Increasing confidence in proteomic spectral deconvolution through mass defect.

MOTIVATION: Confident deconvolution of proteomic spectra is critical for several applications such as de novo sequencing, cross-linking mass spectrometry and handling chimeric mass spectra. RESULTS: In general, all deconvolution algorithms may eventually report mass peaks that are not compatible with the chemical formula of any peptide. We show how to remove these artifacts by considering their mass defects. We introduce Y.A.D.A. 3.0, a fast deconvolution algorithm that can remove peaks with unacceptable mass defects. Our approach is effective for polypeptides with less than 10 kDa, and its essence can be easily incorporated into any deconvolution algorithm. AVAILABILITY AND IMPLEMENTATION: Y.A.D.A. 3.0 is freely available for academic use at http://patternlabforproteomics.org/yada3. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

ProteoCombiner: integrating bottom-up with top-down proteomics data for improved proteoform assessment.

MOTIVATION: We present a high-performance software integrating shotgun with top-down proteomic data. The tool can deal with multiple experiments and search engines. Enable rapid and easy visualization, manual validation and comparison of the identified proteoform sequences including the post-translational modification characterization. RESULTS: We demonstrate the effectiveness of our approach on a large-scale Escherichia coli dataset; ProteoCombiner unambiguously shortlisted proteoforms among those identified by the multiple search engines. AVAILABILITY AND IMPLEMENTATION: ProteoCombiner, a demonstration video and user tutorial are freely available at https://proteocombiner.pasteur.fr, for academic use; all data are thus available from the ProteomeXchange consortium (identifier PXD017618). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterizing protein conformers by cross-linking mass spectrometry and pattern recognition.

MOTIVATION: Chemical cross-linking coupled to mass spectrometry (XLMS) emerged as a powerful technique for studying protein structures and large-scale protein-protein interactions. Nonetheless, XLMS lacks software tailored toward dealing with multiple conformers; this scenario can lead to high-quality identifications that are mutually exclusive. This limitation hampers the applicability of XLMS in structural experiments of dynamic protein systems, where less abundant conformers of the target protein are expected in the sample. RESULTS: We present QUIN-XL, a software that uses unsupervised clustering to group cross-link identifications by their quantitative profile across multiple samples. QUIN-XL highlights regions of the protein or system presenting changes in its conformation when comparing different biological conditions. We demonstrate our software's usefulness by revisiting the HSP90 protein, comparing three of its different conformers. QUIN-XL's clusters correlate directly to known protein 3D structures of the conformers and therefore validates our software. AVAILABILITYAND IMPLEMENTATION: QUIN-XL and a user tutorial are freely available at http://patternlabforproteomics.org/quinxl for academic users. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Top-Down Garbage Collector: a tool for selecting high-quality top-down proteomics mass spectra.

MOTIVATION: We present the first tool for unbiased quality control of top-down proteomics datasets. Our tool can select high-quality top-down proteomics spectra, serve as a gateway for building top-down spectral libraries and, ultimately, improve identification rates. RESULTS: We demonstrate that a twofold rate increase for two E. coli top-down proteomics datasets may be achievable. AVAILABILITY AND IMPLEMENTATION: http://patternlabforproteomics.org/tdgc, freely available for academic use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical Measures Related to Forward Shoulder Posture: A Reliability and Correlational Study.

OBJECTIVES: The purpose of this study was to examine the reliability of clinical measures related to forward shoulder posture (pectoralis minor index [PMI], scapular index [SI], abduction index [AI], acromion to the wall index [AWI] acromion to the treatment table index [ATI], and thoracic curvature [TC]), and to investigate the association (redundancy) among these measures. METHODS: Twenty-one asymptomatic participants participated in this study. Two physiotherapists were trained to perform the clinical measurements. Intraclass correlation coefficients (ICC2,k) were calculated to assess intra- and interrater reliabilities. Pearson product moment correlation was used to investigate the existence of possible redundancy between the measures that showed high intra- and interrater reliabilities. RESULTS: The measures showed ICCs between 0.30 and 0.97. Five measures, PMI, SI, AWI, ATI, and TC, showed appropriate values for intrarater reliability (ICCs 0.77-0.94), and 3 measures, AWI, ATI, and TC, for interrater reliability (ICCs 0.82-0.85). Among measures that showed acceptable intra- and interrater reliability values, 2 measures were redundant, showing high association (AWI vs ATI) (r = 0.80, P < .001). CONCLUSION: For PMI, SI, AWI, ATI, and TC measures, adequate values of intrarater reliability were observed. For AWI, ATI, and TC, adequate values of interrater reliability were found. Two pairs of measures were highly associated (PMI with SI; AWI with ATI), which indicates redundancy among them. Our results suggest that, when the same examiner performs the assessment, the combined use of the PMI, AWI, and TC measures allows a quick but comprehensive evaluation of the presence of forward shoulder posture.

DiagnoProt: a tool for discovery of new molecules by mass spectrometry.

MOTIVATION: Around 75% of all mass spectra remain unidentified by widely adopted proteomic strategies. We present DiagnoProt, an integrated computational environment that can efficiently cluster millions of spectra and use machine learning to shortlist high-quality unidentified mass spectra that are discriminative of different biological conditions. RESULTS: We exemplify the use of DiagnoProt by shortlisting 4366 high-quality unidentified tandem mass spectra that are discriminative of different types of the Aspergillus fungus. AVAILABILITY AND IMPLEMENTATION: DiagnoProt, a demonstration video and a user tutorial are available at http://patternlabforproteomics.org/diagnoprot . CONTACT: andrerfsilva@gmail.com or paulo@pcarvalho.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Evaluation of the Crystal Structure of C-terminal Truncated Apolipoprotein A-I in Solution Reveals Structural Dynamics Related to Lipid Binding.

Apolipoprotein (apo) A-I mediates many of the anti-atherogenic functions attributed to high density lipoprotein. Unfortunately, efforts toward a high resolution structure of full-length apoA-I have not been fruitful, although there have been successes with deletion mutants. Recently, a C-terminal truncation (apoA-I(Δ185-243)) was crystallized as a dimer. The structure showed two helical bundles connected by a long, curved pair of swapped helical domains. To compare this structure to that existing under solution conditions, we applied small angle x-ray scattering and isotope-assisted chemical cross-linking to apoA-I(Δ185-243) in its dimeric and monomeric forms. For the dimer, we found evidence for the shared domains and aspects of the N-terminal bundles, but not the molecular curvature seen in the crystal. We also found that the N-terminal bundles equilibrate between open and closed states. Interestingly, this movement is one of the transitions proposed during lipid binding. The monomer was consistent with a model in which the long shared helix doubles back onto the helical bundle. Combined with the crystal structure, these data offer an important starting point to understand the molecular details of high density lipoprotein biogenesis.

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

Peptide spectrum matching is the current gold standard for protein identification via mass-spectrometry-based proteomics. Peptide spectrum matching compares experimental mass spectra against theoretical spectra generated from a protein sequence database to perform identification, but protein sequences not present in a database cannot be identified unless their sequences are in part conserved. The alternative approach, de novo sequencing, can make it possible to infer a peptide sequence directly from a mass spectrum, but interpreting long lists of peptide sequences resulting from large-scale experiments is not trivial. With this as motivation, PepExplorer was developed to use rigorous pattern recognition to assemble a list of homologue proteins using de novo sequencing data coupled to sequence alignment to allow biological interpretation of the data. PepExplorer can read the output of various widely adopted de novo sequencing tools and converge to a list of proteins with a global false-discovery rate. To this end, it employs a radial basis function neural network that considers precursor charge states, de novo sequencing scores, peptide lengths, and alignment scores to select similar protein candidates, from a target-decoy database, usually obtained from phylogenetically related species. Alignments are performed using a modified Smith-Waterman algorithm tailored for the task at hand. We verified the effectiveness of our approach using a reference set of identifications generated by ProLuCID when searching for Pyrococcus furiosus mass spectra on the corresponding NCBI RefSeq database. We then modified the sequence database by swapping amino acids until ProLuCID was no longer capable of identifying any proteins. By searching the mass spectra using PepExplorer on the modified database, we were able to recover most of the identifications at a 1% false-discovery rate. Finally, we employed PepExplorer to disclose a comprehensive proteomic assessment of the Bothrops jararaca plasma, a known biological source of natural inhibitors of snake toxins. PepExplorer is integrated into the PatternLab for Proteomics environment, which makes available various tools for downstream data analysis, including resources for quantitative and differential proteomics.

The influence of iron on the proteomic profile of Chromobacterium violaceum.

BACKGROUND: Chromobacterium violaceum is a bacterium commonly found in tropical and subtropical regions and is associated with important pharmacological and industrial attributes such as producing substances with therapeutic properties and synthesizing biodegradable polymers. Its genome was sequenced, however, approximately 40% of its genes still remain with unknown functions. Although C. violaceum is known by its versatile capacity of living in a wide range of environments, little is known on how it achieves such success. Here, we investigated the proteomic profile of C. violaceum cultivated in the absence and presence of high iron concentration, describing some proteins of unknown function that might play an important role in iron homeostasis, amongst others. RESULTS: Briefly, C. violaceum was cultivated in the absence and in the presence of 9 mM of iron during four hours. Total proteins were identified by LC-MS and through the PatternLab pipeline. Our proteomic analysis indicates major changes in the energetic metabolism, and alterations in the synthesis of key transport and stress proteins. In addition, it may suggest the presence of a yet unidentified operon that could be related to oxidative stress, together with a set of other proteins with unknown function. The protein-protein interaction network also pinpointed the importance of energetic metabolism proteins to the acclimatation of C. violaceum in high concentration of iron. CONCLUSIONS: This is the first proteomic analysis of the opportunistic pathogen C. violaceum in the presence of high iron concentration. Our data allowed us to identify a yet undescribed operon that might have a role in oxidative stress defense. Our work provides new data that will contribute to understand how this bacterium achieve its capacity of surviving in harsh conditions as well as to open a way to explore the yet little availed biotechnological characteristics of this bacterium with the further exploring of the proteins of unknown function that we showed to be up-regulated in high iron concentration.

Comparative proteomic analysis of the aging soleus and extensor digitorum longus rat muscles using TMT labeling and mass spectrometry.

Sarcopenia describes an age-related decline in skeletal muscle mass, strength, and function that ultimately impairs metabolism and leads to poor balance, frequent falling, limited mobility, and a reduction in quality of life. Here we investigate the pathogenesis of sarcopenia through a proteomic shotgun approach. In brief, we employed tandem mass tags to quantitate and compare the protein profiles obtained from young versus old rat slow-twitch type of muscle (soleus) and a fast-twitch type of muscle (extensor digitorum longus, EDL). Our results disclose 3452 and 1848 proteins identified from soleus and EDL muscles samples, of which 78 and 174 were found to be differentially expressed, respectively. In general, most of the proteins were structural related and involved in energy metabolism, oxidative stress, detoxification, or transport. Aging affected soleus and EDL muscles differently, and several proteins were regulated in opposite ways. For example, pyruvate kinase had its expression and activity different in both soleus and EDL muscles. We were able to verify with existing literature many of our differentially expressed proteins as candidate aging biomarkers and, most importantly, disclose several new candidate biomarkers such as the glioblastoma amplified sequence, zero ß-globin, and prolargin.

DiagnoMass: A proteomics hub for pinpointing discriminative spectral clusters.

MOTIVATION: There are several well-established paradigms for identifying and pinpointing discriminative peptides/proteins using shotgun proteomic data; examples are peptide-spectrum matching, de novo sequencing, open searches, and even hybrid approaches. Such an arsenal of complementary paradigms can provide deep data coverage, albeit some unidentified discriminative peptides remain. RESULTS: We present DiagnoMass, software tool that groups similar spectra into spectral clusters and then shortlists those clusters that are discriminative for biological conditions. DiagnoMass then communicates with proteomic tools to attempt the identification of such clusters. We demonstrate the effectiveness of DiagnoMass by analyzing proteomic data from Escherichia coli, Salmonella, and Shigella, listing many high-quality discriminative spectral clusters that had thus far remained unidentified by widely adopted proteomic tools. DiagnoMass can also classify proteomic profiles. We anticipate the use of DiagnoMass as a vital tool for pinpointing biomarkers. AVAILABILITY: DiagnoMass and related documentation, including a usage protocol, are available at http://www.diagnomass.com.

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V.

Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org .

Leveraging the partition selection bias to achieve a high-quality clustering of mass spectra.

In proteomics, the identification of peptides from mass spectral data can be mathematically described as the partitioning of mass spectra into clusters (i.e., groups of spectra derived from the same peptide). The way partitions are validated is just as important, having evolved side by side with the clustering algorithms themselves and given rise to many partition assessment measures. An assessment measure is said to have a selection bias if, and only if, the probability that a randomly chosen partition scoring a high value depends on the number of clusters in the partition. In the context of clustering mass spectra, this might mislead the validation process to favor clustering algorithms that generate too many (or few) spectral clusters, regardless of the underlying peptide sequence. A selection bias toward the number of peptides is desirable for proteomics as it estimates the number of peptides in a complex protein mixture. Here, we introduce an assessment measure that is purposely biased toward the number of peptide ion species. We also introduce a partition assessment framework for proteomics, called the Partition Assessment Tool, and demonstrate its importance by evaluating the performance of eight clustering algorithms on seven proteomics datasets while discussing the trade-offs involved. SIGNIFICANCE: Clustering algorithms are widely adopted in proteomics for undertaking several tasks such as speeding up search engines, generating consensus mass spectra, and to aid in the classification of proteomic profiles. Choosing which algorithm is most fit for the task at hand is not simple as each algorithm has advantages and disadvantages; furthermore, specifying clustering parameters is also a necessary and fundamental step. For example, deciding on whether to generate "pure clusters" or fewer clusters but accepting noise. With this as motivation, we verify the performance of several widely adopted algorithms on proteomic datasets and introduce a theoretical framework for drawing conclusions on which approach is suitable for the task at hand.

Molecular Architecture of the Antiophidic Protein DM64 and its Binding Specificity to Myotoxin II From Bothrops asper Venom.

DM64 is a toxin-neutralizing serum glycoprotein isolated from Didelphis aurita, an ophiophagous marsupial naturally resistant to snake envenomation. This 64 kDa antitoxin targets myotoxic phospholipases A2, which account for most local tissue damage of viperid snakebites. We investigated the noncovalent complex formed between native DM64 and myotoxin II, a myotoxic phospholipase-like protein from Bothrops asper venom. Analytical ultracentrifugation (AUC) and size exclusion chromatography indicated that DM64 is monomeric in solution and binds equimolar amounts of the toxin. Attempts to crystallize native DM64 for X-ray diffraction were unsuccessful. Obtaining recombinant protein to pursue structural studies was also challenging. Classical molecular modeling techniques were impaired by the lack of templates with more than 25% sequence identity with DM64. An integrative structural biology approach was then applied to generate a three-dimensional model of the inhibitor bound to myotoxin II. I-TASSER individually modeled the five immunoglobulin-like domains of DM64. Distance constraints generated by cross-linking mass spectrometry of the complex guided the docking of DM64 domains to the crystal structure of myotoxin II, using Rosetta. AUC, small-angle X-ray scattering (SAXS), molecular modeling, and molecular dynamics simulations indicated that the DM64-myotoxin II complex is structured, shows flexibility, and has an anisotropic shape. Inter-protein cross-links and limited hydrolysis analyses shed light on the inhibitor's regions involved with toxin interaction, revealing the critical participation of the first, third, and fifth domains of DM64. Our data showed that the fifth domain of DM64 binds to myotoxin II amino-terminal and beta-wing regions. The third domain of the inhibitor acts in a complementary way to the fifth domain. Their binding to these toxin regions presumably precludes dimerization, thus interfering with toxicity, which is related to the quaternary structure of the toxin. The first domain of DM64 interacts with the functional site of the toxin putatively associated with membrane anchorage. We propose that both mechanisms concur to inhibit myotoxin II toxicity by DM64 binding. The present topological characterization of this toxin-antitoxin complex constitutes an essential step toward the rational design of novel peptide-based antivenom therapies targeting snake venom myotoxins.

RawVegetable - A data assessment tool for proteomics and cross-linking mass spectrometry experiments.

We present RawVegetable, a software for mass spectrometry data assessment and quality control tailored toward shotgun proteomics and cross-linking experiments. RawVegetable provides four main modules with distinct features: (A) The charge state chromatogram that independently displays the ion current for each charge state; useful for optimizing the chromatography for highly charged ions and with lower XIC values such as those typically found in cross-linking experiments. (B) The XL-Artefact determination, which flags possible noncovalently associated peptides. (C) The TopN density estimation, for detecting retention time intervals of under or over-sampling, and (D) The chromatography reproducibility module, which provides pairwise comparisons between multiple experiments. RawVegetable, a tutorial, and the example data are freely available for academic use at: http://patternlabforproteomics.org/rawvegetable. SIGNIFICANCE: Chromatography optimization is a critical step for any shotgun proteomic or cross-linking mass spectrometry experiment. Here, we present a nifty solution with several key features, such as displaying individual charge state chromatograms, highlighting chromatographic regions of under- or over-sampling and checking for reproducibility.

A quantitation module for isotope-labeled peptides integrated into PatternLab for proteomics.

We present a new module integrated into the widely adopted PatternLab for proteomics to enable analysis of isotope-labeled peptides produced using dimethyl or SILAC. The accurate quantitation of proteins lies within the heart of proteomics; dimethylation has shown to be reliable, inexpensive, and applicable to any sample type. We validate our algorithm using an M. tuberculosis dataset obtained from two biological conditions; we used three dimethyl labels, one serving as an internal control for labeling a mixture of samples from both biological conditions. This internal control certified the proper functioning of our software. Availability: http://patternlabforproteomics.org, freely available for academic use.

Data on antigen recognition hindrance by antibodies covalently immobilized to Protein G magnetic beads by dimethyl pimelimidate (DMP) cross-linking.

The data presented herein is related to the article entitled "Trypanosoma cruzi immunoproteome: calpain-like CAP5.5 differentially detected throughout distinct stages of human Chagas disease cardiomyopathy" [1]. Electrophoretic analyses under denaturing and reducing conditions indicate that covalent immobilization of human IgG to Protein G magnetic beads by cross-linking with 50 mM dimethyl pimelimidate hinders the recognition of T. cruzi antigens in immunoprecipitation assays.

Trypanosoma cruzi immunoproteome: Calpain-like CAP5.5 differentially detected throughout distinct stages of human Chagas disease cardiomyopathy.

Chagas disease, caused by the protozoan Trypanosoma cruzi, affects millions of people worldwide, especially in Latin America. Approximately 30% of the cases evolve to the chronic symptomatic stage due to cardiac and/or digestive damage, generally accompanied by nervous system impairment. Given the higher frequency and severity of clinical manifestations related to cardiac tissue lesion, the goal of this study was the identification of proteins associated with the disease progression towards its cardiac form. Thus, T. cruzi bloodstream trypomastigotes proteins were submitted to immunoprecipitation using antibodies from patients with the asymptomatic or cardiac (stages B1 and C) forms of the disease and from healthy donors as control. Immunoreactive proteins were identified and quantified based on mass spectrometry analysis and shifts in the recognition profile were further evaluated. Compared to asymptomatic samples, IgG from stage C patients predominantly detected the I/6 autoantigen, whereas IgG from B1 patients resulted in higher yield of dihydrolipoamide acetyltransferase precursor, calpain cysteine peptidase, and two variants of CAP5.5. In this work, CAP5.5 recognition by serum immunoglobulin from patients with early cardiomyopathy generated a 23-fold abundance variation when compared to samples from asymptomatic patients, highlighting the participation of this protein in cardiac form progression of the disease. SIGNIFICANCE: While T. cruzi has become the major cause of infectious cardiomyopathy in Latin America, research groups have been struggling to find alternative treatment, vaccine candidates, and improved diagnostic tests. In addition, the absence of adequate biomarkers to assess cure and progression of disease is a major setback for clinical trials and patients monitoring. Therefore, our findings may contribute to a better understanding of T. cruzi pathogenesis and evaluation of suitable candidates for vaccine and diagnostic tests, besides the clinical applicability of the potential biomarkers for patient follow-up and prognosis. Finally, the identification of T. cruzi proteins recognized by IgG from healthy donors may contribute for the understanding and discovery of epitope conservation among a broad range of pathogens.

Quantitative Proteomic Map of the Trypanosomatid Strigomonas culicis: The Biological Contribution of its Endosymbiotic Bacterium.

Strigomonas culicis is a kinetoplastid parasite of insects that maintains a mutualistic association with an intracellular symbiotic bacterium, which is highly integrated into the protist metabolism: it furnishes essential compounds and divides in synchrony with the eukaryotic nucleus. The protist, conversely, can be cured of the endosymbiont, producing an aposymbiotic cell line, which presents a diminished ability to colonize the insect host. This obligatory association can represent an intermediate step of the evolution towards the formation of an organelle, therefore representing an interesting model to understand the symbiogenesis theory. Here, we used shotgun proteomics to compare the S. culicis endosymbiont-containing and aposymbiotic strains, revealing a total of 11,305 peptides, and up to 2,213 proteins (2,029 and 1,452 for wild type and aposymbiotic, respectively). Gene ontology associated to comparative analysis between both strains revealed that the biological processes most affected by the elimination of the symbiont were the amino acid synthesis, as well as protein synthesis and folding. This large-scale comparison of the protein expression in S. culicis marks a step forward in the comprehension of the role of endosymbiotic bacteria in monoxenous trypanosomatid biology, particularly because trypanosomatids expression is mostly post-transcriptionally regulated.

Characterization of homodimer interfaces with cross-linking mass spectrometry and isotopically labeled proteins.

Cross-linking coupled with mass spectrometry (XL-MS) has emerged as a powerful strategy for the identification of protein-protein interactions, characterization of interaction regions, and obtainment of structural information on proteins and protein complexes. In XL-MS, proteins or complexes are covalently stabilized with cross-linkers and digested, followed by identification of the cross-linked peptides by tandem mass spectrometry (MS/MS). This provides spatial constraints that enable modeling of protein (complex) structures and regions of interaction. However, most XL-MS approaches are not capable of differentiating intramolecular from intermolecular links in multimeric complexes, and therefore they cannot be used to study homodimer interfaces. We have recently developed an approach that overcomes this limitation by stable isotope-labeling of one of the two monomers, thereby creating a homodimer with one 'light' and one 'heavy' monomer. Here, we describe a step-by-step protocol for stable isotope-labeling, followed by controlled denaturation and refolding in the presence of the wild-type protein. The resulting light-heavy dimers are cross-linked, digested, and analyzed by mass spectrometry. We show how to quantitatively analyze the corresponding data with SIM-XL, an XL-MS software with a module tailored toward the MS/MS data from homodimers. In addition, we provide a video tutorial of the data analysis with this protocol. This protocol can be performed in ∼14 d, and requires basic biochemical and mass spectrometry skills.