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 using MS/MS data

The subject of this tutorial is protein identification and characterisation by database searching of MS/MS Data. Peptide Mass Fingerprinting is excluded because it is covered in a separate tutorial. Practical aspects of database searching are emphasised, such as choice of sequence database, effect of mass tolerance, and how to identify post-translational modifications. The relationship between sensitivity and specificity is discussed, as is the challenge of using peptide match information to infer which proteins were present in the sample. Since these tutorials are introductory in nature, most references are to reviews, rather than primary research papers. Some familiarity with mass spectrometry and protein chemistry is assumed. There is an accompanying slide presentation, including speaker notes, and a collection of web-based, practical exercises, designed to reinforce key points. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 6).     

A graph-based filtering method for top-down mass spectral identification

Background

Database search has been the main approach for proteoform identification by top-down tandem mass spectrometry. However, when the target proteoform that produced the spectrum contains post-translational modifications (PTMs) and/or mutations, it is quite time consuming to align a query spectrum against all protein sequences without any PTMs and mutations in a large database. Consequently, it is essential to develop efficient and sensitive filtering algorithms for speeding up database search.

Results

In this paper, we propose a spectrum graph matching (SGM) based protein sequence filtering method for top-down mass spectral identification. It uses the subspectra of a query spectrum to generate spectrum graphs and searches them against a protein database to report the best candidates. As the sequence tag and gaped tag approaches need the preprocessing step to extract and select tags, the SGM filtering method circumvents this preprocessing step, thus simplifying data processing. We evaluated the filtration efficiency of the SGM filtering method with various parameter settings on an Escherichia coli top-down mass spectrometry data set and compared the performances of the SGM filtering method and two tag-based filtering methods on a data set of MCF-7 cells.

Conclusions

Experimental results on the data sets show that the SGM filtering method achieves high sensitivity in protein sequence filtration. When coupled with a spectral alignment algorithm, the SGM filtering method significantly increases the number of identified proteoform spectrum-matches compared with the tag-based methods in top-down mass spectrometry data analysis.

     

Decoding protein modifications using top-down mass spectrometry

Top-down mass spectrometry is an emerging technology which strives to preserve the post-translationally modified forms of proteins present in vivo by measuring them intact, rather than measuring peptides produced from them by proteolysis. The top-down technology is beginning to capture the interest of biologists and mass spectrometrists alike, with a main goal of deciphering interaction networks operative in cellular pathways. Here we outline recent approaches and applications of top-down mass spectrometry as well as an outlook for its future.     

Protein domain identification and improved sequence similarity searching using PSI-BLAST

Protein sequences containing more than one structural domain are problematic when used in homology searches where they can either stop an iterative database search prematurely or cause an explosion of a search to common domains. We describe a method, DOMAINATION, that infers domains and their boundaries in a query sequence from local gapped alignments generated using PSI-BLAST. Through a new technique to recognize domain insertions and permutations, DOMAINATION submits delineated domains as successive database queries in further iterative steps. Assessed over a set of 452 multidomain proteins, the method predicts structural domain boundaries with an overall accuracy of 50% and improves finding distant homologies by 14% compared with PSI-BLAST. DOMAINATION is available as a web based tool at http://mathbio.nimr.mrc.ac.uk, and the source code is available from the authors upon request.     

Protein identification by liquid chromatography-mass spectrometry using retention time prediction

Liquid chromatography has been coupled with mass spectrometry to improve the dynamic range and to reduce the complexity of sample introduced to the mass spectrometer at any given time. The chromatographic separation also provides information on the analytes, such as peptides in enzymatic digests of proteins; information that can be used when identifying the proteins by peptide mass fingerprinting. This paper discusses a recently introduced method based on retention time prediction to extract information from chromatographic separations and the applications of this method to protein identification in organisms with small and large genomes.     

Protein structural domain identification

A simple method for the definition of protein structural domains is described that requires only alpha-carbon coordinate data. The basic method, which encodes no specific aspects of protein structure, captures the essence of most domains but does not give high enough priority to the integrity of beta-sheet structure. This aspect was encouraged both by a bias toward attaining intact beta-sheets and also as an acceptance condition on the final result. The method has only one variable parameter, reflecting the granularity level of the domains, and an attempt was made to set this level automatically for each protein based on the best agreement attained between the domains predicted on the native structure and a set of smoothed coordinates. While not perfect, this feature allowed some tightly packed domains to be separated that would have remained undivided had the best fixed granularity level been used. The quality of the results was high and, when compared with a large collection of accepted domain definitions, only a few could be said to be clearly incorrect. The simplicity of the method allowed its easy extension to the simultaneous definition of domains across related structures in a way that does not involve loss of detail through averaging the structures. This was found to be a useful approach to reconciling differences among structural family members. The method is fast, taking less than 1 s per 100 residues for medium-sized proteins.     

Protein structural class identification directly from NMR spectra using averaged chemical shifts

Knowledge of the three-dimensional structure of proteins is integral to understanding their functions, and a necessity in the era of proteomics. A wide range of computational methods is employed to estimate the secondary, tertiary, and quaternary structures of proteins. Comprehensive experimental methods, on the other hand, are limited to nuclear magnetic resonance (NMR) and X-ray crystallography. The full characterization of individual structures, using either of these techniques, is extremely time intensive. The demands of high throughput proteomics necessitate the development of new, faster experimental methods for providing structural information. As a first step toward such a method, we explore the possibility of determining the structural classes of proteins directly from their NMR spectra, prior to resonance assignment, using averaged chemical shifts. This is achieved by correlating NMR-based information with empirical structure-based information available in widely used electronic databases. The results are analyzed statistically for their significance. The robustness of the method as a structure predictor is probed by applying it to a set of proteins of unknown structure. Our results show that this NMR-based method can be used as a low-resolution tool for protein structural class identification.     

Protein separation and identification using magnetic beads encoded with surface-enhanced Raman spectroscopy

This article presents a prototype of a surface-enhanced Raman spectroscopy (SERS)-encoded magnetic bead of 8 μm diameter. The core part of the bead is composed of a magnetic nanoparticle (NP)-embedded sulfonated polystyrene bead. The outer part of the bead is embedded with Ag NPs on which labeling molecules generating specific SERS bands are adsorbed. A silica shell is fabricated for further bioconjugation and protection of SERS signaling. Benzenethiol, 4-mercaptotoluene, 2-naphthalenethiol, and 4-aminothiophenol are used as labeling molecules. The magnetic SERS beads are used as substrates for protein sensing and screening with easy handling. As a model application, streptavidin-bound magnetic SERS beads are used to illustrate selective separation in a flow cytometry system, and the screened beads are spectrally recognized by Raman spectroscopy. The proposed magnetic SERS beads are likely to be used as a versatile solid support for protein sensing and screening in multiple assay technology.     

Protein identification in complex mixtures

This paper investigates the prospects of successful mass spectrometric protein identification based on mass data from proteolytic digests of complex protein mixtures. Sets of proteolytic peptide masses representing various numbers of digested proteins in a mixture were generated in silico. In each set, different proteins were selected from a protein sequence collection and for each protein the sequence coverage was randomly selected within a particular regime (15-30% or 30-60%). We demonstrate that the Probity algorithm, which is characterized by an optimal tolerance for random interference, employed in an iterative procedure can correctly identify >95% of proteins at a desired significance level in mixtures composed of hundreds of yeast proteins under realistic mass spectrometric experimental constraints. By using a model of the distribution of protein abundance, we demonstrate that the very high efficiency of identification of protein mixtures that can be achieved by appropriate choices of informatics procedures is hampered by limitations of the mass spectrometric dynamic range. The results stress the desire to choose carefully experimental protocols for comprehensive proteome analysis, focusing on truly critical issues such as the dynamic range, which potentially limits the possibilities of identifying low abundance proteins.     

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.     

Protein profiling in human sera for identification of potential lung cancer biomarkers using antibody microarray

To identify potential biomarkers of lung cancer (LC), profiling of proteins in sera obtained from healthy and LC patients was determined using an antibody microarray. Based on our previous study on mRNA expression profiles between patients with LC and healthy persons, 19 proteins of interest were selected as targets for fabrication of an antibody microarray. Antibody to each protein and five nonspecific control antibodies were spotted onto a hydrogel‐coated glass slide and used for profiling of proteins in sera of LC patients in a two‐color fluorescence assay. Forty‐eight human sera samples were analyzed, and expression profiling of proteins were represented by the internally normalized ratio method. Six proteins were distinctly down‐regulated in sera of LC patients; this observation was validated by Wilcoxon test, false discovery rate, and Western blotting. Blind test of other 32 human sera using the antibody microarray followed by hierarchical clustering analysis revealed an approximate sensitivity of 88%, specificity of 80%, and an accuracy of 84%, respectively, in classifying the sera, which supports the potential of the six identified proteins as biomarkers for the prognosis of lung cancer.     

Protein patterns as codes for tumor identification

Particulate-fraction and soluble-fraction proteins from examples of breast carcinoma, Hodgkin's lymphoma, and colon adenocarcinoma and their corresponding normal tissues were resolved on sodium dodecyl sulfate-polyacrylamide gels. The Coomassie blue-stained protein patterns were quantitated using a scanning densitometer and were reduced to simple three-part numerical codes. The codes were (a) specific for each tissue and (b) in the examples of lymphoma and colon adenocarcinoma, indicated a strong similarity between tumors and their respective normal tissues.     

Protein identification pipeline for the homology-driven proteomics

Homology-driven proteomics is a major tool to characterize proteomes of organisms with unsequenced genomes. This paper addresses practical aspects of automated homology-driven protein identifications by LC-MS/MS on a hybrid LTQ Orbitrap mass spectrometer. All essential software elements supporting the presented pipeline are either hosted at the publicly accessible web server, or are available for free download.     

Goal-direction and top-down control

We review the neural mechanisms that support top-down control of behaviour and suggest that goal-directed behaviour uses two systems that work in concert. A basal ganglia-centred system quickly learns simple, fixed goal-directed behaviours while a prefrontal cortex-centred system gradually learns more complex (abstract or long-term) goal-directed behaviours. Interactions between these two systems allow top-down control mechanisms to learn how to direct behaviour towards a goal but also how to guide behaviour when faced with a novel situation.     

A top-down approach to classify enzyme functional classes and sub-classes using random forest

Advancements in sequencing technologies have witnessed an exponential rise in the number of newly found enzymes. Enzymes are proteins that catalyze bio-chemical reactions and play an important role in metabolic pathways. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. Hence, a need for a computing method is felt that can distinguish protein enzyme sequences from those of non-enzymes and reliably predict the function of the former. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been presented. But, these approaches are known to fail for proteins that perform the same function and are dissimilar in their sequence and structure. In this article, we present a supervised machine learning model to predict the function class and sub-class of enzymes based on a set of 73 sequence-derived features. The functional classes are as defined by International Union of Biochemistry and Molecular Biology. Using an efficient data mining algorithm called random forest, we construct a top-down three layer model where the top layer classifies a query protein sequence as an enzyme or non-enzyme, the second layer predicts the main function class and bottom layer further predicts the sub-function class. The model reported overall classification accuracy of 94.87% for the first level, 87.7% for the second, and 84.25% for the bottom level. Our results compare very well with existing methods, and in many cases report better performance. Using feature selection methods, we have shown the biological relevance of a few of the top rank attributes.