Automated protein identification by tandem mass spectrometry: issues and strategies

Using EMBL-EBI Services via Web Interface and Programmatically via Web Services

The European Bioinformatics Institute (EMBL-EBI)'s Job Dispatcher framework provides access to a wide range of core databases and analysis tools that are of key importance in bioinformatics. As well as providing web interfaces to these resources, web services are available using REST and SOAP protocols that enable programmatic access and allow their integration into other applications and analytical workflows and pipelines. This article describes the various options available to researchers and bioinformaticians who would like to use our resources via the web interface employing RESTful web services clients provided in Perl, Python, and Java or who would like to use Docker containers to integrate the resources into analysis pipelines and workflows. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Retrieving data from EMBL-EBI using Dbfetch via the web interface Alternate Protocol 1: Retrieving data from EMBL-EBI using WSDbfetch via the REST interface Alternate Protocol 2: Retrieving data from EMBL-EBI using Dbfetch via RESTful web services with Python client Support Protocol 1: Installing Python REST web services clients Basic Protocol 2: Sequence similarity search using FASTA search via the web interface Alternate Protocol 3: Sequence similarity search using FASTA via RESTful web services with Perl client Support Protocol 2: Installing Perl REST web services clients Basic Protocol 3: Sequence similarity search using NCBI BLAST+ RESTful web services with Python client Basic Protocol 4: Sequence similarity search using HMMER3 phmmer REST web services with Perl client and Docker Support Protocol 3: Installing Docker and running the EMBL-EBI client container Basic Protocol 5: Protein functional analysis using InterProScan 5 RESTful web services with the Python client and Docker Alternate Protocol 4: Protein functional analysis using InterProScan 5 RESTful web services with the Java client Support Protocol 4: Installing Java web services clients Basic Protocol 6: Multiple sequence alignment using Clustal Omega via web interface Alternate Protocol 5: Multiple sequence alignment using Clustal Omega with Perl client and Docker Support Protocol 5: Exploring the RESTful API with OpenAPI User Inferface.

Unveiling the Molecular Footprint: Proteome-Based Biomarkers for Alzheimer's Disease

Alzheimer's disease (AD) is a devastating neurodegenerative disorder characterized by progressive cognitive decline and memory loss. Early and accurate diagnosis of AD is crucial for implementing timely interventions and developing effective therapeutic strategies. Proteome-based biomarkers have emerged as promising tools for AD diagnosis and prognosis due to their ability to reflect disease-specific molecular alterations. There is of great significance for biomarkers in AD diagnosis and management. It emphasizes the limitations of existing diagnostic approaches and the need for reliable and accessible biomarkers. Proteomics, a field that comprehensively analyzes the entire protein complement of cells, tissues, or bio fluids, is presented as a powerful tool for identifying AD biomarkers. There is a diverse range of proteomic approaches employed in AD research, including mass spectrometry, two-dimensional gel electrophoresis, and protein microarrays. The challenges associated with identifying reliable biomarkers, such as sample heterogeneity and the dynamic nature of the disease. There are well-known proteins implicated in AD pathogenesis, such as amyloid-beta peptides, tau protein, Apo lipoprotein E, and clusterin, as well as inflammatory markers and complement proteins. Validation and clinical utility of proteome-based biomarkers are addressing the challenges involved in validation studies and the diagnostic accuracy of these biomarkers. There is great potential in monitoring disease progression and response to treatment, thereby aiding in personalized medicine approaches for AD patients. There is a great role for bioinformatics and data analysis in proteomics for AD biomarker research and the importance of data preprocessing, statistical analysis, pathway analysis, and integration of multi-omics data for a comprehensive understanding of AD pathophysiology. In conclusion, proteome-based biomarkers hold great promise in the field of AD research. They provide valuable insights into disease mechanisms, aid in early diagnosis, and facilitate personalized treatment strategies. However, further research and validation studies are necessary to harness the full potential of proteome-based biomarkers in clinical practice.

Diserinol Isophthalamide: A Novel Reagent for Complexation with Biomolecular Anions in Electrospray Ionization Mass Spectrometry

Transferring biomolecules from solution to vacuum facilitates a detailed analysis of molecular structure and dynamics by isolating molecules of interest from a complex environment. However, inherent in the ion desolvation process is the loss of solvent hydrogen bonding partners, which are critical for the stability of a condensed-phase structure. Thus, transfer of ions to vacuum can favor structural rearrangement, especially near solvent-accessible charge sites, which tend to adopt intramolecular hydrogen bonding motifs in the absence of solvent. Complexation of monoalkylammonium moieties (e.g., lysine side chains) with crown ethers such as 18-crown-6 can disfavor structural rearrangement of protonated sites, but no equivalent ligand has been investigated for deprotonated groups. Herein we describe diserinol isophthalamide (DIP), a novel reagent for the gas-phase complexation of anionic moieties within biomolecules. Complexation is observed to the C-terminus or side chains of the small model peptides GD, GE, GG, DF-OMe, VYV, YGGFL, and EYMPME in electrospray ionization mass spectrometry (ESI-MS) studies. In addition, complexation is observed with the phosphate and carboxylate moieities of phosphoserine and phosphotyrosine. DIP performs favorably in comparison to an existing anion recognition reagent, 1,1'-(1,2-phenylene)bis(3-phenylurea), that exhibits moderate carboxylate binding in organic solvent. This improved performance in ESI-MS experiments is attributed to reduced steric constraints to complexation with carboxylate groups of larger molecules. Overall, diserinol isophthalamide is an effective complexation reagent that can be applied in future work to study retention of solution-phase structure, investigate intrinsic molecular properties, and examine solvation effects.

Functional acellular matrix for tissue repair

In view of their low immunogenicity, biomimetic internal environment, tissue- and organ-like physicochemical properties, and functionalization potential, decellularized extracellular matrix (dECM) materials attract considerable attention and are widely used in tissue engineering. This review describes the composition of extracellular matrices and their role in stem-cell differentiation, discusses the advantages and disadvantages of existing decellularization techniques, and presents methods for the functionalization and characterization of decellularized scaffolds. In addition, we discuss progress in the use of dECMs for cartilage, skin, nerve, and muscle repair and the transplantation or regeneration of different whole organs (e.g., kidneys, liver, uterus, lungs, and heart), summarize the shortcomings of using dECMs for tissue and organ repair after refunctionalization, and examine the corresponding future prospects. Thus, the present review helps to further systematize the application of functionalized dECMs in tissue/organ transplantation and keep researchers up to date on recent progress in dECM usage.

Group-walk: a rigorous approach to group-wise false discovery rate analysis by target-decoy competition

MOTIVATION

Target-decoy competition (TDC) is a commonly used method for false discovery rate (FDR) control in the analysis of tandem mass spectrometry data. This type of competition-based FDR control has recently gained significant popularity in other fields after Barber and Candès laid its theoretical foundation in a more general setting that included the feature selection problem. In both cases, the competition is based on a head-to-head comparison between an (observed) target score and a corresponding decoy (knockoff) score. However, the effectiveness of TDC depends on whether the data are homogeneous, which is often not the case: in many settings, the data consist of groups with different score profiles or different proportions of true nulls. In such cases, applying TDC while ignoring the group structure often yields imbalanced lists of discoveries, where some groups might include relatively many false discoveries and other groups include relatively very few. On the other hand, as we show, the alternative approach of applying TDC separately to each group does not rigorously control the FDR.

RESULTS

We developed Group-walk, a procedure that controls the FDR in the target-decoy/knockoff setting while taking into account a given group structure. Group-walk is derived from the recently developed AdaPT-a general framework for controlling the FDR with side-information. We show using simulated and real datasets that when the data naturally divide into groups with different characteristics Group-walk can deliver consistent power gains that in some cases are substantial. These groupings include the precursor charge state (4% more discovered peptides at 1% FDR threshold), the peptide length (3.6% increase) and the mass difference due to modifications (26% increase).

AVAILABILITY AND IMPLEMENTATION

Group-walk is available at https://cran.r-project.org/web/packages/groupwalk/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Advances and Trends in Omics Technology Development

The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.

SPEQ: quality assessment of peptide tandem mass spectra with deep learning

MOTIVATION

In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may not be identified due to incompleteness of the database or failure of the software. Thus, spectrum quality (SPEQ) assessment tools are helpful programs that can eliminate poor-quality spectra before the database search and highlight the high-quality spectra that are not identified in the initial search. These spectra may be valuable candidates for further analyses.

RESULTS

We propose SPEQ: a spectrum quality assessment tool that uses a deep neural network to classify spectra into high-quality, which are worthy candidates for interpretation, and low-quality, which lack sufficient information for identification. SPEQ was compared with a few other prediction models and demonstrated improved prediction accuracy.

AVAILABILITY AND IMPLEMENTATION

Source code and scripts are freely available at github.com/sor8sh/SPEQ, implemented in Python.

Tailoring to Search Engines: Bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence

Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide–spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptide-level results, we designed methods with one or two liquid chromatography–tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10–40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).

Urine-Based Antigen (Protein) Detection Test for the Diagnosis of Visceral Leishmaniasis

This review describes and appraises a novel protein-based antigen detection test for visceral leishmaniasis (VL). The test detects in patient’s urine six proteins from Leishmania infantum (chagasi) and Leishmania donovani, the etiological agents of VL. The gold standard test for VL is microscopic observation of the parasites in aspirates from spleen, liver, or bone marrow (and lymph node for dogs). Culture of the parasites or detection of their DNA in these aspirates are also commonly used. Serological tests are available but they cannot distinguish patients with active VL from either healthy subjects exposed to the parasites or from subjects who had a successful VL treatment. An antigen detection test based on the agglutination of anti-leishmania carbohydrates antibody coated latex beads has been described. However, the results obtained with this carbohydrate-based test have been conflicting. Using mass spectrometry, we discovered six L. infantum/L. donovani proteins excreted in the urine of VL patients and used them as markers for the development of a robust mAb-based antigen (protein) detection test. The test is assembled in a multiplexed format to simultaneously detect all six markers. Its initial clinical validation showed a sensitivity of 93% and specificity of 100% for VL diagnosis.

Regulation of decellularized matrix mediated immune response

The substantially growing gap between suitable donors and patients waiting for new organ transplantation has compelled tissue engineers to look for suitable patient-specific alternatives. Lately, a decellularized extracellular matrix (dECM), obtained primarily from either discarded human tissues/organs or other species, has shown great promise in the constrained availability of high-quality donor tissues. In this review, we have addressed critical gaps and often-ignored aspects of understanding the innate and adaptive immune response to the dECM. Firstly, although most of the studies claim preservation of the ECM ultrastructure, almost all methods employed for decellularization would inevitably cause a certain degree of disruption to the ECM ultrastructure and modulation in secondary conformations, which may elicit a distinct immunogenic response. Secondly, it is still a major challenge to find ways to conserve the native biochemical, structural and biomechanical cues by making a judicious decision regarding the choice of decellularization agents/techniques. We have critically analyzed various decellularization protocols and tried to find answers on various aspects such as whether the secondary structural conformation of dECM proteins would be preserved after decellularization. Thirdly, to keep the dECM ultrastructure as close to the native ECM we have raised the question "How good is good enough?" Even residual cellular antigens or nucleic acid fragments may elicit antigenicity leading to a low-grade immune response. A combinative knowledge of macrophage plasticity in the decellularized tissue and limits of decellularization will help achieve the native ultrastructure. Lastly, we have shifted our focus on the scientific basis of the presently accepted criteria for decellularization, and the effect on immune response concerning the interaction between the decellularized extracellular matrix and macrophages with the subsequent influence of T-cell activation. Amalgamating suitable decellularization approaches, sufficient knowledge of macrophage plasticity and elucidation of molecular pathways together will help fabricate functional immune informed decellularized tissues in vitro that will have substantial implications for efficient clinical translation and prediction for in vivo reprogramming and tissue regeneration.

Decellularized Extracellular Matrix-based Bioinks for Engineering Tissue- and Organ-specific Microenvironments

Biomaterials-based biofabrication methods have gained much attention in recent years. Among them, 3D cell printing is a pioneering technology to facilitate the recapitulation of unique features of complex human tissues and organs with high process flexibility and versatility. Bioinks, combinations of printable hydrogel and cells, can be utilized to create 3D cell-printed constructs. The bioactive cues of bioinks directly trigger cells to induce tissue morphogenesis. Among the various printable hydrogels, the tissue- and organ-specific decellularized extracellular matrix (dECM) can exert synergistic effects in supporting various cells at any component by facilitating specific physiological properties. In this review, we aim to discuss a new paradigm of dECM-based bioinks able to recapitulate the inherent microenvironmental niche in 3D cell-printed constructs. This review can serve as a toolbox for biomedical engineers who want to understand the beneficial characteristics of the dECM-based bioinks and a basic set of fundamental criteria for printing functional human tissues and organs.

Diagnostic potential of saliva proteome analysis: a review and guide to clinical practice

Proteomic techniques have become popular in medicine and dentistry because of their widespread use in analyzing bodily fluids such as blood, saliva, urine, and gingival crevicular fluids as well as hard tissues such as enamel, dentine, and cementum. This review is a guide to proteomic techniques in general dentistry, summarizing techniques and their clinical application in understanding and diagnosing diseases and their use in identifying biomarkers of various diseases.

Relative proteome quantification of alpha, beta, gamma and delta globin chains in early eluting peaks of Bio-Rad variant II® CE-HPLC of hemoglobin from healthy and beta-thalassemia subjects in Malaysia

This is the first report of QQQ-mass spectrometric identification and quantification of the Hb subunits, alpha, beta, delta and gamma globin peptides, derived from enzymatic-digestion of proteins in the early unknown peaks of the Bio-Rad cation-exchange chromatography of haemoglobin. The objectives were to assess the relationship of the quantity of the free alpha, beta, delta and gamma globin chains with the phenotypic diversity of beta-thalassaemias (β-thal). The results demonstrate that the pools of free globin chains in red blood cells were correlating with the severity of the disease in patients with different phenotypes of β-thal. The mechanism and the regulation of synthesis of free globin chains pool in a normal individual and in patients with different β-thal phenotypes could arise from several mechanisms which will require further investigation. The role of the free globin pool in patients with β-thal for development of novel therapeutic approaches based on these potential targets requires further investigation. Pertinent biomarkers improves the diagnosis of the β-thal, especially in low-income countries where they are most common and allows more effective therapeutic intervention leading to more successful therapeutic outcome.

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Globin chains exist in the fast-eluting unknown HPLC peaks in normal human red blood cells.

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Larger pools of globin chains in patients with β-thal are correlated with the severity of the disease.

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Regulation and mechanisms of free globin chains pool in patients with β-thalassemias requiring further investigation.

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Biomarker with diagnostic and prognostic utility allows more effective therapeutic outcome.

Using EMBL-EBI Services via Web Interface and Programmatically via Web Services

The European Bioinformatics Institute (EMBL-EBI) provides access to a wide range of core databases and analysis tools that are of key importance in bioinformatics. As well as providing web interfaces to these resources, web services are available using REST and SOAP protocols that enable programmatic access and allow their integration into other applications and analytical workflows and pipelines. This article describes the various options available to researchers and bioinformaticians who would like to use our resources via the web interface employing RESTful web service clients provided in Perl, Python, and Java, or would like to use Docker containers to integrate the resources into analysis pipelines and workflows. © 2019 by John Wiley & Sons, Inc.

Recent advancements, challenges, and practical considerations in the mass spectrometry-based analytics of protein biotherapeutics: A viewpoint from the biosimilar industry

The extensive analytical characterization of protein biotherapeutics, especially of biosimilars, is a critical part of the product development and registration. High-resolution mass spectrometry became the primary analytical tool used for the structural characterization of biotherapeutics. Its high instrumental sensitivity and methodological versatility made it possible to use this technique to characterize both the primary and higher-order structure of these proteins. However, even by using high-end instrumentation, analysts face several challenges with regard to how to cope with industrial and regulatory requirements, that is, how to obtain accurate and reliable analytical data in a time- and cost-efficient way. New sample preparation approaches, measurement techniques and data evaluation strategies are available to meet those requirements. The practical considerations of these methods are discussed in the present review article focusing on hot topics, such as reliable and efficient sequencing strategies, minimization of artefact formation during sample preparation, quantitative peptide mapping, the potential of multi-attribute methodology, the increasing role of mass spectrometry in higher-order structure characterization and the challenges of MS-based identification of host cell proteins. On the basis of the opportunities in new instrumental techniques, methodological advancements and software-driven data evaluation approaches, for the future one can envision an even wider application area for mass spectrometry in the biopharmaceutical industry.

Selection of Collision Energies in Proteomics Mass Spectrometry Experiments for Best Peptide Identification: Study of Mascot Score Energy Dependence Reveals Double Optimum

Collision energy is a key parameter determining the information content of beam-type collision induced dissociation tandem mass spectrometry (MS/MS) spectra, and its optimal choice largely affects successful peptide and protein identification in MS-based proteomics. For an MS/MS spectrum, quality of peptide match based on sequence database search, often characterized in terms of a single score, is a complex function of spectrum characteristics, and its collision energy dependence has remained largely unexplored. We carried out electrospray ionization-quadrupole-time of flight (ESI-Q-TOF)-MS/MS measurements on 2807 peptides from tryptic digests of HeLa and E. coli at 21 different collision energies. Agglomerative clustering of the resulting Mascot score versus energy curves revealed that only few of them display a single, well-defined maximum; rather, they feature either a broad plateau or two clear peaks. Nonlinear least-squares fitting of one or two Gaussian functions allowed the characteristic energies to be determined. We found that the double peaks and the plateaus in Mascot score can be associated with the different energy dependence of b- and y-type fragment ion intensities. We determined that the energies for optimum Mascot scores follow separate linear trends for the unimodal and bimodal cases with rather large residual variance even after differences in proton mobility are taken into account. This leaves room for experiment optimization and points to the possible influence of further factors beyond m/ z.

Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides

There are emerging advancements in the strategies used for the discovery and development of food-derived bioactive peptides because of their multiple food and health applications. Bioinformatics and peptidomics are two computational and analytical techniques that have the potential to speed up the development of bioactive peptides from bench to market. Structure-activity relationships observed in peptides form the basis for bioinformatics and in silico prediction of bioactive sequences encrypted in food proteins. Peptidomics, on the other hand, relies on "hyphenated" (liquid chromatography-mass spectrometry-based) techniques for the detection, profiling, and quantitation of peptides. Together, bioinformatics and peptidomics approaches provide a low-cost and effective means of predicting, profiling, and screening bioactive protein hydrolysates and peptides from food. This article discuses the basis, strengths, and limitations of bioinformatics and peptidomics approaches currently used for the discovery and analysis of food-derived bioactive peptides.

Maillard Proteomics: Opening New Pages

Protein glycation is a ubiquitous non-enzymatic post-translational modification, formed by reaction of protein amino and guanidino groups with carbonyl compounds, presumably reducing sugars and α-dicarbonyls. Resulting advanced glycation end products (AGEs) represent a highly heterogeneous group of compounds, deleterious in mammals due to their pro-inflammatory effect, and impact in pathogenesis of diabetes mellitus, Alzheimer's disease and ageing. The body of information on the mechanisms and pathways of AGE formation, acquired during the last decades, clearly indicates a certain site-specificity of glycation. It makes characterization of individual glycation sites a critical pre-requisite for understanding in vivo mechanisms of AGE formation and developing adequate nutritional and therapeutic approaches to reduce it in humans. In this context, proteomics is the methodology of choice to address site-specific molecular changes related to protein glycation. Therefore, here we summarize the methods of Maillard proteomics, specifically focusing on the techniques providing comprehensive structural and quantitative characterization of glycated proteome. Further, we address the novel break-through areas, recently established in the field of Maillard research, i.e., in vitro models based on synthetic peptides, site-based diagnostics of metabolism-related diseases (e.g., diabetes mellitus), proteomics of anti-glycative defense, and dynamics of plant glycated proteome during ageing and response to environmental stress.

Highly sensitive ligand-binding assays in pre-clinical and clinical applications: immuno-PCR and other emerging techniques

Recombinant DNA technology and corresponding innovations in molecular biology, chemistry and medicine have led to novel therapeutic biomacromolecules as lead candidates in the pharmaceutical drug development pipelines. While monoclonal antibodies and other proteins provide therapeutic potential beyond the possibilities of small molecule drugs, the concomitant demand for supportive bioanalytical sample testing creates multiple novel challenges. For example, intact macromolecules can usually not be quantified by mass-spectrometry without enzymatic digestion and isotopically labeled internal standards are costly and/or difficult to prepare. Classical ELISA-type immunoassays, on the other hand, often lack the sensitivity required to obtain pharmacokinetics of low dosed drugs or pharmacodynamics of suitable biomarkers. Here we summarize emerging state-of-the-art ligand-binding assay technologies for pharmaceutical sample testing, which reveal enhanced analytical sensitivity over classical ELISA formats. We focus on immuno-PCR, which combines antibody specificity with the extremely sensitive detection of a tethered DNA marker by quantitative PCR, and alternative nucleic acid-based technologies as well as methods based on electrochemiluminescence or single-molecule counting. Using case studies, we discuss advantages and drawbacks of these methods for preclinical and clinical sample testing.

Conserved mechanism of action of a phosphate analogue in cultured cells

The present study was undertaken to further examine a model for the mechanism of action of thio-phosphate in cells using the cultured human cell line HEK 293 and the bacterium E. coli. Previous work has shown that thio-phosphate is incorporated into the nucleic acids of all types of cells and that this modification leads to the stabilization and accumulation of mRNA in vivo. Evidently, competition for translation occurs between cellular mRNAs, due to the limited number of ribosomes in the cell, which impacts the proteins synthesized. In both E. coli and HEK 293 cells, a global shift in the distribution of proteins on 2D gels was observed when cells were grown in the presence of thio-phosphate. In both cases, a significant fraction of the proteins were impacted, namely 17 % in E. coli and 28 % in HEK 293 cells. In general, those proteins originally expressed at lower levels were enhanced while those at higher levels were reduced. In addition, a correlation was observed between the % initial mass and the fold change observed, supporting the idea of a global shift. Furthermore, the potential identity of some proteins spots was obtained by comparisons with computed MW and pI values for known genes. The candidates were cross-referenced with known mRNA half-lives when available. The analysis supports the idea that a preferential accumulation of proteins derived from mRNAs with half-lives less than the cellular mRNA average occurred, at the expense of those proteins derived from mRNAs with half-lives greater than the cellular average. In summary, thio-phosphate is shown to be a useful tool for predictably shifting the distribution of proteins in a variety of cell types. The fold enhancement is typically 5- to 10-fold. Interestingly, there were some proteins elevated more than 20-fold, identifying potential regulatory responses. Some of these were further characterized using LC-MS/MS.

Extracellular matrix-based biomaterial scaffolds and the host response

Extracellular matrix (ECM) collectively represents a class of naturally derived proteinaceous biomaterials purified from harvested organs and tissues with increasing scientific focus and utility in tissue engineering and repair. This interest stems predominantly from the largely unproven concept that processed ECM biomaterials as natural tissue-derived matrices better integrate with host tissue than purely synthetic biomaterials. Nearly every tissue type has been decellularized and processed for re-use as tissue-derived ECM protein implants and scaffolds. To date, however, little consensus exists for defining ECM compositions or sources that best constitute decellularized biomaterials that might better heal, integrate with host tissues and avoid the foreign body response (FBR). Metrics used to assess ECM performance in biomaterial implants are arbitrary and contextually specific by convention. Few comparisons for in vivo host responses to ECM implants from different sources are published. This review discusses current ECM-derived biomaterials characterization methods including relationships between ECM material compositions from different sources, properties and host tissue response as implants. Relevant preclinical in vivo models are compared along with their associated advantages and limitations, and the current state of various metrics used to define material integration and biocompatibility are discussed. Commonly applied applications of these ECM-derived biomaterials as stand-alone implanted matrices and devices are compared with respect to host tissue responses.

Biomarker detection technologies and future directions

Biomarkers play a vital role in disease detection and treatment follow-up. It is important to note that diseases in the early stage are typically treated with the greatest probability of success. However, due to various technical difficulties in current technologies for the detection of biomarkers, the potential of biomarkers is not explored completely. Therefore, the developments of technologies, which can enable the accurate detection of prostate cancer at an early stage with simple, experimental protocols are highly inevitable. This critical review evaluates the current methods and technologies used in the detection of biomarkers. The aim of this article is to provide a comprehensive review covering the advantages and disadvantages of the biomarker detection methods. Future directions for the development of technologies to achieve highly selective and sensitive detection of biomarkers for point-of-care applications are also commented on.

Scientific workflow optimization for improved peptide and protein identification

Background

Peptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal.

Results

We have applied a new optimization framework for the Taverna scientific workflow management system (http://ms-utils.org/Taverna_Optimization.pdf) to find the best combination of parameters for a given scientific workflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated by several phenomena that can be observed when allowing for larger mass measurement errors in sequence database searches. On-the-fly parameter optimization embedded in scientific workflow management systems enables experts and non-experts alike to extract the maximum amount of information from the data. The same workflows could be used for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but also for other tasks, such as retention time prediction.

Conclusion

Using the optimization framework, we were able to learn about how the data was acquired as well as the explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptides with N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gained with the extension of the common range for the mass measurement error tolerance parameters explored by the optimization framework.

Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists

Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform these data into biological knowledge, for example, how to use these data to answer questions such as: Which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction, and network interrogation. Here we provide an overview of network analysis including a step-by-step guide on how to perform and use this approach to investigate a biological question. In this guide, we also include the software packages that we and others employ for each of the steps of a network analysis workflow.

Formation of gas-phase peptide ions and their dissociation in MALDI: insights from kinetic and ion yield studies

Insights on mechanisms for the generation of gas-phase peptide ions and their dissociation in matrix-assisted laser desorption ionization (MALDI) gained from the kinetic and ion yield studies are presented. Even though the time-resolved photodissociation technique was initially used to determine the dissociation kinetics of peptide ions and their effective temperature, it was replaced by a simpler method utilizing dissociation yields from in-source decay (ISD) and post-source decay (PSD). The ion yields for a matrix and a peptide were measured by repeatedly irradiating a region on a sample and collecting ion signals until the sample in the region was completely depleted. Matrix- and peptide-derived gas-phase cations were found to be generated by pre-formed ion emission or by ion-pair emission followed by anion loss, but not by laser-induced ionization. The total number of ions, that is, matrix plus peptide, was found to be equal to the number of ions emitted from a pure matrix. A matrix plume was found to cool as it expanded, from around 800-1,000 K to 400-500 K. Dissociation of peptide ions along b/y channels was found to occur statistically, that is, following RRKM behavior. Small critical energy (E0  = 0.6-0.7 eV) and highly negative critical entropy (ΔS(‡)  = -30 to -25 eu) suggested that the transition structure was stabilized by multiple intramolecular interactions.

Computational survey of sequence specificity for protein terminal tags covering nine organisms and its application to protein identification

In 1998, Wilkins et al. (J. Mol. Biol. 1998, 278, 599-608) reported high specificity in terminal regions (terminal tags) of 15 519 proteins from five organisms and proposed a methodology for identifying proteins by terminal tags. However, their examined sequence data were not based on complete genome sequences. Here, we examined current proteome data (217 249 entries from UniProt 2013_6 complete/reference proteome for nine organisms including human) in terms of the specificity of terminal tags and their computational annotation. One example from the results indicated that the specificity of N-terminal tags plateaued at 28% at a length of six residues for human; even when using both N- and C-terminal tags, specificity was merely 66%. In order to determine the cause of these low specificities, the annotation of proteins sharing terminal tags with other proteins was examined. The results suggested that a large majority were phylogenetically or functionally related, whereas nonrelated proteins sharing terminal tags made up less than 1% of human proteome data. On the basis of these findings, we constructed the terminal tag sequence database ProteinCarta (http://ms3d.jp/software/proteincarta/), which includes all terminal tags of proteomes from the nine organisms analyzed here, in order to confirm the specificity of terminal tags and to identify the parent protein.

Using EMBL-EBI Services via Web Interface and Programmatically via Web Services

The European Bioinformatics Institute (EMBL-EBI) provides access to a wide range of databases and analysis tools that are of key importance in bioinformatics. As well as providing Web interfaces to these resources, Web Services are available using SOAP and REST protocols that enable programmatic access to our resources and allow their integration into other applications and analytical workflows. This unit describes the various options available to a typical researcher or bioinformatician who wishes to use our resources via Web interface or programmatically via a range of programming languages.

Peptide Peak Detection for Low Resolution MALDI-TOF Mass Spectrometry

A new peak detection method has been developed for rapid selection of peptide and its fragment ion peaks for protein identification using tandem mass spectrometry. The algorithm applies classification of peak intensities present in the defined mass range to determine the noise level. A threshold is then given to select ion peaks according to the determined noise level in each mass range. This algorithm was initially designed for the peak detection of low resolution peptide mass spectra, such as matrix-assisted laser desorption/ionization Time-of-Flight (MALDI-TOF) mass spectra. But it can also be applied to other type of mass spectra. This method has demonstrated obtaining a good rate of number of real ions to noises for even poorly fragmented peptide spectra. The effect of using peak lists generated from this method produces improved protein scores in database search results. The reliability of the protein identifications is increased by finding more peptide identifications. This software tool is freely available at the Mass++ home page (http://www.first-ms3d.jp/english/achievement/software/).

Implications of functional proteomics in breast cancer

Breast cancer is one of the major public health problems of the Western world. Recent advances in genomics and gene expression-profiling approaches have enriched our understanding of this heterogeneous disease. However, progress in functional proteomics in breast cancer research has been relatively slow. Allied with genomics, the functional proteomics approach will be important in improving diagnosis through better classification of breast cancer and in predicting prognosis and response to different therapies, including chemotherapy, hormonal therapy, and targeted therapy. In this review, we will present functional proteomic approaches with a focus on the recent clinical implications of utilizing the reverse-phase protein array platform in breast cancer research.

Small-molecule mechanism of action studies in Caenorhabditis elegans

A general protocol for exogenous small-molecule pull-down experiments with Caenorhabditis elegans is described; it provides a link between small-molecule screens in worms and existing mutant and RNAi technologies, thereby enabling organismal mechanism of action studies for the natural product clovanemagnolol. Forward chemical genetic screens followed by mechanism of action studies with C. elegans, when coupled with genetic validation of identified targets to reproduce the small molecule's phenotypic effects, provide a unique platform for discovering the biological targets of compounds that affect multicellular processes. First, the use of an immobilized FK506 derivative and soluble competition experiments with optimally prepared soluble C. elegans proteome successfully identified interactions with FK506 binding proteins 1 to 6. This approach was used to determine an unknown mechanism of action for clovanemagnolol, a small molecule that promotes axonal branching in both primary neuronal cultures and in vivo in C. elegans. Following the synthesis of an appropriately functionalized solid-phase reagent bearing a clovanemagnolol analogue pull-down experiments employing soluble competition identified kinesin light chain-1 (KLC-1), a protein involved in axonal cargo transport, as a putative target. This was corroborated through the use of mutant worms lacking klc-1 and possessing GFP neuronal labeling, reproducing the axonal branching phenotype induced by the small molecule clovanemagnolol.

Membrane-SPINE: a biochemical tool to identify protein-protein interactions of membrane proteins in vivo

Membrane proteins are essential for cell viability and are therefore important therapeutic targets(1-3). Since they function in complexes(4), methods to identify and characterize their interactions are necessary(5). To this end, we developed the Membrane Strep-protein interaction experiment, called Membrane-SPINE(6). This technique combines in vivo cross-linking using the reversible cross-linker formaldehyde with affinity purification of a Strep-tagged membrane bait protein. During the procedure, cross-linked prey proteins are co-purified with the membrane bait protein and subsequently separated by boiling. Hence, two major tasks can be executed when analyzing protein-protein interactions (PPIs) of membrane proteins using Membrane-

SPINE

first, the confirmation of a proposed interaction partner by immunoblotting, and second, the identification of new interaction partners by mass spectrometry analysis. Moreover, even low affinity, transient PPIs are detectable by this technique. Finally, Membrane-SPINE is adaptable to almost any cell type, making it applicable as a powerful screening tool to identify PPIs of membrane proteins.

Multisite phosphorylation of c-Jun at threonine 91/93/95 triggers the onset of c-Jun pro-apoptotic activity in cerebellar granule neurons

Cerebellar granule cell (CGC) apoptosis by trophic/potassium (TK) deprivation is a model of election to study the interplay of pro-apoptotic and pro-survival signaling pathways in neuronal cell death. In this model, the c-Jun N-terminal kinase (JNK) induces pro-apoptotic genes through the c-Jun/activator protein 1 (AP-1) transcription factor. On the other side, a survival pathway initiated by lithium leads to repression of pro-apoptotic c-Jun/AP-1 target genes without interfering with JNK activity. Yet, the mechanism by which lithium inhibits c-Jun activity remains to be elucidated. Here, we used this model system to study the regulation and function of site-specific c-Jun phosphorylation at the S63 and T91/T93 JNK sites in neuronal cell death. We found that TK-deprivation led to c-Jun multiphosphorylation at all three JNK sites. However, immunofluorescence analysis of c-Jun phosphorylation at single cell level revealed that the S63 site was phosphorylated in all c-Jun-expressing cells, whereas the response of T91/T93 phosphorylation was more sensitive, mirroring the switch-like apoptotic response of CGCs. Conversely, lithium prevented T91T93 phosphorylation and cell death without affecting the S63 site, suggesting that T91T93 phosphorylation triggers c-Jun pro-apoptotic activity. Accordingly, a c-Jun mutant lacking the T95 priming site for T91/93 phosphorylation protected CGCs from apoptosis, whereas it was able to induce neurite outgrowth in PC12 cells. Vice versa, a c-Jun mutant bearing aspartate substitution of T95 overwhelmed lithium-mediate protection of CGCs from TK-deprivation, validating that inhibition of T91/T93/T95 phosphorylation underlies the effect of lithium on cell death. Mass spectrometry analysis confirmed multiphosphorylation of c-Jun at T91/T93/T95 in cells. Moreover, JNK phosphorylated recombinant c-Jun at T91/T93 in a T95-dependent manner. On the basis of our results, we propose that T91/T93/T95 multiphosphorylation of c-Jun functions as a sensitivity amplifier of the JNK cascade, setting the threshold for c-Jun pro-apoptotic activity in neuronal cells.

Developing microRNA screening as a functional genomics tool for disease research

Originally discovered as regulators of developmental timing in C. elegans, microRNAs (miRNAs) have emerged as modulators of nearly every cellular process, from normal development to pathogenesis. With the advent of whole genome libraries of miRNA mimics suitable for high throughput screening, it is possible to comprehensively evaluate the function of each member of the miRNAome in cell-based assays. Since the relatively few microRNAs in the genome are thought to directly regulate a large portion of the proteome, miRNAome screening, coupled with the identification of the regulated proteins, might be a powerful new approach to gaining insight into complex biological processes.

Natural variation in floral nectar proteins of two Nicotiana attenuata accessions

BACKGROUND

Floral nectar (FN) contains not only energy-rich compounds to attract pollinators, but also defense chemicals and several proteins. However, proteomic analysis of FN has been hampered by the lack of publically available sequence information from nectar-producing plants. Here we used next-generation sequencing and advanced proteomics to profile FN proteins in the opportunistic outcrossing wild tobacco, Nicotiana attenuata.

RESULTS

We constructed a transcriptome database of N. attenuata and characterized its nectar proteome using LC-MS/MS. The FN proteins of N. attenuata included nectarins, sugar-cleaving enzymes (glucosidase, galactosidase, and xylosidase), RNases, pathogen-related proteins, and lipid transfer proteins. Natural variation in FN proteins of eleven N. attenuata accessions revealed a negative relationship between the accumulation of two abundant proteins, nectarin1b and nectarin5. In addition, microarray analysis of nectary tissues revealed that protein accumulation in FN is not simply correlated with the accumulation of transcripts encoding FN proteins and identified a group of genes that were specifically expressed in the nectary.

CONCLUSIONS

Natural variation of identified FN proteins in the ecological model plant N. attenuata suggests that nectar chemistry may have a complex function in plant-pollinator-microbe interactions.

PRIMe Update: innovative content for plant metabolomics and integration of gene expression and metabolite accumulation

PRIMe (http://prime.psc.riken.jp/), the Platform for RIKEN Metabolomics, is a website that was designed and implemented to support research and analyses ranging from metabolomics to transcriptomics. To achieve functional genomics and annotation of unknown metabolites, we established the following PRIMe contents: MS2T, a library comprising >1 million entries of untargeted tandem mass spectrometry (MS/MS) data of plant metabolites; AtMetExpress LC-MS, a database of transcriptomics and metabolomics approaches in Arabidopsis developmental stages (AtMetExpress Development LC-MS) and a data set of the composition of secondary metabolites among 20 Arabidopsis ecotypes (AtMetExpress 20 ecotypes LC-MS); and ReSpect, hybrid reference MS/MS data resources (acquisitions and literature). PRIMeLink is a new web application that allows access to the innovative data resources of PRIMe. The MS2T library was generated from a set of MS/MS spectra acquired using the automatic data acquisition function of mass spectrometry. To increase the understanding of mechanisms driving variations in metabolic profiles among plant tissues, we further provided the AtMetExpress Development LC-MS database in PRIMe, facilitating the investigation of relationships between gene expression and metabolite accumulation. This information platform therefore provides an integrative analysis resource by linking Arabidopsis transcriptome and metabolome data. Moreover, we developed the ReSpect database, a plant-specific MS/MS data resource, which allows users to identify candidate structures from the suite of complex phytochemical structures. Finally, we integrated the three databases into PRIMeLink and established a walk-through link between transcriptome and metabolome information. PRIMeLink offers a bi-directional searchable function, from the gene and the metabolite perspective, to search for targets seamlessly and effectively.

Delivering value from sperm proteomics for fertility

Fertilization of an egg by a spermatozoon sets the stage for mammalian development. Viable sperm are a prerequisite for successful fertilization and beyond. Spermatozoa have a unique cell structure where haploid genomic DNA is located in a tiny cytoplasmic space in the head, mitochondria in the midpiece and then the tail, all enclosed by several layers of membrane. Proteins in sperm play vital roles in motility, capacitation, fertilization, egg activation and embryo development. Molecular defects in these proteins are associated with low fertility or in some cases, infertility. This review will first summarize genesis, molecular anatomy and physiology of spermatozoa, fertilization, embryogenesis and then those proteins playing important roles in various aspects of sperm physiology.

Platelet proteomics

Platelets are small cell fragments, produced by megakaryocytes, in the bone marrow. They play an important role in hemostasis and diverse thrombotic disorders. They are therefore primary targets of antithrombotic therapies. They are implicated in several pathophysiological pathways, such as inflammation or wound repair. In blood circulation, platelets are activated by several pathways including subendothelial matrix and thrombin, triggering the formation of the platelet plug. Studying their proteome is a powerful approach to understand their biology and function. However, particular attention must be paid to different experimental parameters, such as platelet quality and purity. Several technologies are involved during the platelet proteome processing, yielding information on protein identification, characterization, localization, and quantification. Recent technical improvements in proteomics combined with inter-disciplinary strategies, such as metabolomic, transcriptomics, and bioinformatics, will help to understand platelets biological mechanisms. Therefore, a comprehensive analysis of the platelet proteome under different environmental conditions may contribute to elucidate complex processes relevant to platelet function regarding bleeding disorders or platelet hyperreactivity and identify new targets for antiplatelet therapy.

Strategies to dissect parasite proteomes

Proteomes are complex and dynamic entities that are still poorly understood, but the application of proteomic technologies has become invaluable in many areas of biology, including parasitology. These technologies can be exploited to identify proteins in both complex or relatively simple samples, that formerly could only be characterized by targeted approaches such as Western blotting. Quantitative proteomic approaches can reveal modulations in protein expression that accompany phenotypes of interest. Proteomic approaches have been exploited to understand some of the molecular basis for host:parasite interactions and to elucidate phenotypes such as virulence, antigenicity and drug resistance. Many of the same technologies can also be more easily applied to targeted sub-proteomes.

Examples from several studies on pathogen proteomes and sub-proteomes, from bacteria to helminths, are presented to illustrate the potential and limitations of proteomic technologies.

OpenKnowledge for peer-to-peer experimentation in protein identification by MS/MS

BACKGROUND

Traditional scientific workflow platforms usually run individual experiments with little evaluation and analysis of performance as required by automated experimentation in which scientists are being allowed to access numerous applicable workflows rather than being committed to a single one. Experimental protocols and data under a peer-to-peer environment could potentially be shared freely without any single point of authority to dictate how experiments should be run. In such environment it is necessary to have mechanisms by which each individual scientist (peer) can assess, locally, how he or she wants to be involved with others in experiments. This study aims to implement and demonstrate simple peer ranking under the OpenKnowledge peer-to-peer infrastructure by both simulated and real-world bioinformatics experiments involving multi-agent interactions.

METHODS

A simulated experiment environment with a peer ranking capability was specified by the Lightweight Coordination Calculus (LCC) and automatically executed under the OpenKnowledge infrastructure. The peers such as MS/MS protein identification services (including web-enabled and independent programs) were made accessible as OpenKnowledge Components (OKCs) for automated execution as peers in the experiments. The performance of the peers in these automated experiments was monitored and evaluated by simple peer ranking algorithms.

RESULTS

Peer ranking experiments with simulated peers exhibited characteristic behaviours, e.g., power law effect (a few dominant peers dominate), similar to that observed in the traditional Web. Real-world experiments were run using an interaction model in LCC involving two different types of MS/MS protein identification peers, viz., peptide fragment fingerprinting (PFF) and de novo sequencing with another peer ranking algorithm simply based on counting the successful and failed runs. This study demonstrated a novel integration and useful evaluation of specific proteomic peers and found MASCOT to be a dominant peer as judged by peer ranking.

CONCLUSION

The simulated and real-world experiments in the present study demonstrated that the OpenKnowledge infrastructure with peer ranking capability can serve as an evaluative environment for automated experimentation.

Sample preparation techniques for the untargeted LC-MS-based discovery of peptides in complex biological matrices

Although big progress has been made in sample pretreatment over the last years, there are still considerable limitations when it comes to overcoming complexity and dynamic range problems associated with peptide analyses from biological matrices. Being the little brother of proteomics, peptidomics is a relatively new field of research aiming at the direct analysis of the small proteins, called peptides, many of which are not amenable for typical trypsin-based analytics. In this paper, we present an overview of different techniques and methods currently used for reducing a sample's complexity and for concentrating low abundant compounds to enable successful peptidome analysis. We focus on techniques which can be employed prior to liquid chromatography coupled to mass spectrometry for peptide detection and identification and indicate their advantages as well as their shortcomings when it comes to the untargeted analysis of native peptides from complex biological matrices.

Enhanced peptide quantification using spectral count clustering and cluster abundance

Background

Quantification of protein expression by means of mass spectrometry (MS) has been introduced in various proteomics studies. In particular, two label-free quantification methods, such as spectral counting and spectra feature analysis have been extensively investigated in a wide variety of proteomic studies. The cornerstone of both methods is peptide identification based on a proteomic database search and subsequent estimation of peptide retention time. However, they often suffer from restrictive database search and inaccurate estimation of the liquid chromatography (LC) retention time. Furthermore, conventional peptide identification methods based on the spectral library search algorithms such as SEQUEST or SpectraST have been found to provide neither the best match nor high-scored matches. Lastly, these methods are limited in the sense that target peptides cannot be identified unless they have been previously generated and stored into the database or spectral libraries.

To overcome these limitations, we propose a novel method, namely Quantification method based on Finding the Identical Spectral set for a Homogenous peptide (Q-FISH) to estimate the peptide's abundance from its tandem mass spectrometry (MS/MS) spectra through the direct comparison of experimental spectra. Intuitively, our Q-FISH method compares all possible pairs of experimental spectra in order to identify both known and novel proteins, significantly enhancing identification accuracy by grouping replicated spectra from the same peptide targets.

Results

We applied Q-FISH to Nano-LC-MS/MS data obtained from human hepatocellular carcinoma (HCC) and normal liver tissue samples to identify differentially expressed peptides between the normal and disease samples. For a total of 44,318 spectra obtained through MS/MS analysis, Q-FISH yielded 14,747 clusters. Among these, 5,777 clusters were identified only in the HCC sample, 6,648 clusters only in the normal tissue sample, and 2,323 clusters both in the HCC and normal tissue samples. While it will be interesting to investigate peptide clusters only found from one sample, further examined spectral clusters identified both in the HCC and normal samples since our goal is to identify and assess differentially expressed peptides quantitatively. The next step was to perform a beta-binomial test to isolate differentially expressed peptides between the HCC and normal tissue samples. This test resulted in 84 peptides with significantly differential spectral counts between the HCC and normal tissue samples. We independently identified 50 and 95 peptides by SEQUEST, of which 24 and 56 peptides, respectively, were found to be known biomarkers for the human liver cancer. Comparing Q-FISH and SEQUEST results, we found 22 of the differentially expressed 84 peptides by Q-FISH were also identified by SEQUEST. Remarkably, of these 22 peptides discovered both by Q-FISH and SEQUEST, 13 peptides are known for human liver cancer and the remaining 9 peptides are known to be associated with other cancers.

Conclusions

We proposed a novel statistical method, Q-FISH, for accurately identifying protein species and simultaneously quantifying the expression levels of identified peptides from mass spectrometry data. Q-FISH analysis on human HCC and liver tissue samples identified many protein biomarkers that are highly relevant to HCC. Q-FISH can be a useful tool both for peptide identification and quantification on mass spectrometry data analysis. It may also prove to be more effective in discovering novel protein biomarkers than SEQUEST and other standard methods.