Molecular interaction databases are playing an ever more important role in our understanding of the biology of the cell. An increasing number of resources exist to provide these data and many of these have adopted the controlled vocabularies and agreed-upon standardized data formats produced by the Molecular Interaction workgroup of the Human Proteome Organization Proteomics Standards Initiative (HUPO PSI-MI). Use of these standards allows each resource to establish PSICQUIC service, making data from multiple resources available to the user in response to a single query. This cooperation between databases has been taken a stage further, with the establishment of the IMEx consortium which aims to maximize the curation power of numerous data resources, and provide the user with a non-redundant, consistently annotated set of interaction data.

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.

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 datasets. 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 datasets 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.

Mass spectrometry analysis of intact protein complexes has emerged as an established technology for assessing the composition and connectivity within dynamic, heterogeneous multiprotein complexes at low concentrations and in the context of mixtures. As this technology continues to move forward, one of the main challenges is to integrate the information content of such intact protein complex measurements with other mass spectrometry approaches in structural biology. Methods such as H/D exchange, oxidative foot-printing, chemical cross-linking, affinity purification, and ion mobility separation add complementary information that allows access to every level of protein structure and organization. Here, we survey the structural information that can be retrieved by such experiments, demonstrate the applicability of integrative mass spectrometry approaches in structural proteomics, and look to the future to explore upcoming innovations in this rapidly-advancing area.

Our ability to study protein-protein interactions has grown by leaps and bounds in recent years, enabling numerous large-scale studies to be performed in a variety of organisms. Despite this success, some classes of proteins, including those bound to chromatin, remain difficult to characterize through proteomic approaches. Some of the problems faced by researchers studying chromatin-bound proteins include low complex solubility, heterogeneous sample composition, and numerous transient interactions which can be further complicated by the presence of DNA itself. To tackle these issues, a number of innovative protocols have been developed to better study the various facets of chromatin biology. In this review, we will discuss novel approaches to study protein-DNA interactions as well as protein complexes affecting chromatin.

Proteins are a remarkable class of molecules that exhibit wide diversity of shapes or topological features that underpin protein interactions and give rise to biological function. In addition to quantitation of abundance levels of proteins in biological systems under a variety of conditions, the field of proteome research has as a primary mission the assignment of function for proteins and if possible, illumination of factors that enable function. For many years, chemical cross-linking methods have been used to provide structural data on single purified proteins and purified protein complexes. However, these methods also offer the alluring possibility to extend capabilities to complex biological samples such as cell lysates or intact living cells where proteins may exhibit native topological features that do not exist in purified form. Recent efforts are beginning to provide glimpses of protein complexes and topologies in cells that suggest continued development will yield novel capabilities to view functional topological features of many proteins and complexes as they exist in cells, tissues or other complex samples. This review will describe rationale, challenges and a few success stories along the path of development of cross-linking technologies for measurement of in vivo protein interaction topologies.

Modular protein interaction domains (PIDs) that recognize linear peptide motifs are found in hundreds of proteins within the human genome. Some PIDs such as SH2, 14-3-3, Chromo, and Bromo domains serve to recognize posttranslational modification (PTM) of amino acids (such as phosphorylation, acetylation, methylation, etc.) and translate these into discrete cellular responses. Other modules such as SH3 and PSD-95/Discs-large/ZO-1 (PDZ) domains recognize linear peptide epitopes and serve to organize protein complexes based on localization and regions of elevated concentration. In both cases, the ability to nucleate-specific signaling complexes is in large part dependent on the selectivity of a given protein module for its cognate peptide ligand. High-throughput analysis of peptide-binding domains by peptide or protein arrays, phage display, mass spectrometry, or other HTP techniques provides new insight into the potential protein-protein interactions prescribed by individual or even whole families of modules. Systems level analyses have also promoted a deeper understanding of the underlying principles that govern selective protein-protein interactions and how selectivity evolves. Lastly, there is a growing appreciation for the limitations and potential pitfalls of high-throughput analysis of protein-peptide interactomes. This review will examine some of the common approaches utilized for large-scale studies of PIDs and suggest a set of standards for the analysis and validation of datasets from large-scale studies of peptide-binding modules. We will also highlight how data from large-scale studies of modular interaction domain families can provide insight into systems level properties such as the linguistics of selective interactions.

Label-based quantitative mass spectrometry analysis of affinity purified complexes, with its built-in negative controls and relative ease of use, is an increasingly popular choice for defining protein-protein interactions and multiprotein complexes. This approach, which differentially labels proteins/peptides from two or more populations and combines them prior to analysis, permits direct comparison of a protein pulldown (e.g. affinity purified tagged protein) to that of a control pulldown (e.g. affinity purified tag alone) in a single mass spectrometry (MS) run, thus avoiding the variability inherent in separate runs. The use of quantitative techniques has been driven in large part by significant improvements in the resolution and sensitivity of high-end mass spectrometers. Importantly, the availability of commercial reagents and open source identification/quantification software has made these powerful techniques accessible to non-specialists. Benefits and drawbacks of the most popular labeling-based approaches are discussed here, and key steps/strategies for the use of labeling in quantitative immunoprecipitation experiments detailed.

Network visualization of the interactome has been become routine in systems biology research. Not only does it serve as an illustration on the cellular organization of protein-protein interactions, it also serves as a biological context for gaining insights from high-throughput data. However, the challenges to produce an effective visualization have been great owing to the fact that the scale, biological context, and dynamics of any given interactome are too large and complex to be captured by a single visualization. Visualization design therefore requires a pragmatic tradeoff between capturing biological concept and being comprehensible. In this review, we focus on the biological interpretation of different network visualizations. We will draw on examples predominantly from our experiences but elaborate them in the context of the broader field. A rich variety of networks will be introduced including interactomes and the complexome in 2D, interactomes in 2.5D and 3D, and dynamic networks.

Membrane-bound proteins are one of the most important protein types in the cell, and are involved in many major cell processes and signaling pathways. Most proteins, including those at membranes, must interact with other proteins to form complexes which are essential for their function. In this review, we describe some of the major non-mass spectrometry-based methods and technologies used for the investigation of intracellular membrane protein complexes, including Tango, fluorescence/bioluminescence resonance energy transfer (F/BRET), luminescence-based mammalian interactome mapping (LUMIER), protein-fragment complementation assay (PCA), and membrane yeast two-hybrid assay (MYTH). We highlight the advantages and drawbacks of these methods, describe recent studies utilizing these methods, and discuss some of the major findings in the study of membrane protein-based cell pathways.

The physical interaction of proteins is subject to intense investigation that has revealed that proteins are assembled into large densely connected networks. In this review, we will examine how signaling pathways can be combined to form higher order protein interaction networks. By using network graph theory, these interaction networks can be further analyzed for global organization, which has revealed unique aspects of the relationships between protein networks and complex biological phenotypes. Moreover, several studies have shown that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer progression. These relationships suggest a novel paradigm for treatment of complex multigenic disease where the protein interaction network is the target of therapy more so than individual molecules within the network.

Protein interactions have been at the focus of computational biology in recent years. In particular, interest has come from two different communities-structural and systems biology. Here, we will discuss key systems and structural biology methods that have been used for analysis and prediction of protein-protein interactions and the insight these approaches have provided on the nature and organization of protein-protein interactions inside cells.

Cellular functions are defined by the dynamic interactions of proteins within macromolecular networks. Deciphering these complex interplays is the key to getting a comprehensive picture of cellular behavior and to understanding biological systems, from a simple bacterial cell to highly regulated neuronal cells or cancerous tissue. In the last decade, affinity purification (AP) coupled to mass spectrometry has emerged as a powerful tool to comprehensively study interaction networks and their macromolecular assemblies. This review discusses recent advances in AP approaches, from cell lysis to the importance of sample preparation and the choice of AP matrix as well as the development of different epitope tags and strategies to study dynamic interactions, with an emphasis on RNA-protein interaction networks.

Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.

We utilized a setup based on extensive pre-fractionation of proteolytic peptides and nanoflow reversed-phase LC-MS/MS to identify the (sub)proteome of human follicular fluid (FF). In this in-depth screen, 246 specific proteins were identified, the majority of which are involved in coagulation- and immune-response pathways. Our aim is to define a set of FF protein markers, which could predict oocyte quality.

Peroxiredoxin 6 (PRDX6), a 1-Cys peroxiredoxin, is a bifunctional enzyme acting both as a glutathione peroxidase and a phospholipase A2. However, the underlying mechanisms and their regulation mechanisms are not well understood. Because post-translational modifications (PTMs) have been shown to play important roles in the function of many proteins, we undertook, in this study, to identify the PTMs in PRDX6 utilizing proteomic tools including nanoUPLC-ESI-q-TOF MS/MS employing selectively excluded mass screening analysis (SEMSA) in conjunction with MOD(i) and MODmap algorithm. We chose PRDX6 obtained from liver tissues from two inbred mouse strains, C57BL/6J and C3H/HeJ, which vary in their susceptibility to high-fat diet-induced obesity and atherosclerosis, and a B16F10 melanoma cell line for this study. When PRDX6 protein samples were separated on 2D-PAGE based on pI, several PRDX6 spots appeared. They were purified and the low abundant PTMs in each PRDX6 spot were analyzed. Unexpected mass shifts (Δm =-34,+25,+64,+87,+103,+134,+150,+284 Da) observed at active site cysteine residue (Cys47) were quantified using precursor ion intensities. Mass differences of -34,+25, and +64 Da are presumed to reflect the conversion of cysteine to dehydroalanine, cyano, and Cys-SO(2)-SH, respectively. We also detected acrylamide adducts of sulfenic and sulfinic acids (+87 and +103 Da) as well as unknown modifications (+134,+150,+284 Da). Comprehensive analysis of these PTMs revealed that the PRDX6 exists as a heterogeneous mixture of molecules containing a multitude of PTMs. Several of these modifications occur at cysteine residue in the enzyme active site. Other modifications observed, in PRDX6 from mouse liver tissues included, among others, mono- and dioxidation at Trp and Met, acetylation at Lys, and deamidation at Asn and Gln. Comprehensive identification of the diverse PTMs occurring in this bifunctional PRDX6 enzyme should help understand how PRDX6 plays key roles in oxidative stresses.