Proteomic Identification Of Proteins Research Articles

Proteomic Identification of Proteins as Potential Biomarkers of Nonmeat Components in Meat Products

Proteomic Identification of Proteins as Potential Biomarkers of Nonmeat Components in Meat Products

Protein Identification by Database Searching of Mass Spectrometry Data in the Teaching of Proteomics

Mass spectrometry is essential for large-scale protein identification in proteomics. By database searching of mass spectrometry data, it is possible to identify proteins without the need to interpret MS/MS spectra (de novo sequencing), which is time-consuming and not feasible as a systematic tool in proteomic analysis. Many authors have reported initiatives to teach protein mass spectrometry to undergraduate students, but database searching is usually not emphasized. Here, we propose a class about database searching of mass spectrometry data for protein identification in order to complement a theoretical proteomics course. It includes a presentation about the theory of database searching and a practical, computer-based activity in which students can learn how to perform analysis of mass spectrometry data for proteomics purposes. Our class model fills a widespread gap in the teaching of proteomics and improves students’ understanding of protein identification by mass spectrometry.

Mass spectrometry-based protein identification in proteomics-a review.

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.

Proteomic identification of protein markers of stages of heart formation in humans

On the basis of the results of proteomic analysis and mass spectrometric identification of human myocardium proteins exhibiting pronounced quantitative changes in the dynamics of prenatal cardiogenesis, changes in the expression level of proteins of three families (mitochondrial, contractile, and heat shock) have been identified. The complex of human myocardium mitochondrial proteins (for example, α and β isoforms of ATP synthase, aconitase 2, creatine phosphokinase M-subunit, and 60-kDa heat shock protein) largely finishes its development according to the adult type by developmental week 24. The formation of the protein composition of human myocardium contractile structures (for example, desmin, myosin regulatory light chain 2, fetal ventricular essential isoform 1, canonical α-tropomyosin, and fetal isoform 6) reflects the initial stage of myofibril development until developmental week 8 (replacement of fetal isoforms of contractile proteins with adult ones with the involvement of the phosphorylated isoform of 27-kDa heat shock protein), the stage of their qualitative and quantitative structuring by developmental weeks 20–24, and the final formation of the adult phenotype of contractile structures by 2 years of life.

Proteins Differentially Expressed in the Pancreas of Hepatic Alcohol Dehydrogenase-Deficient Deer Mice Fed Ethanol For 3 Months.

The aim of this study was to identify differentially expressed proteins in the pancreatic tissue of hepatic alcohol dehydrogenase-deficient deer mice fed ethanol to understand metabolic basis and mechanism of alcoholic chronic pancreatitis. Mice were fed liquid diet containing 3.5 g% ethanol daily for 3 months, and differentially expressed pancreatic proteins were identified by protein separation using 2-dimensional gel electrophoresis and identification by mass spectrometry. Nineteen differentially expressed proteins were identified by applying criteria established for protein identification in proteomics. An increased abundance was found for ribosome-binding protein 1, 60S ribosomal protein L31-like isoform 1, histone 4, calcium, and adenosine triphosphate (ATP) binding proteins and the proteins involved in antiapoptotic processes and endoplasmic reticulum function, stress, and/or homeostasis. Low abundance was found for endoA cytokeratin, 40S ribosomal protein SA, amylase 2b isoform precursor, serum albumin, and ATP synthase subunit β and the proteins involved in cell motility, structure, and conformation. Chronic ethanol feeding in alcohol dehydrogenase-deficient deer mice differentially expresses pancreatic functional and structural proteins, which can be used to develop biomarker(s) of alcoholic chronic pancreatitis, particularly amylase 2b precursor, and 60 kDa heat shock protein and those involved in ATP synthesis and blood osmotic pressure.

Mutational analysis employing a phylogenetic mass tree approach in a study of the evolution of the influenza virus

A mass based approach has been advanced to enable mutations associated with the evolution of proteins to be both charted and interrogated using phylogenetic trees built solely from the masses of peptides generated upon protein proteolysis. The modified MassTree algorithm identifies and displays all such mutations and calculates the frequency of a particular mutation across a tree. Its significance in terms of its position(s) on the tree is scored, where mutations that occur toward the basis of the tree are weighted more favourably. A comparison with data generated from a conventional sequence based tree demonstrates the reliability of mutational analysis employing this approach. Although illustrated for the study of the evolution of influenza hemagglutinin in this work, the approach has far broader applicability and can be applied to investigate the evolution of any organism. In the case of simple microorganisms this can be achieved even without the separation of component proteins. Given the central role that mass map or fingerprint data plays in protein identification in proteomics, this work demonstrates that such data can be successfully employed in a phylogenetics strategy to better understand and predict future evolutionary trends from the perspective of functional proteins expressed by the organism.

The effect of low-level kelp supplementation on digestive enzyme activity levels in cultured abalone Haliotis midae fed formulated feeds

Previous studies have found that macroalgal inclusion in formulated diets for cultured abalone improves feed utilisation, and it was suggested that seaweed-associated bacteria supply enzymes that aid in the animal's digestion. In the present study, we hypothesised that kelp supplementation in formulated feed affects the profile of digestive enzymes in farmed Haliotis midae. Commercially reared sub-adult abalone fed a kelp-supplemented (Ecklonia maxima; 0.88% dry weight) or kelp-free control feed were collected for analysis of the amylase, alginate lyase, laminarinase, fucoidanase and protease activities in the gut. Levels of polysaccharidase and protease activity did not significantly differ between the diet treatments. However, enzyme-activity levels were more variable in abalone fed the kelp-free diet as compared with those fed the kelp-supplemented diet (coefficients of variation: 73%, 48.3–60.2% and 31.9% [control diet] versus 42.7%, 13.6–33.8% and 14.6% [KS diet] for amylase, macroalgal polysaccharidases and acid protease activity, respectively). We suggest that the presence of dietary kelp modulates the abalone's gut bacteria and their nutrient exchange. Proteomic identification of proteins in abalone gut sections showed that exogenous enzymes associated with the gut microbiome aid in bacterial utilisation of digested molecules, whereas abalone endogenous enzymes degrade the macronutrients in formulated feeds.

Identification of an intrinsic determinant critical for maspin subcellular localization and function.

Maspin, a multifaceted tumor suppressor, belongs to the serine protease inhibitor superfamily, but only inhibits serine protease-like enzymes such as histone deacetylase 1 (HDAC1). Maspin is specifically expressed in epithelial cells and it is differentially regulated during tumor progression. A new emerging consensus suggests that a shift in maspin subcellular localization from the nucleus to the cytoplasm stratifies with poor cancer prognosis. In the current study, we employed a rational mutagenesis approach and showed that maspin reactive center loop (RCL) and its neighboring sequence are critical for maspin stability. Further, when expressed in multiple tumor cell lines, single point mutation of Aspartate346 (D346) to Glutamate (E346), maspinD346E, was predominantly nuclear, whereas wild type maspin (maspinWT) was both cytoplasmic and nuclear. Evidence from cellular fractionation followed by immunological and proteomic protein identification, combined with the evidence from fluorescent imaging of endogenous proteins, fluorescent protein fusion constructs, as well as bimolecular fluorescence complementation (BiFC) showed that the increased nuclear enrichment of maspinD346E was, at least in part, due to its increased affinity to HDAC1. MaspinD346E was also more potent than maspinWT as an HDAC inhibitor. Taken together, our evidence demonstrates that D346 is a critical cis-element in maspin sequence that determines the molecular context and subcellular localization of maspin. A mechanistic model derived from our evidence suggests a new window of opportunity for the development of maspin-based biologically competent HDAC inhibitors for cancer treatment.

A new peptide retention time prediction method for mass spectrometry based proteomic analysis by a serial and parallel support vector machine model

The online reversed-phase liquid chromatography (RPLC) contributes a lot for the large scale mass spectrometry based protein identification in proteomics. Retention time (RT) as an important evidence can be used to distinguish the false positive/true positive peptide identifications. Because of the nonlinear concentration curve of organic phase in the whole range of run time and the interactions among peptides, the sequence based RT prediction of peptides has low accuracy and is difficult to generalize in practice, and thus is less effective in the validation of peptide identifications. A serial and parallel support vector machine (SP-SVM) method was proposed to characterize the nonlinear effect of organic phase concentration and the interactions among peptides. The SP-SVM contains a support vector regression (SVR) only for model training (named as p-SVR) and 4 SVM models (named as C-SVM, 1-SVR, s-SVR and n-SVR) for the RT prediction. After distinguishing the peptide chromatographic behavior by C-SVM, 1-SVR and s-SVR were used to predict the peptide RT specifically to improve the accuracy. Then the peptide RT was normalized by n-SVR to characterize the peptide interactions. The prediction accuracy was improved significantly by applying this method to the processing of the complex sample dataset. The coefficient of the determination between predictive and experimental RTs reaches 0. 95, the prediction error range was less than 20% of the total LC run time for more than 95% cases, and less than 10% of the total LC run time for more than 70% cases. The performance of this model reaches the best of known so far. More important, the SP-SVM method provides a framework to take into account the interactions among peptides in chromatographic separation, and its performance can be improved further by introducing new data processing and experiment strategy.

Serum protein identification and quantification of the corona of 5, 15 and 80 nm gold nanoparticles

When nanoparticles (NP) enter the body they come into contact with body fluids containing proteins which can adsorb to their surface. These proteins may influence the NP interactions with the biological vicinity, eventually determining their biological fate inside the body. Adsorption of the most abundantly binding proteins was studied after an in vitro 24 hr incubation of monodisperse, negatively charged 5, 15 and 80 nm gold spheres (AuNP) in mouse serum by a two-step analysis: proteomic protein identification and quantitative protein biochemistry. The adsorbed proteins were separated from non-adsorbed proteins by centrifugation and gel electrophoresis and identified using a MALDI-TOF-MS-Proteomics-Analyzer. Quantitative analysis of proteins in gel bands by protein densitometry, required the focus on predominantly binding serum proteins. Numerous proteins adsorbed to the AuNP depending on their size, e.g. apolipoproteins or complement C3. The qualitative and quantitative amount of adsorbed proteins differed between 5, 15 and 80 nm AuNP. Band intensities of adsorbed proteins decreased with increasing AuNP sizes based not only on their mass but also on their surface area. Summarizing, the AuNP surface is covered with serum proteins containing transport and immune related proteins among others. Hence, protein binding depends on the size, surface area and curvature of the AuNP.

Tyrosine Modifications in Aging

The understanding of physiological and pathological processes involving protein oxidation, particularly under conditions of aging and oxidative stress, can be aided by proteomic identification of proteins that accumulate oxidative post-translational modifications only if these detected modifications are connected to functional consequences. The modification of tyrosine (Tyr) residues can elicit significant changes in protein structure and function, which, in some cases, may contribute to biological aging and age-related pathologies, such as atherosclerosis, neurodegeneration, and cataracts. Studies characterizing proteins in which Tyr has been modified to 3-nitrotyrosine, 3,4-dihydroxyphenylalanine, 3,3'-dityrosine and other cross-links, or 3-chlorotyrosine are reviewed, with an emphasis on structural and functional consequences. Distinguishing between inconsequential modifications and functionally significant ones requires careful biochemical and biophysical analysis of target proteins, as well as innovative methods for isolating the effects of the multiple modifications that often occur under oxidizing conditions. The labor-intensive task of isolating and characterizing individual modified proteins must continue, especially given the expanding list of known modifications. Emerging approaches, such as genetic and metabolic incorporation of unnatural amino acids, hold promise for additional focused studies of this kind.

PI: An open-source software package for validation of the SEQUEST result and visualization of mass spectrum

BackgroundTandem mass spectrometry (MS/MS) has emerged as the leading method for high- throughput protein identification in proteomics. Recent technological breakthroughs have dramatically increased the efficiency of MS/MS data generation. Meanwhile, sophisticated algorithms have been developed for identifying proteins from peptide MS/MS data by searching available protein sequence databases for the peptide that is most likely to have produced the observed spectrum. The popular SEQUEST algorithm relies on the cross-correlation between the experimental mass spectrum and the theoretical spectrum of a peptide. It utilizes a simplified fragmentation model that assigns a fixed and identical intensity for all major ions and fixed and lower intensity for their neutral losses. In this way, the common issues involved in predicting theoretical spectra are circumvented. In practice, however, an experimental spectrum is usually not similar to its SEQUEST -predicted theoretical one, and as a result, incorrect identifications are often generated.ResultsBetter understanding of peptide fragmentation is required to produce more accurate and sensitive peptide sequencing algorithms. Here, we designed the software PI of novel and exquisite algorithms that make a good use of intensity property of a spectrum.ConclusionsWe designed the software PI with the novel and effective algorithms which made a good use of intensity property of the spectrum. Experiments have shown that PI was able to validate and improve the results of SEQUEST to a more satisfactory degree.

Increased Protein Nitration in Mitochondrial Diseases: Evidence for Vessel Wall Involvement

Mitochondrial diseases (MD) are heterogeneous disorders because of impairment of respiratory chain function leading to oxidative stress. We hypothesized that in MD the vascular endothelium may be affected by increased oxidative/nitrative stress causing a reduction of nitric oxide availability. We therefore, investigated the pathobiology of vasculature in MD patients by assaying the presence of 3-nitrotyrosine in muscle biopsies followed by the proteomic identification of proteins which undergo tyrosine nitration. We then measured the flow-mediated vasodilatation as a proof of altered nitric oxide generation/bioactivity. Here, we show that 3-nitrotyrosine staining is specifically located in the small vessels of muscle tissue and that the reaction is stronger and more evident in a significant percentage of vessels from MD patients as compared with controls. Eleven specific proteins which are nitrated under pathological conditions were identified; most of them are involved in energy metabolism and are located mainly in mitochondria. In MD patients the flow-mediated vasodilatation was reduced whereas baseline arterial diameters, blood flow velocity and endothelium-independent vasodilatation were similar to controls. The present results provide evidence that in MD the vessel wall is a target of increased oxidative/nitrative stress.

Better score function for peptide identification with ETD MS/MS spectra

BackgroundTandem mass spectrometry (MS/MS) has become the primary way for protein identification in proteomics. A good score function for measuring the match quality between a peptide and an MS/MS spectrum is instrumental for the protein identification. Traditionally the to-be-measured peptides are fragmented with the collision induced dissociation (CID) method. More recently, the electron transfer dissociation (ETD) method was introduced and has proven to produce better fragment ion ladders for larger and more basic peptides. However, the existing software programs that analyze ETD MS/MS data are not as advanced as they are for CID.ResultsTo take full advantage of ETD data, in this paper we develop a new score function to evaluate the match between a peptide and an ETD MS/MS spectrum. Experiments on real data demonstrated that this newly developed score function significantly improved the de novo sequencing accuracy of the PEAKS software on ETD data.ConclusionA new and better score function for ETD MS/MS peptide identification was developed. The method used to develop our ETD score function can be easily reused to train new score functions for other types of MS/MS data.

Evaluation of a generalized use of the log Sum(k+1)AA descriptor in a QSRR model to predict peptide retention on RPLC systems

At the current state of knowledge, the rational optimization of the chromatographic separation of peptides, as well as the identification of proteins in proteomics are challenges for analytical chemists. In this paper the generalized applicability of a recently derived descriptor log Sum(k+1)AA in a QSRR equation to model peptide retention in RP-LC systems was evaluated. For that purpose, two sets of peptides analyzed on dissimilar RP-LC systems were considered. A first set of 28 peptides was measured on 17 columns/systems, while a second of 70 peptides was eluted on four. The aim of this work was to confirm the usefulness of the partly experimental log Sum(k+1)AA descriptor for the prediction of peptides retention compared to the initially applied, fully experimental log SumAA descriptor. The verification of the predictive abilities of both QSRR models, applying either the initial or the alternative descriptor, was done by using the leave-one-out and leave-three-out cross-validation procedures. The results seem to demonstrate that the QSRR model with log Sum(k+1)AA, for which the retention measurement of only seven out of 20 existing amino acids is necessary, possesses similar or in some cases even better predictive abilities than that containing log SumAA.

Isatin binding proteins in rat brain: In situ imaging, quantitative characterization of specific [3H]isatin binding, and proteomic profiling

Isatin (indole-2,3-dione) is an endogenous indole that has a distinct and discontinuous distribution in the brain and in other mammalian tissues and body fluids. Its output is increased under conditions of stress and anxiety. Its biological targets remain poorly characterized, although [(3)H]isatin binding sites have been demonstrated in various brain structures. In this study, by using a real-time beta-imager, [(3)H]isatin radioligand binding analysis, and proteomic identification of proteins specifically bound to the affinity sorbent 5-aminocaproyl-isatin-Sepharose, we have investigated the distribution of [(3)H]isatin specific binding sites in the rat brain, characterized their K(d) and B(max), and identified some individual brain isatin binding proteins. The binding of [(3)H]isatin to rat brain sections was saturable and characterized by K(d) values (of 0.2-0.3 microM) consistent with physiological concentrations. The highest B(max) was found in the hypothalamus, consistent with a role in stress. In most brain regions, the homologous inhibition of [(3)H]isatin binding by increasing concentrations of cold isatin demonstrated complex behavior suggesting involvement of various binding proteins characterized by different affinity to isatin. Affinity chromatography of Triton X-100 lysates of whole-brain homogenates on 5-aminocaproyl-isatin-Sepharose followed by subsequent proteomic analysis resulted in identification of 25 individual proteins, including glyceraldehyde-3-phosphate dehydrogenase, one of few previously reported isatin binding proteins, and a group of cytoskeleton-related proteins. These binding sites may be related to the known antiproliferative and proapoptotic activities of isatin.

Predictions of peptides' retention times in reversed‐phase liquid chromatography as a new supportive tool to improve protein identification in proteomics

One of the initial steps of proteomic analysis is peptide separation. However, little information from RP-HPLC, employed for peptides separation, is utilized in proteomics. Meanwhile, prediction of the retention time for a given peptide, combined with routine MS/MS data analysis, could help to improve the confidence of peptide identifications. Recently, a number of models has been proposed to characterize quantitatively the structure of a peptide and to predict its gradient RP-HPLC retention at given separation conditions. The chromatographic behavior of peptides has usually been related to their amino acid composition. However, different values of retention coefficients of the same amino acid in different peptides at different neighborhoods were commonly observed. Therefore, specific retention coefficients were derived by regression analysis or by artificial neural networks (ANNs) with the use of a set of peptides retention. In the review, various approaches for peptide elution time prediction in RP-HPLC are presented and critically discussed. The contribution of sequence dependent parameters (e.g., amphipathicity or peptide sequence) and peptide physicochemical descriptors (e.g., hydrophobicity or peptide length) that have been shown to affect the peptide retention time in LC are considered and analyzed. The predictive capability of the retention time prediction models based on quantitative structure-retention relationships (QSRRs) are discussed in details. Advantages and limitations of various retention prediction strategies are identified. It is concluded that proper processing of chromatographic data by statistical learning techniques can result in information of direct use for proteomics, which is otherwise wasted.

Proteomics of brain extracellular fluid (ECF) and cerebrospinal fluid (CSF)

Mass spectrometry has become the gold standard for the identification of proteins in proteomics. In this review, I will discuss the available literature on proteomic experiments that analyze human cerebrospinal fluid (CSF) and brain extracellular fluid (ECF), mostly obtained by cerebral microdialysis. Both materials are of high diagnostic value in clinical neurology, for example, in cerebrovascular disorders like stroke, neurodegenerative diseases like Alzheimer's Disease, Parkinson's Disease, amyotrophic lateral sclerosis (ALS), traumatic brain injury and cerebral infectious and inflammatory disease, such as multiple sclerosis. Moreover, there are standard procedures for sampling. In a number of studies in recent years, biomarkers have been proposed in CSF and ECF for improved diagnosis or to control therapy, based on proteomics and mass spectrometry. I will also discuss the needs for a transition of research-based experimental screening with mass spectrometry to fast and reliable diagnostic instrumentation for clinical use.

Proteomic identification of proteins in the human brain: Towards a more comprehensive understanding of neurodegenerative disease

Proteomics has revealed itself as a powerful tool in the identification and determination of proteins and their biological significance. More recently, several groups have taken advantage of the high-throughput nature of proteomics in order to gain a more in-depth understanding of the human brain. In turn, this information has provided researchers with invaluable insight into the potential pathways and mechanisms involved in the pathogenesis of several neurodegenerative disorders, e.g., Alzheimer and Parkinson disease. Furthermore, these findings likely will improve methods to diagnose disease and monitor disease progression as well as generate novel targets for therapeutic intervention. Despite these advances, comprehensive understanding of the human brain proteome remains challenging, and requires development of improved sample enrichment, better instrumentation, and innovative analytic techniques. In this review, we will focus on the most recent progress related to identification of proteins in the human brain under normal as well as pathological conditions, mainly Alzheimer and Parkinson disease, their potential application in biomarker discovery, and discuss current advances in protein identification aimed at providing a more comprehensive understanding of the brain.

Prediction of Missed Cleavage Sites in Tryptic Peptides Aids Protein Identification in Proteomics

Protein identification via peptide mass fingerprinting (PMF) remains a key component of high-throughput proteomics experiments in post-genomic science. Candidate protein identifications are made using bioinformatic tools from peptide peak lists obtained via mass spectrometry (MS). These algorithms rely on several search parameters, including the number of potential uncut peptide bonds matching the primary specificity of the hydrolytic enzyme used in the experiment. Typically, up to one of these "missed cleavages" are considered by the bioinformatics search tools, usually after digestion of the in silico proteome by trypsin. Using two distinct, nonredundant datasets of peptides identified via PMF and tandem MS, a simple predictive method based on information theory is presented which is able to identify experimentally defined missed cleavages with up to 90% accuracy from amino acid sequence alone. Using this simple protocol, we are able to "mask" candidate protein databases so that confident missed cleavage sites need not be considered for in silico digestion. We show that that this leads to an improvement in database searching, with two different search engines, using the PMF dataset as a test set. In addition, the improved approach is also demonstrated on an independent PMF data set of known proteins that also has corresponding high-quality tandem MS data, validating the protein identifications. This approach has wider applicability for proteomics database searching, and the program for predicting missed cleavages and masking Fasta-formatted protein sequence databases has been made available via http:// ispider.smith.man.ac uk/MissedCleave.