feeding state-related neuromodulation. Two of these latter neuropeptides were determined to be upregulated in PO tissue from fed crabs, and one of these two peptides influenced heartbeat in ex vivo preparations. Overall, the results presented here identify a cohort of neuropeptides that are poised to influence feeding-related behaviors, providing valuable opportunities for future functional studies.

Proteomic profile of pre-implantational ovine embryos produced in vivo

The present study was conducted to decipher the proteome of in vivo-produced pre-implantation ovine embryos. Ten locally adapted Morana Nova ewes received hormonal treatment and were inseminated 12 hr after ovulation. Six days later, 54 embryos (morula and blastocyst developmental state) were recovered from eight ewes and pooled to obtain sufficient protein for proteomic analysis. Extracted embryo proteins were analysed by LC-MS/MS, followed by identification based on four database searches (PEAKS, Proteome Discoverer software, SearchGUI software, PepExplorer). Identified proteins were analysed for gene ontology terms, protein clusters and interactions. Genes associated with the ovine embryo proteome were screened for miRNA targets using data sets of TargetScan (http://www.targetscan.org) and mIRBase (http://www.mirbase.org) servers. There were 667 proteins identified in the ovine embryos. Biological processes of such proteins were mainly related to cellular process and regulation, and molecular functions, to binding and catalytic activity. Analysis of the embryo proteins revealed 49 enriched functional clusters, linked to energy metabolism (TCA cycle, pyruvate and glycolysis metabolism), zona pellucida (ZP), MAPK signalling pathway, tight junction, binding of sperm to ZP, translation, proteasome, cell cycle and calcium/phospholipid binding. Sixteen miRNAs were related to 25 pre-implantation ovine embryo genes, all conserved in human, bovine and ovine species. The interaction network generated by miRNet showed four key miRNAs (hsa-mir-106b-5p; hsa-mir-30-5p; hsa-mir-103a-5p and hsa-mir-106a-5p) with potential interactions with embryo-expressed genes. Functional analysis of the network indicated that miRNAs modulate genes related to cell cycle, regulation of stem cell and embryonic cell differentiation, among others. Retrieved miRNAs also modulate the expression of genes involved in cell signalling pathways, such as MAPK, Wnt, TGF-beta, p53 and Toll-like receptor. The current study describes the first major proteomic profile of 6-day-old ovine embryos produced in vivo, setting a comprehensive foundation for our understanding of embryo physiology in the ovine species.

ProAlanase is an Effective Alternative to Trypsin for Proteomics Applications and Disulfide Bond Mapping

Trypsin is the protease of choice in bottom-up proteomics. However, its application can be limited by the amino acid composition of target proteins and the pH of the digestion solution. In this study we characterize ProAlanase, a protease from the fungus Aspergillus niger that cleaves primarily on the C-terminal side of proline and alanine residues. ProAlanase achieves high proteolytic activity and specificity when digestion is carried out at acidic pH (1.5) for relatively short (2 h) time periods. To elucidate the potential of ProAlanase in proteomics applications, we conducted a series of investigations comprising comparative multi-enzymatic profiling of a human cell line proteome, histone PTM analysis, ancient bone protein identification, phosphosite mapping and de novo sequencing of a proline-rich protein and disulfide bond mapping in mAb. The results demonstrate that ProAlanase is highly suitable for proteomics analysis of the arginine- and lysine-rich histones, enabling high sequence coverage of multiple histone family members. It also facilitates an efficient digestion of bone collagen thanks to the cleavage at the C terminus of hydroxyproline which is highly prevalent in collagen. This allows to identify complementary proteins in ProAlanase- and trypsin-digested ancient bone samples, as well as to increase sequence coverage of noncollagenous proteins. Moreover, digestion with ProAlanase improves protein sequence coverage and phosphosite localization for the proline-rich protein Notch3 intracellular domain (N3ICD). Furthermore, we achieve a nearly complete coverage of N3ICD protein by de novo sequencing using the combination of ProAlanase and tryptic peptides. Finally, we demonstrate that ProAlanase is efficient in disulfide bond mapping, showing high coverage of disulfide-containing regions in a nonreduced mAb.

De novo sequencing of proteins by mass spectrometry

INTRODUCTION

Proteins are crucial for every cellular activity and unraveling their sequence and structure is a crucial step to fully understand their biology. Early methods of protein sequencing were mainly based on the use of enzymatic or chemical degradation of peptide chains. With the completion of the human genome project and with the expansion of the information available for each protein, various databases containing this sequence information were formed.

AREAS COVERED

De novo protein sequencing, shotgun proteomics and other mass-spectrometric techniques, along with the various software are currently available for proteogenomic analysis. Emphasis is placed on the methods for de novo sequencing, together with potential and shortcomings using databases for interpretation of protein sequence data.

EXPERT OPINION

As mass-spectrometry sequencing performance is improving with better software and hardware optimizations, combined with user-friendly interfaces, de-novo protein sequencing becomes imperative in shotgun proteomic studies. Issues regarding unknown or mutated peptide sequences, as well as, unexpected post-translational modifications (PTMs) and their identification through false discovery rate searches using the target/decoy strategy need to be addressed. Ideally, it should become integrated in standard proteomic workflows as an add-on to conventional database search engines, which then would be able to provide improved identification.

Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

GiTx1(β/κ-theraphotoxin-Gi1a), a novel toxin from the venom of Brazilian tarantula Grammostola iheringi (Mygalomorphae, Theraphosidae): Isolation, structural assessments and activity on voltage-gated ion channels

Spider venoms, despite their toxicity, represent rich sources of pharmacologically active compounds with biotechnological potential. However, in view of the large diversity of the spider species, the full potential of their venom molecules is still far from being known. In this work, we report the purification and structural and functional characterization of GiTx1 (β/κ-TRTX-Gi1a), the first toxin purified from the venom of the Brazilian tarantula spider Grammostola iheringi. GiTx1 was purified by chromatography, completely sequenced through automated Edman degradation and tandem mass spectrometry and its structure was predicted by molecular modeling. GiTx1 has a MW of 3.585 Da, with the following amino acid sequence: SCQKWMWTCDQKRPCCEDMVCKLWCKIIK. Pharmacological activity of GiTx1 was characterized by electrophysiology using whole-cell patch clamp on dorsal root ganglia neurons (DRG) and two-electrode voltage-clamp on voltage-gated sodium and potassium channels subtypes expressed in Xenopus laevis oocytes. GiTx1, at 2 μM, caused a partial block of inward (∼40%) and outward (∼20%) currents in DRG cells, blocked rNav1.2, rNav1.4 and mNav1.6 and had a significant effect on VdNav, an arachnid sodium channel isoform. IC₅₀ values of 156.39 ± 14.90 nM for Nav1.6 and 124.05 ± 12.99 nM for VdNav, were obtained. In addition, this toxin was active on rKv4.3 and hERG potassium channels, but not Shaker IR or rKv2.1 potassium channels. In summary, GiTx1 is a promiscuous toxin with multiple effects on different types of ion channels.

The interaction between the natural metalloendopeptidase inhibitor BJ46a and its target toxin jararhagin analyzed by structural mass spectrometry and molecular modeling

Snakebite envenoming affects millions of people worldwide, being officially considered a neglected tropical disease by the World Health Organization. The antivenom is effective in neutralizing the systemic effects of envenomation, but local effects are poorly neutralized, often leading to permanent disability. The natural resistance of the South American pit viper Bothrops jararaca to its venom is partly attributed to BJ46a, a natural snake venom metalloendopeptidase inhibitor. Upon complex formation, BJ46a binds non-covalently to the metalloendopeptidase, rendering it unable to exert its proteolytic activity. However, the structural features that govern this interaction are largely unknown. In this work, we applied structural mass spectrometry techniques (cross-linking-MS and hydrogen-deuterium exchange MS) and in silico analyses (molecular modeling, docking, and dynamics simulations) to understand the interaction between BJ46a and jararhagin, a metalloendopeptidase from B. jararaca venom. We explored the distance restraints generated from XL-MS experiments to guide the modeling of BJ46a and jararhagin, as well as the protein-protein docking simulations. HDX-MS data pinpointed regions of protection/deprotection at the interface of the BJ46a-jararhagin complex which, in addition to the molecular dynamics simulation data, reinforced our proposed interaction model. Ultimately, the structural understanding of snake venom metalloendopeptidases inhibition by BJ46a could lead to the rational design of drugs to improve anti-snake venom therapeutics, alleviating the high morbidity rates currently observed.

Combination of Proteogenomics with Peptide De Novo Sequencing Identifies New Genes and Hidden Posttranscriptional Modifications

Next-generation sequencing techniques have considerably increased the number of completely sequenced eukaryotic genomes. These genomes are mostly automatically annotated, and ab initio gene prediction is commonly combined with homology-based search approaches and often supported by transcriptomic data. The latter in particular improve the prediction of intron splice sites and untranslated regions. However, correct prediction of translation initiation sites (TIS), alternative splice junctions, and protein-coding potential remains challenging. Here, we present an advanced proteogenomics approach, namely, the combination of proteogenomics and de novo peptide sequencing analysis, in conjunction with Blast2GO and phylostratigraphy. Using the model fungus Sordaria macrospora as an example, we provide a comprehensive view of the proteome that not only increases the functional understanding of this multicellular organism at different developmental stages but also immensely enhances the genome annotation quality.

Proteogenomics combines proteomics, genomics, and transcriptomics and has considerably improved genome annotation in poorly investigated phylogenetic groups for which homology information is lacking. Furthermore, it can be advantageous when reinvestigating well-annotated genomes. Here, we applied an advanced proteogenomics approach, combining standard proteogenomics with peptide de novo sequencing, to refine annotation of the well-studied model fungus Sordaria macrospora. We investigated samples from different developmental and physiological conditions, resulting in the detection of 104 so-far hidden proteins and annotation changes in 575 genes, including 389 splice site refinements. Significantly, our approach provides peptide-level evidence for 113 single-amino-acid variations and 15 C-terminal protein elongations originating from A-to-I RNA editing, a phenomenon recently detected in fungi. Coexpression and phylostratigraphic analysis of the refined proteome suggest that new functions in evolutionarily young genes correlate with distinct developmental stages. In conclusion, our advanced proteogenomics approach supports and promotes functional studies of fungal model systems.

Proteome Dataset of Qualea grandiflora Mart. (Vochysiaceae) by LC-MS/MS Label-Free Identification in Response to Aluminum

This dataset brief is about the descriptive proteome of Qualea grandiflora plants by label free mass spectrometry (LC-MS/MS). Q. grandiflora is a plant that accumulates aluminum (Al) in high quantities and requires it for growth and development. Although quite relevant for the understanding of Al effects on plants, the proteome of Q. grandiflora has not been studied yet. Therefore, the current proteome analysis identifies a total of 2010 proteins. Furthermore, the identified Q. grandiflora root proteins are associated with several crucial molecular functions, biological processes, and cellular sites. Hence, the proteome analysis of Q. grandiflora will contribute to unravel how plants evolved to cope with high levels of Al in soils. All data can be accessed at the Centre for Computational Mass Spectrometry - MassIVE MSV000082284 - https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=adb9647282a5421a9cffe3124c060f46.

Data Independent Acquisition Mass Spectrometry Method for Improved Neuropeptidomic Coverage in Crustacean Neural Tissue Extracts

Neuropeptides are an important class of signaling molecules in the nervous and neuroendocrine system, but they are challenging to study due to their low concentration in vivo in the presence of numerous interfering artifacts. Often the limitation of mass spectrometry analyses of neuropeptides in complex tissue extracts is not due to neuropeptides being below the detection limit but due to ions not being selected for tandem mass spectrometry during the liquid chromatography elution time and therefore not being identified. In this study, a data independent acquisition (DIA) method was developed to improve the coverage of neuropeptides in neural tissue from the model organism C. borealis. The optimal mass-to-charge ratio range and isolation window were determined and subsequently used to detect more neuropeptides in extracts from the brain and pericardial organs than the conventional data dependent acquisition method. The DIA method led to the detection of almost twice as many neuropeptides in the brain and approximately 1.5-fold more neuropeptides in the pericardial organs. The technical and biological reproducibility were also explored and found to be improved over the original method, with 56% of neuropeptides detected in 3 out of 3 replicate injections and 62% in 3 out of 3 biological replicates. Furthermore, 68 putative novel neuropeptides were detected and identified with de novo sequencing. The quantitative accuracy of the method was also explored. The developed method is anticipated to be useful for gaining a deeper profiling of neuropeptides, especially those in low abundance, in a variety of sample types.

Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?

While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies. The main objective of this work is to provide a detailed evaluation on the performance of de novo sequencing algorithms on high-resolution data. For this purpose, we processed four experimental data sets acquired from different instrument types from collision-induced dissociation and higher energy collisional dissociation (HCD) fragmentation mode using the software packages Novor, PEAKS and PepNovo. Moreover, the accuracy of these algorithms is also tested on ground truth data based on simulated spectra generated from peak intensity prediction software. We found that Novor shows the overall best performance compared with PEAKS and PepNovo with respect to the accuracy of correct full peptide, tag-based and single-residue predictions. In addition, the same tool outpaced the commercial competitor PEAKS in terms of running time speedup by factors of around 12-17. Despite around 35% prediction accuracy for complete peptide sequences on HCD data sets, taken as a whole, the evaluated algorithms perform moderately on experimental data but show a significantly better performance on simulated data (up to 84% accuracy). Further, we describe the most frequently occurring de novo sequencing errors and evaluate the influence of missing fragment ion peaks and spectral noise on the accuracy. Finally, we discuss the potential of de novo sequencing for now becoming more widely used in the field.

Proteomic Deep Mining the Venom of the Red-Headed Krait, Bungarus flaviceps

The use of -omics technologies allows for the characterization of snake venom composition at a fast rate and at high levels of detail. In the present study, we investigated the protein content of Red-headed Krait (Bungarus flaviceps) venom. This analysis revealed a high diversity of snake venom protein families, as evidenced by high-throughput mass spectrometric analysis. We found all six venom protein families previously reported in a transcriptome study of the venom gland of B. flaviceps, including phospholipases A₂ (PLA₂s), Kunitz-type serine proteinase inhibitors (KSPIs), three-finger toxins (3FTxs), cysteine-rich secretory proteins (CRISPs), snaclecs, and natriuretic peptides. A combined approach of automated database searches and de novo sequencing of tandem mass spectra, followed by sequence similarity searches, revealed the presence of 12 additional toxin families. De novo sequencing alone was able to identify 58 additional peptides, and this approach contributed significantly to the comprehensive description of the venom. Abundant protein families comprise 3FTxs (22.3%), KSPIs (19%), acetylcholinesterases (12.6%), PLA₂s (11.9%), venom endothelial growth factors (VEGFs, 8.4%), nucleotidases (4.3%), and C-type lectin-like proteins (snaclecs, 3.3%); an additional 11 toxin families are present at significantly lower concentrations, including complement depleting factors, a family not previously detected in Bungarus venoms. The utility of a multifaceted approach toward unraveling the proteome of snake venoms, employed here, allowed detection of even minor venom components. This more in-depth knowledge of the composition of B. flaviceps venom facilitates a better understanding of snake venom molecular evolution, in turn contributing to more effective treatment of krait bites.

A Potential Golden Age to Come-Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics

In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community.

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.

A multi-protease, multi-dissociation, bottom-up-to-top-down proteomic view of the Loxosceles intermedia venom

Venoms are a rich source for the discovery of molecules with biotechnological applications, but their analysis is challenging even for state-of-the-art proteomics. Here we report on a large-scale proteomic assessment of the venom of Loxosceles intermedia, the so-called brown spider. Venom was extracted from 200 spiders and fractioned into two aliquots relative to a 10 kDa cutoff mass. Each of these was further fractioned and digested with trypsin (4 h), trypsin (18 h), pepsin (18 h), and chymotrypsin (18 h), then analyzed by MudPIT on an LTQ-Orbitrap XL ETD mass spectrometer fragmenting precursors by CID, HCD, and ETD. Aliquots of undigested samples were also analyzed. Our experimental design allowed us to apply spectral networks, thus enabling us to obtain meta-contig assemblies, and consequently de novo sequencing of practically complete proteins, culminating in a deep proteome assessment of the venom. Data are available via ProteomeXchange, with identifier PXD005523.

Tandem Mass Spectrum Sequencing: An Alternative to Database Search Engines in Shotgun Proteomics

Protein identification via database searches has become the gold standard in mass spectrometry based shotgun proteomics. However, as the quality of tandem mass spectra improves, direct mass spectrum sequencing gains interest as a database-independent alternative. In this chapter, the general principle of this so-called de novo sequencing is introduced along with pitfalls and challenges of the technique. The main tools available are presented with a focus on user friendly open source software which can be directly applied in everyday proteomic workflows.

Novel Catalytically-Inactive PII Metalloproteinases from a Viperid Snake Venom with Substitutions in the Canonical Zinc-Binding Motif

Snake venom metalloproteinases (SVMPs) play key biological roles in prey immobilization and digestion. The majority of these activities depend on the hydrolysis of relevant protein substrates in the tissues. Hereby, we describe several isoforms and a cDNA clone sequence, corresponding to PII SVMP homologues from the venom of the Central American pit viper Bothriechis lateralis, which have modifications in the residues of the canonical sequence of the zinc-binding motif HEXXHXXGXXH. As a consequence, the proteolytic activity of the isolated proteins was undetectable when tested on azocasein and gelatin. These PII isoforms comprise metalloproteinase and disintegrin domains in the mature protein, thus belonging to the subclass PIIb of SVMPs. PII SVMP homologues were devoid of hemorrhagic and in vitro coagulant activities, effects attributed to the enzymatic activity of SVMPs, but induced a mild edema. One of the isoforms presents the characteristic RGD sequence in the disintegrin domain and inhibits ADP- and collagen-induced platelet aggregation. Catalytically-inactive SVMP homologues may have been hitherto missed in the characterization of snake venoms. The presence of such enzymatically-inactive homologues in snake venoms and their possible toxic and adaptive roles deserve further investigation.

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for the analysis of shotgun proteomic data. The contained modules allow for formatting of sequence databases, peptide spectrum matching, statistical filtering and data organization, extracting quantitative information from label-free and chemically labeled data, and analyzing statistics for differential proteomics. PatternLab also has modules to perform similarity-driven studies with de novo sequencing data, to evaluate time-course experiments and to highlight the biological significance of data with regard to the Gene Ontology database. The PatternLab for proteomics 4.0 package brings together all of these modules in a self-contained software environment, which allows for complete proteomic data analysis and the display of results in a variety of graphical formats. All updates to PatternLab, including new features, have been previously tested on millions of mass spectra. PatternLab is easy to install, and it is freely available from http://patternlabforproteomics.org.

Navigating through metaproteomics data: a logbook of database searching

Metaproteomic research involves various computational challenges during the identification of fragmentation spectra acquired from the proteome of a complex microbiome. These issues are manifold and range from the construction of customized sequence databases, the optimal setting of search parameters to limitations in the identification search algorithms themselves. In order to assess the importance of these individual factors, we studied the effect of strategies to combine different search algorithms, explored the influence of chosen database search settings, and investigated the impact of the size of the protein sequence database used for identification. Furthermore, we applied de novo sequencing as a complementary approach to classic database searching. All evaluations were performed on a human intestinal metaproteome dataset. Pyrococcus furiosus proteome data were used to contrast database searching of metaproteomic data to a classic proteomic experiment. Searching against subsets of metaproteome databases and the use of multiple search engines increased the number of identifications. The integration of P. furiosus sequences in a metaproteomic sequence database showcased the limitation of the target-decoy-controlled false discovery rate approach in combination with large sequence databases. The selection of varying search engine parameters and the application of de novo sequencing represented useful methods to increase the reliability of the results. Based on our findings, we provide recommendations for the data analysis that help researchers to establish or improve analysis workflows in metaproteomics.