Average peptide score: a useful parameter for identification of proteins derived from database searches of liquid chromatography/tandem mass spectrometry data

Insight into the structure-activity relationship of thermal hysteresis activity of cod collagen peptides through peptidomics and bioinformatics approaches

To elucidate the correlation between variations in thermal hysteresis activity (THA) and the physicochemical properties and structure, antifreeze peptides (AFPs) of isolated fractions (CCP-1 and CCP-2) were characterized on based peptidomics and bioinformatics. The results revealed a positive correlation between the THA of cod collagen antifreeze peptide (CCAFP) and peptide chain length, isoelectric point, and hydrophobic amino acid content. Notably, the THA of CCP-1, which has higher alkaline amino acid content, was 2.60 °C at a concentration of 10 mg/mL, significantly higher than CCP (1.90 °C) and CCP-2 (2.27 °C). Glycine, proline, and valine were the vital amino acids to the formation of hydrogen bonds. Conversely, aspartic and glutamic acids at terminal regions of AFPs tended to introduce kinks in their structures. This distortion reduced binding sites for ice crystals, thereby decreasing their THA, providing a theory for understanding the physicochemical properties and structure of AFPs that influence their THA.

Label-free-based proteomics analysis reveals differential proteins of sheep, goat, and cow milk

Regarding the limited information on species protein differences between sheep, goat, and cow milk, we analyzed the differentially expressed proteins in sheep, goat, and cow milk and their functional differences using label-free proteomics technology to identify potential biomarkers. In all, 770 proteins and 2,914 peptide segments were identified. Statistical analysis showed significant differences in the relative abundances of the 74 proteins among sheep, goat, and cow milk. CSN3 and LALBA can be used as potential biomarkers for goat milk, XDH can be used as a potential biomarker for cow milk, and CTSB and BPIFB1 can be used as potential biomarkers for sheep milk. Functional analysis using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes showed that these significantly different proteins were enriched by different pathways, including thyroid hormone synthesis and glycerol phospholipid metabolism. The data revealed differences in the amounts and physiological functions of the milk proteins of different species, which may provide an important basis for research on the nutritional composition of dairy products and adulteration identification technology.

False discovery rate: the Achilles' heel of proteogenomics

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

Differential proteomic analysis of human erythroblasts undergoing apoptosis induced by epo-withdrawal

The availability of Erythropoietin (Epo) is essential for the survival of erythroid progenitors. Here we study the effects of Epo removal on primary human erythroblasts grown from peripheral blood CD34(+) cells. The erythroblasts died rapidly from apoptosis, even in the presence of SCF, and within 24 hours of Epo withdrawal 60% of the cells were Annexin V positive. Other classical hallmarks of apoptosis were also observed, including cytochrome c release into the cytosol, loss of mitochondrial membrane potential, Bax translocation to the mitochondria and caspase activation. We adopted a 2D DIGE approach to compare the proteomes of erythroblasts maintained for 12 hours in the presence or absence of Epo. Proteomic comparisons demonstrated significant and reproducible alterations in the abundance of proteins between the two growth conditions, with 18 and 31 proteins exhibiting altered abundance in presence or absence of Epo, respectively. We observed that Epo withdrawal induced the proteolysis of the multi-functional proteins Hsp90 alpha, Hsp90 beta, SET, 14-3-3 beta, 14-3-3 gamma, 14-3-3 epsilon, and RPSA, thereby targeting multiple signaling pathways and cellular processes simultaneously. We also observed that 14 proteins were differentially phosphorylated and confirmed the phosphorylation of the Hsp90 alpha and Hsp90 beta proteolytic fragments in apoptotic cells using Nano LC mass spectrometry. Our analysis of the global changes occurring in the proteome of primary human erythroblasts in response to Epo removal has increased the repertoire of proteins affected by Epo withdrawal and identified proteins whose aberrant regulation may contribute to ineffective erythropoiesis.

The plant cell wall-decomposing machinery underlies the functional diversity of forest fungi

Brown rot decay removes cellulose and hemicellulose from wood--residual lignin contributing up to 30% of forest soil carbon--and is derived from an ancestral white rot saprotrophy in which both lignin and cellulose are decomposed. Comparative and functional genomics of the "dry rot" fungus Serpula lacrymans, derived from forest ancestors, demonstrated that the evolution of both ectomycorrhizal biotrophy and brown rot saprotrophy were accompanied by reductions and losses in specific protein families, suggesting adaptation to an intercellular interaction with plant tissue. Transcriptome and proteome analysis also identified differences in wood decomposition in S. lacrymans relative to the brown rot Postia placenta. Furthermore, fungal nutritional mode diversification suggests that the boreal forest biome originated via genetic coevolution of above- and below-ground biota.

Merging molecular electron microscopy and mass spectrometry by carbon film-assisted endoproteinase digestion

Many fundamental processes in the cell are performed by complex macromolecular assemblies that comprise a large number of proteins. Numerous macromolecular assemblies are structurally rather fragile and may suffer during purification, resulting in the partial dissociation of the complexes. These limitations can be overcome by chemical fixation of the assemblies, and recently introduced protocols such as gradient fixation during ultracentrifugation (GraFix) offer advantages for the analysis of fragile macromolecular assemblies. The irreversible fixation, however, is thought to render macromolecular samples useless for studying their protein composition. We therefore developed a novel approach that possesses the advantages of fixation for structure determination by single particle electron microscopy while still allowing a correlative compositional analysis by mass spectrometry. In this method, which we call "electron microscopy carbon film-assisted digestion", macromolecular assemblies are chemically fixed and then adsorbed onto electron microscopical carbon films. Parallel, identically prepared specimens are then subjected to structural investigation by electron microscopy and proteomics analysis by mass spectrometry of the digested sample. As identical sample preparation protocols are used for electron microscopy and mass spectrometry, the results of both methods can directly be correlated. In addition, we demonstrate improved sensitivity and reproducibility of electron microscopy carbon film-assisted digestion as compared with standard protocols. We show that sample amounts of as low as 50 fmol are sufficient to obtain a comprehensive protein composition of two model complexes. We suggest our approach to be an optimization technique for the compositional analysis of macromolecules by mass spectrometry in general.

Computational approaches to peptide identification via tandem MS

The peptide identification problem lies at the heart of modern proteomic methodology, from which the presence of a particular protein or proteins in a sample may be inferred. The challenge is to find the most likely amino acid sequence, which corresponds to each tandem mass spectrum that has been collected, and produce some kind of score and associated statistical measure that the putative identification is correct. This approach assumes that the peptide (and parent protein) sequence in question is known and is present in the database which is to be searched, as opposed to de novo methods, which seek to identify the peptide ab initio. This chapter will provide an overview of the methods that common, popular software tools employ to search protein sequence databases to provide the non-expert reader with sufficient background to appreciate the choices they can make. This will cover the approaches used to compare experimental and theoretical spectra and some of the methods used to validate and provide higher confidence in the assignments.

Proteomics analysis of the nucleolus in adenovirus-infected cells

Adenoviruses replicate primarily in the host cell nucleus, and it is well established that adenovirus infection affects the structure and function of host cell nucleoli in addition to coding for a number of nucleolar targeted viral proteins. Here we used unbiased proteomics methods, including high throughput mass spectrometry coupled with stable isotope labeling by amino acids in cell culture (SILAC) and traditional two-dimensional gel electrophoresis, to identify quantitative changes in the protein composition of the nucleolus during adenovirus infection. Two-dimensional gel analysis revealed changes in six proteins. By contrast, SILAC-based approaches identified 351 proteins with 24 proteins showing at least a 2-fold change after infection. Of those, four were previously reported to have aberrant localization and/or functional relevance during adenovirus infection. In total, 15 proteins identified as changing in amount by proteomics methods were examined in infected cells using confocal microscopy. Eleven of these proteins showed altered patterns of localization in adenovirus-infected cells. Comparing our data with the effects of actinomycin D on the nucleolar proteome revealed that adenovirus infection apparently specifically targets a relatively small subset of nucleolar antigens at the time point examined.

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

LC-MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re-assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.

The minor salivary gland proteome in Sjögren's syndrome

OBJECTIVE

To identify the global protein expression (the proteome) in the minor salivary glands from primary Sjögren's syndrome (pSS) patients and non-SS controls.

MATERIALS AND METHODS

Minor labial salivary glands were obtained from six pSS patients and from six age-matched non-SS controls, lysed in SDS buffer and pooled into two groups, respectively. The lysates were analysed by liquid chromatography electrospray ionization combined with tandem mass spectrometry. Also, the proteins were separated by two-dimensional polyacrylamide gel electrophoresis and protein spots were subjected to mass spectrometry.

RESULTS

Heat shock proteins, mucins, carbonic anhydrases, enolase, vimentin and cyclophilin B were among the proteins identified. The differences in the proteomes of minor salivary glands from pSS patients and non-SS controls were mainly related to ribosomal proteins, immunity and stress. Alpha-defensin-1 and calmodulin were among six proteins exclusively identified in pSS patients.

CONCLUSION

We have identified several minor salivary gland proteins that may have implications for clarifying the SS pathophysiology. This experiment adds to the knowledge of proteins produced in salivary glands in health and disease, and may form the basis of further studies on biomarkers of prognostic and diagnostic value.

Determining the protein repertoire of Cryptosporidium parvum sporozoites

The genome of the intracellular parasite Cryptosporidium parvum has recently been sequenced, but protein expression data for the invasive stages of this important zoonotic gastrointestinal pathogen are limited. In this paper a comprehensive analysis of the expressed protein repertoire of an excysted oocyst/sporozoite preparation of C. parvum is presented. Three independent proteome platforms were employed which yielded more than 4800 individual protein identifications representing 1237 nonredundant proteins, corresponding to approximately 30% of the predicted proteome. Peptide data were mapped to the corresponding locations on the C. parvum genome and a publicly accessible interface for proteome data was developed for data-mining and visualisation at CryptoDB (http://cryptodb.org). These data provide a timely and valuable resource for improved annotation of the genome, verification of predicted hypothetical proteins and identification of proteins not predicted by current gene models. The data indicated the expression of proteins likely to be important to the invasion and intracellular establishment of the parasite, including surface proteins, constituents of the remnant mitochondrion and apical organelles. Comparison of the expressed proteome with existing transcriptional data indicated only a weak correlation. For approximately half the proteome there was limited functional and structural information, highlighting the limitations in the current understanding of Cryptosporidium biology.

MASCOT HTML and XML parser: An implementation of a novel object model for protein identification data

Protein identification using MS is an important technique in proteomics as well as a major generator of proteomics data. We have designed the protein identification data object model (PDOM) and developed a parser based on this model to facilitate the analysis and storage of these data. The parser works with HTML or XML files saved or exported from MASCOT MS/MS ions search in peptide summary report or MASCOT PMF search in protein summary report. The program creates PDOM objects, eliminates redundancy in the input file, and has the capability to output any PDOM object to a relational database. This program facilitates additional analysis of MASCOT search results and aids the storage of protein identification information. The implementation is extensible and can serve as a template to develop parsers for other search engines. The parser can be used as a stand-alone application or can be driven by other Java programs. It is currently being used as the front end for a system that loads HTML and XML result files of MASCOT searches into a relational database. The source code is freely available at http://www.ccbm.jhu.edu and the program uses only free and open-source Java libraries.

Ampicillin/penicillin-binding protein interactions as a model drug-target system to optimize affinity pull-down and mass spectrometric strategies for target and pathway identification

The identification and validation of the targets of active compounds identified in cell-based assays is an important step in preclinical drug development. New analytical approaches that combine drug affinity pull-down assays with mass spectrometry (MS) could lead to the identification of new targets and druggable pathways. In this work, we investigate a drug-target system consisting of ampicillin- and penicillin-binding proteins (PBPs) to evaluate and compare different amino-reactive resins for the immobilization of the affinity compound and mass spectrometric methods to identify proteins from drug affinity pull-down assays. First, ampicillin was immobilized onto various amino-reactive resins, which were compared in the ampicillin-PBP model with respect to their nonspecific binding of proteins from an Escherichia coli membrane extract. Dynal M-270 magnetic beads were chosen to further study the system as a model for capturing and identifying the targets of ampicillin, PBPs that were specifically and covalently bound to the immobilized ampicillin. The PBPs were identified, after in situ digestion of proteins bound to ampicillin directly on the beads, by using either one-dimensional (1-D) or two-dimensional (2-D) liquid chromatography (LC) separation techniques followed by tandem mass spectrometry (MS/MS) analysis. Alternatively, an elution with N-lauroylsarcosine (sarcosyl) from the ampicillin beads followed by in situ digestion and 2-D LC-MS/MS analysis identified proteins potentially interacting noncovalently with the PBPs or the ampicillin. The in situ approach required only little time, resources, and sample for the analysis. The combination of drug affinity pull-down assays with in situ digestion and 2-D LC-MS/MS analysis is a useful tool in obtaining complex information about a primary drug target as well as its protein interactors.

Confident protein identification using the average peptide score method coupled with search-specific, ab initio thresholds

Perhaps the greatest difficulty in interpreting large sets of protein identifications derived from mass spectrometric methods is whether or not to trust the results. For such experiments, the level of confidence in each protein identification made needs to be far greater than the often used 95% significance threshold to avoid the identification of many false-positives. To provide higher confidence results, we have developed an innovative scoring strategy coupling the recently published Average Peptide Score (APS) method with pre-filtering of peptide identifications, using a simple peptide quality filter. Iterative generation of these filters in conjunction with reversed database searching is used to determine the correct levels at which the APS and peptide quality thresholds should be set to return virtually zero false-positive reports. This proceeds without the need to reference a known dataset.