Applications In Proteomics Research Research Articles

Detecting apoptosis signal-regulating kinase 1 protein expression in diabetica nephropathy rats using magnetic hydrophilic nanomaterials

Apoptosis signal-regulating kinase 1 (ASK1) signaling is involved in the occurrence and progression of diabetic nephropathy (DN) renal fibrosis, yet the detection of ASK1 requires further investigation. To explore the effect of magnetic nanomaterials combined with ASK1 signaling on renal fibrosis, a DN rat model was generated. Thirty Wistar rats were randomly allocated into three experimental models: control, sham-operated, and DN model groups. Concentrations of postoperative serum creatinine (Scr) and blood urea nitrogen (BUN) were measured to determine the success of the DN model group. Chitosan coated magnetic hydrophilic nanomaterials (Fe3O4@CS MCNCs) were prepared by a “one-pot reaction” method, and further characterized to assess their protein adsorption properties. ASK1 protein expression was analyzed by combining conventional immunohistochemistry and Western blotting techniques with the magnetic nanomaterials. Serum Scr and BUN levels in the DN model group were significantly increased (P < 0.01), indicating that the model was successfully generated. Fe3O4@CS MCNCs were characterized and demonstrated effective hydrophilicity, magnetism, and protein adsorption capacity, highlighting its potential application in proteomic research. The detection of ASK1 protein expression was consistent with that of immunohistochemistry and Western blotting when using the magnetic nanomaterials. Furthermore, ASK1 protein expression in the DN model group was significantly increased compared with the control and sham-operated groups. In this study, magnetic hydrophilic nanomaterials were demonstrated to be feasible for protein detection. In addition, the results indicated the potential involvement of ASK1 signaling in the process of renal fibrosis in DN rats.

Challenges and Advances in the Fabrication of Monolithic Bioseparation Materials and their Applications in Proteomics Research.

High-performance liquid chromatography integrated with tandem mass spectrometry (HPLC-MS/MS) has become a powerful technique for proteomics research. Its performance heavily depends on the separation efficiency of HPLC, which in turn depends on the chromatographic material. As the "heart" of the HPLC system, the chromatographic material is required to achieve excellent column efficiency and fast analysis. Monolithic materials, fabricated as continuous supports with interconnected skeletal structure and flow-through pores, are regarded as an alternative to particle-packed columns. Such materials are featured with easy preparation, fast mass transfer, high porosity, low back pressure, and miniaturization, and are next-generation separation materials for high-throughput proteins and peptides analysis. Herein, the recent progress regarding the fabrication of various monolithic materials is reviewed. Special emphasis is placed on studies of the fabrication of monolithic capillary columns and their applications in separation of biomolecules by capillary liquid chromatography (cLC). The applications of monolithic materials in the digestion, enrichment, and separation of phosphopeptides and glycopeptides from biological samples are also considered. Finally, advances in comprehensive 2D HPLC separations using monolithic columns are also shown.

Development of a rapid and improved two‐dimensional electrophoresis method for the separation of protein lysate

Researchers frequently use two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) prior to mass spectrometric analysis in a proteomics approach. The i2D-PAGE method, which 'inverts' the dimension of protein separation of the conventional 2D-PAGE, is presented in this publication. Protein lysate of Channa striata, a freshwater snakehead fish, was separated based on its molecular weight in the first dimension and its isoelectric point in the second dimension. The first-dimension separation was conducted on a gel-free separation device, and the protein mixture was fractionated into 12 fractions in chronological order of increasing molecular weight. The second-dimension separation featured isoelectric focusing, which further separated the proteins within the same fraction according to their respective isoelectric point. Advantages of i2D-PAGE include better visualisation of the isolated protein, easy identification on protein isoforms, shorter running time, customisability and reproducibility. Erythropoietin standard was applied to i2D-PAGE to show its effectiveness for separating protein isoforms. Various staining methods such as Coomassie blue staining and silver staining are also applicable to i2D-PAGE. Overall, the i2D-PAGE separation method effectively separates protein lysate and is suitable for application in proteomics research.

Development of LysargiNase, a mirror trypsin and its application in proteomics

Proteomics is a fast-growing discipline that aims at systematic identification, quantification of proteins and their post-translational modifications in cells. Mass spectrometry-based shotgun proteomics technology is currently one of the mainstream methods for proteomics research. With this method, proteins need to be digested to peptides by site-specific proteases before they can be detected with mass spectrometry. Therefore, site-specific proteases played key roles in this process and so far, a variety of specific proteases have been developed and used in proteomics study. Particularly, the identification, characterization and development of proteases that cleave at the N-termini of corresponding amino acid residues, which are just mirrors to those of typical C-termini proteases, provide novel tools for proteomics analysis. In this review, we summarized the proprieties of LysargiNase, a most recently identified mirror trypsin, and its applications in proteomics research to promote its more widespread usage.

Superhydrophobic candle soot/PDMS substrate for one-step enrichment and desalting of peptides in MALDI MS analysis

Superhydrophobic substrate is applied in matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) detection due to its confinement effect. The weak interaction of superhydrophobic surface with water/salts makes it potential in one-step enrichment and desalting of peptide in MALDI MS analysis. We fabricate a superhydrophobic substrate by spin-coating poly(dimethyl siloxane) (PDMS) on a candle soot layer. On this substrate, the peptide analytes can be confined and enriched in a small area due to the confinement effect and its strong hydrophobic interactions with PDMS. Meanwhile, the desalting can be easily realized by removing the residual solution after the absorption of analyst molecules due to the weak interaction between water/salt contaminants and the superhydrophobic surface. Using this substrate, angiotensin III (Ang III) in the presence of salt with high concentration (2 M or saturated) can be analyzed, and the peptide sequence coverage of 10 μg/mL myoglobin (MYO) and bovine serum albumin (BSA) digests is enhanced to 51% and 26%, which is 37% and 21% analyzed with the commercial ZipTipC18 pipette tips. The LOD of bacitracin A (Bac A) in milk with this substrate is 100 pM and nearly 360 times lower than the LOD of standard testing method. This substrate has potential practical applications in proteomics research and actual sample analysis.

Machine learning applications in proteomics research: How the past can boost the future

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.

Inhibition of protein carbamylation in urea solution using ammonium-containing buffers

Urea solution is one of the most commonly employed protein denaturants for protease digestion in proteomic studies. However, it has long been recognized that urea solution can cause carbamylation at the N termini of proteins/peptides and at the side chain amino groups of lysine and arginine residues. Protein/peptide carbamylation blocks protease digestion and affects protein identification and quantification in mass spectrometry analysis by blocking peptide amino groups from isotopic/isobaric labeling and changing peptide charge states, retention times, and masses. In addition, protein carbamylation during sample preparation makes it difficult to study in vivo protein carbamylation. In this study, we compared the peptide carbamylation in urea solutions of different buffers and found that ammonium-containing buffers were the most effective buffers to inhibit protein carbamylation in urea solution. The possible mechanism of carbamylation inhibition by ammonium-containing buffers is discussed, and a revised procedure for the protease digestion of proteins in urea and ammonium-containing buffers was developed to facilitate its application in proteomic research.

Mass spectrometry imaging: linking molecule profiles to tissue spatial distribution

Advances in Bioanalytical Mass Spectrometry: 5th Symposium of the Göttingen Proteomics ForumMass Spectrometry with Spatial Resolution: MALDI-Imaging and Laser Capture MicroscopyGöttingen, Germany, 22 November 2012MALDI mass spectrometry imaging (MSI) combines the speed and molecular specificity of MALDI-MS detection with information on spatial organization. In the last years, MSI found large application in proteomics research for determining the spatial distribution of compounds in biological tissues and started to draw increasing interest in clinical research. To shed light on the new developments in the field of MSI, the Göttingen Proteomics Forum organized a symposium that was held in Göttingen as part of the series of regular symposia organized by the members of the Göttingen Proteomics Forum. The symposium was on 22 November 2012, with more than 80 delegates that attended the event entitled ‘Mass spectrometry with spatial resolution: MALDI-imaging and laser capture microscopy’. The one-day agenda consisted of nine oral presentations from renowned experts in the field with subsequent discussion sessions. As usual, the meeting was fruitful and offered a good platform for discussion between the delegates and proteomics specialists.

Immobilization of trypsin via reactive polymer grafting from magnetic nanoparticles for microwave-assisted digestion.

The effort to stabilize enzymes and improve their activity has generated great interest because of their wide application in proteomics research, bioenergy conversion, bioassays and so on. In this work, biocompatible reactive polymer, poly (glycidyl methacrylate), grafted from magnetic nanoparticles by atom transfer radical polymerization method, has been firstly proposed to immobilize enzymes for microwave-assisted digestion. Meanwhile, trypsin was chosen as a model enzyme. Resulting from the increased functionality, the immobilization amount of the enzyme on the magnetic nanoparticle surface has been greatly improved. Furthermore, the enzyme immobilized magnetic nanoparticles have exhibited excellent repeatability and stability. The influence of the polymer chain length on digestion efficiency has been investigated both at 37 °C and under microwave. It has been found that the digestion efficiency increases with the lengthened polymer brushes due to the increased immobilization amount. Utilizing cytochrome C as a model protein for digestion, the performance of this immobilized biocatalyst has been demonstrated and this digestion assisted with microwave could be completed within 15 s. This study offers insight into the design of polymer brushes on the surface of magnetic nanoparticles for high digestion efficiency in the future.

Profiling Lipid–protein Interactions Using Nonquenched Fluorescent Liposomal Nanovesicles and Proteome Microarrays

Fluorescent liposomal nanovesicles (liposomes) are commonly used for lipid research and/or signal enhancement. However, the problem of self-quenching with conventional fluorescent liposomes limits their applications because these liposomes must be lysed to detect the fluorescent signals. Here, we developed a nonquenched fluorescent (NQF)1 liposome by optimizing the proportion of sulforhodamine B (SRB) encapsulant and lissamine rhodamine B-dipalmitoyl phosphatidylethanol (LRB-DPPE) on a liposomal surface for signal amplification. Our study showed that 0.3% of LRB-DPPE with 200 μm of SRB provided the maximal fluorescent signal without the need to lyse the liposomes. We also observed that the NQF liposomes largely eliminated self-quenching effects and produced greatly enhanced signals than SRB-only liposomes by 5.3-fold. To show their application in proteomics research, we constructed NQF liposomes that contained phosphatidylinositol 3,5-bisphosphate (PI(3,5)P2) and profiled its protein interactome using a yeast proteome microarray. Our profiling led to the identification of 162 PI(3,5)P2-specific binding proteins (PI(3,5)P2-BPs). We not only recovered many proteins that possessed known PI(3,5)P2-binding domains, but we also found two unknown Pfam domains (Pfam-B_8509 and Pfam-B_10446) that were enriched in our dataset. The validation of many newly discovered PI(3,5)P2-BPs was performed using a bead-based affinity assay. Further bioinformatics analyses revealed that the functional roles of 22 PI(3,5)P2-BPs were similar to those associated with PI(3,5)P2, including vesicle-mediated transport, GTPase, cytoskeleton, and kinase. Among the 162 PI(3,5)P2-BPs, we found a novel motif, HRDIKP[ES]NJLL that showed statistical significance. A docking simulation showed that PI(3,5)P2 interacted primarily with lysine or arginine side chains of the newly identified PI(3,5)P2-binding kinases. Our study showed that this new tool would greatly benefit profiling lipid-protein interactions in high-throughput studies.

Online Mass Spectrometric Analysis of Proteins/Peptides Following Electrolytic Cleavage of Disulfide Bonds

The disulfide bond bridge is an important post-translational modification for proteins. This study presents a structural analysis of biologically active peptides and proteins containing disulfide bonds using electrochemistry (EC) online combined with desorption electrospray ionization mass spectrometry (DESI-MS), in which the sample undergoes electrolytic disulfide cleavage in an electrochemical flow cell followed by MS detection. Using this EC/DESI-MS method, the disulfide-containing peptides can be quickly identified from enzymatic digestion mixtures, simply based on the abrupt decrease in their relative ion abundances after electrolysis. Peptide mass mapping and tandem MS analysis of the ions of the resulting free peptide chains can possibly establish the disulfide linkage pattern and sequence the precursor peptides. In this regard, the method provides much more chemical information than previous analogous electrochemical analyses. In addition, derivatization of thiols by selective selenamide reagents is useful for easy recognition of reduced peptide ions and the number of their free thiols. Furthermore, electrolytic reduction of proteins (e.g., α-lactalbumin) leads to increased charges on the detected protein ions, revealing the role of disulfide bonds on maintaining protein conformation. This electrochemical mass spectrometric method is fast (completed in few minutes) and does not need chemical reductants, potentially having valuable applications in proteomics research.

Guidelines for experimental design and data analysis of proteomic mass spectrometry-based experiments

The end of the 1990s saw a surge in the use of mass spectrometric applications in proteomics research. A huge amount of data was being generated. But proportionally little of the acquired data was being validated biologically. As the years went by it became evident that many false positives were being presented as bona fide results. In 2004, Molecular and Cellular Proteomics (MCP) generated guidelines for proteomics data (Carr et al. 2004). The editorial that accompanies the guidelines published in MCP in 2004 discusses the background to the criteria for publishable MS data. Those guidelines were largely adopted by Proteomics and J. Proteome Res. in subsequent years (see author proteomics data guidelines of respective journals). In 2006, the guidelines were made official and in 2009, MCP had a meeting in Paris to revise and update the guidelines, rendering the criteria for publication more applicable and broadened to include quantitative data (Bradshaw et al. 2006; Chalkley et al. 2009). It is essential, that journals, such as Amino Acids, publishing mass spectrometric proteomics data also adhere to these guidelines. We hope that other journals who publish mass spectrometry proteomics data will follow suit.

Comparison of SYPRO Ruby and Flamingo fluorescent stains for application in proteomic research

Fluorescent dyes are widely used for the detection and quantitation of proteins separated by polyacrylamide gel electrophoresis. SYPRO Ruby is one such fluorescent dye widely used for this purpose. More recently, another fluorescent dye, Flamingo, is available for expression proteomic research. Using a standard ultraviolet (UV) transilluminator and a charge-coupled device (CCD)-based imaging system, the relative sensitivity of these two different fluorescent stains with regard to detection of protein spots separated by two-dimensional gel electrophoresis (2D–GE) and identification by liquid chromatography–electrospray ionization–tandem mass spectrometry (LC–ESI–MS/MS) were compared. Using mouse kidney and liver homogenates as well as Escherichia coli extract, we detected a greater number of protein spots using Flamingo compared with SYPRO Ruby. In addition, when we compared the number of matched peptides and the percentage of amino acid residues identified for 22 different protein spots of mouse kidney proteome, we observed a higher number of matched peptides and a higher percentage of amino acid residues for the majority of the proteins using Flamingo compared with SYPRO Ruby. Also, we were able to characterize a protein spot that can be detected by Flamingo only. Therefore, we recommend Flamingo over SYPRO Ruby to be used for studies on expression proteomics.

Affinity‐MS – Methods and applications in proteomics research

PROTEOMICSVolume 9, Issue 6 p. 1418-1419 EditorialFree Access Affinity-MS – Methods and applications in proteomics research Thomas Joos, Thomas JoosSearch for more papers by this author Thomas Joos, Thomas JoosSearch for more papers by this author First published: 17 March 2009 https://doi.org/10.1002/pmic.200990019Citations: 3AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume9, Issue6No. 6 March 2009Pages 1418-1419 RelatedInformation

Using an electro-microchip, a nanogold probe, and silver enhancement in an immunoassay

This paper presents a novel immunoassay that uses an electro-microchip to detect the immuno-reaction signal, gold nanoparticles (ANPs) as a label of antigen or antibody and as a catalyst for silver precipitation, and the silver enhancement reaction to magnify the detection signal. This study is based on the direct immunoassay (two-layer format) and the sandwich immunoassay (three-layer format). The ANPs were introduced into the electro-microchip by the specific binding of the antibodies-ANPs conjugates and then were coupled with silver enhancement to produce black spots of silver metal. The silver precipitation constructs a “bridge” between two electrodes of the electro-microchip allowing electrons to pass. The variation of impedance can be easily measured with a commercial LCR meter. Various gap sizes (20, 50, 100, and 200 μm) of the electrodes of electro-microchips were designed for the sensitivity study. The experimental data show that a chip with a 20 μm gap has the highest sensitivity. There was a significant difference in impedance between the experiment sample and the negative control after 10 min of reaction time. The proposed method requires less time and fewer steps than the conventional enzyme-linked immunosorbent assay (ELISA). In addition, it shows a high detection sensitivity (10 μg/mL of 1st antibody (IgG) immobilized on slides and 1 ng/mL of antigen (protein A)). There is a clear distinction between the signal intensity and the logarithm of the sample concentration. The proposed new immunoassay method has potential applications in proteomics research and clinical diagnosis.

Comparison of SYPRO Ruby and Deep Purple Using Commonly Available UV Transilluminator: Wide-Scale Application in Proteomic Research

Fluorescent stains are becoming increasingly useful in proteomics research involving protein expression as well as post-translational modification studies and are particularly useful for samples which are expensive and scarce. The fluorescent dyes Deep Purple and SYPRO Ruby are widely used in protein expression studies. Using UV transillumination and Charged Coupled Device (CCD) based imaging system, their relative sensitivity to detect proteins separated by two-dimensional polyacrylamide gel electrophoresis and downstream protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) was compared. Using mouse liver homogenate, we detected a greater number of spots using SYPRO Ruby over Deep Purple stain. However, the number of matched peptides and the percentage of amino acid residues identified for 21 different proteins were comparable suggesting their equivalency for LC-MS/MS identification. In spite of comparable MS compatibility, we recommend the use of SYPRO Ruby for expression proteomics due to its higher sensitivity in detecting protein spots.

An immunoassay using an electro-microchip, nanogold probe and silver enhancement

This paper presents a novel immunoassay using an electro-microchip to detect the immuno-reaction signal, gold nanoparticles (ANPs) as a label of antigen or antibody and as a catalyst for silver precipitation, and the silver enhancement reaction to magnify the detection signal. This study is based on the direct immunoassay (two-layer format) and the sandwich immunoassay (three-layer format). The ANPs are introduced to the electro-microchip by the specific binding of the antibodies–ANPs conjugates and then coupled with silver enhancement to produce black spots of silver metal. The silver precipitation constructs a “bridge” between two electrodes of the electro-microchip allowing the electrons to pass, and the variation of the impedance can be easily measured with a commercial LCR meter. Different gap sizes (20, 50, 100, and 200 μm) of the electrodes of electro-microchips were designed for the sensitivity study. The experimental data show that a chip with a 200 μm gap has the highest sensitivity. There is a significant difference in impedance between the experiment and the negative control after 10 min reaction time. The proposed method requires less time and fewer steps than the conventional enzyme-linked immunosorbent assay. In addition, it shows a high detection sensitivity [10 μg/mL of 1st antibody (IgG) immobilized on slides and 10 ng/mL of antigen (protein A)], and there is a clear distinction between the signal intensity and the logarithm of the sample concentration. This new immunoassay has potential applications in proteomics research and clinical diagnosis.

Prediction of peptides observable by mass spectrometry applied at the experimental set level

BackgroundWhen proteins are subjected to proteolytic digestion and analyzed by mass spectrometry using a method such as 2D LC MS/MS, only a portion of the proteotypic peptides associated with each protein will be observed. The ability to predict which peptides can and cannot potentially be observed for a particular experimental dataset has several important applications in proteomics research including calculation of peptide coverage in terms of potentially detectable peptides, systems biology analysis of data sets, and protein quantification.ResultsWe have developed a methodology for constructing artificial neural networks that can be used to predict which peptides are potentially observable for a given set of experimental, instrumental, and analytical conditions for 2D LC MS/MS (a.k.a Multidimensional Protein Identification Technology [MudPIT]) datasets. Neural network classifiers constructed using this procedure for two MudPIT datasets exhibit 10-fold cross validation accuracy of about 80%. We show that a classifier constructed for one dataset has poor predictive performance with the other dataset, thus demonstrating the need for dataset specific classifiers. Classification results with each dataset are used to compute informative percent amino acid coverage statistics for each protein in terms of the predicted detectable peptides in addition to the percent coverage of the complete sequence. We also demonstrate the utility of predicted peptide observability for systems analysis to help determine if proteins that were expected but not observed generate sufficient peptides for detection.ConclusionClassifiers that accurately predict the likelihood of detecting proteotypic peptides by mass spectrometry provide proteomics researchers with powerful new approaches for data analysis. We demonstrate that the procedure we have developed for building a classifier based on an individual experimental data set results in classifiers with accuracy comparable to those reported in the literature based on large training sets collected from multiple experiments. Our approach allows the researcher to construct a classifier that is specific for the experimental, instrument, and analytical conditions of a single experiment and amenable to local, condition-specific, implementation. The resulting classifiers have application in a number of areas such as determination of peptide coverage for protein identification, pathway analysis, and protein quantification.

Alternative non-antibody scaffolds for molecular recognition

Originally proposed one decade ago, the idea of engineering proteins outside the immunoglobulin family for novel binding functions has evolved as a powerful technology. Several classes of protein scaffolds proved to yield reagents with specificities and affinities in a range that was previously considered unique to antibodies. Such engineered protein scaffolds are usually obtained by designing a random library with mutagenesis focused at a loop region or at an otherwise permissible surface area and by selection of variants against a given target via phage display or related techniques. Whereas a plethora of protein scaffolds has meanwhile been proposed, only few of them were actually demonstrated to yield specificities towards different kinds of targets and to offer practical benefits such as robustness, smaller size, and ease of expression that justify their use as a true alternative to conventional antibodies or their recombinant fragments. Currently, the most promising scaffolds with broader applicability are protein A, the lipocalins, a fibronectin domain, an ankyrin consensus repeat domain, and thioredoxin. Corresponding binding proteins are not only of interest as research reagents or for separation in biotechnology but also as potential biopharmaceuticals, especially in the areas of cancer, autoimmune and infectious diseases as well as for in vivo diagnostics. The medical prospects have boosted high commercial expectations, and many of the promising scaffolds are under development by biotech start-up companies. Although some issues still have to be addressed, for example immunogenicity, effector functions, and plasma half-life in the context of therapeutic use or low-cost high-throughput selection for applications in proteomics research, it has become clear that scaffold-derived binding proteins will play an increasing role in biotechnology and medicine.

Bioinformatic Application in Proteomic Research on Biomarker Discovery and Drug Target Validation

Novel biomarker identification and drug target validation are highly complex and resource-intensive processes, requiring an integral use of various tools, approaches and information. The recently developed proteomic technology features high-throughput parallel analysis of thousands of proteins in individual patients and amount populations and thus opens up the possibility of providing more details at a global level on the molecular mechanisms. With regularly updated public databases, bioinformatics can contribute to these processes by providing functional information of target candidates and correlating this information to the biological pathways. In this review, we outline recent advances of bioinformatic application in proteomic research on biomarker discovery and drug target validation. Specifically, we highlight how bioinformatics can facilitate the proteomic studies of biomarker identification and drug target validation. Keywords: Bioinformatics, biomarker discovery, drug design, drug development, proteomics