Algorithm for accurate similarity measurements of peptide mass fingerprints and its application

Expression, purification and identification of isotope-labeled recombinant cystatin C protein in Escheichia coli intended for absolute quantification using isotope dilution mass spectrometry

Isotope-labeled proteins are expected to be used as internal standard proteins in the field of protein quantification by isotope dilution mass spectrometry (ID/MS). To achieve the absolute quantification of Cystatin C (Cys C) based on ID/MS, we aims to obtain ¹⁵N isotope-labeled recombinant Cys C (¹⁵N-Cys C) protein. Firstly, the Cys C gene was optimized based on the preferred codons of Escherichia coli, and inserted into the pET-28a(+) expression plasmid. Then, the plasmid was transformed into TOP10 and BL21 strains, and ¹⁵N-Cys C was expressed in M9 medium using ¹⁵N as the only nitrogen source. ¹⁵N-Cys C was detected by SDS-PAGE, protein immunoblotting and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). The characteristic peptides obtained from ¹⁵N-Cys C were analyzed by a Q Exactive Plus MS system. Results showed that 53.06% of the codons were optimized. The codon adaptation index of the Cys C genes increased from 0.31 to 0.95, and the GC content was adjusted from 64.85% to 54.88%. The purity of ¹⁵N-Cys C was higher than 95%. MALDI-TOF MS analysis showed that the m/z of ¹⁵N-Cys C had changed from 13 449 to 14 850. The characteristic peptides showed that 619.79 m/z (M+2H)²⁺ was the parent ion of ¹⁵N-Cys C and that the secondary ions of ¹⁵N-labeled peptides from y⁺⁵ to y⁺⁹ were 616.27 m/z, 716.33 m/z, 788.39 m/z, 936.43 m/z, and 1052.46 m/z, respectively. In conclusion, we successfully expressed, purified and identified of ¹⁵N-Cys C protein in Escheichia coli intended for absolute quantification using ID/MS.

Performance comparison of three trypsin columns used in liquid chromatography

Trypsin is the most widely used enzyme in proteomic research due to its high specificity. Although the in-solution digestion is predominantly used, it has several drawbacks, such as long digestion times, autolysis, and intolerance to high temperatures or organic solvents. To overcome these shortcomings trypsin was covalently immobilized on solid support and tested for its proteolytic activity. Trypsin was immobilized on bridge-ethyl hybrid silica sorbent with 300Å pores, packed in 2.1×30mm column and compared with Perfinity and Poroszyme trypsin columns. Catalytic efficiency of enzymatic reactors was tested using N_α-Benzoyl-l-arginine 4-nitroanilide hydrochloride as a substrate. The impact of buffer pH, mobile phase flow rate, and temperature on enzymatic activity was investigated. Digestion speed generally increased with the temperature from 20 to 37°C. Digestion speed also increased with pH from 7.0 to 9.0; the activity of prototype enzyme reactor was highest at pH 9.0, when it activity exceeded both commercial reactors. Preliminary data for fast protein digestion are presented.

Insight into Trypsin Miscleavage: Comparison of Kinetic Constants of Problematic Peptide Sequences

Trypsin, a high fidelity protease, is the most widely used enzyme for protein digestion in proteomic research. Optimal digestion conditions are well-known and so are the expected cleavage products. However, missed cleavage sites are frequently observed when acidic amino acids, aspartic and glutamic acids, are present near the cleavage site. Also, the sequence motifs with successive lysine and/or arginine residues represent a source of missed cleaved sites. In spite of an adverse role of missed cleaved peptides on proteomic research, the digestion kinetics of these problematic sequences is not well-known. In this work, synthetic peptides with various sequence motifs were used as trypsin substrates. Cleavage products were analyzed with reversed-phase high performance liquid chromatography, and the kinetic constants for selected missed cleavage sites were calculated. Relative digestion speed for lysine and arginine sites is compared, including the digestion motifs flanked with aspartic and glutamic acid. Our findings show that DK and DTR motifs are cleaved by trypsin with 3 orders of magnitude lower speed than the arginine site. These motifs are likely to produce missed cleavage peptides in protein tryptic digests even at prolonged digestion times.

Absolute multiplexed protein quantification using QconCAT technology

In addition to protein identification, protein quantification is becoming a key output of proteomic experiments. Although relative quantification techniques are more commonplace and central to discovery proteomics, most assays require absolute quantification. The growth in systems biology has also increased the demand for absolute protein abundance values for input into models. QconCATs are created by concatenating peptide sequences taken from the target proteins into artificial proteins. The QconCAT acts as a source of internal standards and enables parallel absolute quantification of multiple proteins. QconCATs are typically applied in targeted proteomic workflows and so benefit from the greater sensitivity and wider dynamic range of these approaches. In this chapter, we discuss the design, construction, expression, and deployment of a QconCAT and the resulting experiments required for multiplex absolute quantification.

Prediction of missed proteolytic cleavages for the selection of surrogate peptides for quantitative proteomics

Quantitative proteomics experiments are usually performed using proteolytic peptides as surrogates for their parent proteins, inferring protein amounts from peptide-level quantitation. This process is frequently dependent on complete digestion of the parent protein to its limit peptides so that their signal is truly representative. Unfortunately, proteolysis is often incomplete, and missed cleavage peptides are frequently produced that are unlikely to be optimal surrogates for quantitation, particularly for label-mediated approaches seeking to derive absolute values. We have generated a predictive computational tool that is able to predict which candidate proteolytic peptide bonds are likely to be missed by the standard enzyme trypsin. Our cross-validated prediction tool uses support vector machines and achieves high accuracy in excess of 0.94 precision (PPV), with attendant high sensitivity of 0.79, across multiple proteomes. We believe this is a useful tool for selecting candidate quantotypic peptides, seeking to minimize likely loss owing to missed cleavage, which will be a boon for quantitative proteomic pipelines as well as other areas of proteomics. Our results are discussed in the context of recent results examining the kinetics of missed cleavages in proteomic digestion protocols, and show agreement with observed experimental trends. The software has been made available at http://king.smith.man.ac.uk/mcpred .

A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.

Feature-matching pattern-based support vector machines for robust peptide mass fingerprinting

Peptide mass fingerprinting, regardless of becoming complementary to tandem mass spectrometry for protein identification, is still the subject of in-depth study because of its higher sample throughput, higher level of specificity for single peptides and lower level of sensitivity to unexpected post-translational modifications compared with tandem mass spectrometry. In this study, we propose, implement and evaluate a uniform approach using support vector machines to incorporate individual concepts and conclusions for accurate PMF. We focus on the inherent attributes and critical issues of the theoretical spectrum (peptides), the experimental spectrum (peaks) and spectrum (masses) alignment. Eighty-one feature-matching patterns derived from cleavage type, uniqueness and variable masses of theoretical peptides together with the intensity rank of experimental peaks were proposed to characterize the matching profile of the peptide mass fingerprinting procedure. We developed a new strategy including the participation of matched peak intensity redistribution to handle shared peak intensities and 440 parameters were generated to digitalize each feature-matching pattern. A high performance for an evaluation data set of 137 items was finally achieved by the optimal multi-criteria support vector machines approach, with 491 final features out of a feature vector of 35,640 normalized features through cross training and validating a publicly available "gold standard" peptide mass fingerprinting data set of 1733 items. Compared with the Mascot, MS-Fit, ProFound and Aldente algorithms commonly used for MS-based protein identification, the feature-matching patterns algorithm has a greater ability to clearly separate correct identifications and random matches with the highest values for sensitivity (82%), precision (97%) and F1-measure (89%) of protein identification. Several conclusions reached via this research make general contributions to MS-based protein identification. Firstly, inherent attributes showed comparable or even greater robustness than other explicit. As an inherent attribute of an experimental spectrum, peak intensity should receive considerable attention during protein identification. Secondly, alignment between intense experimental peaks and properly digested, unique or non-modified theoretical peptides is very likely to occur in positive peptide mass fingerprinting. Finally, normalization by several types of harmonic factors, including missed cleavages and mass modification, can make important contributions to the performance of the procedure.

Simultaneous phenotyping and quantification of α-1-antitrypsin by liquid chromatography-tandem mass spectrometry

BACKGROUND

α-1-Antitrypsin (A1AT) deficiency results from a genetic disorder at 2 common loci. Diagnosis requires quantification of A1AT and subsequent identification of the specific variant. The current algorithm of laboratory testing for the diagnosis of A1AT deficiency uses a combination of quantification (nephelometry), genotyping, and/or phenotyping. We developed a multiple reaction monitoring liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous quantification of A1AT and identification of the 2 most common deficiency alleles present in 95% of the patients with A1AT deficiency.

METHODS

Serum samples (n = 40) were digested with trypsin, and appropriate ¹³C/¹⁵N-labeled standard peptides were added. We performed LC-MS/MS analysis with a 0.5- by 150-mm C18 column and H₂O:acetonitrile:n-propanol:formic acid (A:98:1:1:0.2 and B:10:80:10:0.2; flow 12 μL/min) mobile phase in positive ion mode on a TSQ Quantum triple quadrupole MS system. We measured the A1AT concentration by comparison to a calibration curve and determined the phenotype by the presence or absence of variant peptides. We compared the results to the current phenotyping assay by isoelectric focusing (IEF) and the immunonephelometry quantitative assay.

RESULTS

For A1AT allele detection, in 39 of 40 samples the LC-MS/MS results were identical to those obtained by IEF gel electrophoresis. The single discrepant result was rerun by IEF at a lower dilution, and the results were in concordance. The A1AT quantification by LC-MS/MS also compared favorably with nephelometry.

CONCLUSIONS

The LC-MS/MS method correlates well with current phenotyping and nephelometric assays and has the potential to improve the laboratory diagnosis of genetic A1AT deficiency.

Novel amidinating cross-linker for facilitating analyses of protein structures and interactions

A novel bifunctional thioimidate cross-linking reagent (diethyl suberthioimidate) that modifies amines without sacrificing their native basicity is developed. Intermolecular cross-linking of neurotensin and intramolecular cross-linking of cytochrome c under physiological conditions is investigated with this reagent. Because it does not perturb the electrostatic properties of a protein, it is unlikely to lead to artifactual conclusions about native protein structure. The interpeptide cross-links formed with this reagent are easily separated from other tryptic fragments using strong cation exchange chromatography, and they have a readily identified mass spectrometric signature. The use of this novel amidinating protein cross-linking reagent holds great promise for efficient, large-scale structural analysis of complex systems.

Separating the wheat from the chaff: unbiased filtering of background tandem mass spectra improves protein identification

Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.

Evaluating reproducibility and similarity of mass and intensity data in complex spectra--applications to tubulin

We present a data processing approach based on the spectral dot product for evaluating spectral similarity and reproducibility. The method introduces 95% confidence intervals on the spectral dot product to evaluate the strength of spectral correlation; it is the only calculation described to date that accounts for both the non-normal sampling distribution of the dot product and the number of peaks the spectra have in common. These measures of spectral similarity allow for the recursive generation of a consensus spectrum, which incorporates the most consistent features from statistically similar replicate spectra. Taking the spectral dot product and 95% confidence intervals between consensus spectra from different samples yields the similarity between these samples. Applying the data analysis scheme to replicates of brain tubulin CNBr peptides enables a robust comparison of tubulin isotype expression and post-translational modification patterns in rat and cow brains.

From the genome sequence to the proteome and back: evaluation of E. coli genome annotation with a 2-D gel-based proteomics approach

The ambition of systems biology to understand complex biological systems at the molecular level implies that we need to have a concrete and correct understanding of each molecular entity and its function. However, even for the best-studied organism, Escherichia coli, a large number of proteins have never been identified and characterised from wild-type cells, and/or await unravelling of their biological role. Instead, the ORF models for these proteins have been predicted by suitable algorithms and/or through comparison with known, homologous proteins from other organisms, approaches which may be prone to error. In the present study, we used a combination of 2-DE, MALDI-TOF-MS and PMF to identify 1151 different proteins in E. coli K12 JM109. Comparison of the experimental with the theoretical Mr and pI values (4000 experimental values each) allowed the identification of numerous proteins with incorrect or incomplete ORF annotations in the current E. coli genome databases. Several inconsistencies in genome annotation were verified experimentally, and up to 55 candidates await further investigation. Our findings demonstrate how an up-to-date 2-D gel-based proteomics approach can be used for improving the annotation of prokaryotic genomes. They also highlight the need for harmonization among the different E. coli genome databases.

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.

Mass spectrometry-based proteomics for the detection of plant pathogens

Plant diseases caused by fungi, oomycetes, viruses, and bacteria are devastating both to the economy and to the food supply of a nation. Therefore, the development of new, rapid methods to identify these pathogens is a highly important area of research that is of international concern. MS-based proteomics has become a powerful and increasingly popular approach to not only identify these pathogens, but also to better understand their biology. However, there is a distinction between identifying a pathogen protein and identifying a pathogen based upon the detection of one of its proteins and this must be considered before the general application of MS for plant pathogen detection is made. There has been a recent push in the proteomics community to make data from large-scale proteomics experiments publicly available in the form of a centralized repository. Such a resource could enable the use of MS as a universal plant pathogen detection technology.

Detection and Identification of Protein Isoforms Using Cluster Analysis of MALDI−MS Mass Spectra

We describe an approach to screen large sets of MALDI-MS mass spectra for protein isoforms separated on two-dimensional electrophoresis gels. Mass spectra are matched against each other by utilizing extracted peak mass lists and hierarchical clustering. The output is presented as dendrograms in which protein isoforms cluster together. Clustering could be applied to mass spectra from different sample sets, dates, and instruments, revealed similarities between mass spectra, and was a useful tool to highlight peptide peaks of interest for further investigation. Shared peak masses in a cluster could be identified and were used to create novel peak mass lists suitable for protein identification using peptide mass fingerprinting. Complex mass spectra consisting of more than one protein were deconvoluted using information from other mass spectra in the same cluster. The number of peptide peaks shared between mass spectra in a cluster was typically found to be larger than the number of peaks that matched to calculated peak masses in databases, thus modified peaks are probably among the shared peptides. Clustering increased the number of peaks associated with a given protein.

Mathematical tools in analytical mass spectrometry

Over the last few decades, mass spectrometry has become a powerful tool for exploring various aspects of molecular processes occurring in biological systems. Such exploration is leading to a greater understanding of various complex life processes; unraveling these processes poses the greatest challenge to contemporary bioscience. With due respect to sample preparation, data analysis is rapidly becoming a major obstacle to the conversion of experimental knowledge into valid conclusions. It is interesting to note that many problems related to mass spectrometry can be solved using techniques from computer science, graph theory and discrete mathematics. The aim of this manuscript is to recollect several essays that demonstrate the power and the need to apply such skills to mass spectrometry data interpretation. Special attention is paid to situations where traditional chemical analysis reaches its limits but mathematical reasoning can still allow us to reach valid conclusions.

Identification of nicotinamide N-methyltransferase as a novel serum tumor marker for colorectal cancer

PURPOSE

The goal of this study was to identify and validate novel serum markers of human colorectal cancer as potential candidates for noninvasive detection of early colorectal neoplasm.

EXPERIMENTAL DESIGN

Employing two-dimensional gel electrophoresis and mass spectrometry, we analyzed 16 matched colorectal cancer and adjacent normal tissue samples. Proteins found to be elevated in cancer tissue were further validated by generating antibodies which were used for immunoblotting of tissue samples and for the development of highly sensitive immunoassays for assessment of serum samples.

RESULTS

In total, 735 different proteins were identified in colon tissue. Strong elevation in colorectal cancer for five proteins was confirmed by immunoblot analysis: transforming growth factor-beta induced protein ig-h3 (betaIG-H3), nicotinamide N-methyltransferase (NNMT), nucleoside diphosphate kinase A (nm23-H1), purine nucleoside phosphorylase (PNPH), and mannose-6-phosphate receptor binding protein 1 (M6P1). Elevated levels of NNMT, which is not predicted to be secreted but is known as a cytoplasmic protein, were found in serum from patients with colorectal cancer. Employing a receiver-operating characteristic curve based on the measurement of 109 patients with colorectal cancer and 317 healthy controls, we obtained an area under the curve of 0.84 for NNMT, which was superior to the established tumor marker carcinoembryogenic antigen with an area under the curve of 0.78.

CONCLUSIONS

It is proposed that NNMT serum levels may have significance in the early detection and in the management of patients with colorectal cancer.

Method for the elucidation of the elemental composition of low molecular mass chemicals using exact masses of product ions and neutral losses: application to environmental chemicals measured by liquid chromatography with hybrid quadrupole/time-of-flight mass spectrometry

A method for elucidating the elemental compositions of low molecular weight chemicals, based primarily on mass measurements made using liquid chromatography (LC) with time-of-flight mass spectrometry (TOFMS) and quadrupole/time-of-flight mass spectrometry (LC/QTOFMS), was developed and tested for 113 chemicals of environmental interest with molecular masses up to approximately 400 Da. As the algorithm incorporating the method is not affected by differences in the instrument used, or by the ionization method and other ionization conditions, the method is useful not only for LC/TOFMS, but also for all kinds of mass spectra measured with higher accuracy and precision (uncertainties of a few mDa) employing all ionization methods and on-line separation techniques. The method involves calculating candidate compositions for intact ionized molecules (ionized forms of the sample molecule that have lost or gained no more than a proton, i.e., [M+H](+) or [M-H](-)) as well as for fragment ions and corresponding neutral losses, and eliminating those atomic compositions for the molecules that are inconsistent with the corresponding candidate compositions of fragment ions and neutral losses. Candidate compositions were calculated for the measured masses of the intact ionized molecules and of the fragment ions and corresponding neutral losses, using mass uncertainties of 2 and 5 mDa, respectively. Compositions proposed for the ionized molecule that did not correspond to the sum of the compositions of a candidate fragment ion and its corresponding neutral loss were discarded. One, 2-5, 6-10, 11-20, and >20 candidate compositions were found for 65%, 39%, 1%, 1%, and 0%, respectively, for the 124 ionized molecules formed from the 113 chemicals tested (both positive and negative ions were obtained from 11 of the chemicals). However, no candidate composition was found for 2% of the test cases (i.e., 3 chemicals), for each of which the measured mass of one of the product ions was in error by 5-6.7 mDa.