Current trends in computational inference from mass spectrometry-based proteomics

Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.     

Proteomic studies in plants

Proteomics is a leading technology for the high-throughput analysis of proteins on a genome-wide scale. With the completion of genome sequencing projects and the development of analytical methods for protein characterization, proteomics has become a major field of functional genomics. The initial objective of proteomics was the large-scale identification of all protein species in a cell or tissue. The applications are currently being extended to analyze various functional aspects of proteins such as post-translational modifications, protein-protein interactions, activities and structures. Whereas the proteomics research is quite advanced in animals and yeast as well as Escherichia coli, plant proteomics is only at the initial phase. Major studies of plant proteomics have been reported on subcellular proteomes and protein complexes (e.g. proteins in the plasma membranes, chloroplasts, mitochondria and nuclei). Here several plant proteomics studies will be presented, followed by a recent work using multidimensional protein identification technology (MudPIT).     

Cardiovascular proteomic analysis

Here, we report on our proteomic studies in the field of cardiovascular medicine. Our research has been focused on understanding the role of proteins in cardiovascular disease with a particular focus on epigenetic regulation and biomarker discovery, with the objective of better understanding cardiovascular pathophysiology to lead to the development of new and better diagnostic and therapeutic methods. We have used mass spectrometry for over 5 years as a viable method to investigate protein-protein interactions and post-translational modifications in cellular proteins as well as a method to investigate the role of extra-cellular proteins. Use of mass spectrometry not only as a research tool but also as a potential diagnostic tool is a topic of interest. In addition to these functional proteomics studies, structural proteomic studies are also done with expectations to allow for pinpoint drug design and therapeutic intervention. Collectively, our proteomics studies are focused on understanding the functional role and potential therapeutically exploitable property of proteins in cardiovascular disease from both intra-cellular and extra-cellular aspects with both functional as well as structural proteomics approaches to allow for comprehensive analysis.     

Design and initial characterization of the SC-200 proteomics standard mixture

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate?=?1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.     

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false-positive protein interactions present in unfiltered data sets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS data sets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.     

Proteome analysis at the level of subcellular structures.

The targeting of proteins to particular subcellular sites is an important principle of the functional organization of cells at the molecular level. In turn, knowledge about the subcellular localization of a protein is a characteristic that may provide a hint as to the function of the protein. The combination of classic biochemical fractionation techniques for the enrichment of particular subcellular structures with the large-scale identification of proteins by mass spectrometry and bioinformatics provides a powerful strategy that interfaces cell biology and proteomics, and thus is termed 'subcellular proteomics'. In addition to its exceptional power for the identification of previously unknown gene products, the analysis of proteins at the subcellular level is the basis for monitoring important aspects of dynamic changes in the proteome such as protein transloction. This review summarizes data from recent subcellular proteomics studies with an emphasis on the type of data that can retrieved from such studies depending on the design of the analytical strategy.     

Systems approach to explore components and interactions in the presynapse

The application of proteomic techniques to neuroscientific research provides an opportunity for a greater understanding of nervous system structure and function. As increasing amounts of neuroproteomic data become available, it is necessary to formulate methods to integrate these data in a meaningful way to obtain a more comprehensive picture of neuronal subcompartments. Furthermore, computational methods can be used to make biologically relevant predictions from large proteomic data sets. Here, we applied an integrated proteomics and systems biology approach to characterize the presynaptic (PRE) nerve terminal. For this, we carried out proteomic analyses of presynaptically enriched fractions, and generated a PRE literature‐based protein–protein interaction network. We combined these with other proteomic analyses to generate a core list of 117 PRE proteins, and used graph theory‐inspired algorithms to predict 92 additional components and a PRE complex containing 17 proteins. Some of these predictions were validated experimentally, indicating that the computational analyses can identify novel proteins and complexes in a subcellular compartment. We conclude that the combination of techniques (proteomics, data integration, and computational analyses) used in this study are useful in obtaining a comprehensive understanding of functional components, especially low‐abundance entities and/or interactions in the PRE nerve terminal.     

New dimensions in the study of protein complexes using quantitative mass spectrometry

Mass spectrometry combined with affinity purification techniques has evolved as a prime tool for the in-depth study of distinct protein complexes and protein-protein interactions. It fueled proteome-wide studies leading to the establishment of intricate cellular protein interaction networks. Recent innovative advancements in quantitative protein mass spectrometry act as driving force for the design of ingenious strategies in interaction proteomics facilitating the acquisition of interaction data with improved accuracy and, most intriguingly, the elucidation of functional aspects by monitoring transient interactions as well as dynamic changes in composition, stoichiometry, localization and post-translational modification of protein complexes under various conditions.     

Combinatorial depletion analysis to assemble the network architecture of the SAGA and ADA chromatin remodeling complexes

Despite the availability of several large‐scale proteomics studies aiming to identify protein interactions on a global scale, little is known about how proteins interact and are organized within macromolecular complexes. Here, we describe a technique that consists of a combination of biochemistry approaches, quantitative proteomics and computational methods using wild‐type and deletion strains to investigate the organization of proteins within macromolecular protein complexes. We applied this technique to determine the organization of two well‐studied complexes, Spt–Ada–Gcn5 histone acetyltransferase (SAGA) and ADA, for which no comprehensive high‐resolution structures exist. This approach revealed that SAGA/ADA is composed of five distinct functional modules, which can persist separately. Furthermore, we identified a novel subunit of the ADA complex, termed Ahc2, and characterized Sgf29 as an ADA family protein present in all Gcn5 histone acetyltransferase complexes. Finally, we propose a model for the architecture of the SAGA and ADA complexes, which predicts novel functional associations within the SAGA complex and provides mechanistic insights into phenotypical observations in SAGA mutants.     

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

Exploring sequence-structure relationships in the tyrosine kinome space: functional classification of the binding specificity mechanisms for cancer therapeutics

MOTIVATION: Evolutionary and structural conservation patterns shared by more than 500 of identified protein kinases have led to complex sequence-structure relationships of cross-reactivity for kinase inhibitors. Understanding the molecular basis of binding specificity for protein kinases family, which is the central problem in discovery of cancer therapeutics, remains challenging as the inhibitor selectivity is not readily interpreted from chemical proteomics studies, neither it is easily discernable directly from sequence or structure information. We present an integrated view of sequence-structure-binding relationships in the tyrosine kinome space in which evolutionary analysis of the kinases binding sites is combined with computational proteomics profiling of the inhibitor-protein interactions. This approach provides a functional classification of the binding specificity mechanisms for cancer agents targeting protein tyrosine kinases. RESULTS: The proposed functional classification of the kinase binding specificities explores mechanisms in which structural plasticity of the tyrosine kinases and sequence variation of the binding-site residues are linked with conformational preferences of the inhibitors in achieving effective drug binding. The molecular basis of binding specificity for tyrosine kinases may be largely driven by conformational adaptability of the inhibitors to an ensemble of structurally different conformational states of the enzyme, rather than being determined by their phylogenetic proximity in the kinome space or differences in the interactions with the variable binding-site residues. This approach provides a fruitful functional linkage between structural bioinformatics analysis and disease by unraveling the molecular basis of kinase selectivity for the prominent kinase drugs (Imatinib, Dasatinib and Erlotinib) which is consistent with structural and proteomics experiments.     

Chemical proteomics and functional proteomics strategies for protein kinase inhibitor validation and protein kinase substrate identification: Applications to protein kinase CK2

Since protein kinases have been implicated in numerous human diseases, kinase inhibitors have emerged as promising therapeutic agents. Despite this promise, there has been a relative lag in the development of unbiased strategies to validate both inhibitor specificity and the ability to inhibit target activity within living cells. To overcome these limitations, our efforts have been focused on the development of systematic strategies that employ chemical and functional proteomics. We utilized these strategies to evaluate small molecule inhibitors of protein kinase CK2, a constitutively active kinase that has recently emerged as target for anti-cancer therapy in clinical trials. Our chemical proteomics strategies used ATP or CK2 inhibitors immobilized on sepharose beads together with mass spectrometry to capture and identify binding partners from cell extracts. These studies have verified that interactions between CK2 and its inhibitors occur in complex mixtures. However, in the case of CK2 inhibitors related to 4,5,6,7-tetrabromo-1H-benzotriazole (TBB), our work has also revealed off-targets for the inhibitors. To complement these studies, we devised functional proteomics approaches to identify proteins that exhibit decreases in phosphorylation when cells are treated with CK2 inhibitors. To identify and validate those proteins that are direct substrates for CK2, we have also employed mutants of CK2 with decreased inhibitor sensitivity. Overall, our studies have yielded systematic platforms for studying CK2 inhibitors which we believe will foster efforts to define the biological functions of CK2 and to rigorously investigate its potential as a candidate for molecular-targeted therapy. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases (2012).     

Synergistic computational and experimental proteomics approaches for more accurate detection of active serine hydrolases in yeast

An analysis of the structurally and catalytically diverse serine hydrolase protein family in the Saccharomyces cerevisiae proteome was undertaken using two independent but complementary, large-scale approaches. The first approach is based on computational analysis of serine hydrolase active site structures; the second utilizes the chemical reactivity of the serine hydrolase active site in complex mixtures. These proteomics approaches share the ability to fractionate the complex proteome into functional subsets. Each method identified a significant number of sequences, but 15 proteins were identified by both methods. Eight of these were unannotated in the Saccharomyces Genome Database at the time of this study and are thus novel serine hydrolase identifications. Three of the previously uncharacterized proteins are members of a eukaryotic serine hydrolase family, designated as Fsh (family of serine hydrolase), identified here for the first time. OVCA2, a potential human tumor suppressor, and DYR-SCHPO, a dihydrofolate reductase from Schizosaccharomyces pombe, are members of this family. Comparing the combined results to results of other proteomic methods showed that only four of the 15 proteins were identified in a recent large-scale, "shotgun" proteomic analysis and eight were identified using a related, but similar, approach (neither identifies function). Only 10 of the 15 were annotated using alternate motif-based computational tools. The results demonstrate the precision derived from combining complementary, function-based approaches to extract biological information from complex proteomes. The chemical proteomics technology indicates that a functional protein is being expressed in the cell, while the computational proteomics technology adds details about the specific type of function and residue that is likely being labeled. The combination of synergistic methods facilitates analysis, enriches true positive results, and increases confidence in novel identifications. This work also highlights the risks inherent in annotation transfer and the use of scoring functions for determination of correct annotations.     

Characterisation of protein microheterogeneity and protein complexes using on-chip immunoaffinity purification-mass spectrometry.

Post-translational modification of proteins may influence their interactions with other plasma proteins, as well as having an effect on many aspects of the metabolism of the protein, such as receptor binding, tissue uptake, degradation and excretion. Many post-translational modifications occur in a physiological context, while others are specific for certain diseases, which is why they are of diagnostic importance in clinical proteomics. Analytical approaches to the study of post-translational modifications and protein complexes through the combined use of on-chip immunological affinity purification on a surface-enhanced laser desorption/ionisation platform and subsequent mass spectrometry are illustrated in the author's own work relating to plasma transthyretin (TTR) and retinol-binding protein (RBP). In those studies, both the aspects of post-translational modifications of TTR and the formation of a protein complex between TTR and RBP have been discussed. Such aspects are of diagnostic interest in clinical proteomics, especially with regard to the modification of TTR in relation to the occurrence of amyloidotic diseases.     

Proteomics approaches to study genetic and metabolic disorders

Several proteomics approaches to study different aspects of genetic and metabolic diseases are presented. The choice of technique is strongly dependent on the biological question to be addressed and the availability and amount of sample. In general, there are three approaches that may be used to study genetic and metabolic diseases: protein profiling of complex biological samples, identification of affected proteins, or a functional proteomics approach to study protein interactions and function.     

Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions

Background  

In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions. Qualitative labels are essential for the molecular biologist to experimentally verify predicted interactions.