Diagnostic protein discovery using liquid chromatography/mass spectrometry for proteolytic peptide targeting

A peptide targeting method has been developed for diagnostic protein discovery, which combines proteolytic digestion of fractionated plasma proteins and liquid chromatography coupled to electrospray time-of-flight mass spectrometry (LC/ESI-TOFMS) profiling. Proteolysis prior to profiling overcomes molecular weight limitations and compensates for the poor sensitivity of matrix-assisted laser desorption/ionization (MALDI) protein profiling. LC/MS increases the peak capacity compared to crude fractionation techniques or single sample MALDI analysis. Differentially expressed peptides are targeted in the mass chromatograms using bioinformatic techniques and subsequently sequenced with MALDI tandem MS. In a model study comparing pancreatic cancer patients to controls, 74% of the peptide targets were successfully sequenced. This profiling method was superior to previous experiments using single sample MALDI analysis for protein profiling or proteolytic peptide profiling, because more potential protein markers were identified.     

Improving peptide identification using an empirical peptide retention time database

Peptide retention time (RT) is independent of tandem mass spectrometry (MS/MS) parameters and can be combined with MS/MS information to enhance peptide identification. In this paper, we utilized peptide empirical RT and MS/MS for peptide identification. This new approach resulted in the construction of an Empirical Peptide Retention Time Database (EPRTD) based on peptides showing a false‐positive rate (FPR) ≤1%, detected in several liquid chromatography (LC)/MS/MS analyses. In subsequent experiments, the RT of peptides with FPR >1% was compared with empirical data derived from the EPRTD. If the experimental RT was within a specified time range of the empirical value, the corresponding MS/MS spectra were accepted as positive. Application of the EPRTD approach to simple samples (known protein mixtures) and complex samples (human urinary proteome) revealed that this method could significantly enhance peptide identification without compromising the associated confidence levels. Further analysis indicated that the EPRTD approach could improve low‐abundance peptides and with the expansion of the EPRTD the number of peptide identifications will be increased. This approach is suitable for large‐scale clinical proteomics research, in which tens of LC/MS/MS analyses are run for different samples with similar components. Copyright © 2008 John Wiley & Sons, Ltd.     

An efficient parallelization of phosphorylated peptide and protein identification

Protein sequence database search based on tandem mass spectrometry is an essential method for protein identification. As the computational demand increases, parallel computing has become an important technique for accelerating proteomics data analysis. In this paper, we discuss several factors which could affect the runtime of the pFind search engine and build an estimation model. Based on this model, effective on‐line and off‐line scheduling methods were developed. An experiment on the public dataset from PhosphoPep consisting of 100 RAW files of phosphopeptides shows that the speedup on 100 processors is 83.7. The parallel version can complete the identification task within 9 min, while a stand‐alone process on a single PC takes more than 10 h. On another larger dataset consisting of 1 366 471 spectra, the speedup on 320 processors is 258.9 and the efficiency is 80.9%. Our approach can be applied to other similar search engines. Copyright © 2010 John Wiley & Sons, Ltd.     

The use of proteotypic peptide libraries for protein identification

This paper describes an algorithm to apply proteotypic peptide sequence libraries to protein identifications performed using tandem mass spectrometry (MS/MS). Proteotypic peptides are those peptides in a protein sequence that are most likely to be confidently observed by current MS-based proteomics methods. Libraries of proteotypic peptide sequences were compiled from the Global Proteome Machine Database for Homo sapiens and Saccharomyces cerevisiae model species proteomes. These libraries were used to scan through collections of tandem mass spectra to discover which proteins were represented by the data sets, followed by detailed analysis of the spectra with the full protein sequences corresponding to the discovered proteotypic peptides. This algorithm (Proteotypic Peptide Profiling, or P3) resulted in sequence-to-spectrum matches comparable to those obtained by conventional protein identification algorithms using only full protein sequences, with a 20-fold reduction in the time required to perform the identification calculations. The proteotypic peptide libraries, the open source code for the implementation of the search algorithm and a website for using the software have been made freely available. Approximately 4% of the residues in the H. sapiens proteome were required in the proteotypic peptide library to successfully identify proteins.     

A novel approach for protein identification from complex cell proteome using modified peptide mass fingerprinting algorithm

A unique peptide based search algorithm for identification of protein mixture using PMF is proposed. The proposed search algorithm utilizes binary search and heapsort programs to generate frequency chart depicting the unique peptides corresponding to all proteins in a proteome. The use of binary search program significantly reduces the time for frequency chart preparation to ～2 s for a proteome comprising ～23 000 proteins. The algorithm was applied to a three‐protein mixture identification, host cell protein (HCP) analysis, and a simulation‐generated data set. It was found that the algorithm could identify at least one unique peptide of a protein even in the presence of fourfold higher concentration of another protein. In addition, two HCPs that are known to be difficult to remove were missed by MS/MS approach and were exclusively identified using the presented algorithm. Thus, the proposed algorithm when used along with standard proteomic approaches present avenues for enhanced protein identification efficiency, particularly for applications such as HCP analysis in biopharmaceutical research, where identification of low‐abundance proteins are generally not achieved due to dynamic range limitations between the target product and HCPs.     

Miniaturized membrane-based reversed-phase chromatography and enzyme reactor for protein digestion,peptide separation,and protein identification using electrospray ionization mass spectrometry

Separation and determination of water- and fat-soluble vitamins by micellar (MEKC) and microemulsion electrokinetic chromatography (MEEKC) are compared. MEKC is only useful in the quantitative analysis of water-soluble vitamins when sodium dodecylsulfate (SDS) is used as the surfactant. However, the separation of mixtures containing water- and fat-soluble vitamins is only achieved by MEEKC using a microemulsion prepared by mixing SDS as the surfactant, butanol as the co-surfactant, octane as the non-polar modifier and propanol as the second co-surfactant. The injection time and the solvent used for the dilution of samples have a significant effect on the analysis of lypophilic compounds. The most reproducible results in the analysis of fat-soluble vitamins are obtained by using the same microemulsion electrolyte as the solvent for samples and an injection time of 10 s.     

Confident protein identification using the average peptide score method coupled with search-specific, ab initio thresholds

Perhaps the greatest difficulty in interpreting large sets of protein identifications derived from mass spectrometric methods is whether or not to trust the results. For such experiments, the level of confidence in each protein identification made needs to be far greater than the often used 95% significance threshold to avoid the identification of many false-positives. To provide higher confidence results, we have developed an innovative scoring strategy coupling the recently published Average Peptide Score (APS) method with pre-filtering of peptide identifications, using a simple peptide quality filter. Iterative generation of these filters in conjunction with reversed database searching is used to determine the correct levels at which the APS and peptide quality thresholds should be set to return virtually zero false-positive reports. This proceeds without the need to reference a known dataset.     

The information encrypted in accurate peptide masses-improved protein identification and assistance in glycopeptide identification and characterization

Analytically useful information from accurate mass data for peptides with an error of 相似文献   

Quantitative kinetics of proteolytic enzymes determined by a surface concentration-based assay using peptide arrays

Peptide arrays have emerged as a key technology for drug discovery, diagnosis, and cell biology. Despite the promise of these arrays, applications of peptide arrays to quantitative analysis of enzyme kinetics have been limited due to the difficulty in obtaining quantitative information of enzymatic reaction products. In this study, we developed a new approach for the quantitative kinetics analysis of proteases using fluorescence-conjugated peptide arrays, a surface concentration-based assay with solid-phase peptide standards using dry-off measurements, and compared it with an applied concentration-based assay. For fabrication of the peptide arrays, substrate peptides of cMMP-3, caspase-3, caspase-9, and calpain-1 were functionalized with TAMRA and cysteine, and were immobilized onto amine-functionalized arrays using a heterobifunctional linker, N-[γ-maleimidobutyloxy]succinimide ester. The proteolytic activities of the four enzymes were quantitatively analyzed by calculating changes induced by enzymatic reactions in the concentrations of peptides bound to array surfaces. In addition, this assay was successfully applied for calculating the Michaelis constant (K(m,surf)) for the four enzymes. Thus, this new assay has a strong potential for use in the quantitative evaluation of proteases, and for drug discovery through kinetics studies including the determination of K(m) and V(max).     

On-line coupling of micro-enzyme reactor with micro-membrane chromatography for protein digestion, peptide separation, and protein identification using electrospray ionization mass spectrometry

To miniaturize high-performance membrane chromatography, a poly(vinylidene fluoride) membrane medium, employed as the stationary phase, is sandwiched between two poly(dimethylsiloxane) substrates containing the microchannels. The microchannels are fabricated by the capillary molding technique, involving the use of capillaries as the channel template and the fluid inlet/outlet. The micro(micro)-membrane chromatography system is coupled with a micro-enzyme reactor containing immobilized trypsins for performing rapid protein digestion, peptide separation, and protein identification using electrospray ionization mass spectrometry. Separation performance of cytochrome c digest in micro-membrane chromatography is compared with the results obtained from a regular reversed-phase micro-liquid chromatography. The efficacy and the potentials of micro-membrane chromatography in tryptic mapping are reported. On-line integration of the micro-enzyme reactor with micro-chromatographic separation techniques and electrospray ionization mass spectrometry clearly provides a microanalytical platform for automated sample handling, minimized sample loss, and reduced sample consumption. It also provides enhanced detection sensitivity and dynamic range for the analysis of complex protein mixtures such as cell lysates in proteomics research.     

Identification of tissue-specific targeting peptide

Using phage display technique, we identified tissue-targeting peptide sets that recognize specific tissues (bone-marrow dendritic cell, kidney, liver, lung, spleen and visceral adipose tissue). In order to rapidly evaluate tissue-specific targeting peptides, we performed machine learning studies for predicting the tissue-specific targeting activity of peptides on the basis of peptide sequence information using four machine learning models and isolated the groups of peptides capable of mediating selective targeting to specific tissues. As a representative liver-specific targeting sequence, the peptide ??DKNLQLH?? was selected by the sequence similarity analysis. This peptide has a high degree of homology with protein ligands which can interact with corresponding membrane counterparts. We anticipate that our models will be applicable to the prediction of tissue-specific targeting peptides which can recognize the endothelial markers of target tissues.     

Making and breaking peptide bonds: protein engineering using sortase

Sortases are a class of bacterial enzymes that possess transpeptidase activity. It is their ability to site-specifically break a peptide bond and then reform a new bond with an incoming nucleophile that makes sortase an attractive tool for protein engineering. This technique has been adopted for a range of applications, from chemistry-based to cell biology and technology. In this Minireview we provide a brief overview of the biology of sortase enzymes and current applications in protein engineering. We identify areas that lend themselves to further innovation and that suggest new applications.     

Simplifying sample handling for protein identification by peptide mass fingerprint using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

An ultrasonic bath, an ultrasonic probe and a sonoreactor were used to speed up the kinetics of the reactions involved in each step of the sample handling for in-gel protein identification by peptide mass fingerprint, PMF, using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The following steps were successfully accelerated using ultrasonic energy: gel washing, protein reduction, and protein alkylation. As a result, a reduction comprising 80% to 90% of the total time involved in the classic approach was achieved. In addition the sample handling was also drastically simplified. The number of peptides identified and the protein sequence coverage obtained for the new procedure were comparable to those obtained with the traditional sample treatment for the following protein standards: glycogen phosphorylase b, BSA, ovalbumin, carbonic anhydrase, trypsin inhibitor and alpha-lactalbumin. Finally, as a proof of the procedure, specific proteins were identified from complex protein mixtures obtained from three different sulphate-reducing bacteria: Desulfovibrio desulfuricans G20, Desulfuvibrio gigas NCIB 9332, and Desulfuvibrio desulfuricans ATCC 27774.     

Absolute protein quantification by LC-ICP-MS using MeCAT peptide labeling

Nowadays, the most common strategies used in quantitative proteomics are based on isotope-coded labeling followed by specific molecule mass spectrometry. The implementation of inductively coupled plasma mass spectrometry (ICP-MS) for quantitative purposes can solve important drawbacks such as lack of sensitivity, structure-dependent responses, or difficulties in absolute quantification. Recently, lanthanide-containing labels as metal-coded affinity tag (MeCAT) reagents have been introduced, increasing the interest and scope of elemental mass spectrometry techniques for quantitative proteomics. In this work one of the first methodologies for absolute quantification of peptides and proteins using MeCAT labeling is presented. Liquid chromatography (LC) interfaced to ICP-MS has been used to separate and quantify labeled peptides while LC coupled to electrospray ionization mass spectrometry served for identification tasks. Synthetic-labeled peptides were used as standards to calibrate the response of the detector with compounds as close as possible to the target species. External calibration was employed as a quantification technique. The first step to apply this approach was MeCAT-Eu labeling and quantification by isotope dilution ICP-MS of the selected peptides. The standards were mixed in different concentrations and subjected to reverse-phase chromatography before ICP-MS detection to consider the column effect over the peptides. Thus, the prepared multi-peptide mix allowed a calibration curve to be obtained in a single chromatographic run, correcting possible non-quantitative elutions of the peptides from the column. The quantification strategy was successfully applied to other labeled peptides and to standard proteins such as digested lysozyme and bovine serum albumin.     

In-capillary self-assembly and proteolytic cleavage of polyhistidine peptide capped quantum dots

A new method using fluorescence coupled capillary electrophoresis (CE-FL) for monitoring self-assembly and proteolytic cleavage of hexahistidine peptide capped quantum dots (QDs) inside a capillary has been developed in this report. QDs and the ATTO 590-labeled hexahistidine peptide (H6-ATTO) were injected into a capillary, sequentially. Their self-assembly inside the capillary was driven by a metal-affinity force which yielded a new fluorescence signal due to Förster resonance energy transfer (FRET). The highly efficient separation of fluorescent complexes and the FRET process were analyzed using CE-FL. The self-assembly of QDs and biomolecules was found to effectively take place inside the capillary. The kinetics of the assembly was monitored by CE-FL, and the approach was extended to the study of proteolytic cleavage of surface conjugated peptides. Being the first in-depth analysis of in-capillary nanoparticle–biomolecule assembly, the novel approach reported here provides inspiration to the development of QD-based FRET probes for biomedical applications.