Protein identification using 2D-LC-MS/MS

Multidimensional liquid chromatography techniques have been coupled to tandem mass spectrometry to provide a robust method to identify proteins in complex mixtures. Data acquisition is interfaced directly with search algorithms for identification through cross-correlation with databases. This review describes the most recent advances in methodologies for protein identification by mass spectrometry and describes the limitations of the application of the technologies.     

Protein identification by syringe pump-driven reversed-phase LC-MS/MS

In typical mass spectrometry-based protein identification using peptide fragmentation fingerprinting, front-end separation plays a critical role in successful peptide sequencing. This separation step demands a great deal of time and usually is the rate-limiting step for the whole process. Here we provide an alternative separation method, based on a simple nanoflow delivery system, that is able to shorten the separation time considerably. This system consists of a 25-mul syringe connected to a manually packed reversed-phase mini-capillary column that can be directly coupled to an electrospray ionization tandem mass spectrometer. A syringe pump is then used to deliver the peptide mixtures at a nanoscale flow rate. We examined the efficiency and efficacy of this method by analyzing the tryptic peptides of bovine serum albumin and of 10 Escherichia coli proteins separated by two-dimensional gel electrophoresis (2DE). The results showed that identification of each protein could be achieved successfully within 25 min by using the disposable mini-capillary column. Moreover, all 2DE-separated E. coli proteins were identified at high confidence levels. Together, our data suggest that this method is a suitable option for mass spectrometry-based protein identification.     

Evaluation of Chemical Derivatisation Methods for Protein Identification using MALDI MS/MS

In proteomic studies, assigning protein identity from organisms whose genomes are yet to be completely sequenced remains a challenging task. For these organisms, protein identification is typically based on cross species matching of amino acid sequence obtained from collision induced dissociation (CID) of peptides using mass spectrometry. The most direct approach of de novo sequencing is slow and often difficult, due to the complexity of the resultant CID spectra. For MALDI-MS, this problem has been addressed by using chemical derivatisation to direct peptide fragmentation, thereby simplifying CID spectra and facilitating de novo interpretation. In this study, milk whey proteins from the tammar wallaby (Macropus eugenii) were used to evaluate three chemical derivatisation methods compatible with MALDI MS/MS. These methods included (i) guanidination and sulfonation using chemically-assisted fragmentation (CAF), (ii) guanidination and sulfonation using 4-sulfophenyl isothiocyanate (SPITC) and (iii) derivatising the epsilon-amino group of lysine residues with Lys Tag 4H. Derivatisation with CAF and SPITC resulted in more protein identification than Lys Tag 4H. Sulfonation using SPITC was the preferred method due to the low cost per experiment, the reactivity with both lysine and arginine terminated peptides and the resultant simplified MS/MS spectra.*Australian Peptide Conference Issue.**This project was funded by an ARC Linkage grant to Deane supported by TGR Biosciences and facilitated by access to the Australian Proteome Analysis Facility established under the Australian Government’s Major National Research Facilities program.     

Web-based MS/MS data analysis

This tutorial focuses on three MS/MS data analysis programs currently available via a web interface: Mascot, Phenyx and X!Tandem. Although these programs process the same input and often produce comparable outputs, subtle differences remain. The use of parameters that are requested in the on-line forms and the subsequent interpretation of results are illustrated and explained via a single example.     

Protein identification using top-down

In the last two years, because of advances in protein separation and mass spectrometry, top-down mass spectrometry moved from analyzing single proteins to analyzing complex samples and identifying hundreds and even thousands of proteins. However, computational tools for database search of top-down spectra against protein databases are still in their infancy. We describe MS-Align+, a fast algorithm for top-down protein identification based on spectral alignment that enables searches for unexpected post-translational modifications. We also propose a method for evaluating statistical significance of top-down protein identifications and further benchmark various software tools on two top-down data sets from Saccharomyces cerevisiae and Salmonella typhimurium. We demonstrate that MS-Align+ significantly increases the number of identified spectra as compared with MASCOT and OMSSA on both data sets. Although MS-Align+ and ProSightPC have similar performance on the Salmonella typhimurium data set, MS-Align+ outperforms ProSightPC on the (more complex) Saccharomyces cerevisiae data set.     

Protein and peptide identification algorithms using MS for use in high-throughput, automated pipelines

Current proteomics experiments can generate vast quantities of data very quickly, but this has not been matched by data analysis capabilities. Although there have been a number of recent reviews covering various aspects of peptide and protein identification methods using MS, comparisons of which methods are either the most appropriate for, or the most effective at, their proposed tasks are not readily available. As the need for high-throughput, automated peptide and protein identification systems increases, the creators of such pipelines need to be able to choose algorithms that are going to perform well both in terms of accuracy and computational efficiency. This article therefore provides a review of the currently available core algorithms for PMF, database searching using MS/MS, sequence tag searches and de novo sequencing. We also assess the relative performances of a number of these algorithms. As there is limited reporting of such information in the literature, we conclude that there is a need for the adoption of a system of standardised reporting on the performance of new peptide and protein identification algorithms, based upon freely available datasets. We go on to present our initial suggestions for the format and content of these datasets.     

Rapid protein identification using monolithic enzymatic microreactor and LC-ESI-MS/MS

A monolithic enzymatic microreactor was prepared in a fused-silica capillary by in situ polymerization of acrylamide, glycidyl methacrylate (GMA) and ethylene dimethacrylate (EDMA) in the presence of a binary porogenic mixture of dodecanol and cyclohexanol, followed by ammonia solution treatment, glutaraldehyde activation and trypsin modification. The choice of acrylamide as co-monomer was found useful to improve the efficiency of trypsin modification, thus, to increase the enzyme activity. The optimized microreactor offered very low back pressure, enabling the fast digestion of proteins flowing through the reactor. The performance of the monolithic microreactor was demonstrated with the digestion of cytochrome c at high flow rate. The digests were then characterized by CE and HPLC-MS/MS with the sequence coverage of 57.7%. The digestion efficiency was found over 230 times as high as that of the conventional method. In addition, for the first time, protein digestion carried out in a mixture of water and ACN was compared with the conventional aqueous reaction using MS/MS detection, and the former solution was found more compatible and more efficient for protein digestion.     

First identification of tannin-binding proteins in saliva of Papio hamadryas using MS/MS mass spectrometry

Hamadryas baboons possess salivary proline-rich proteins (PRP), as indicated by the presence of pink-staining protein bands using 1D SDS gel electrophoresis and Coomassie R250 staining. The ability of these protein bands to interact with tannic acid was further examined. In a tannin-binding assay using 5 μg tannic acid mixed with hamadryas whole saliva, we recently found four distinct protein bands of apparently 72, 55, 20, and 15 kDa that were precipitated during the experiments. In this work, we were able to identify these protein bands in a follow-up analysis using MS/MS mass spectrometry after excising such bands out of air-dried gels. Albumin and α-amylase were present in the tannic acid-protein complexes, with albumin already known to nonspecifically interact with a great diversity of chemical compounds. More interesting, we also identified a basic PRP and a cystatin precursor protein. This was the first successful attempt to identify a PRP from precipitated tannin-protein complexes in hamadryas baboons using MS/MS mass spectrometry. On the other hand, the role of cystatins in tannin binding is not yet well understood. However, there are recent reports on cystatin expression in saliva of rats responding to astringent dietary compounds. In conclusion, the follow-up data on tannin-binding proteins present in salivary secretions from hamadryas baboons adds important knowledge to primate physiology and feeding ecology, in order to shed light on the establishment and development of food adaptations in primates. It also demonstrates that tannin binding is characteristic for PRP, but might not be restricted to this particular group of proteins in primate species.     

PRIMA: peptide robust identification from MS/MS spectra

In proteomics, tandem mass spectrometry is the key technology for peptide sequencing. However, partially due to the deficiency of peptide identification software, a large portion of the tandem mass spectra are discarded in almost all proteomics centers because they are not interpretable. The problem is more acute with the lower quality data from low end but more popular devices such as the ion trap instruments. In order to deal with the noisy and low quality data, this paper develops a systematic machine learning approach to construct a robust linear scoring function, whose coefficients are determined by a linear programming. A prototype, PRIMA, was implemented. When tested with large benchmarks of varying qualities, PRIMA consistently has higher accuracy than commonly used software MASCOT, SEQUEST and X! Tandem.     

Detection and identification of plasma proteins that bind GlialCAM using ProteinChip arrays, SELDI-TOF MS, and nano-LC MS/MS

In order to fully understand biological processes it is essential to identify interactions in protein complexes. There are several techniques available to study this type of interactions, such as yeast two-hybrid screens, affinity chromatography, and coimmunoprecipitation. We propose a novel strategy to identify protein-protein interactions, comprised of first detecting the interactions using ProteinChips and SELDI-TOF MS, followed by the isolation of the interacting proteins through affinity beads and RP-HPLC and finally identifying the proteins using nano-LC MS/MS. The advantages of this new strategy are that the primary high-throughput screening of samples can be performed with small amounts of sample, no specific antibody is needed and the proteins represented on the SELDI-TOF MS spectra can be identified with high confidence. Furthermore, the method is faster and less labor-intensive than other current approaches. Using this novel method, we isolated and identified the interactions of two mouse plasma proteins, mannose binding lectin C and properdin, with GlialCAM, a type 1 transmembrane glycoprotein that belongs to the Ig superfamily.     

Large improvements in MS/MS-based peptide identification rates using a hybrid analysis

We report a hybrid search method combining database and spectral library searches that allows for a straightforward approach to characterizing the error rates from the combined data. Using these methods, we demonstrate significantly increased sensitivity and specificity in matching peptides to tandem mass spectra. The hybrid search method increased the number of spectra that can be assigned to a peptide in a global proteomics study by 57-147% at an estimated false discovery rate of 5%, with clear room for even greater improvements. The approach combines the general utility of using consensus model spectra typical of database search methods with the accuracy of the intensity information contained in spectral libraries. A common scoring metric based on recent developments linking data analysis and statistical thermodynamics is used, which allows the use of a conservative estimate of error rates for the combined data. We applied this approach to proteomics analysis of Synechococcus sp. PCC 7002, a cyanobacterium that is a model organism for studies of photosynthetic carbon fixation and biofuels development. The increased specificity and sensitivity of this approach allowed us to identify many more peptides involved in the processes important for photoautotrophic growth.     

The identification of anaerobic bacteria using MALDI-TOF MS

Matrix Assisted Laser Desorption and Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has gained more and more popularity for the identification of bacteria. Several studies show that bacterial diagnosticis is being revolutionized by the application of MALDI-TOF MS. For anaerobic bacteria, MALDI-TOF MS has been used for the identification of Prevotella spp., Fusobacterium spp., Clostridium spp., Bacteroides spp. and Gram-positive anaerobic cocci. However, to identify bacteria reliably, an extensive database is essential. For routine identification of anaerobic bacteria available databases need to be optimised.     

An N-glycopeptide MS/MS data analysis workflow leveraging two complementary glycoproteomic software tools for more confident identification and assignments

Complete coverage of all N-glycosylation sites on the SARS-CoV2 spike protein would require the use of multiple proteases in addition to trypsin. Subsequent identification of the resulting glycopeptides by searching against database often introduces assignment errors due to similar mass differences between different permutations of amino acids and glycosyl residues. By manually interpreting the individual MS² spectra, we report here the common sources of errors in assignment, especially those introduced by the use of chymotrypsin. We show that by applying a stringent threshold of acceptance, erroneous assignment by the commonly used Byonic software can be controlled within 15%, which can be reduced further if only those also confidently identified by a different search engine, pGlyco3, were considered. A representative site-specific N-glycosylation pattern could be constructed based on quantifying only the overlapping subset of N-glycopeptides identified at higher confidence. Applying the two complimentary glycoproteomic software in a concerted data analysis workflow, we found and confirmed that glycosylation at several sites of an unstable Omicron spike protein differed significantly from those of the stable trimeric product of the parental D614G variant.     

Optimal construction of theoretical spectra for MS/MS spectra identification

We derive the optimal number of peaks (defined as the minimum number that provides the required efficiency of spectra identification) in the theoretical spectra as a function of (i) the experimental accuracy, sigma, of the measured ratio m/z; (ii) experimental spectrum density; (iii) size of the database; (iv) number of peaks in the theoretical spectra; and (v) types of ions that the peaks represent. We show that if theoretical spectra are constructed including b and y ions alone, then for sigma = 0.5, which is typical for high-throughput data, peptide chains of eight amino acids or longer can be identified based on the positions of peaks alone, at a rate of false identification below 1%. To discriminate between shorter peptides, additional (e.g., intensity-inferred) information is necessary. We derive the dependence of the probability of false identification on the number of peaks in the theoretical spectra and on the types of ions that the peaks represent. Our results suggest that the class of mass spectrum identification problems, for which more elaborate development of fragmentation rules (such as intensity model) is required, can be reduced to the problems that involve homologous peptides.     

Characterization of betaines using electrospray MS/MS

Betaines are an important class of naturally occurring compounds that function as compatible solutes or osmoprotectants. Because of the permanent positive charge on the quaternary ammonium moiety, mass spectrometric analysis has been approached by desorption methods, including fast atom bombardment and plasma desorption mass spectrometry. Here we show that electrospray ionization MS gives comparable results to plasma desorption MS for a range of authentic betaine standards and betaines purified from plant extracts by ion exchange chromatography. A distinct advantage of electrospray ionization MS over plasma desorption MS is the capability of obtaining product ion spectra via MS/MS of selected parent ions, and hence structural information to discriminate between ions of identical mass.     

MassSieve: Panning MS/MS peptide data for proteins

We present MassSieve, a Java‐based platform for visualization and parsimony analysis of single and comparative LC‐MS/MS database search engine results. The success of mass spectrometric peptide sequence assignment algorithms has led to the need for a tool to merge and evaluate the increasing data set sizes that result from LC‐MS/MS‐based shotgun proteomic experiments. MassSieve supports reports from multiple search engines with differing search characteristics, which can increase peptide sequence coverage and/or identify conflicting or ambiguous spectral assignments.     

High-throughput identification and screening of novel Methylobacterium species using whole-cell MALDI-TOF/MS analysis

Methylobacterium species are ubiquitous α-proteobacteria that reside in the phyllosphere and are fed by methanol that is emitted from plants. In this study, we applied whole-cell matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis (WC-MS) to evaluate the diversity of Methylobacterium species collected from a variety of plants. The WC-MS spectrum was reproducible through two weeks of cultivation on different media. WC-MS spectrum peaks of M. extorquens strain AM1 cells were attributed to ribosomal proteins, but those were not were also found. We developed a simple method for rapid identification based on spectra similarity. Using all available type strains of Methylobacterium species, the method provided a certain threshold similarity value for species-level discrimination, although the genus contains some type strains that could not be easily discriminated solely by 16S rRNA gene sequence similarity. Next, we evaluated the WC-MS data of approximately 200 methylotrophs isolated from various plants with MALDI Biotyper software (Bruker Daltonics). Isolates representing each cluster were further identified by 16S rRNA gene sequencing. In most cases, the identification by WC-MS matched that by sequencing, and isolates with unique spectra represented possible novel species. The strains belonging to M. extorquens, M. adhaesivum, M. marchantiae, M. komagatae, M. brachiatum, M. radiotolerans, and novel lineages close to M. adhaesivum, many of which were isolated from bryophytes, were found to be the most frequent phyllospheric colonizers. The WC-MS technique provides emerging high-throughputness in the identification of known/novel species of bacteria, enabling the selection of novel species in a library and identification without 16S rRNA gene sequencing.     

Comparison of MS/MS methods for protein identification from 2D-PAGE

We have compared the use of a low resolution MALDI-Ion Trap MS/MS and a high-resolution ESI-TOF-MS/MS for the analysis of spots from 2D gels. The main criteria were speed and accuracy of protein identification. The results obtained using the MALDI-MS/MS system are comparable to those from the LC-MS/MS system in terms of accuracy, but less low-level proteins are identified while the time required for the analysis is dramatically reduced.