Error-tolerant EST database searches by tandem mass spectrometry and multiTag software

The MultiTag method (Sunyaev et al., Anal. Chem. 2003 15, 1307-1315) employs multiple error-tolerant searches with peptide sequence tags (Mann and Wilm, Anal. Chem. 1994, 66, 4390-4399) for the identification of proteins from organisms with unsequenced genomes. Here we demonstrate that the error-tolerant capabilities of MultiTag increased the number of peptide alignments and improved the confidence of identifications in an EST database. The MultiTag outperformed conventional database searching software that only utilizes stringent matching of tandem mass spectra to nucleotide sequences of ESTs.     

A database of unique protein sequence identifiers for proteome studies

In proteome studies, identification of proteins requires searching protein sequence databases. The public protein sequence databases (e.g., NCBInr, UniProt) each contain millions of entries, and private databases add thousands more. Although much of the sequence information in these databases is redundant, each database uses distinct identifiers for the identical protein sequence and often contains unique annotation information. Users of one database obtain a database-specific sequence identifier that is often difficult to reconcile with the identifiers from a different database. When multiple databases are used for searches or the databases being searched are updated frequently, interpreting the protein identifications and associated annotations can be problematic. We have developed a database of unique protein sequence identifiers called Sequence Globally Unique Identifiers (SEGUID) derived from primary protein sequences. These identifiers serve as a common link between multiple sequence databases and are resilient to annotation changes in either public or private databases throughout the lifetime of a given protein sequence. The SEGUID Database can be downloaded (http://bioinformatics.anl.gov/SEGUID/) or easily generated at any site with access to primary protein sequence databases. Since SEGUIDs are stable, predictions based on the primary sequence information (e.g., pI, Mr) can be calculated just once; we have generated approximately 500 different calculations for more than 2.5 million sequences. SEGUIDs are used to integrate MS and 2-DE data with bioinformatics information and provide the opportunity to search multiple protein sequence databases, thereby providing a higher probability of finding the most valid protein identifications.     

Antibody screening database for protein kinetic modeling

Knowledge-based proteomic studies rely on the availability of quality antibodies. The increasing number of commercially available antibodies covers a wide range of protein networks; however, performance of each antibody can vary, depending on what type of cells, treatments, and time points are studied. Here, we describe an antibody database in which we screened 279 antibodies against multiple cell lysates after various treatments and from different time points. We applied these quality-confirmed antibodies on protein arrays, showing their utility for protein kinetic modeling.     

Evaluation of an algorithm for the automated sequential assignment of protein backbone resonances: A demonstration of the connectivity tracing assignment tools (CONTRAST) software package

Summary The peptide sequential assignment algorithm presented here was implemented as a macro within the CONnectivity TRacing ASsignment Tools (CONTRAST) computer software package. The algorithm provides a semi- or fully automated global means of sequentially assigning the NMR backbone resonances of proteins. The program's performance is demonstrated here by its analysis of realistic computer-generated data for III^Glc, a 168-residue signal-transducing protein of Escherichia coli [Pelton et al. (1991) Biochemistry, 30, 10043–10057]. Missing experimental data (19 resonances) were generated so that a complete assignment set could be tested. The algorithm produces sequential assignments from appropriate peak lists of nD NMR data. It quantifies the ambiguity of each assignment and provides ranked alternatives. A best first approach, in which high-scoring local assignments are made before and in preference to lower scoring assignments, is shown to be superior (in terms of the current set of CONTRAST scoring routines) to approaches such as simulated annealing that seek to maximize the combined scores of the individual assignments. The robustness of the algorithm was tested by evaluating the effects of imposed frequency imprecision (scatter), added false signals (noise), missing peaks (incomplete data), and variation in userdefined tolerances on the performance of the algorithm.     

DigesTip: a new device for a rapid and efficient in-solution protein digestion

DigesTip is a new device for in-solution protein digestion, based on a patent pending technology, able to immobilize enzymes (trypsin, in this case) on a solid surface, keeping their activity preserved. DigesTip is a standard pipette tip, usable both by human and by robots. Its main performances are: very short digestion time (1 min) and usability with low protein sample concentrations (5 microg/mL). DigesTip obtains a clear signal in MS measurements and its usage rules out several preparative steps.     

A database for allergenic proteins and tools for allergenicity prediction

The AllergenPro database has developed a web-based system that will provide information about allergen in microbes, animals and plants. The database has three major parts and functions:(i) database list; (ii) allergen search; and (iii) allergenicity prediction. The database contains 2,434 allergens related information readily available in the database such as on allergens in rice microbes (712 records), animals (617 records) and plants (1,105 records). Furthermore, this database provides bioinformatics tools for allergenicity prediction. Users can search for specific allergens by various methods and can run tools for allergenicity prediction using three different methods.

Availability

The database is available for free at http://www.niab.go.kr/nabic/     

Automated methods for improved protein identification by peptide mass fingerprinting

In order to maximize protein identification by peptide mass fingerprinting noise peaks must be removed from spectra and recalibration is often required. The preprocessing of the spectra before database searching is essential but is time-consuming. Nevertheless, the optimal database search parameters often vary over a batch of samples. For high-throughput protein identification, these factors should be set automatically, with no or little human intervention. In the present work automated batch filtering and recalibration using a statistical filter is described. The filter is combined with multiple data searches that are performed automatically. We show that, using several hundred protein digests, protein identification rates could be more than doubled, compared to standard database searching. Furthermore, automated large-scale in-gel digestion of proteins with endoproteinase LysC, and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis, followed by subsequent trypsin digestion and MALDI-TOF analysis were performed. Several proteins could be identified only after digestion with one of the enzymes, and some less significant protein identifications were confirmed after digestion with the other enzyme. The results indicate that identification of especially small and low-abundance proteins could be significantly improved after sequential digestions with two enzymes.     

Including shared peptides for estimating protein abundances: A significant improvement for quantitative proteomics

Inferring protein abundances from peptide intensities is the key step in quantitative proteomics. The inference is necessarily more accurate when many peptides are taken into account for a given protein. Yet, the information brought by the peptides shared by different proteins is commonly discarded. We propose a statistical framework based on a hierarchical modeling to include that information. Our methodology, based on a simultaneous analysis of all the quantified peptides, handles the biological and technical errors as well as the peptide effect. In addition, we propose a practical implementation suitable for analyzing large data sets. Compared to a method based on the analysis of one protein at a time (that does not include shared peptides), our methodology proved to be far more reliable for estimating protein abundances and testing abundance changes. The source codes are available at http://pappso.inra.fr/bioinfo/all_p/.     

A new scoring function for top-down spectral deconvolution

Background

Top-down mass spectrometry plays an important role in intact protein identification and characterization. Top-down mass spectra are more complex than bottom-up mass spectra because they often contain many isotopomer envelopes from highly charged ions, which may overlap with one another. As a result, spectral deconvolution, which converts a complex top-down mass spectrum into a monoisotopic mass list, is a key step in top-down spectral interpretation.

Results

In this paper, we propose a new scoring function, L-score, for evaluating isotopomer envelopes. By combining L-score with MS-Deconv, a new software tool, MS-Deconv+, was developed for top-down spectral deconvolution. Experimental results showed that MS-Deconv+ outperformed existing software tools in top-down spectral deconvolution.

Conclusions

L-score shows high discriminative ability in identification of isotopomer envelopes. Using L-score, MS-Deconv+ reports many correct monoisotopic masses missed by other software tools, which are valuable for proteoform identification and characterization.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1140) contains supplementary material, which is available to authorized users.     

Direct visualization and profiling of protein misfolding and aggregation in live cells

Over the past few years, research tools have been developed to monitor the multistep protein aggregation process in live cells, a process that has been associated with a growing number of human diseases. Herein, we describe recent advances in methods that can either survey the distribution of aggregation at the level of the cellular proteome using mass spectroscopy or discern the multistep aggregation process of specific proteins of interest via fluorescence signals. Future development and application of such technologies are expected to provide insights on mechanisms, diagnosis, and treatment of diseases rooted in protein aggregation.     

Differentially isotope-coded N-terminal protein sulphonation: combining protein identification and quantification

Most proteomic labelling technologies intend to improve protein quantification and/or facilitate (de novo) peptide sequencing. We present here a novel stable-isotope labelling method to simultaneously identify and quantify protein components in complex mixtures by specifically derivatizing the N-terminus of proteins with 4-sulphophenyl isothiocyanate (SPITC). Our approach combines protein identification with quantification through differential isotope-coded labelling at the protein N-terminus prior to digestion. The isotope spacing of 6 Da (unlabelled vs. six-fold 13C-labelled tag) between derivatized peptide pairs enables the detection on different MS platforms (MALDI and ESI). Optimisation of the reaction conditions using SPITC was performed on three model proteins. Improved detection of the N-terminally derivatized peptide compared to the native analogue was observed in negative-ion MALDI-MS. Simpler fragmentation patterns compared to native peptides facilitated protein identification. The 13C-labelled SPITC resulted in convenient peptide pair spacing without isotopic overlap and hence facilitated relative quantification by MALDI-TOF/TOF and LC-ESI-MS/MS. The combination of facilitated identification and quantification achieved by differentially isotope-coded N-terminal protein tagging with light/heavy SPITC represents, to our knowledge, a new approach to quantitative proteomics.     

Evaluation of algorithms for protein identification from sequence databases using mass spectrometry data

In this work, the commonly used algorithms for mass spectrometry based protein identification, Mascot, MS-Fit, ProFound and SEQUEST, were studied in respect to the selectivity and sensitivity of their searches. The influence of various search parameters were also investigated. Approximately 6600 searches were performed using different search engines with several search parameters to establish a statistical basis. The applied mass spectrometric data set was chosen from a current proteome study. The huge amount of data could only be handled with computational assistance. We present a software solution for fully automated triggering of several peptide mass fingerprinting (PMF) and peptide fragmentation fingerprinting (PFF) algorithms. The development of this high-throughput method made an intensive evaluation based on data acquired in a typical proteome project possible. Previous evaluations of PMF and PFF algorithms were mainly based on simulations.     

The maize two-dimensional gel protein database: towards an integrated genome analysis program

This paper describes the first maize database of proteins separated by two-dimensional electrophoresis. Fifty-six coleoptile proteins and 18 leaf proteins from two maize lines were partially microsequenced. Thirty-six proteins (49%) displayed high similarity with database proteins. Nine of these proteins, representing five different functions, had never been described in maize. No conclusive function could be found for 45 polypeptides (61% of the microsequenced proteins). In addition, an alternative identification method, based on amino acid analysis, allowed candidates to be proposed for 17 proteins out of 44 additional proteins analyzed in the coleoptiles. These results are stored in a database which also includes, when available, genetic information about the chromosomal location of structural genes and regulatory factors of proteins. This database is being used in the context of a project on the genetic mapping of the expressed genome in maize.     

A novel tandem affinity purification strategy for the efficient isolation and characterisation of native protein complexes

Isolation and dissection of native multiprotein complexes is a central theme in functional genomics. The development of the tandem affinity purification (TAP) tag has enabled an efficient and large-scale purification of native protein complexes. However, the TAP tag features a size of 21 kDa and requires time consuming cleavage. By combining a tandem Strep-tag II with a FLAG-tag we were able to reduce the size of the TAP (SF-TAP) tag to 4.6 kDa. Both moieties have a medium affinity and avidity to their immobilised binding partners. This allows the elution of SF-tagged proteins under native conditions using desthiobiotin in the first step and the FLAG octapeptide in the second step. The SF-TAP protocol represents an efficient, fast and straightforward purification of protein complexes from mammalian cells within 2.5 h. The power of this novel method is demonstrated by the purification of Raf associated protein complexes from HEK293 cells and subsequent analysis of their protein interaction network by dissection of interaction patterns from the Raf binding partners MEK1 and 14-3-3.     

Functional analysis tools for post‐translational modification: a post‐translational modification database for analysis of proteins and metabolic pathways

Post‐translational modifications (PTMs) are critical regulators of protein function, and nearly 200 different types of PTM have been identified. Advances in high‐resolution mass spectrometry have led to the identification of an unprecedented number of PTM sites in numerous organisms, potentially facilitating a more complete understanding of how PTMs regulate cellular behavior. While databases have been created to house the resulting data, most of these resources focus on individual types of PTM, do not consider quantitative PTM analyses or do not provide tools for the visualization and analysis of PTM data. Here, we describe the Functional Analysis Tools for Post‐Translational Modifications (FAT‐PTM) database ( https://bioinformatics.cse.unr.edu/fat-ptm/ ), which currently supports eight different types of PTM and over 49 000 PTM sites identified in large‐scale proteomic surveys of the model organism Arabidopsis thaliana. The FAT‐PTM database currently supports tools to visualize protein‐centric PTM networks, quantitative phosphorylation site data from over 10 different quantitative phosphoproteomic studies, PTM information displayed in protein‐centric metabolic pathways and groups of proteins that are co‐modified by multiple PTMs. Overall, the FAT‐PTM database provides users with a robust platform to share and visualize experimentally supported PTM data, develop hypotheses related to target proteins or identify emergent patterns in PTM data for signaling and metabolic pathways.     

Reproducibility of mass spectrometry based protein profiles for diagnosis of ovarian cancer across clinical studies: A systematic review

The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

myProMS, a web server for management and validation of mass spectrometry-based proteomic data

Curation and interpretation of protein databank-search results by human experts are key aspects of MS-based proteomic data acquisition. These tasks are often overlooked due to the vast amount of data to inspect. We have developed myProMS, a web server designed to ease search results validation and interpretation by improving data organization, mining and sharing between MS specialists and biologists during MS-based collaborative projects. A demo is accessible at http://bioinfo.curie.fr/myproms.     

Phosphorylation of human high mobility group N1 protein by protein kinase CK2

High mobility group (HMG) N1 protein, formerly known as HMG 14, is a member of the chromosomal HMG protein family. Protein kinase CK2 was previously reported to be able to phosphorylate bovine HMGN1 in vitro; Ser89 and Ser99, corresponding to Ser88 and Ser98 in human HMGN1, were shown to be major and minor recognition sites, respectively. In this report, we employed mass spectrometry and examined both the extent and the sites of phosphorylation in HMGN1 protein catalyzed by recombinant human protein kinase CK2. We found that five serine residues, i.e., Ser6, Ser7, Ser85, Ser88, and Ser98, in HMGN1 can be phosphorylated by the kinase in vitro. All five sites were previously shown to be phosphorylated in MCF-7 human breast cancer cells in vivo. Among these five sites, Ser6, Ser7, and Ser85 were new sites of phosphorylation induced by protein kinase CK2 in vitro.     

Peptide enrichment and protein fractionation using selective electrophoresis

In recent times, the analysis of the peptidome has become increasingly valuable to gain a better understanding of the critical roles native peptides play in biological processes. Here, we show a technique using a novel electrophoretic device named MF10, for the fractionation of proteins and peptides based on size and also pH in low volume liquid phase under an electric field. A 1 microM, 7-protein and peptide standard mix ranging from 1 to 25 kDa has been used to show peptide migration into a fraction contained by 1-5 kDa membranes. Simultaneous fractionation of the higher mass protein standards to the correct fraction also occurred. To assess the MF10's ability to fractionate more complex samples, human plasma was used to enrich for the peptidome below 5 kDa in the presence of the proteome. Peptide enrichment was achieved while simultaneously fractionating higher mass proteins to three other mass restricted fractions. The utility of this approach is demonstrated with the identification (with at least 2 ppm mass accuracy) of 76 unique peptides, equating to 22 proteins enriched to the 1-5 kDa fraction of the MF10.