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

Seed proteomics

Rather than providing a single specific protocol, the inclusive area of seed proteomics is reviewed; methods are described and compared and primary literature citations are provided. The limitations and challenges of proteomics as an approach to study seed biology are emphasized. The proteomic analysis of seeds encounters some specific problems that do not impinge on analyses of other plant cells, tissues, or organs. There are anatomic considerations. Seeds comprise the seed coat, the storage organ(s), and the embryonic axis. Are these to be studied individually or as a composite? The physiological status of the seeds must be considered; developing, mature, or germinating? If mature, are they quiescent or dormant? If mature and quiescent, then orthodox or recalcitrant? The genetic uniformity of the population of seeds being compared must be considered. Finally, seeds are protein-rich and the extreme abundance of the storage proteins results in a study-subject with a dynamic range that spans several orders of magnitude. This represents a problem that must be dealt with if the study involves analysis of proteins that are of "normal" to low abundance. Several different methods of prefractionation are described and the results compared.

SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software

Significance Analysis of INTeractome (SAINT) is a statistical method for probabilistically scoring protein-protein interaction data from affinity purification-mass spectrometry (AP-MS) experiments. The utility of the software has been demonstrated in many protein-protein interaction mapping studies, yet the extensive testing also revealed some practical drawbacks. In this paper, we present a new implementation, SAINTexpress, with simpler statistical model and quicker scoring algorithm, leading to significant improvements in computational speed and sensitivity of scoring. SAINTexpress also incorporates external interaction data to compute supplemental topology-based scores to improve the likelihood of identifying co-purifying protein complexes in a probabilistically objective manner. Overall, these changes are expected to improve the performance and user experience of SAINT across various types of high quality datasets.

BIOLOGICAL SIGNIFICANCE

We present SAINTexpress, an upgraded implementation of Significance Analysis of INTeractome (SAINT) for filtering high confidence interaction data from affinity purification-mass spectrometry (AP-MS) experiments. SAINTexpress features faster computation and incorporation of external data sources into the scoring, improving the performance and user experience of SAINT across various types of datasets. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis

Background

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts.

Results

We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance.

Conclusions

Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely possible. Fruitful future directions may include investigating more sophisticated schemes for converting spectral counts to probabilities and applying the framework to direct protein complex prediction methods.

Identifying protein complexes based on multiple topological structures in PPI networks

Various computational algorithms are developed to identify protein complexes based on only one of specific topological structures in protein-protein interaction (PPI) networks, such as cliques, dense subgraphs, core-attachment structures and starlike structures. However, protein complexes exhibit intricate connections in a PPI network. They cannot be fully detected by only single topological structure. In this paper, we propose an algorithm based on multiple topological structures to identify protein complexes from PPI networks. In the proposed algorithm, four single topological structure based algorithms are first employed to identify raw predictions with specific topological structures, respectively. Those raw predictions are trimmed according to their topological information or GO annotations. Similar results are carefully merged before generating final predictions. Numerical experiments are conducted on a yeast PPI network of DIP and a human PPI network of HPRD. The predicted results show that the multiple topological structure based algorithm can not only obtain a more number of predictions, but also generate results with high accuracy in terms of f-score, matching with known protein complexes and functional enrichments with GO.

The CRAPome: a contaminant repository for affinity purification-mass spectrometry data

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/.

The restricted metabolism of the obligate organohalide respiring bacterium Dehalobacter restrictus: lessons from tiered functional genomics

Dehalobacter restrictus strain PER-K23 is an obligate organohalide respiring bacterium, which displays extremely narrow metabolic capabilities. It grows only via coupling energy conservation to anaerobic respiration of tetra- and trichloroethene with hydrogen as sole electron donor. Dehalobacter restrictus represents the paradigmatic member of the genus Dehalobacter, which in recent years has turned out to be a major player in the bioremediation of an increasing number of organohalides, both in situ and in laboratory studies. The recent elucidation of the D. restrictus genome revealed a rather elaborate genome with predicted pathways that were not suspected from its restricted metabolism, such as a complete corrinoid biosynthetic pathway, the Wood-Ljungdahl (WL) pathway for CO2 fixation, abundant transcriptional regulators and several types of hydrogenases. However, one important feature of the genome is the presence of 25 reductive dehalogenase genes, from which so far only one, pceA, has been characterized on genetic and biochemical levels. This study describes a multi-level functional genomics approach on D. restrictus across three different growth phases. A global proteomic analysis allowed consideration of general metabolic pathways relevant to organohalide respiration, whereas the dedicated genomic and transcriptomic analysis focused on the diversity, composition and expression of genes associated with reductive dehalogenases.

Defining the extracellular matrix using proteomics

The cell microenvironment has a profound influence on the behaviour, growth and survival of cells. The extracellular matrix (ECM) provides not only mechanical and structural support to cells and tissues but also binds soluble ligands and transmembrane receptors to provide spatial coordination of signalling processes. The ability of cells to sense the chemical, mechanical and topographical features of the ECM enables them to integrate complex, multiparametric information into a coherent response to the surrounding microenvironment. Consequently, dysregulation or mutation of ECM components results in a broad range of pathological conditions. Characterization of the composition of ECM derived from various cells has begun to reveal insights into ECM structure and function, and mechanisms of disease. Proteomic methodologies permit the global analysis of subcellular systems, but extracellular and transmembrane proteins present analytical difficulties to proteomic strategies owing to the particular biochemical properties of these molecules. Here, we review advances in proteomic approaches that have been applied to furthering our understanding of the ECM microenvironment. We survey recent studies that have addressed challenges in the analysis of ECM and discuss major outcomes in the context of health and disease. In addition, we summarize efforts to progress towards a systems-level understanding of ECM biology.

Proteomic approaches to the study of papillomavirus-host interactions

The identification of interactions between viral and host cellular proteins has provided major insights into papillomavirus research, and these interactions are especially relevant to the role of papillomaviruses in the cancers with which they are associated. Recent advances in mass spectrometry technology and data processing now allow the systematic identification of such interactions. This has led to an improved understanding of the different pathologies associated with the many papillomavirus types, and the diverse nature of these viruses is reflected in the spectrum of interactions with host proteins. Here we review a history of proteomic approaches, particularly as applied to the papillomaviruses, and summarize current techniques. Current proteomic studies on the papillomaviruses use yeast-two-hybrid or affinity purification-mass spectrometry approaches. We detail the advantages and disadvantages of each and describe current examples of papillomavirus proteomic studies, with a particular focus on the HPV E6 and E7 oncoproteins.

Plant protein interactomes

Protein-protein interactions are a critical element of biological systems, and the analysis of interaction partners can provide valuable hints about unknown functions of a protein. In recent years, several large-scale protein interaction studies have begun to unravel the complex networks through which plant proteins exert their functions. Two major classes of experimental approaches are used for protein interaction mapping: analysis of direct interactions using binary methods such as yeast two-hybrid or split ubiquitin, and analysis of protein complexes through affinity purification followed by mass spectrometry. In addition, bioinformatics predictions can suggest interactions that have evaded detection by other methods or those of proteins that have not been investigated. Here we review the major approaches to construct, analyze, use, and carry out quality control on plant protein interactome networks. We present experimental and computational approaches for large-scale mapping, methods for validation or smaller-scale functional studies, important bioinformatics resources, and findings from recently published large-scale plant interactome network maps.

Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT

Significance Analysis of INTeractome (SAINT) is a software package for scoring protein-protein interactions based on label-free quantitative proteomics data (e.g., spectral count or intensity) in affinity purification-mass spectrometry (AP-MS) experiments. SAINT allows bench scientists to select bona fide interactions and remove nonspecific interactions in an unbiased manner. However, there is no 'one-size-fits-all' statistical model for every dataset, since the experimental design varies across studies. Key variables include the number of baits, the number of biological replicates per bait, and control purifications. Here we give a detailed account of input data format, control data, selection of high-confidence interactions, and visualization of filtered data. We explain additional options for customizing the statistical model for optimal filtering in specific datasets. We also discuss a graphical user interface of SAINT in connection to the LIMS system ProHits, which can be installed as a virtual machine on Mac OS X or Windows computers.

Next-generation proteomics: towards an integrative view of proteome dynamics

Next-generation sequencing allows the analysis of genomes, including those representing disease states. However, the causes of most disorders are multifactorial, and systems-level approaches, including the analysis of proteomes, are required for a more comprehensive understanding. The proteome is extremely multifaceted owing to splicing and protein modifications, and this is further amplified by the interconnectivity of proteins into complexes and signalling networks that are highly divergent in time and space. Proteome analysis heavily relies on mass spectrometry (MS). MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis. Here we describe this emerging next generation of proteomics and highlight recent applications.

Label-free quantitative proteomics trends for protein-protein interactions

Understanding protein interactions within the complexity of a living cell is challenging, but techniques coupling affinity purification and mass spectrometry have enabled important progress to be made in the past 15 years. As identification of protein-protein interactions is becoming easier, the quantification of the interaction dynamics is the next frontier. Several quantitative mass spectrometric approaches have been developed to address this issue that vary in their strengths and weaknesses. While isotopic labeling approaches continue to contribute to the identification of regulated interactions, techniques that do not require labeling are becoming increasingly used in the field. Here, we describe the major types of label-free quantification used in interaction proteomics, and discuss the relative merits of data dependent and data independent acquisition approaches in label-free quantification. This article is part of a Special Issue entitled: From protein structures to clinical applications.

Affinity-purification coupled to mass spectrometry: basic principles and strategies

Identifying the interactions established by a protein of interest can be a critical step in understanding its function. This is especially true when an unknown protein of interest is demonstrated to physically interact with proteins of known function. While many techniques have been developed to characterize protein-protein interactions, one strategy that has gained considerable momentum over the past decade for identification and quantification of protein-protein interactions, is affinity-purification followed by mass spectrometry (AP-MS). Here, we briefly review the basic principles used in affinity-purification coupled to mass spectrometry, with an emphasis on tools (both biochemical and computational), which enable the discovery and reporting of high quality protein-protein interactions.

Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments

Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., co-occurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability. The scoring methods and platforms described include: CompPASS, SAINT, Decontaminator, MINT, IntAct, STRING, and FunCoup. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing interdisciplinary field of investigation.

Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell

Mapping and understanding of the protein interaction networks with their key modules and hubs can provide deeper insights into the molecular machinery underlying complex phenotypes. In this article, we present the basic characteristics and definitions of protein networks, starting with a distinction of the different types of associations between proteins. We focus the review on protein-protein interactions (PPIs), a subset of associations defined as physical contacts between proteins that occur by selective molecular docking in a particular biological context. We present such definition as opposed to other types of protein associations derived from regulatory, genetic, structural or functional relations. To determine PPIs, a variety of binary and co-complex methods exist; however, not all the technologies provide the same information and data quality. A way of increasing confidence in a given protein interaction is to integrate orthogonal experimental evidences. The use of several complementary methods testing each single interaction assesses the accuracy of PPI data and tries to minimize the occurrence of false interactions. Following this approach there have been important efforts to unify primary databases of experimentally proven PPIs into integrated databases. These meta-databases provide a measure of the confidence of interactions based on the number of experimental proofs that report them. As a conclusion, we can state that integrated information allows the building of more reliable interaction networks. Identification of communities, cliques, modules and hubs by analysing the topological parameters and graph properties of the protein networks allows the discovery of central/critical nodes, which are candidates to regulate cellular flux and dynamics.

Mass spectrometry approaches to study mammalian kinase and phosphatase associated proteins

Reversible phosphorylation events regulate critical aspects of cellular biology by affecting protein conformation, cellular localization, enzymatic activity and associations with interaction partners. Kinases and phosphatases interact not only with their substrates but also with regulatory subunits and other proteins, including scaffolds. In recent years, affinity purification coupled to mass spectrometry (AP-MS) has proven to be a powerful tool to identify protein-protein interactions (PPIs) involving kinases and phosphatases. In this review we outline general considerations for successful AP-MS, and describe strategies that we have used to characterize the interactions of kinases and phosphatases in human cells.