Quantitative analysis of membrane fouling by protein mixtures using MALDI-MS

Binary aqueous solutions of bovine serum albumin (BSA) and beta-lactoglobulin (bLG) were subject to flux-stepping and constant flux ultrafiltration to identify the apparent critical flux and to study the mechanisms and factors affecting fouling when the membrane is permeable to one protein component. Membranes from these filtration experiments were analyzed using matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) to locate and quantify levels of fouling below and above the apparent critical flux. Hydrophilic (PLTK) regenerated cellulose and hydrophobic (PBTK) polysulfone asymmetric membranes were used, both of 30 kDa nominal molecular weight cut-off. For the hydrophilic PLTK membrane, protein deposition was shown to depend on electrostatic forces, exhibiting little or no fouling when the proteins had the same charge sign as that of the membrane. This was found to apply for both dilute equal mass-per-unit-volume and equimolar binary mixtures. For the PBTK membrane, hydrophobic protein-membrane attractive forces were sufficiently strong to cause deposition of bLG even in the presence of repulsive electrostatic forces. For the PBTK membrane deposition exceeded monolayer coverage below and above apparent critical flux conditions but for the PLTK membrane this generally occurred when the apparent critical flux was exceeded. MALDI-MS was shown to be a facile direct analytical technique for individually quantifying adsorbed proteins on membrane surfaces at levels as low as 50 fmol/mm(2). The high levels of compound specificity inherent to mass spectrometry make this approach especially suited to the quantification of individual components in mixed deposits. In this study, MALDI-MS was found to be successful in identifying and quantifying the protein species responsible for fouling.     

Clustering of MS spectra for improved protein identification rate and screening for protein variants and modifications by MALDI-MS/MS

It is an established fact that allelic variation and post-translational modifications create different variants of proteins, which are observed as isoelectric and size subspecies in two-dimensional gel based proteomics. Here we explore the stromal proteome of spinach and Arabidopsis chloroplast and show that clustering of mass spectra is a useful tool for investigating such variants and detecting modified peptides with amino acid substitutions or post-translational modifications. This study employs data mining by hierarchical clustering of MALDI-MS spectra, using the web version of the SPECLUST program (http://bioinfo.thep.lu.se/speclust.html). The tool can also be used to remove peaks of contaminating proteins and to improve protein identification, especially for species without a fully sequenced genome. Mutually exclusive peptide peaks within a cluster provide a good starting point for MS/MS investigation of modified peptides, here exemplified by the identification of an A to E substitution that accounts for the isoelectric heterogeneity in protein isoforms.     

Detection and identification of Vibrio species using whole-cell protein pattern analysis

Outbreaks of foodborne diseases associated with Vibrio species such as V. parahaemolyticus, V. vulnificus, and V. cholerae frequently occur in countries having a dietary habit of raw seafood consumption. For rapid identification of different Vibrio species involved in foodborne diseases, whole-cell protein pattern analysis for 13 type strains of 12 Vibrio species was performed using SDS-PAGE analysis. Pathogenic Vibrio species such as V. parahaemolyticus, V. vulnificus, V. cholerae, V. alginolyticus, V. fluvialis, and V. mimicus were included in the 12 Vibrio species used in this study. Each of the 12 Vibrio species showed clearly specific band patterns of its own. Two different strains of V. parahaemolyticus showed two different SDS-PAGE wholecell protein patterns, giving the possibility of categorizing isolated strains in the same V. parahaemolyticus species into two subgroups. The 36 Vibrio isolates collected from sushi restaurants in Busan were all identified as V. parahaemolyticus by comparing their protein patterns with those of Vibrio type strains. The identified isolates were categorized into two different subgroups of V. parahaemolyticus. The whole-cell protein pattern analysis by SDS-PAGE can be used as a specific, rapid, and simple identification method for Vibrio spp. involved in foodborne diseases at the subspecies level.     

Optimal analysis of complex protein mass spectra

Due to physical and chemical phenomena, a simple sample can give rise to a complex mass spectrum with many more peaks than the number of molecular species present in the sample. We link peaks within and between different spectra, and come up with an advanced analysis approach to produce reliable estimates of the molecule masses and abundances. By linking peaks, we can locate multiple‐charge peaks at the correct position in the spectrum, we can deconvolute complex regions with many overlapping peaks by including information from related regions with lower complexity and higher resolution, and we reduce the total number of observed peaks in a spectrum to a much smaller number of underlying molecular species. In this paper we properly model 29 952 peaks in 64 spectra, using only 39 location parameters and one shape parameter. This major reduction from many different molecules to a limited set of molecular species reduces the statistical test multiplicity for biomarker discovery and therefore we imply that the reduction should eventually increase the biomarker discovery power significantly, too.     

Large-scale protein identification using mass spectrometry

Recent achievements in genomics have created an infrastructure of biological information. The enormous success of genomics promptly induced a subsequent explosion in proteomics technology, the emerging science for systematic study of proteins in complexes, organelles, and cells. Proteomics is developing powerful technologies to identify proteins, to map proteomes in cells, to quantify the differential expression of proteins under different states, and to study aspects of protein-protein interaction. The dynamic nature of protein expression, protein interactions, and protein modifications requires measurement as a function of time and cellular state. These types of studies require many measurements and thus high throughput protein identification is essential. This review will discuss aspects of mass spectrometry with emphasis on methods and applications for large-scale protein identification, a fundamental tool for proteomics.     

Intensity-based protein identification by machine learning from a library of tandem mass spectra

Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomics owing in part to robust spectral interpretation algorithms. Widely used algorithms do not fully exploit the intensity patterns present in mass spectra. Here, we demonstrate that intensity pattern modeling improves peptide and protein identification from MS/MS spectra. We modeled fragment ion intensities using a machine-learning approach that estimates the likelihood of observed intensities given peptide and fragment attributes. From 1,000,000 spectra, we chose 27,000 with high-quality, nonredundant matches as training data. Using the same 27,000 spectra, intensity was similarly modeled with mismatched peptides. We used these two probabilistic models to compute the relative likelihood of an observed spectrum given that a candidate peptide is matched or mismatched. We used a 'decoy' proteome approach to estimate incorrect match frequency, and demonstrated that an intensity-based method reduces peptide identification error by 50-96% without any loss in sensitivity.     

Bacterial species identification from MALDI-TOF mass spectra through data analysis and machine learning

At present, there is much variability between MALDI-TOF MS methodology for the characterization of bacteria through differences in e.g., sample preparation methods, matrix solutions, organic solvents, acquisition methods and data analysis methods. After evaluation of the existing methods, a standard protocol was developed to generate MALDI-TOF mass spectra obtained from a collection of reference strains belonging to the genera Leuconostoc, Fructobacillus and Lactococcus. Bacterial cells were harvested after 24 h of growth at 28 °C on the media MRS or TSA. Mass spectra were generated, using the CHCA matrix combined with a 50:48:2 acetonitrile:water:trifluoroacetic acid matrix solution, and analyzed by the cell smear method and the cell extract method. After a data preprocessing step, the resulting high quality data set was used for PCA, distance calculation and multi-dimensional scaling. Using these analyses, species-specific information in the MALDI-TOF mass spectra could be demonstrated. As a next step, the spectra, as well as the binary character set derived from these spectra, were successfully used for species identification within the genera Leuconostoc, Fructobacillus, and Lactococcus. Using MALDI-TOF MS identification libraries for Leuconostoc and Fructobacillus strains, 84% of the MALDI-TOF mass spectra were correctly identified at the species level. Similarly, the same analysis strategy within the genus Lactococcus resulted in 94% correct identifications, taking species and subspecies levels into consideration. Finally, two machine learning techniques were evaluated as alternative species identification tools. The two techniques, support vector machines and random forests, resulted in accuracies between 94% and 98% for the identification of Leuconostoc and Fructobacillus species, respectively.     

Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra

Abstract An artificial neural network was trained to distinguish between three putatively novel species of Streptomyces using normalised, scaled pyrolysis mass spectra from three representative strains of each of the taxa, each sampled in triplicate. Once trained, the artificial neural network was challenged with spectral data from the original organisms, the 'training set', from additional members of the putative novel taxa and from over a hundred strains representing six other actinomycete genera. All of the streptomycetes were correctly identified but many of the other actinomycetes were mis-identified. A modified network topology was developed to recognise the mass spectral patterns of the non-streptomycete strains. The resultant neural network correctly identified the streptomycetes, whereas all of the remaining actinomycetes were recognised as unknown organisms. The improved artificial neural network provides a rapid, reliable and cost-effective method of identifying members of the three target streptomycete taxa.     

Analysis of intact protein isoforms by mass spectrometry

The diverse proteome of an organism arises from such events as single nucleotide substitutions at the DNA level, different RNA processing, and dynamic enzymatic post-translational modifications. This minireview focuses on the measurement of intact proteins to describe the diversity found in proteomes. The field of biological mass spectrometry has steadily advanced, enabling improvements in the characterization of single proteins to proteins derived from cells or tissues. In this minireview, we discuss the basic technology for "top-down" intact protein analysis. Furthermore, examples of studies involved with the qualitative and quantitative analysis of full-length polypeptides are provided.     

Rapid protein identification using direct infusion nanoelectrospray ionization mass spectrometry

Current protein identification techniques are largely based on MALDI-TOF mass fingerprinting and LC-ESI MS/MS sequence tag analysis. Here we describe an improved method for rapid protein identification that uses direct infusion nanoelectrospray quadrupole time-of-flight (nanoESI QTOF) MS. Protein digests were analyzed without LC separation using nanoESI on a QSTAR XL MS/MS system in information dependent data acquisition mode. The protein identification conditions and parameters were extensively evaluated with in-solution and in-gel digested protein samples. Rapid identification of proteins was achieved and compared directly to the results obtained on the same samples using nanoflow HPLC-MS/MS on the QSTAR system. The increased throughput, reproducibility, the high data quality, and the ease of use make the direct infusion system an efficient and affordable technique for protein identification analysis.     

Robust protein quantitation in chromatographic fractions using MALDI-MS of tryptic peptides

A method is introduced to evaluate protein concentrations using the height sum of all MALDI-MS peaks that unambiguously match theoretic tryptic peptide masses of the protein sought after. The method uses native chromatographic protein fractionation prior to digestion but does not require any depletion, labeling, derivatization, or preparation of a compound similar to the analyte. All peak heights of tryptic peptides are normalized with the peak height of a unique standard peptide added to the MALDI-MS samples. The sum of normalized peak heights, S(n), or the normalized mean peak height, M(n), reflects the concentration of the respective protein. For fractions containing various proteins, S(n) and M(n) can be used to compare concentrations of a protein between different fractions. For fractions with one predominating protein, they can be used to estimate concentration ratios between fractions, or to quantify the fractional protein concentration after calibration with pure protein solutions. Initial native fractionation retains the possibility to apply all conventional analytic procedures. Moreover, it renders the method relatively robust to MS mass accuracy. The method was validated with albumin, transferrin, alpha1-antitrypsin, and immunoglobulin G within highly complex chromatographic fractions of pathological and normal sera, which contained the respective intact native protein in dominating as well as minor concentrations. The correlation found between S(n) and the protein concentration as determined with ELISA showed that the method can be applied to select markers for distinguishing between normal and pathological serum samples.     

Rapid and accurate peptide identification from tandem mass spectra

Mass spectrometry, the core technology in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of proteins in a complex biological sample. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We describe a database search program called Crux that reimplements and extends the widely used database search program Sequest. For speed, Crux uses a peptide indexing scheme to rapidly retrieve candidate peptides for a given spectrum. For each peptide in the target database, Crux generates shuffled decoy peptides on the fly, providing a good null model and, hence, accurate false discovery rate estimates. Crux also implements two recently described postprocessing methods: a p value calculation based upon fitting a Weibull distribution to the observed scores, and a semisupervised method that learns to discriminate between target and decoy matches. Both methods significantly improve the overall rate of peptide identification. Crux is implemented in C and is distributed with source code freely to noncommercial users.     

Two-dimensional mass spectra generated from the analysis of 15N-labeled and unlabeled peptides for efficient protein identification and de novo peptide sequencing

Protein identification has been greatly facilitated by database searches against protein sequences derived from product ion spectra of peptides. This approach is primarily based on the use of fragment ion mass information contained in a MS/MS spectrum. Unambiguous protein identification from a spectrum with low sequence coverage or poor spectral quality can be a major challenge. We present a two-dimensional (2D) mass spectrometric method in which the numbers of nitrogen atoms in the molecular ion and the fragment ions are used to provide additional discriminating power for much improved protein identification and de novo peptide sequencing. The nitrogen number is determined by analyzing the mass difference of corresponding peak pairs in overlaid spectra of (15)N-labeled and unlabeled peptides. These peptides are produced by enzymatic or chemical cleavage of proteins from cells grown in (15)N-enriched and normal media, respectively. It is demonstrated that, using 2D information, i.e., m/z and its associated nitrogen number, this method can, not only confirm protein identification results generated by MS/MS database searching, but also identify peptides that are not possible to identify by database searching alone. Examples are presented of analyzing Escherichia coli K12 extracts that yielded relatively poor MS/MS spectra, presumably from the digests of low abundance proteins, which can still give positive protein identification using this method. Additionally, this 2D MS method can facilitate spectral interpretation for de novo peptide sequencing and identification of posttranslational or other chemical modifications. We envision that this method should be particularly useful for proteome expression profiling of organelles or cells that can be grown in (15)N-enriched media.     

2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts

Chemical shifts of amino acids in proteins are the most sensitive and easily obtainable NMR parameters that reflect the primary, secondary, and tertiary structures of the protein. In recent years, chemical shifts have been used to identify secondary structure in peptides and proteins, and it has been confirmed that ¹H^α, ¹³C^α, ¹³C^β, and ¹³C′ NMR chemical shifts for all 20 amino acids are sensitive to their secondary structure. Currently, most of the methods are purely based on one-dimensional statistical analyses of various chemical shifts for each residue to identify protein secondary structure. However, it is possible to achieve an increased accuracy from the two-dimensional analyses of these chemical shifts. The 2DCSi approach performs two-dimension cluster analyses of ¹H^α, ¹H^N, ¹³C^α, ¹³C^β, ¹³C′, and ¹⁵N^H chemical shifts to identify protein secondary structure and the redox state of cysteine residue. For the analysis of paired chemical shifts of 6 data sets, each of the 20 amino acids has its own 15 two-dimension cluster scattering diagrams. Accordingly, the probabilities for identifying helix and extended structure were calculated by using our scoring matrix. Compared with existing the chemical shift-based methods, it appears to improve the prediction accuracy of secondary structure identification, particularly in the extended structure. In addition, the probability of the given residue to be helix or extended structure is displayed, allows the users to make decisions by themselves. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Grant sponsor: National Science Council of ROC; Grant numbers: NSC-94-2323-B006- 001, NSC-93-2212-E-006.     

Fast tandem mass spectra-based protein identification regardless of the number of spectra or potential modifications examined

MOTIVATION: Comparing tandem mass spectra (MSMS) against a known dataset of protein sequences is a common method for identifying unknown proteins; however, the processing of MSMS by current software often limits certain applications, including comprehensive coverage of post-translational modifications, non-specific searches and real-time searches to allow result-dependent instrument control. This problem deserves attention as new mass spectrometers provide the ability for higher throughput and as known protein datasets rapidly grow in size. New software algorithms need to be devised in order to address the performance issues of conventional MSMS protein dataset-based protein identification. METHODS: This paper describes a novel algorithm based on converting a collection of monoisotopic, centroided spectra to a new data structure, named 'peptide finite state machine' (PFSM), which may be used to rapidly search a known dataset of protein sequences, regardless of the number of spectra searched or the number of potential modifications examined. The algorithm is verified using a set of commercially available tryptic digest protein standards analyzed using an ABI 4700 MALDI TOFTOF mass spectrometer, and a free, open source PFSM implementation. It is illustrated that a PFSM can accurately search large collections of spectra against large datasets of protein sequences (e.g. NCBI nr) using a regular desktop PC; however, this paper only details the method for identifying peptide and subsequently protein candidates from a dataset of known protein sequences. The concept of using a PFSM as a peptide pre-screening technique for MSMS-based search engines is validated by using PFSM with Mascot and XTandem. AVAILABILITY: Complete source code, documentation and examples for the reference PFSM implementation are freely available at the Proteome Commons, http://www.proteomecommons.org and source code may be used both commercially and non-commercially as long as the original authors are credited for their work.     

Direct analysis of protein complexes using mass spectrometry.

We describe a rapid, sensitive process for comprehensively identifying proteins in macromolecular complexes that uses multidimensional liquid chromatography (LC) and tandem mass spectrometry (MS/MS) to separate and fragment peptides. The SEQUEST algorithm, relying upon translated genomic sequences, infers amino acid sequences from the fragment ions. The method was applied to the Saccharomyces cerevisiae ribosome leading to the identification of a novel protein component of the yeast and human 40S subunit. By offering the ability to identify >100 proteins in a single run, this process enables components in even the largest macromolecular complexes to be analyzed comprehensively.     

Detection,quantification and identification of fungal extracellular laccases using polyclonal antibody and mass spectrometry

This study presents a combined method to analyze extracellular fungal laccases using a new anti-laccase antibody together with the identification of tryptic laccase peptides by mass spectrometry (nanoLC–ESI–MS/MS). The polyclonal anti-laccase antibody LccCbr2 was raised against peptides designed from the copper binding region II of fungal laccases using in silico data obtained from GenBank database. As a consequence, detection requires denaturation of the enzymes due to the stable conformation of the copper binding region II. The specificity of the antibody was shown with denatured laccase Lcc1 of Coprinopsis cinerea and laccase of Hypholoma fasciculare. LccCbr2 detected amounts as low as 5 ng of highly purified laccase, indicating a possible use of the antibody for quantification of laccase proteins. Denatured extracellular laccases from culture supernatants of the basidiomycetes C. cinerea, H. fasciculare, Lentinula edodes, Mycena sp., Piriformospora indica, Pleurotus cornucopiae, Pleurotus ostreatus, Pycnoporus cinnabarinus, Trametes versicolor and furthermore the ascomycete Verpa conica were detected with apparent molecular masses between 60 and 70 kDa by LccCbr2. The identity of extracellular laccases from C. cinerea, H. fasciculare, P. ostreatus, P. cinnabarinus and T. versicolor were verified by tryptic peptides using nanoLC–ESI–MS/MS.     

Detection, quantification and identification of fungal extracellular laccases using polyclonal antibody and mass spectrometry

This study presents a combined method to analyze extracellular fungal laccases using a new anti-laccase antibody together with the identification of tryptic laccase peptides by mass spectrometry (nanoLC–ESI–MS/MS). The polyclonal anti-laccase antibody LccCbr2 was raised against peptides designed from the copper binding region II of fungal laccases using in silico data obtained from GenBank database. As a consequence, detection requires denaturation of the enzymes due to the stable conformation of the copper binding region II. The specificity of the antibody was shown with denatured laccase Lcc1 of Coprinopsis cinerea and laccase of Hypholoma fasciculare. LccCbr2 detected amounts as low as 5 ng of highly purified laccase, indicating a possible use of the antibody for quantification of laccase proteins. Denatured extracellular laccases from culture supernatants of the basidiomycetes C. cinerea, H. fasciculare, Lentinula edodes, Mycena sp., Piriformospora indica, Pleurotus cornucopiae, Pleurotus ostreatus, Pycnoporus cinnabarinus, Trametes versicolor and furthermore the ascomycete Verpa conica were detected with apparent molecular masses between 60 and 70 kDa by LccCbr2. The identity of extracellular laccases from C. cinerea, H. fasciculare, P. ostreatus, P. cinnabarinus and T. versicolor were verified by tryptic peptides using nanoLC–ESI–MS/MS.