Selective enrichment of tryptophan-containing peptides from protein digests employing a reversible derivatization with malondialdehyde and solid-phase capture on hydrazide beads

A method for the selective enrichment of tryptophan-containing peptides from complex peptide mixtures such as protein digests is presented. It is based on the reversible reaction of tryptophan with malondialdehyde and trapping of the derivatized Trp-peptides on hydrazide beads via the free aldehyde group of the modified peptides. The peptides are subsequently recovered in their native form by specific cleavage reactions for further (mass spectrometric) analysis. The method was optimized and evaluated using a tryptic digest of a mixture of 10 model proteins, demonstrating a significant reduction in sample complexity while still allowing the identification of all proteins. The applicability of the tryptophan-specific enrichment procedure to complex biological samples is demonstrated for a total yeast cell lysate. Analysis of the processed fraction by 1D-LC-MS/MS confirms the specificity of the enrichment procedure, as more than 85% of the peptides recovered from the enrichment step contained tryptophan. The reduction in sample complexity also resulted in the identification of additional proteins in comparison to the untreated lysate.     

Evaluation of prefractionation methods as a preparatory step for multidimensional based chromatography of serum proteins

Prefractionations of proteins prior to their proteolysis, chromatography, and MS/MS analyses help reduce complexity and increase the yield of protein identifications. A number of methods were evaluated here for prefractionating serum samples distributed to the participating laboratories as part of the human Plasma Proteome Project. These methods include strong cation exchange (SCX) chromatography, slicing of SDS-PAGE gel bands, and liquid-phase IEF of the proteins. The fractionated proteins were trypsinized and the resulting peptides were resolved and analyzed by multidimensional protein identification technology coupled to IT MS/MS. The MS/MS spectra were clustered, combined, and searched against the IPI protein databank using Pep-Miner. The identification results were evaluated for the efficacy of the different prefractionation methodologies to identify larger numbers of proteins at higher confidence and to achieve the best coverage of the proteins with the identified peptides. Prefractionation based on SCX resulted in the largest number of identified proteins, followed by gel slices and then the liquid-phase IEF. An important observation was that each of the methods revealed a set of unique proteins, some identified with high confidence. Therefore, for comprehensive identification of the serum proteins, several different prefractionation approaches should be used in parallel.     

Positional proteomics: selective recovery and analysis of N-terminal proteolytic peptides

Bottom-up proteomics is the analysis of peptides derived from single proteins or protein mixtures, and because each protein generates tens of peptides, there is scope for controlled reduction in complexity. We report here a new strategy for selective isolation of the N-terminal peptides of a protein mixture, yielding positionally defined peptides. The method is tolerant of several fragmentation methods, and the databases that must be searched are substantially less complex.     

Identification and quantification of peptides and proteins secreted from prostate epithelial cells by unbiased liquid chromatography tandem mass spectrometry using goodness of fit and analysis of variance

The proteins secreted by prostate cancer cells (PC3(AR)6) were separated by strong anion exchange chromatography, digested with trypsin and analyzed by unbiased liquid chromatography tandem mass spectrometry with an ion trap. The spectra were matched to peptides within proteins using a goodness of fit algorithm that showed a low false positive rate. The parent ions for MS/MS were randomly and independently sampled from a log-normal population and therefore could be analyzed by ANOVA. Normal distribution analysis confirmed that the parent and fragment ion intensity distributions were sampled over 99.9% of their range that was above the background noise. Arranging the ion intensity data with the identified peptide and protein sequences in structured query language (SQL) permitted the quantification of ion intensity across treatments, proteins and peptides. The intensity of 101,905 fragment ions from 1421 peptide precursors of 583 peptides from 233 proteins separated over 11 sample treatments were computed together in one ANOVA model using the statistical analysis system (SAS) prior to Tukey-Kramer honestly significant difference (HSD) testing. Thus complex mixtures of proteins were identified and quantified with a high degree of confidence using an ion trap without isotopic labels, multivariate analysis or comparing chromatographic retention times.     

A metal-coded affinity tag approach to quantitative proteomics

The quantitative analysis of protein mixtures is pivotal for the understanding of variations in the proteome of living systems. Therefore, approaches have been recently devised that generally allow the relative quantitative analysis of peptides and proteins. Here we present proof of concept of the new metal-coded affinity tag (MeCAT) technique, which allowed the quantitative determination of peptides and proteins. A macrocyclic metal chelate complex (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)) loaded with different lanthanides (metal(III) ions) was the essential part of the tag. The combination of DOTA with an affinity anchor for purification and a reactive group for reaction with amino acids constituted a reagent that allowed quantification of peptides and proteins in an absolute fashion. For the quantitative determination, the tagged peptides and proteins were analyzed using flow injection inductively coupled plasma MS, a technique that allowed detection of metals with high precision and low detection limits. The metal chelate complexes were attached to the cysteine residues, and the course of the labeling reaction was followed using SDS-PAGE and MALDI-TOF MS, ESI MS, and inductively coupled plasma MS. To limit the width in isotopic signal spread and to increase the sensitivity for ESI analysis, we used the monoisotopic lanthanide macrocycle complexes. Peptides tagged with the reagent loaded with different metals coelute in liquid chromatography. In first applications with proteins, the calculated detection limit for bovine serum albumin for example was 110 amol, and we have used MeCAT to analyze proteins of the Sus scrofa eye lens as a model system. These data showed that MeCAT allowed quantification not only of peptides but also of proteins in an absolute fashion at low concentrations and in complex mixtures.     

Quantitative analysis of human cerebrospinal fluid proteins using a combination of cysteine tagging and amine-reactive isobaric labeling

Highly complex and dynamic protein mixtures are hardly comprehensively resolved by direct shotgun proteomic analysis. As many proteins of biological interest are of low abundance, numerous analytical methodologies have been developed to reduce sample complexity and go deeper into proteomes. The present work describes an analytical strategy to perform cysteinyl-peptide subset enrichment and relative quantification through successive cysteine and amine-isobaric tagging. A cysteine-reactive covalent capture tag (C3T) allowed derivatization of cysteines and specific isolation on a covalent capture (CC) resin. The 6-plex amine-reactive tandem mass tags (TMT) served for relative quantification of the targeted peptides. The strategy was first evaluated on a model protein mixture with increasing concentrations to assess the specificity of the enrichment and the quantitative performances of the workflow. It was then applied to human cerebrospinal fluid (CSF) from post-mortem and ante-mortem samples. These studies confirmed the specificity of the C3T and the CC technique to cysteine-containing peptides. The model protein mixture analysis showed high precision and accuracy of the quantification with coefficients of variation and mean absolute errors of less than 10% on average. The CSF experiments demonstrated the potential of the strategy to study complex biological samples and identify differential brain-related proteins. In addition, the quantification data were highly correlated with a classical TMT experiment (i.e., without C3T cysteine-tagging and enrichment steps). Altogether, these results legitimate the use of this quantitative C3T strategy to enrich and relatively quantify cysteine-containing peptides in complex mixtures.     

Precursor-ion mass re-estimation improves peptide identification on hybrid instruments

Mass spectrometry-based proteomics experiments have become an important tool for studying biological systems. Identifying the proteins in complex mixtures by assigning peptide fragmentation spectra to peptide sequences is an important step in the proteomics process. The 1-2 ppm mass-accuracy of hybrid instruments, like the LTQ-FT, has been cited as a key factor in their ability to identify a larger number of peptides with greater confidence than competing instruments. However, in replicate experiments of an 18-protein mixture, we note parent masses deviate 171 ppm, on average, for ion-trap data directed identifications and 8 ppm, on average, for preview Fourier transform (FT) data directed identifications. These deviations are neither caused by poor calibration nor by excessive ion-loading and are most likely due to errors in parent mass estimation. To improve these deviations, we introduce msPrefix, a program to re-estimate a peptide's parent mass from an associated high-accuracy full-scan survey spectrum. In 18-protein mixture experiments, msPrefix parent mass estimates deviate only 1 ppm, on average, from the identified peptides. In a cell lysate experiment searched with a tolerance of 50 ppm, 2295 peptides were confidently identified using native data and 4560 using msPrefixed data. Likewise, in a plasma experiment searched with a tolerance of 50 ppm, 326 peptides were identified using native data and 1216 using msPrefixed data. msPrefix is also able to determine which MS/MS spectra were possibly derived from multiple precursor ions. In complex mixture experiments, we demonstrate that more than 50% of triggered MS/MS may have had multiple precursor ions and note that spectra with multiple candidate ions are less likely to result in an identification using TANDEM. These results demonstrate integration of msPrefix into traditional shotgun proteomics workflows significantly improves identification results.     

Enrichment of N-terminal cysteinyl-peptides by covalent capture

The detection of low abundance proteins in complex biological samples is still a challenge in proteomics. To circumvent this obstacle a number of strategies involving the targeting of subsets of proteins or peptides were developed.The following work describes a new approach to simplify peptide mixtures by enrichment of N-terminal cysteinyl peptides (and to some extent N-terminal threonine peptides). The strategy is based on the use of an isolation method, so-called covalent capture (CC), which relies on the formation of a covalent bond between an N-terminal free cysteine or N-terminal free threonine and an aldehyde fixed on a solid support. The CC is highly selective. It permits extensive washes of the resin for the elimination of non-specific moieties before the release of the captured peptides. The application of the CC to proteomics was evaluated on tryptic peptides of standard proteins and test protein mixtures. The procedure demonstrated a significant reduction in sample complexity, while allowing the identification of N-terminal cysteinyl peptides hidden in the non-fractionated samples.This new strategy provides an efficient tool to existing proteomics approaches to reduce sample complexity and potentially identify less abundance proteins.     

Enrichment of N-terminal cysteinyl-peptides by covalent capture

The detection of low abundance proteins in complex biological samples is still a challenge in proteomics. To circumvent this obstacle a number of strategies involving the targeting of subsets of proteins or peptides were developed.The following work describes a new approach to simplify peptide mixtures by enrichment of N-terminal cysteinyl peptides (and to some extent N-terminal threonine peptides). The strategy is based on the use of an isolation method, so-called covalent capture (CC), which relies on the formation of a covalent bond between an N-terminal free cysteine or N-terminal free threonine and an aldehyde fixed on a solid support. The CC is highly selective. It permits extensive washes of the resin for the elimination of non-specific moieties before the release of the captured peptides. The application of the CC to proteomics was evaluated on tryptic peptides of standard proteins and test protein mixtures. The procedure demonstrated a significant reduction in sample complexity, while allowing the identification of N-terminal cysteinyl peptides hidden in the non-fractionated samples.This new strategy provides an efficient tool to existing proteomics approaches to reduce sample complexity and potentially identify less abundance proteins.     

Capture of peptides with N-terminal serine and threonine: a sequence-specific chemical method for Peptide mixture simplification

The objective of this study was to evaluate a sequence-specific chemistry for the ability to specifically capture peptides that contain N-terminal serine or threonine residues from mixtures. The first step is the oxidation of the 1,2-amino alcohol structure -CH(NH(2))CH(OH)- of peptides containing N-terminal serine or threonine with periodate. The newly formed aldehyde reacts with a labeling reagent containing a hydrazide, RCONHNH(2), to form a hydrazone-peptide conjugate, RCONHN=CH-peptide. Biotin-labeled conjugates can then be isolated by affinity purification with streptavidin. The method described in this report can be useful in simplifying the complex mixtures of peptides that are generated in typical proteomic analysis, where proteins are digested with trypsin and analyzed using liquid chromatography mass spectrometry data. The sequence-specific peptide selection not only reduces the complexity of digest mixtures, but also provides additional information for peptide identification. The targeted peptides are those that have either serine or threonine adjacent to a protease cleavage site. The sequence information should greatly aid in both database matching for protein identification and for de novo sequence determination.     

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis,and the application of statistical tools for data analysis and interpretation

Proteomic approaches to biological research that will prove the most useful and productive require robust, sensitive, and reproducible technologies for both the qualitative and quantitative analysis of complex protein mixtures. Here we applied the isotope-coded affinity tag (ICAT) approach to quantitative protein profiling, in this case proteins that copurified with lipid raft plasma membrane domains isolated from control and stimulated Jurkat human T cells. With the ICAT approach, cysteine residues of the two related protein isolates were covalently labeled with isotopically normal and heavy versions of the same reagent, respectively. Following proteolytic cleavage of combined labeled proteins, peptides were fractionated by multidimensional chromatography and subsequently analyzed via automated tandem mass spectrometry. Individual tandem mass spectrometry spectra were searched against a human sequence database, and a variety of recently developed, publicly available software applications were used to sort, filter, analyze, and compare the results of two repetitions of the same experiment. In particular, robust statistical modeling algorithms were used to assign measures of confidence to both peptide sequences and the proteins from which they were likely derived, identified via the database searches. We show that by applying such statistical tools to the identification of T cell lipid raft-associated proteins, we were able to estimate the accuracy of peptide and protein identifications made. These tools also allow for determination of the false positive rate as a function of user-defined data filtering parameters, thus giving the user significant control over and information about the final output of large-scale proteomic experiments. With the ability to assign probabilities to all identifications, the need for manual verification of results is substantially reduced, thus making the rapid evaluation of large proteomic datasets possible. Finally, by repeating the experiment, information relating to the general reproducibility and validity of this approach to large-scale proteomic analyses was also obtained.     

Prediction of band profiles of mixtures of bradykinin and kallidin from data acquired by competitive frontal analysis

The competitive adsorption isotherms of two closely related peptides, bradykinin and kallidin, were measured by frontal analysis on a Zorbax SB-C18 microbore column. An aqueous soluton at 20% acetonitrile (0.1% TFA) was used as the mobile phase. The competitive isotherm data were fitted to four different models: Langmuir, Bilangmuir, Langmuir-Freundlich, and Toth. These data fitted best to a Bilangmuir isotherm model. The influence of the pressure on the retention factors of the two peptides was found to be small and was not investigated in detail. The band profiles of large samples of the single components and of their mixtures were recorded. The overloaded profiles calculated using either the equilibrium-dispersive or POR model are in excellent agreement with the experimental profiles in all cases. Our results confirm that the competitive isotherm data derived from mixtures may suffice for a reasonably accurate prediction of the band profiles of all mixtures of the two components, provided their composition is close to 1/1.     

The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools

Tandem mass spectrometry (MS/MS) is frequently used in the identification of peptides and proteins. Typical proteomic experiments rely on algorithms such as SEQUEST and MASCOT to compare thousands of tandem mass spectra against the theoretical fragment ion spectra of peptides in a database. The probabilities that these spectrum-to-sequence assignments are correct can be determined by statistical software such as PeptideProphet or through estimations based on reverse or decoy databases. However, many of the software applications that assign probabilities for MS/MS spectra to sequence matches were developed using training data sets from 3D ion-trap mass spectrometers. Given the variety of types of mass spectrometers that have become commercially available over the last 5 years, we sought to generate a data set of reference data covering multiple instrumentation platforms to facilitate both the refinement of existing computational approaches and the development of novel software tools. We analyzed the proteolytic peptides in a mixture of tryptic digests of 18 proteins, named the "ISB standard protein mix", using 8 different mass spectrometers. These include linear and 3D ion traps, two quadrupole time-of-flight platforms (qq-TOF), and two MALDI-TOF-TOF platforms. The resulting data set, which has been named the Standard Protein Mix Database, consists of over 1.1 million spectra in 150+ replicate runs on the mass spectrometers. The data were inspected for quality of separation and searched using SEQUEST. All data, including the native raw instrument and mzXML formats and the PeptideProphet validated peptide assignments, are available at http://regis-web.systemsbiology.net/PublicDatasets/.     

Identifying differences in protein expression levels by spectral counting and feature selection

Spectral counting is a strategy to quantify relative protein concentrations in pre-digested protein mixtures analyzed by liquid chromatography online with tandem mass spectrometry. In the present study, we used combinations of normalization and statistical (feature selection) methods on spectral counting data to verify whether we could pinpoint which and how many proteins were differentially expressed when comparing complex protein mixtures. These combinations were evaluated on real, but controlled, experiments (yeast lysates were spiked with protein markers at different concentrations to simulate differences), which were therefore verifiable. The following normalization methods were applied: total signal, Z-normalization, hybrid normalization, and log preprocessing. The feature selection methods were: the Golub index, the Student t-test, a strategy based on the weighting used in a forward-support vector machine (SVM-F) model, and SVM recursive feature elimination. The results showed that Z-normalization combined with SVM-F correctly identified which and how many protein markers were added to the yeast lysates for all different concentrations. The software we used is available at http://pcarvalho.com/patternlab.     

Profiling human brain proteome by multi-dimensional separations coupled with MS

In our initial attempt to analyze the human brain proteome, we applied multi-dimensional protein separation and identification techniques using a combination of sample fractionation, 1-D SDS-PAGE, and MS analysis. The complexity of human brain proteome requires multiple fractionation strategies to extend the range and total number of proteins identified. According to the method of Klose (Methods Mol. Biol. 1999, 112, 67), proteins of the temporal lobe of human brain were fractionated into (i) cytoplasmic and nucleoplasmic, (ii) membrane and other structural, and (iii) DNA-binding proteins. Each fraction was then separated by SDS-PAGE, and the resulting gel line was cut into approximately 50 bands. After trypsin digestion, the resulting peptides from each band were analyzed by RP-LC/ESI-MS/MS using an LTQ spectrometer. The SEQUEST search program, which searched against the IPI database, was used for peptide sequence identification, and peptide sequences were validated by reversed sequence database search and filtered by the Protein Hit Score. Ultimately, 1533 proteins could be detected from the human brain. We classified the identified proteins according to their distribution on cellular components. Among these proteins, 24% were membrane proteins. Our results show that the multiple separation strategy is effective for high-throughput characterization of proteins from complex proteomic mixtures.     

A Computer Program to Compare Sequence Fingerprints of Homologous Proteins for the Rapid Assessment of Their Primary Structure Differences

We have developed a computer program for the rapid assessment of the primary structure differences between a protein of unknown sequence and a homologous known protein. Both proteins are reduced, alkylated, and digested with the same hydrolytic agent. The unfractionated peptide mixtures are submitted to automatic sequence analysis. Based on the knowledge of the reference sequence, the program utilizes the analysis data to identify all the potential peptides present in the two mixtures, determining their primary structure, homology degree, and molecular weight calculated both as integer MH⁺ and average mass variables. These fingerprints allow the user to easily identify the structural differences between the two proteins and clarify possible doubts by a mass spectrometric analysis of the two mixtures. In order to verify the utility of the program, we provide an application example using the already reported data of two homologous proteins.     

An automated method for scanning LC-MS data sets for significant peptides and proteins, including quantitative profiling and interactive confirmation

Differential quantification of proteins and peptides by LC-MS is a promising method to acquire knowledge about biological processes, and for finding drug targets and biomarkers. However, differential protein analysis using LC-MS has been held back by the lack of suitable software tools. Large amounts of experimental data are easily generated in protein and peptide profiling experiments, but data analysis is time-consuming and labor-intensive. Here, we present a fully automated method for scanning LC-MS/MS data for biologically significant peptides and proteins, including support for interactive confirmation and further profiling. By studying peptide mixtures of known composition, we demonstrate that peptides present in different amounts in different groups of samples can be automatically screened for using statistical tests. A linear response can be obtained over almost 3 orders of magnitude, facilitating further profiling of peptides and proteins of interest. Furthermore, we apply the method to study the changes of endogenous peptide levels in mouse brain striatum after administration of reserpine, a classical model drug for inducing Parkinson disease symptoms.     

Interaction of linear cationic peptides with phospholipid membranes and polymers of sialic acid

Polysialic acid (PSA) is a natural anionic polymer typically occurring on the outer surface of cell membranes. PSA is involved in cell signaling and intermolecular interactions with proteins and peptides. The antimicrobial potential of peptides is usually evaluated in model membranes consisting of lipid bilayers but devoid of either PSA or its analogs. The goal of this work was to investigate the possible effect of PSA on the structure of melittin (Mlt) and latarcins Ltc1K, Ltc2a, and the activity of these peptides with respect to model membranes. These peptides are linear cationic ones derived from the venom of bee (Mlt) and spider (both latarcins). The length of each of the peptides is 26 amino acid residues, and they all have antimicrobial activity. However, they differ with respect to conformational mobility, hydrophobic characteristics, and overall charge. In this work, using circular dichroism spectroscopy, we show that the peptides adopt an α-helical conformation upon interaction with either PSA or phospholipid liposomes formed of either zwitterionic or anionic phospholipids or their mixtures. The extent of helicity depends on the amino acid sequence and properties of the medium. Based on small angle X-ray scattering data and the analysis of the fluorescence spectrum of the Trp residue in Mlt, we conclude that the peptide forms an oligomeric complex consisting of α-helical Mlt and several PSA molecules. Both latarcins, unlike Mlt, the most hydrophobic of the peptides, interact weakly with zwitterionic liposomes. However, they bind anionic liposomes or those composed of anionic/zwitterionic lipid mixtures. Latarcin Ltc1K forms associates on liposomes composed of zwitterionic/anionic lipid mixture. The structure of the peptide associates is either disordered or of β-sheet conformation. In all other cases the studied peptides adopt predominately α-helical conformation. In addition, we demonstrate that PSA inhibits membranolytic activity of Mlt and latarcin Ltc1K. These data suggest that the peptides, due to their high conformational lability, can vary structural and amphiphilic properties in the presence of PSA. As a result, various scenarios of the interaction of the peptides with membranes, whose surface is abundant with anionic polysaccharides, can take place. This can account for difficulties in understanding the structure-functional relationships in interactions of linear cationic peptides with biological membranes.     

Systematic assessment of the benefits and caveats in mining microbial post-translational modifications from shotgun proteomic data: the response of Shewanella oneidensis to chromate exposure

Microbes are known to regulate both gene expression and protein activity through the use of post-translational modifications (PTMs). Common PTMs involved in cellular signaling and gene control include methylations, acetylations, and phosphorylations, whereas oxidations have been implicated as an indicator of stress. Shewanella oneidensis MR-1 is a Gram-negative bacterium that demonstrates both respiratory versatility and the ability to sense and adapt to diverse environmental conditions. The data set used in this study consisted of tandem mass spectra derived from midlog phase aerobic cultures of S. oneidensis either native or shocked with 1 mM chromate [Cr(VI)]. In this study, three algorithms (DBDigger, Sequest, and InsPecT) were evaluated for their ability to scrutinize shotgun proteomic data for evidence of PTMs. The use of conservative scoring filters for peptides or proteins versus creating a subdatabase first from a nonmodification search was evaluated with DBDigger. The use of higher-scoring filters for peptide identifications was found to result in optimal identifications of PTM peptides with a 2% false discovery rate (FDR) for the total data set using the DBDigger algorithm. However, the FDR climbs to unacceptably high levels when only PTM peptides are considered. Sequest was evaluated as a method for confirming PTM peptides putatively identified using DBDigger; however, there was a low identification rate ( approximately 25%) for the searched spectra. InsPecT was found to have a much lower, and thus more acceptable, FDR than DBDigger for PTM peptides. Comparisons between InsPecT and DBDigger were made with respect to both the FDR and PTM peptide identifications. As a demonstration of this approach, a number of S. oneidensis chemotaxis proteins as well as low-abundance signal transduction proteins were identified as being post-translationally modified in response to chromate challenge.