Spectral clustering in peptidomics studies helps to unravel modification profile of biologically active peptides and enhances peptide identification rate

Optimized protocol for shotgun label-free proteomic analysis of pancreatic islets

Pancreatic islets are crucial in diabetes research. Consequently, this protocol aims at optimizing both the protein-extraction process and the proteomic analysis via shotgun methods for pancreatic islets. Six protocols were tested, combining three types of chemical extraction with two mechanical extraction methods. Furthermore, two protocols incorporated a surfactant to enhance enzymatic cleavage. The steps involved extraction and concentration of protein, protein quantification, reduction, alkylation, digestion, purification and desalination, sample concentration to ∼1 µl, and proteomic analysis using the mass spectrometer. The most effective protocol involves either a milder chemical extraction paired with a more intensive mechanical process, or a more robust chemical extraction paired with a gentle mechanical process, tailored to the sample's characteristics. Additionally, it was observed that the use of a surfactant proved ineffective for these types of samples. Protocol 5 was recently used with success to examine metabolic changes in pancreatic islets of non-obese diabetic mice exposed to low doses of fluoride ions (F-) and the primary pathways altered by the treatment.

The challenges of peptidomics in complementing proteomics in a clinical context

Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.

Mining proteomic data to expose protein modifications in Methanosarcina mazei strain Gö1

Proteomic tools identify constituents of complex mixtures, often delivering long lists of identified proteins. The high-throughput methods excel at matching tandem mass spectrometry data to spectra predicted from sequence databases. Unassigned mass spectra are ignored, but could, in principle, provide valuable information on unanticipated modifications and improve protein annotations while consuming limited quantities of material. Strategies to "mine" information from these discards are presented, along with discussion of features that, when present, provide strong support for modifications. In this study we mined LC-MS/MS datasets of proteolytically-digested concanavalin A pull down fractions from Methanosarcina mazei Gö1 cell lysates. Analyses identified 154 proteins. Many of the observed proteins displayed post-translationally modified forms, including O-formylated and methyl-esterified segments that appear biologically relevant (i.e., not artifacts of sample handling). Interesting cleavages and modifications (e.g., S-cyanylation and trimethylation) were observed near catalytic sites of methanogenesis enzymes. Of 31 Methanosarcina protein N-termini recovered by concanavalin A binding or from a previous study, only M. mazei S-layer protein MM1976 and its M. acetivorans C2A orthologue, MA0829, underwent signal peptide excision. Experimental results contrast with predictions from algorithms SignalP 3.0 and Exprot, which were found to over-predict the presence of signal peptides. Proteins MM0002, MM0716, MM1364, and MM1976 were found to be glycosylated, and employing chromatography tailored specifically for glycopeptides will likely reveal more. This study supplements limited, existing experimental datasets of mature archaeal N-termini, including presence or absence of signal peptides, translation initiation sites, and other processing. Methanosarcina surface and membrane proteins are richly modified.

Clustering analyses in peptidomics revealed that peptide profiles of infant formulae are descriptive

Prompted by the accumulating evidence on bioactive moieties of milk-derived peptides, novel methods were applied to compare the peptide composition among commercially available hydrolysate formulations and to determine batch-to-batch variations of protein hydrolysate products. Despite the availability of general methods to measure, for example, the degree of hydrolysis and peptide mass distribution at a high level, the objective of this study was to more qualitatively compare peptide sequences and composition. By a comprehensive approach combining peptidomics technologies and multivariate clustering analyses, the peptide profiles of different hydrolyzed milk protein formulations were compared. Moreover, peptide profiles of various hydrolysate batches that had been produced over a period of 5 years were included. Coupling of identified peptide sequences to the position in their corresponding milk proteins produced numerical datasets that subsequently were utilized for multivariate data analyses. These analyses revealed that batch-to-batch variation in the peptide profiles of a specific extensively hydrolyzed casein preparation was low. Moreover, extensive multivariate evaluations revealed that the peptide profiles of different commercially available hydrolyzed milk protein formulations provided a descriptive and distinct signature. Overall, the described methodology may contribute to the field of peptide research as observed dissimilarities in peptide profiles of similar products may be related to differences in their overall functionality.

Functional neuropeptidomics in invertebrates

Neuropeptides are key messengers in almost all physiological processes. They originate from larger precursors and are extensively processed to become bioactive. Neuropeptidomics aims to comprehensively identify the collection of neuropeptides in an organism, organ, tissue or cell. The neuropeptidome of several invertebrates is thoroughly explored since they are important model organisms (and models for human diseases), disease vectors and pest species. The charting of the neuropeptidome is the first step towards understanding peptidergic signaling. This review will first discuss the latest developments in exploring the neuropeptidome. The physiological roles and modes of action of neuropeptides can be explored in two ways, which are largely orthogonal and therefore complementary. The first way consists of inferring the functions of neuropeptides by a forward approach where neuropeptide profiles are compared under different physiological conditions. Second is the reverse approach were neuropeptide collections are used to screen for receptor-binding. This is followed by localization studies and functional tests. This review will focus on how these different functional screening methods contributed to the field of invertebrate neuropeptidomics and expanded our knowledge of peptidergic signaling. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

Platelets of mice heterozygous for neurobeachin, a candidate gene for autism spectrum disorder, display protein changes related to aberrant protein kinase A activity

BACKGROUND

Neurobeachin (NBEA) has been identified as a candidate gene for autism spectrum disorders (ASD) in several unrelated patients with alterations in the NBEA gene. The exact function of NBEA, a multidomain scaffolding protein, is currently unknown. It contains an A-kinase anchoring protein (AKAP) domain which binds the regulatory subunit of protein kinase A (PKA) thereby confining its activity to specific subcellular regions. NBEA has been implicated in post-Golgi membrane trafficking and in regulated secretion. The mechanism of regulated secretion is largely conserved between neurons and platelets, and the morphology of platelet dense granules was found to be abnormal in several ASD patients, including one with NBEA haploinsufficiency. Platelet dense granules are secreted upon vascular injury when platelets are exposed to for instance collagen. Dense granules contain serotonin, ATP and ADP, which are necessary for platelet plug formation and vascular contraction.

METHODS

To further investigate possible roles for NBEA in secretion or dense granule morphology, platelets from Nbea+/- mice were analyzed morphometrically, functionally and biochemically. A differential proteomics and peptidomics screen was performed between Nbea+/- and Nbea+/+ mice, in which altered Talin-1 cleavage was further investigated and validated in brain samples. Finally, the phosphorylation pattern of PKA substrates was analyzed.

RESULTS

Platelet dense granules of Nbea+/- mice had a reduced surface area and abnormal dense-core halo, but normal serotonin-content. Nbea haploinsufficiency did not affect platelet aggregation and ATP secretion after collagen stimulation, although the platelet shape change was more pronounced. Furthermore, peptidomics revealed that Nbea+/- platelets contain significantly reduced levels of several actin-interacting peptides. Decreased levels were detected of the actin-binding head and rod domain of Talin-1, which are cleavage products of Calpain-2. This is most likely due to increased PKA-mediated phosphorylation of Calpain-2, which renders the enzyme less active. Analysis of other PKA substrates revealed both increased and reduced phosphorylation.

CONCLUSION

Our results show the pleiotropic effects of alterations in PKA activity due to Nbea haploinsufficiency, highlighting the important function of the AKAP domain in Nbea in regulating and confining PKA activity. Furthermore, these results suggest a role for Nbea in remodeling the actin cytoskeleton of platelets.

Improving the identification rate of endogenous peptides using electron transfer dissociation and collision-induced dissociation

Tandem mass spectrometry (MS/MS) combined with bioinformatics tools have enabled fast and systematic protein identification based on peptide-to-spectrum matches. However, it remains challenging to obtain accurate identification of endogenous peptides, such as neuropeptides, peptide hormones, peptide pheromones, venom peptides, and antimicrobial peptides. Since these peptides are processed at sites that are difficult to predict reliably, the search of their MS/MS spectra in sequence databases needs to be done without any protease setting. In addition, many endogenous peptides carry various post-translational modifications, making it essential to take these into account in the database search. These characteristics of endogenous peptides result in a huge search space, frequently leading to poor confidence of the peptide characterizations in peptidomics studies. We have developed a new MS/MS spectrum search tool for highly accurate and confident identification of endogenous peptides by combining two different fragmentation methods. Our approach takes advantage of the combination of two independent fragmentation methods (collision-induced dissociation and electron transfer dissociation). Their peptide spectral matching is carried out separately in both methods, and the final score is built as a combination of the two separate scores. We demonstrate that this approach is very effective in discriminating correct peptide identifications from false hits. We applied this approach to a spectral data set of neuropeptides extracted from mouse pituitary tumor cells. Compared to conventional MS-based identification, i.e., using a single fragmentation method, our approach significantly increased the peptide identification rate. It proved also highly effective for scanning spectra against a very large search space, enabling more accurate genome-wide searches and searches including multiple potential post-translational modifications.

Learning from decoys to improve the sensitivity and specificity of proteomics database search results

The statistical validation of database search results is a complex issue in bottom-up proteomics. The correct and incorrect peptide spectrum match (PSM) scores overlap significantly, making an accurate assessment of true peptide matches challenging. Since the complete separation between the true and false hits is practically never achieved, there is need for better methods and rescoring algorithms to improve upon the primary database search results. Here we describe the calibration and False Discovery Rate (FDR) estimation of database search scores through a dynamic FDR calculation method, FlexiFDR, which increases both the sensitivity and specificity of search results. Modelling a simple linear regression on the decoy hits for different charge states, the method maximized the number of true positives and reduced the number of false negatives in several standard datasets of varying complexity (18-mix, 49-mix, 200-mix) and few complex datasets (E. coli and Yeast) obtained from a wide variety of MS platforms. The net positive gain for correct spectral and peptide identifications was up to 14.81% and 6.2% respectively. The approach is applicable to different search methodologies- separate as well as concatenated database search, high mass accuracy, and semi-tryptic and modification searches. FlexiFDR was also applied to Mascot results and showed better performance than before. We have shown that appropriate threshold learnt from decoys, can be very effective in improving the database search results. FlexiFDR adapts itself to different instruments, data types and MS platforms. It learns from the decoy hits and sets a flexible threshold that automatically aligns itself to the underlying variables of data quality and size.

Open MS/MS spectral library search to identify unanticipated post-translational modifications and increase spectral identification rate

Motivation: Identification of post-translationally modified proteins has become one of the central issues of current proteomics. Spectral library search is a new and promising computational approach to mass spectrometry-based protein identification. However, its potential in identification of unanticipated post-translational modifications has rarely been explored. The existing spectral library search tools are designed to match the query spectrum to the reference library spectra with the same peptide mass. Thus, spectra of peptides with unanticipated modifications cannot be identified.

Results: In this article, we present an open spectral library search tool, named pMatch. It extends the existing library search algorithms in at least three aspects to support the identification of unanticipated modifications. First, the spectra in library are optimized with the full peptide sequence information to better tolerate the peptide fragmentation pattern variations caused by some modification(s). Second, a new scoring system is devised, which uses charge-dependent mass shifts for peak matching and combines a probability-based model with the general spectral dot-product for scoring. Third, a target-decoy strategy is used for false discovery rate control. To demonstrate the effectiveness of pMatch, a library search experiment was conducted on a public dataset with over 40 000 spectra in comparison with SpectraST, the most popular library search engine. Additional validations were done on four published datasets including over 150 000 spectra. The results showed that pMatch can effectively identify unanticipated modifications and significantly increase spectral identification rate.

Availability:http://pfind.ict.ac.cn/pmatch/

Contact:yfu@ict.ac.cn; rxsun@ict.ac.cn

Supplementary information:Supplementary data are available at Bioinformatics online.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.