Proteomics strategies for protein identification

Proteomics approaches to characterize the immune responses in cancer

Despite the dynamic development of cancer research, annually millions of people die of cancer. The human immune system is the major 'guard' against tumor development. Unfortunately, cancer cells have the ability to evade the immune system and continue to grow. The proper understanding of the intricate immune response in tumorigenesis remains the holy grail of cancer immunology and designing effective immunotherapy. To decode the immune responses in cancer, in recent years, proteomics studies have received considerable attention. Proteomics studies focus on the detection and quantification of proteins, which are the effectors of biological functions, and as such, are proven to reflect the cell state more accurately, in comparison to genomic or transcriptomic studies. In this review, we discuss the proteomics studies applied to characterize the immune responses in cancer and tumor immune microenvironment heterogeneity. Further, we describe emerging single-cell proteomics approaches that have the potential to be applied in cancer immunity studies.

Integration of transcriptomic and proteomic approaches for snake venom profiling

INTRODUCTION

Snake venoms contain many protein and peptide isoforms with high levels of sequence variation, even within a single species.

AREAS COVERED

In this review, we highlight several examples, from both published and unpublished work in our lab, demonstrating how a combined venom gland transcriptome and proteome methodology allows for comprehensive characterization of venoms, including those from understudied rear-fanged snake species, and we provide recommendations for using these approaches.

EXPERT OPINION

When characterizing venoms, peptide mass fingerprinting using databases built predominately from protein sequences originating from model organisms can be disadvantageous, especially when the intention is to document protein diversity. Therefore, the use of species-specific venom gland transcriptomes corrects for the absence of these unique peptide sequences in databases. The integration of transcriptomics and proteomics improves the accuracy of either approach alone for venom profiling.

Advances in mass spectrometry-based glycoproteomics: An update covering the period 2017-2021

Protein glycosylation is one of the most common posttranslational modifications, and plays an essential role in a wide range of biological processes such as immune response, intercellular signaling, inflammation, host-pathogen interaction, and protein stability. Glycoproteomics is a proteomics subfield dedicated to identifying and characterizing the glycans and glycoproteins in a given cell or tissue. Aberrant glycosylation has been associated with various diseases such as Alzheimer's disease, viral infections, inflammation, immune deficiencies, congenital disorders, and cancers. However, glycoproteomic analysis remains challenging because of the low abundance, site-specific heterogeneity, and poor ionization efficiency of glycopeptides during LC-MS analyses. Therefore, the development of sensitive and accurate approaches to efficiently characterize protein glycosylation is crucial. Methods such as metabolic labeling, enrichment, and derivatization of glycopeptides, coupled with different mass spectrometry techniques and bioinformatics tools, have been developed to achieve sophisticated levels of quantitative and qualitative analyses of glycoproteins. This review attempts to update the recent developments in the field of glycoproteomics reported between 2017 and 2021.

Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects

Omics technologies, namely genomics, transcriptomics, proteomics, metabolomics, and phenomics, are becoming an integral part of virtually every commercial cereal crop breeding program, as they provide substantial dividends per unit time in both pre-breeding and breeding phases. Continuous advances in omics assure time efficiency and cost benefits to improve cereal crops. This review provides a comprehensive overview of the established omics methods in five major cereals, namely rice, sorghum, maize, barley, and bread wheat. We cover the evolution of technologies in each omics section independently and concentrate on their use to improve economically important agronomic as well as biotic and abiotic stress-related traits. Advancements in the (1) identification, mapping, and sequencing of molecular/structural variants; (2) high-density transcriptomics data to study gene expression patterns; (3) global and targeted proteome profiling to study protein structure and interaction; (4) metabolomic profiling to quantify organ-level, small-density metabolites, and their composition; and (5) high-resolution, high-throughput, image-based phenomics approaches are surveyed in this review.

Proteomes Are of Proteoforms: Embracing the Complexity

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

Machine learning-aided protein identification from multidimensional signatures

The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of a novel method for protein identification in a microfluidic format.

Mass spectrometric investigations of caloric restriction mimetics

Caloric restriction (CR) is an innovative therapy used in tumor tissue and tumor model studies to promote cell death and decrease cell viability. Caloric restriction mimetics (CRMs) are a class of drugs that induce CR and starvation conditions within a cell. When used simultaneously with other chemotherapy agents, the effects are synergistic and effective at promoting tumor cell death. In this review, we discuss CRMs and their potential as cancer therapeutics. Firstly, we establish an overview of CR and its impacts on healthy and tumor cells. CR and CRM drugs have shown to decrease age-related diseases and can act as an anti-cancer agent. As it can be challenging for an individual to diligently stick to a diet that would induce CR, CRMs are even more desirable. Then, we discuss the drug class by highlighting three CRMs: resveratrol, (-)-hydroxycitric acid, and rapamycin. These CRMs are commonly known for their dietary effects, but the underlying mechanisms that drive cellular metabolic and proteomic changes show promise as a cancer therapeutic. Lastly, we highlight the use of mass spectrometry and proteomic techniques on experiments utilizing CRM drugs to understand the cellular pathways impacted by this drug class, leading to a better understanding of the anti-cancer properties and potentials of CRM.

Ion Mobility and Gas-Phase Covalent Labeling Study of the Structure and Reactivity of Gaseous Ubiquitin Ions Electrosprayed from Aqueous and Denaturing Solutions

Gas-phase ion/ion chemistry was coupled to ion mobility/mass spectrometry analysis to correlate the structure of gaseous ubiquitin to its solution structures with selective covalent structural probes. Collision cross section (CCS) distributions were measured to ensure the ubiquitin ions were not unfolded when they were introduced to the gas phase. Aqueous solutions stabilizing the native state of ubiquitin yielded folded ubiquitin structures with CCS values consistent with previously published literature. Denaturing solutions favored several families of unfolded conformations for most of the charge states evaluated. Gas-phase covalent labeling via ion/ion reactions was followed by collision-induced dissociation of the intact, labeled protein to determine which residues were labeled. Ubiquitin 5+ and 6+ electrosprayed from aqueous conditions were covalently modified preferentially at the lysine 29 and arginine 54 positions, indicating that elements of three-dimensional structure were maintained in the gas phase. On the other hand, most ubiquitin ions produced in denaturing conditions were labeled at various other lysine residues, likely due to the availability of additional sites following methanol- and low-pH-induced unfolding. These data support the conservation of ubiquitin structural elements in the gas phase. The research presented here provides the basis for residue-specific characterization of biomolecules in the gas phase.

Covalent fragment libraries in drug discovery

Targeted covalent inhibitors and chemical probes have become integral parts of drug discovery approaches. Given the advantages of fragment-based drug discovery, screening electrophilic fragments emerged as a promising alternative to discover and validate novel targets and to generate viable chemical starting points even for targets that are barely tractable. In this review, we present recent principles and considerations in the design of electrophilic fragment libraries from the selection of the appropriate covalent warhead through the design of the covalent fragment to the compilation of the library. We then summarize recent screening methodologies of covalent fragments against surrogate models, proteins, and the whole proteome, or living cells. Finally, we highlight recent drug discovery applications of covalent fragment libraries.

Beyond mass spectrometry, the next step in proteomics

Proteins can be the root cause of a disease, and they can be used to cure it. The need to identify these critical actors was recognized early (1951) by Sanger; the first biopolymer sequenced was a peptide, insulin. With the advent of scalable, single-molecule DNA sequencing, genomics and transcriptomics have since propelled medicine through improved sensitivity and lower costs, but proteomics has lagged behind. Currently, proteomics relies mainly on mass spectrometry (MS), but instead of truly sequencing, it classifies a protein and typically requires about a billion copies of a protein to do it. Here, we offer a survey that illuminates a few alternatives with the brightest prospects for identifying whole proteins and displacing MS for sequencing them. These alternatives all boast sensitivity superior to MS and promise to be scalable and seem to be adaptable to bioinformatics tools for calling the sequence of amino acids that constitute a protein.

Proteomic Biomarkers for the Detection of Endometrial Cancer

Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.

Cannabinoid synthases and osmoprotective metabolites accumulate in the exudates of Cannabis sativa L. glandular trichomes

Cannabinoids are terpenophenolic compounds produced by Cannabis sativa L., which accumulate in storage cavities of glandular trichomes as a part of the exudates. We investigated if tetrahydrocannabinolic acid synthase and cannabidiolic acid synthase, which are involved in the last step of cannabinoid biosynthesis, are also secreted into Cannabis trichome exudates. The exudates were collected by microsuction from storage cavities of Cannabis glandular trichomes and were subjected for proteomic and metabolomic analyses. The catalytic activity of the exudates was documented by cannabigerolic acid biotransformation studies under hydrophobic conditions. Electrophoretic separations revealed protein bands at ˜65 kDa, which were further identified as tetrahydrocannabinolic acid synthase and cannabidiolic acid synthase. The accumulation of the enzymes in trichome exudates increased substantially during the flowering period in the drug-type Cannabis plants. The content of cannabinoids increased significantly after incubating hexane-diluted trichome exudates with cannabigerolic acid. In this study, we showed that Cannabis glandular trichomes secrete and accumulate cannabinoid synthases in storage cavities, and the enzymes able to convert cannabigerolic acid under hydrophobic trichome-mimicking conditions. Metabolite profiling of the exudates revealed compounds with hydrophilic, osmoprotective and amphiphilic properties, which may play a role in providing a necessary aqueous microenvironment, which enables enzyme solubility and biocatalysis under hydrophobic conditions of glandular trichomes.

Targeted Proteomics Comes to the Benchside and the Bedside: Is it Ready for Us?

While mass spectrometry (MS)-based quantification of small molecules has been successfully used for decades, targeted MS has only recently been used by the proteomics community to investigate clinical questions such as biomarker verification and validation. Targeted MS holds the promise of a paradigm shift in the quantitative determination of proteins. Nevertheless, targeted quantitative proteomics requires improvisation in making sample processing, instruments, and data analysis more accessible. In the backdrop of the genomic era reaching its zenith, certain questions arise: is the proteomic era about to come? If we are at the beginning of a new future for protein quantification, are we prepared to incorporate targeted proteomics at the benchside for basic research and at the bedside for the good of patients? Here, an overview of the knowledge required to perform targeted proteomics as well as its applications is provided. A special emphasis is placed on upcoming areas such as peptidomics, proteoform research, and mass spectrometry imaging, where the utilization of targeted proteomics is expected to bring forth new avenues. The limitations associated with the acceptance of this technique for mainstream usage are also highlighted. Also see the video abstract here https://youtu.be/mieB47B8gZw.

Advances in mass spectrometry-based glycoproteomics

Protein glycosylation, an important PTM, plays an essential role in a wide range of biological processes such as immune response, intercellular signaling, inflammation, and host-pathogen interaction. Aberrant glycosylation has been correlated with various diseases. However, studying protein glycosylation remains challenging because of low abundance, microheterogeneities of glycosylation sites, and poor ionization efficiency of glycopeptides. Therefore, the development of sensitive and accurate approaches to characterize protein glycosylation is crucial. The identification and characterization of protein glycosylation by MS is referred to as the field of glycoproteomics. Methods such as enrichment, metabolic labeling, and derivatization of glycopeptides in conjunction with different MS techniques and bioinformatics tools, have been developed to achieve an unequivocal quantitative and qualitative characterization of glycoproteins. This review summarizes the recent developments in the field of glycoproteomics over the past 6 years (2012 to 2018).

Evaluation and optimization of reduction and alkylation methods to maximize peptide identification with MS-based proteomics

Mass spectrometry (MS) has become an increasingly important technique to analyze proteins. In popular bottom-up MS-based proteomics, reduction and alkylation are routine steps to facilitate peptide identification. However, incomplete reactions and side reactions may occur, which compromise the experimental results. In this work, we systematically evaluated the reduction step with commonly used reagents, i.e., dithiothreitol, 2-mercaptoethanol, tris(2-carboxyethyl)phosphine, or tris(3-hydroxypropyl)phosphine, and alkylation with iodoacetamide, acrylamide, N-ethylmaleimide, or 4-vinylpyridine. By using digested peptides from a yeast whole-cell lysate, the number of proteins and peptides identified were very similar using four different reducing reagents. The results from four alkylating reagents, however, were dramatically different with iodoacetamide giving the highest number of peptides with alkylated cysteine and the lowest number of peptides with incomplete cysteine alkylation and side reactions. Alkylation conditions with iodoacetamide were further optimized. To identify more peptides with cysteine, thiopropyl-sepharose 6B resins were used to enrich them, and the optimal conditions were employed for the reduction and alkylation. The enrichment resulted in over three times more cysteine-containing peptides than without enrichment. Systematic evaluation of the reduction and alkylation with different reagents can aid in a better design of bottom-up proteomic experiments.

Proteomics: A new perspective for cancer

In the past decades, several ground breaking discoveries in life science were made. The completion of sequencing the human genome certainly belongs to the key tasks successfully completed, representing a true milestone in the biomedicine. The accomplishment of the complete genome also brings along a new, even more challenging task for scientists: The characterization of the human proteome. Proteomics, the main tool for proteome research, is a relatively new and extremely dynamically evolving branch of science, focused on the evaluation of gene expression at proteome level. Due to the specific properties of proteins, current proteomics deals with different issues, such as protein identification, quantification, characterization of post-translational modification, structure and function elucidation, and description of possible interactions. This field incorporates technologies that can be applied to serum and tissue in order to extract important biological information in the form of biomarkers to aid clinicians and scientists in understanding the dynamic biology of their system of interest, such as a patient with cancer. The present review article provides a detail description of proteomics and its role in cancer research.

A Cluster of Proteins Implicated in Kidney Disease Is Increased in High-Density Lipoprotein Isolated from Hemodialysis Subjects

Cardiovascular disease is the leading cause of death in end-stage renal disease (ESRD) patients treated with hemodialysis. An important contributor might be a decline in the cardioprotective effects of high-density lipoprotein (HDL). One important factor affecting HDL's cardioprotective properties may involve the alterations of protein composition in HDL. In the current study, we used complementary proteomics approaches to detect and quantify relative levels of proteins in HDL isolated from control and ESRD subjects. Shotgun proteomics analysis of HDL isolated from 20 control and 40 ESRD subjects identified 63 proteins in HDL. Targeted quantitative proteomics by isotope-dilution selective reaction monitoring revealed that 22 proteins were significantly enriched and 6 proteins were significantly decreased in ESRD patients. Strikingly, six proteins implicated in renal disease, including B2M, CST3, and PTGDS, were markedly increased in HDL of uremic subjects. Moreover, several of these proteins (SAA1, apoC-III, PON1, etc.) have been associated with atherosclerosis. Our observations indicate that the HDL proteome is extensively remodeled in uremic subjects. Alterations of the protein cargo of HDL might impact HDL's proposed cardioprotective properties. Quantifying proteins in HDL may be useful in the assessment of cardiovascular risk in patients with ESRD and in assessing response to therapeutic interventions.

Magnetite-doped polydimethylsiloxane (PDMS) for phosphopeptide enrichment

A moldable, reusable magnetite-doped polydimethylsiloxane (PDMS) substrate for phosphopeptide enrichment.

Extending the coverage of spectral libraries: a neighbor-based approach to predicting intensities of peptide fragmentation spectra

Searching spectral libraries in MS/MS is an important new approach to improving the quality of peptide and protein identification. The idea relies on the observation that ion intensities in an MS/MS spectrum of a given peptide are generally reproducible across experiments, and thus, matching between spectra from an experiment and the spectra of previously identified peptides stored in a spectral library can lead to better peptide identification compared to the traditional database search. However, the use of libraries is greatly limited by their coverage of peptide sequences: even for well-studied organisms a large fraction of peptides have not been previously identified. To address this issue, we propose to expand spectral libraries by predicting the MS/MS spectra of peptides based on the spectra of peptides with similar sequences. We first demonstrate that the intensity patterns of dominant fragment ions between similar peptides tend to be similar. In accordance with this observation, we develop a neighbor-based approach that first selects peptides that are likely to have spectra similar to the target peptide and then combines their spectra using a weighted K-nearest neighbor method to accurately predict fragment ion intensities corresponding to the target peptide. This approach has the potential to predict spectra for every peptide in the proteome. When rigorous quality criteria are applied, we estimate that the method increases the coverage of spectral libraries available from the National Institute of Standards and Technology by 20-60%, although the values vary with peptide length and charge state. We find that the overall best search performance is achieved when spectral libraries are supplemented by the high quality predicted spectra.

Characterization of Carotenoid-protein Complexes and Gene Expression Analysis Associated with Carotenoid Sequestration in Pigmented Cassava (Manihot Esculenta Crantz) Storage Root

Carotenoid-protein complex (CPC) was isolated from chromoplast-enriched suspensions of cassava storage root (CSR) using size exclusion chromatography and characterized. Peptide sequences (LC_MS/MS spectrum) obtained from CPC and their corresponding proteins were obtained using publically available databases. Small Heat Shock Proteins (sHSPs) were the most abundant proteins identified in the CPC. Western blot analysis showed that Fribrillin and Or-protein were present in chromoplast-enriched suspensions of yellow root but not in the complex or white root. Results from qRT-PCR helped identify an isoform of HSP21 possessing four single point mutations in the intense yellow CSR that may be responsible for increased sequestration of b-carotene.

Monocyte-to-macrophage differentiation: synthesis and secretion of a complex extracellular matrix

Although monocyte- and macrophage-derived molecules are known to promote extracellular matrix (ECM) disruption and destabilization, it is less appreciated that they also synthesize molecules contributing to ECM formation, stabilization, and function. We have identified and characterized the synthesis of proteoglycans and related proteins, some not previously known to be associated with macrophages. Proteoglycan extracts of [³⁵S]sulfate- and ³⁵S-trans amino acid-radiolabeled culture media from THP-1 monocytes induced to differentiate by treatment with phorbol myristate acetate revealed three major proteins of ∼25, 90, and 100 kDa following chondroitin ABC lyase digestion. The 25-kDa protein was predominant for monocytes, whereas the 90- and 100-kDa proteins were predominant for macrophages. Tandem mass spectrometry identified (i) the 25-kDa core protein as serglycin, (ii) the 90-kDa core protein as inter-α-inhibitor heavy chain 2 (IαIHC2), and (iii) the 100-kDa core as amyloid precursor-like protein 2 (APLP2). Differentiation was also associated with (i) a >500-fold increase in mRNA for TNF-stimulated gene-6, an essential cofactor for heavy chain-mediated matrix stabilization; (ii) a >800-fold increase in mRNA for HAS2, which is responsible for hyaluronan synthesis; and (iii) a 3-fold increase in mRNA for versican, which interacts with hyaluronan. Biochemical evidence is also presented for an IαIHC2-APLP2 complex, and immunohistochemical staining of human atherosclerotic lesions demonstrates similar staining patterns for APLP2 and IαIHC2 with macrophages, whereas serglycin localizes to the underlying glycosaminoglycan-rich region. These findings indicate that macrophages synthesize many of the molecules participating in ECM formation and function, suggesting a novel role for these molecules in the differentiation of macrophages in the development of atherosclerosis.

Applications of graph theory in protein structure identification

There is a growing interest in the identification of proteins on the proteome wide scale. Among different kinds of protein structure identification methods, graph-theoretic methods are very sharp ones. Due to their lower costs, higher effectiveness and many other advantages, they have drawn more and more researchers' attention nowadays. Specifically, graph-theoretic methods have been widely used in homology identification, side-chain cluster identification, peptide sequencing and so on. This paper reviews several methods in solving protein structure identification problems using graph theory. We mainly introduce classical methods and mathematical models including homology modeling based on clique finding, identification of side-chain clusters in protein structures upon graph spectrum, and de novo peptide sequencing via tandem mass spectrometry using the spectrum graph model. In addition, concluding remarks and future priorities of each method are given.

Plant organelle proteomics: collaborating for optimal cell function

Organelle proteomics describes the study of proteins present in organelle at a particular instance during the whole period of their life cycle in a cell. Organelles are specialized membrane bound structures within a cell that function by interacting with cytosolic and luminal soluble proteins making the protein composition of each organelle dynamic. Depending on organism, the total number of organelles within a cell varies, indicating their evolution with respect to protein number and function. For example, one of the striking differences between plant and animal cells is the plastids in plants. Organelles have their own proteins, and few organelles like mitochondria and chloroplast have their own genome to synthesize proteins for specific function and also require nuclear-encoded proteins. Enormous work has been performed on animal organelle proteomics. However, plant organelle proteomics has seen limited work mainly due to: (i) inter-plant and inter-tissue complexity, (ii) difficulties in isolation of subcellular compartments, and (iii) their enrichment and purity. Despite these concerns, the field of organelle proteomics is growing in plants, such as Arabidopsis, rice and maize. The available data are beginning to help better understand organelles and their distinct and/or overlapping functions in different plant tissues, organs or cell types, and more importantly, how protein components of organelles behave during development and with surrounding environments. Studies on organelles have provided a few good reviews, but none of them are comprehensive. Here, we present a comprehensive review on plant organelle proteomics starting from the significance of organelle in cells, to organelle isolation, to protein identification and to biology and beyond. To put together such a systematic, in-depth review and to translate acquired knowledge in a proper and adequate form, we join minds to provide discussion and viewpoints on the collaborative nature of organelles in cell, their proper function and evolution.

Proteomics and islet research

The complementary disciplines of genomics and proteomics offer better insights into the molecular mechanisms of diseases. While genomics hunts for defining our static genetic substrate, proteomics explores the structure and function of proteins expressed by a cell or tissue type under specified conditions. In the past decade, proteomics has been revolutionized by the application of techniques such as two-dimensional gel electrophoresis (2DGE), mass spectrometry (MS), and protein arrays. These techniques have tremendous potential for biomarker development, target validation, diagnosis, prognosis, and optimization of treatment in medical care, especially in the field of islet and diabetes research. This chapter will highlight the contributions of proteomic technologies toward the dissection of complex network of signaling molecules regulating islet function, the identification of potential biomarkers, and the understanding of mechanisms involved in the pathogenesis of diabetes.

Understanding and exploiting Peptide fragment ion intensities using experimental and informatic approaches

Tandem mass spectrometry is a widely used tool in proteomics. This section will address the properties that describe how protonated peptides fragment when activated by collisions in a mass spectrometer and how that information can be used to identify proteins. A review of the mobile proton model is presented, along with a summary of commonly observed peptide cleavage enhancements, including the proline effect. The methods used to elucidate peptide dissociation chemistry by using both small groups of model peptides and large datasets are also discussed. Finally, the role of peak intensity in commercially available and developmental peptide identification algorithms is examined.

Laser-capture microdissection and protein extraction for protein fingerprint of OSCC and OLK

To find new biomarkers and establish histopathology protein fingerprint models for early detection of oral squamous cell carcinoma (OSCC), laser capture microdissection (LCM) technology was utilized in 21 OSCC tissues and 7 oral leukoplaque (OLK) tissues as well as their adjacent normal tissues. Each sample was then detected by SELDI-TOF-MS technology and CM10 protein chip as well as bioinformatics tools. Three proteomic biomarker patterns were identified. Pattern 1 distinguishes patients with OLK from healthy individuals. Pattern 2 distinguishes patients with OSCC from healthy individuals. Pattern 3 distinguishes patients with OSCC from patients with OLK. The analysis yielded both a specificity and a sensitivity of 90.48% for pattern 1, a specificity of 100.00% and a sensitivity of 85.71% for pattern 2, and a specificity of 100.00% and a sensitivity of 85.71% for pattern 3. Proteome mass/charge 3714, 3515, and 4944 built the distinguished protein peaks between the OSCC tumor and adjacent normal tissues. The accuracy of the blind prediction was 90.48% (38/42). M/Z 15122 and 7569 built the distinguished protein peaks between the OLK and adjacent normal tissues. M/Z 3738 and 11366 built the distinguished protein peaks between the OSCC and the OLK. By employing LCM technology combined with SELDI-TOF-MS technology and bioinformatics approaches, histopathology would not only facilitate the discovery of better biomarkers for OSCC and OLK, but also provide a useful tool for molecular diagnosis by potential biomarker.

The fundamental flaws of immunoassays and potential solutions using tandem mass spectrometry

Immunoassays have made it possible to measure dozens of individual proteins and other analytes in human samples for help in establishing the diagnosis and prognosis of disease. In too many cases the results of those measurements are misleading and can lead to unnecessary treatment or missed opportunities for therapeutic interventions. These cases stem from problems inherent to immunoassays performed with human samples, which include a lack of concordance across platforms, autoantibodies, anti-reagent antibodies, and the high-dose hook effect. Tandem mass spectrometry may represent a detection method capable of alleviating many of the flaws inherent to immunoassays. We review our understanding of the problems associated with immunoassays on human specimens and describe methodologies using tandem mass spectrometry that could solve some of those problems. We also provide a critical discussion of the potential pitfalls of novel mass spectrometric approaches in the clinical laboratory.

Proteomic diagnosis models from serum for early detection of oral squamous cell carcinoma

The early diagnosis of oral squamous cell carcinoma (OSCC) is crucial to prevent deformity and malfunction post-surgery, as well as to allow clinicians to make a rapid decision about treatment. The aim of this study was to search a serum diagnostic model at a molecular level for OSCC. After collection and processing of serum from 28 OSCC patients and 32 healthy volunteers in the Department of Stomatology at the university hospital, samples were detected using Surface Enhanced Laser Desorption Ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) technology with CM10 protein chip and bioinformatics. Seven protein mass peaks were screened out to build a serum diagnosis model with a significant P value, respectively, and the sensitivity, specificity, and total accuracy were 93.75%, 92.86%, and 93.33%. The use of serum protein fingerprint provides a promising approach for early diagnostics, which could benefit determining preventative and therapeutic stages of patients with OSCC.

Peptide fragmentation induced by radicals at atmospheric pressure

A novel ion dissociation technique, which is capable of providing an efficient fragmentation of peptides at essential atmospheric pressure conditions, is developed. The fragmentation patterns observed often contain c-type fragments that are specific to electron capture dissociation/electron transfer dissociation (ECD/ETD), along with the y-/b-type fragments that are specific to collision-activated dissociation (CAD). In the presented experimental setup, ion fragmentation takes place within a flow reactor located in the atmospheric pressure region between the ion source and the mass spectrometer. According to a proposed mechanism, the fragmentation results from the interaction of ESI-generated analyte ions with the gas-phase radical species produced by a corona discharge source.

Comparative analysis of proteome differential regulation during cell dedifferentiation in Arabidopsis

Cell dedifferentiation is a cell fate switching process in which differentiated cells undergo genome reprogramming to regain the competency of cell division and organ regeneration. The molecular mechanism underlying the cell dedifferentiation process remains obscure. In this report, we investigate the cell dedifferentiation process in Arabidopsis using a shotgun proteomics approach. A total of 758 proteins are identified by two or more matched peptides. Comparative analyses at four time points using two label-free methods reveal that 193 proteins display up-regulation and 183 proteins display down-regulation within 48 h. While the results of the two label-free quantification methods match well with each other, comparison with previously published 2-DE gel results reveal that label-free quantification results differ substantially from those of the 2-DE method for proteins with peptides common to multiple proteins, suggesting a limitation of the label-free methods in quantifying proteins with closely related family members in complex samples. Our results show that the shotgun approach and the traditional 2-DE gel approach complement each other in both protein identification and quantification. An interesting observation is that core histones and histone variants are subjected to extensive down-regulation, indicating that there is a dramatic change in the chromatin during cell differentiation.

"Unknown genome" proteomics: a new NADP-dependent epimerase/dehydratase revealed by N-terminal sequencing, inverted PCR, and high resolution mass spectrometry

We present here a new approach that enabled the identification of a new protein from a bacterial strain with unknown genomic background using a combination of inverted PCR with degenerate primers derived from N-terminal protein sequences and high resolution peptide mass determination of proteolytic digests from two-dimensional electrophoretic separation. Proteins of the sulfate-reducing bacterium Desulfotignum phosphitoxidans specifically induced in the presence of phosphite were separated by two-dimensional gel electrophoresis as a series of apparent soluble and membrane-bound isoforms with molecular masses of approximately 35 kDa. Inverted PCR based on N-terminal sequences and high resolution peptide mass fingerprinting by Fourier transform-ion cyclotron resonance mass spectrometry provided the identification of a new NAD(P) epimerase/dehydratase by specific assignment of peptide masses to a single ORF, excluding other possible ORF candidates. The protein identification was ascertained by chromatographic separation and sequencing of internal proteolytic peptides. Metal ion affinity isolation of tryptic peptides and high resolution mass spectrometry provided the identification of five phosphorylations identified in the domains 23-47 and 91-118 of the protein. In agreement with the phosphorylations identified, direct molecular weight determination of the soluble protein eluted from the two-dimensional gels by mass spectrometry provided a molecular mass of 35,400 Da, which is consistent with an average degree of three phosphorylations.

Proteomics Discovery of Disease Biomarkers

Recent technological developments in proteomics have shown promising initiatives in identifying novel biomarkers of various diseases. Such technologies are capable of investigating multiple samples and generating large amount of data end-points. Examples of two promising proteomics technologies are mass spectrometry, including an instrument based on surface enhanced laser desorption/ionization, and protein microarrays. Proteomics data must, however, undergo analytical processing using bioinformatics. Due to limitations in proteomics tools including shortcomings in bioinformatics analysis, predictive bioinformatics can be utilized as an alternative strategy prior to performing elaborate, high-throughput proteomics procedures. This review describes mass spectrometry, protein microarrays, and bioinformatics and their roles in biomarker discovery, and highlights the significance of integration between proteomics and bioinformatics.

Computational methods for protein identification from mass spectrometry data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology.

Fast and accurate identification of semi-tryptic peptides in shotgun proteomics

MOTIVATION

One of the major problems in shotgun proteomics is the low peptide coverage when analyzing complex protein samples. Identifying more peptides, e.g. non-tryptic peptides, may increase the peptide coverage and improve protein identification and/or quantification that are based on the peptide identification results. Searching for all potential non-tryptic peptides is, however, time consuming for shotgun proteomics data from complex samples, and poses a challenge for a routine data analysis.

RESULTS

We hypothesize that non-tryptic peptides are mainly created from the truncation of regular tryptic peptides before separation. We introduce the notion of truncatability of a tryptic peptide, i.e. the probability of the peptide to be identified in its truncated form, and build a predictor to estimate a peptide's truncatability from its sequence. We show that our predictions achieve useful accuracy, with the area under the ROC curve from 76% to 87%, and can be used to filter the sequence database for identifying truncated peptides. After filtering, only a limited number of tryptic peptides with the highest truncatability are retained for non-tryptic peptide searching. By applying this method to identification of semi-tryptic peptides, we show that a significant number of such peptides can be identified within a searching time comparable to that of tryptic peptide identification.

Proteomic applications in ecotoxicology

Within the growing body of proteomics studies, issues addressing problems of ecotoxicology are on the rise. Generally speaking, ecotoxicology uses quantitative expression changes of distinct proteins known to be involved in toxicological responses as biomarkers. Unlike these directed approaches, proteomics examines how multiple expression changes are associated with a contamination that is suspected to be detrimental. Consequently, proteins involved in toxicological responses that have not been described previously may be revealed. Following identification of key proteins indicating exposure or effect, proteomics can potentially be employed in environmental risk assessment. To this end, bioinformatics may unveil protein patterns specific to an environmental stress that would constitute a classifier able to distinguish an exposure from a control state. The combined use of sets of marker proteins associated with a given pollution impact may prove to be more reliable, as they are based not only on a few unique markers which are measured independently, but reflect the complexity of a toxicological response. Such a proteomic pattern might also integrate some of the already established biomarkers of environmental toxicity. Proteomics applications in ecotoxicology may also comprise functional examination of known classes of proteins, such as glutathione transferases or metallothioneins, to elucidate their toxicological responses.

Neuroproteomics and its applications in research on nicotine and other drugs of abuse

The rapidly growing field of neuroproteomics is able to track changes in protein expression and protein modifications underlying various physiological conditions, including the neural diseases related to drug addiction. Thus, it presents great promise in characterizing protein function, biochemical pathways, and networks to understand the mechanisms underlying drug dependence. In this article, we first provide an overview of proteomics technologies and bioinformatics tools available to analyze proteomics data. Then we summarize the recent applications of proteomics to profile the protein expression pattern in animal or human brain tissues after the administration of nicotine, alcohol, amphetamine, butorphanol, cocaine, and morphine. By comparing the protein expression profiles in response to chronic nicotine exposure with those appearing in response to treatment with other drugs of abuse, we identified three biological processes that appears to be regulated by multiple drugs of abuse: energy metabolism, oxidative stress response, and protein degradation and modification. Such similarity indicates that despite the obvious differences among their chemical properties and the receptors with which they interact, different substances of abuse may cause some similar changes in cellular activities and biological processes in neurons.

Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications

Recent advances in high-throughput technologies enable quantitative monitoring of the abundance of various biological molecules and allow determination of their variation between biological states on a genomic scale. Two popular platforms are DNA microarrays that measure messenger RNA transcript levels, and gel-free proteomic analyses that quantify protein abundance. Obviously, no single approach can fully unravel the complexities of fundamental biology and it is equally clear that integrative analysis of multiple levels of gene expression would be valuable in this endeavor. However, most integrative transcriptomic and proteomic studies have thus far either failed to find a correlation or only observed a weak correlation. In addition to various biological factors, it is suggested that the poor correlation could be quite possibly due to the inadequacy of available statistical tools to compensate for biases in the data collection methodologies. To address this issue, attempts have recently been made to systematically investigate the correlation patterns between transcriptomic and proteomic datasets, and to develop sophisticated statistical tools to improve the chances of capturing a relationship. The goal of these efforts is to enhance understanding of the relationship between transcriptomes and proteomes so that integrative analyses may be utilized to reveal new biological insights that are not accessible through one-dimensional datasets. In this review, we outline some of the challenges associated with integrative analyses and present some preliminary statistical solutions. In addition, some new applications of integrated transcriptomic and proteomic analysis to the investigation of post-transcriptional regulation are also discussed.

Shotgun proteomics implicates protease inhibition and complement activation in the antiinflammatory properties of HDL

HDL lowers the risk for atherosclerotic cardiovascular disease by promoting cholesterol efflux from macrophage foam cells. However, other antiatherosclerotic properties of HDL are poorly understood. To test the hypothesis that the lipoprotein carries proteins that might have novel cardioprotective activities, we used shotgun proteomics to investigate the composition of HDL isolated from healthy subjects and subjects with coronary artery disease (CAD). Unexpectedly, our analytical strategy identified multiple complement-regulatory proteins and a diverse array of distinct serpins with serine-type endopeptidase inhibitor activity. Many acute-phase response proteins were also detected, supporting the proposal that HDL is of central importance in inflammation. Mass spectrometry and biochemical analyses demonstrated that HDL3 from subjects with CAD was selectively enriched in apoE, raising the possibility that HDL carries a unique cargo of proteins in humans with clinically significant cardiovascular disease. Collectively, our observations suggest that HDL plays previously unsuspected roles in regulating the complement system and protecting tissue from proteolysis and that the protein cargo of HDL contributes to its antiinflammatory and antiatherogenic properties.

Understanding proteomics

The purpose of this article is to describe proteomics, to discuss the importance of proteomics, to review different methods for protein measurement, and to illustrate how knowledge of proteomics might improve patient care. Among common laboratory determinations are those involving enzymatic (protein) function. Although the presence or activity of proteins may be seen clinically as incidental, proteins represent the engines through which critical life processes ensue. A selected review of the literature is presented to define and explain proteomics and to review the various techniques to measure proteins. A case-study approach is used to illustrate how proteomics can be utilized for detecting and monitoring disease. The human genome has been completely sequenced and proteomics has emerged as a way to unravel the biochemical and physiological mechanisms of diseases at the functional level. This review includes the recent discoveries regarding proteomics and its importance in the detection and treatment of disease.