Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein-protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.     

Allosteric Regulation of the Hsp90 Dynamics and Stability by Client Recruiter Cochaperones: Protein Structure Network Modeling

The fundamental role of the Hsp90 chaperone in supporting functional activity of diverse protein clients is anchored by specific cochaperones. A family of immune sensing client proteins is delivered to the Hsp90 system with the aid of cochaperones Sgt1 and Rar1 that act cooperatively with Hsp90 to form allosterically regulated dynamic complexes. In this work, functional dynamics and protein structure network modeling are combined to dissect molecular mechanisms of Hsp90 regulation by the client recruiter cochaperones. Dynamic signatures of the Hsp90-cochaperone complexes are manifested in differential modulation of the conformational mobility in the Hsp90 lid motif. Consistent with the experiments, we have determined that targeted reorganization of the lid dynamics is a unifying characteristic of the client recruiter cochaperones. Protein network analysis of the essential conformational space of the Hsp90-cochaperone motions has identified structurally stable interaction communities, interfacial hubs and key mediating residues of allosteric communication pathways that act concertedly with the shifts in conformational equilibrium. The results have shown that client recruiter cochaperones can orchestrate global changes in the dynamics and stability of the interaction networks that could enhance the ATPase activity and assist in the client recruitment. The network analysis has recapitulated a broad range of structural and mutagenesis experiments, particularly clarifying the elusive role of Rar1 as a regulator of the Hsp90 interactions and a stability enhancer of the Hsp90-cochaperone complexes. Small-world organization of the interaction networks in the Hsp90 regulatory complexes gives rise to a strong correspondence between highly connected local interfacial hubs, global mediator residues of allosteric interactions and key functional hot spots of the Hsp90 activity. We have found that cochaperone-induced conformational changes in Hsp90 may be determined by specific interaction networks that can inhibit or promote progression of the ATPase cycle and thus control the recruitment of client proteins.     

Recognition pliability is coupled to structural heterogeneity: a calmodulin intrinsically disordered binding region complex

Protein interactions within regulatory networks should adapt in a spatiotemporal-dependent dynamic environment, in order to process and respond to diverse and versatile cellular signals. However, the principles governing recognition pliability in protein complexes are not well understood. We have investigated a region of the intrinsically disordered protein myelin basic protein (MBP(145-165)) that interacts with calmodulin, but that also promiscuously binds other biomolecules (membranes, modifying enzymes). To characterize this interaction, we implemented an NMR spectroscopic approach that calculates, for each conformation of the complex, the maximum occurrence based on recorded pseudocontact shifts and residual dipolar couplings. We found that the MBP(145-165)-calmodulin interaction is characterized by structural heterogeneity. Quantitative comparative analysis indicated that distinct conformational landscapes of structural heterogeneity are sampled for different calmodulin-target complexes. Such structural heterogeneity in protein complexes could potentially explain the way that transient and promiscuous protein interactions are optimized and tuned in complex regulatory networks.     

Exploration of the Dynamic Properties of Protein Complexes Predicted from Spatially Constrained Protein-Protein Interaction Networks

Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and visualization of the hidden dynamics within protein interaction networks.     

Amyloidogenic Regions and Interaction Surfaces Overlap in Globular Proteins Related to Conformational Diseases

Protein aggregation underlies a wide range of human disorders. The polypeptides involved in these pathologies might be intrinsically unstructured or display a defined 3D-structure. Little is known about how globular proteins aggregate into toxic assemblies under physiological conditions, where they display an initially folded conformation. Protein aggregation is, however, always initiated by the establishment of anomalous protein-protein interactions. Therefore, in the present work, we have explored the extent to which protein interaction surfaces and aggregation-prone regions overlap in globular proteins associated with conformational diseases. Computational analysis of the native complexes formed by these proteins shows that aggregation-prone regions do frequently overlap with protein interfaces. The spatial coincidence of interaction sites and aggregating regions suggests that the formation of functional complexes and the aggregation of their individual subunits might compete in the cell. Accordingly, single mutations affecting complex interface or stability usually result in the formation of toxic aggregates. It is suggested that the stabilization of existing interfaces in multimeric proteins or the formation of new complexes in monomeric polypeptides might become effective strategies to prevent disease-linked aggregation of globular proteins.     

The advancement of chemical cross-linking and mass spectrometry for structural proteomics: from single proteins to protein interaction networks

During the last 15 years, chemical cross-linking combined with mass spectrometry (MS) and computational modeling has advanced from investigating 3D-structures of isolated proteins to deciphering protein interaction networks. In this article, the author discusses the advent, the development and the current status of the chemical cross-linking/MS strategy in the context of recent technological developments. A direct way to probe in vivo protein–protein interactions is by site-specific incorporation of genetically encoded photo-reactive amino acids or by non-directed incorporation of photo-reactive amino acids. As the chemical cross-linking/MS approach allows the capture of transient and weak interactions, it has the potential to become a routine technique for unraveling protein interaction networks in their natural cellular environment.     

Errata

Abstract

Mass spectrometry (MS)-based proteomics is an unrivaled tool for studying complex biological systems and diseases in the post-genomic era. In recent years, MS has emerged as a powerful structural biological tool to characterize protein conformation and conformational dynamics. The advantages of MS in structural studies are most evident for membrane proteins such as GPCRs (G protein-coupled receptors), where other well-established structural methods such as X-ray crystallography and NMR remain challenging. For proteins with available high-resolution structures, MS-based structural strategies can provide valuable, previously inaccessible information on protein conformational changes and dynamics, protein motion/flexibility, ligand–protein binding, and protein–protein interfaces. In the past several years, we have developed and adapted a number of MS-based structural approaches, such as CDSiL-MS (Conformational changes and Dynamics using Stable-isotope Labeling and MS), CXMS (Crosslinking/MS) and HDXMS (Hydrogen-Deuterium Exchange MS), to study protein structures and conformational dynamics in human β2-adrenegic receptor (β2AR) signaling. In this mini-review, we will highlight several examples demonstrating the power of MS in structural analysis to better elucidate the structural basis of GPCR signaling, particularly through the β-arrestin-mediated GPCR signaling pathway.     

Quantitative analysis of protein interaction network dynamics in yeast

Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.     

Statistical analysis of structural determinants for protein–DNA‐binding specificity

DNA‐binding proteins play critical roles in biological processes including gene expression, DNA packaging and DNA repair. They bind to DNA target sequences with different degrees of binding specificity, ranging from highly specific (HS) to nonspecific (NS). Alterations of DNA‐binding specificity, due to either genetic variation or somatic mutations, can lead to various diseases. In this study, a comparative analysis of protein–DNA complex structures was carried out to investigate the structural features that contribute to binding specificity. Protein–DNA complexes were grouped into three general classes based on degrees of binding specificity: HS, multispecific (MS), and NS. Our results show a clear trend of structural features among the three classes, including amino acid binding propensities, simple and complex hydrogen bonds, major/minor groove and base contacts, and DNA shape. We found that aspartate is enriched in HS DNA binding proteins and predominately binds to a cytosine through a single hydrogen bond or two consecutive cytosines through bidentate hydrogen bonds. Aromatic residues, histidine and tyrosine, are highly enriched in the HS and MS groups and may contribute to specific binding through different mechanisms. To further investigate the role of protein flexibility in specific protein–DNA recognition, we analyzed the conformational changes between the bound and unbound states of DNA‐binding proteins and structural variations. The results indicate that HS and MS DNA‐binding domains have larger conformational changes upon DNA‐binding and larger degree of flexibility in both bound and unbound states. Proteins 2016; 84:1147–1161. © 2016 Wiley Periodicals, Inc.     

Modularity in the evolution of yeast protein interaction network

Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.     

Proteomics of Arabidopsis redox proteins in response to methyl jasmonate

Protein redox regulation is increasingly recognized as an important switch of protein activity in yeast, bacteria, mammals and plants. In this study, we identified proteins with potential thiol switches involved in jasmonate signaling, which is essential for plant defense. Methyl jasmonate (MeJA) treatment led to enhanced production of hydrogen peroxide in Arabidopsis leaves and roots, indicating in vivo oxidative stress. With monobromobimane (mBBr) labeling to capture oxidized sulfhydryl groups and 2D gel separation, a total of 35 protein spots that displayed significant redox and/or total protein expression changes were isolated. Using LC–MS/MS, the proteins in 33 spots were identified in both control and MeJA-treated samples. By comparative analysis of mBBr and SyproRuby gel images, we were able to determine many proteins that were redox responsive and proteins that displayed abundance changes in response to MeJA. Interestingly, stress and defense proteins constitute a large group that responded to MeJA. In addition, many cysteine residues involved in the disulfide dynamics were mapped based on tandem MS data. Identification of redox proteins and their cysteine residues involved in the redox regulation allows for a deeper understanding of the jasmonate signaling networks.     

Allosteric communication in cysteinyl tRNA synthetase: a network of direct and indirect readout

Protein structure networks are constructed for the identification of long-range signaling pathways in cysteinyl tRNA synthetase (CysRS). Molecular dynamics simulation trajectory of CysRS-ligand complexes were used to determine conformational ensembles in order to gain insight into the allosteric signaling paths. Communication paths between the anticodon binding region and the aminoacylation region have been identified. Extensive interaction between the helix bundle domain and the anticodon binding domain, resulting in structural rigidity in the presence of tRNA, has been detected. Based on the predicted model, six residues along the communication paths have been examined by mutations (single and double) and shown to mediate a coordinated coupling between anticodon recognition and activation of amino acid at the active site. This study on CysRS clearly shows that specific key residues, which are involved in communication between distal sites in allosteric proteins but may be elusive in direct structure analysis, can be identified from dynamics of protein structure networks.     

A network analysis of changes in molecular interactions in cellular signaling

Multiprotein complexes play an essential role in the propagation and integration of cellular signals. However, systems level analyses of signaling-dependent changes in the pattern of molecular interactions are still missing. Signaling in T-lymphocytes is one prominent example in which multiprotein complexes orchestrate signal transduction. We implemented peptide microarrays comprising a set of interaction motifs of signaling proteins for network-based analyses of signaling-dependent changes in molecular interactions. Lysates of resting or stimulated cells were incubated on these arrays, and the binding of signaling proteins was detected by immunofluorescence. Signaling-dependent complex formation led to changes of signals on the microarrays in two ways. 1) Masking of a binding site of a signaling protein for a peptide on the array resulted in a signal decrease. 2) Interaction of a protein with a second protein, which in turn binds to a peptide on the array, resulted in a signal increase for the first protein. Dissipation of complexes led to the reverse changes. Competition with peptides corresponding to interaction motifs provided detailed information on the architecture of complexes; lack of individual signaling proteins revealed the functional interdependence of interactions in the network. We show that complex formation through phosphorylation of the scaffolding protein LAT (linker for activation of T-cells) acted as a signal amplifier. PLCgamma1 deficiency increased the resting state levels of LAT-dependent complexes and augmented the recruitment of the phosphatase SHPTP2 into complexes. For the analysis of signaling networks, the parallel detection of changes in interactions enabled the identification of functional interdependencies with minimum a priori knowledge.     

Generation of branched actin networks: assembly and regulation of the N‐WASP and WAVE molecular machines

The Arp2/3 complex is a molecular machine that generates branched actin networks responsible for membrane remodeling during cell migration, endocytosis, and other morphogenetic events. This machine requires activators, which themselves are multiprotein complexes. This review focuses on recent advances concerning the assembly of stable complexes containing the most‐studied activators, N‐WASP and WAVE proteins, and the level of regulation that is provided by these complexes. N‐WASP is the paradigmatic auto‐inhibited protein, which is activated by a conformational opening. Even though this regulation has been successfully reconstituted in vitro with isolated N‐WASP, the native dimeric complex with a WIP family protein has unique additional properties. WAVE proteins are part of a pentameric complex, whose basal state and activated state when bound to the Rac GTPase were recently clarified. Moreover, this review attempts to put together diverse observations concerning the WAVE complex in the conceptual frame of an in vivo assembly pathway that has gained support from the recent identification of a precursor.     

The HSP90 complex of plants

Heat shock protein 90 (HSP90) is a highly conserved and essential molecular chaperone involved in maturation and activation of signaling proteins in eukaryotes. HSP90 operates as a dimer in a conformational cycle driven by ATP binding and hydrolysis. HSP90 often functions together with co-chaperones that regulate the conformational cycle and/or load a substrate "client" protein onto HSP90. In plants, immune sensing NLR (nucleotide-binding domain and leucine-rich repeat containing) proteins are among the few known client proteins of HSP90. In the process of chaperoning NLR proteins, co-chaperones, RAR1 and SGT1 function together with HSP90. Recent structural and functional analyses indicate that RAR1 dynamically controls conformational changes of the HSP90 dimer, allowing SGT1 to bridge the interaction between NLR proteins and HSP90. Here, we discuss the regulation of NLR proteins by HSP90 upon interaction with RAR1 and SGT1, emphasizing the recent progress in our understanding of the structure and function of the complex. This article is part of a Special Issue entitled: Heat Shock Protein 90 (HSP90).     

Changes in Conformation and Stability upon Formation of Complexes of Erythropoietin (EPO) and Soluble EPO Receptor

Erythropoietin (EPO) is a glycoprotein hormone which belongs to the four-helical-bundle cytokine family and regulates the level of circulating red blood cells. The EPO receptor (EPOR) belongs to the cytokine-receptor family of proteins. While many of the downstream events following receptor/ligand interaction have been defined, both ligand-induced receptor dimerization and conformational changes induced by binding have been implicated as the initial step in signal transduction. In a recent paper [Philo et al. (1996), Biochemistry 38, 1681–1691] we described the formation of both 1:1 and 2:1 EPOR/EPO complexes. In this paper, we examine changes in protein conformation and stability resulting from the formation of both 1:1 and 2:1 complexes of the soluble extracellular domain of EPOR and the recombinant EPO derived from either Chinese hamster ovary cells or from Escherichia coli cells. Occupation of the first binding site results in a slight conformational change that is apparent in both the far- and near-UV circular dichroism spectra. Formation of the 2:1 complex results in an even greater change in conformation which involves the local environment of one or more aromatic amino acids, accompanied perhaps by a small increase in helical content of the complex. This change in local conformation could occur in the EPO molecule, in the EPOR, in both EPOR molecules due to dimerization, or in all molecules in the trimer. The 1:1 complex exhibits increased stability to thermal-induced denaturation relative to the individual protein component; indeed, the E. coli-derived (nonglycosylated) EPO stays folded in the complex at temperatures where the EPO alone would have unfolded and precipitated. Glycosylation of the receptor increases the reversibility of thermal denaturation, but does not affect the temperature at which this unfolding reaction occurs.     

Relative solvent accessible surface area predicts protein conformational changes upon binding

Protein interactions are often accompanied by significant changes in conformation. We have analyzed the relationships between protein structures and the conformational changes they undergo upon binding. Based upon this, we introduce a simple measure, the relative solvent accessible surface area, which can be used to predict the magnitude of binding-induced conformational changes from the structures of either monomeric proteins or bound subunits. Applying this to a large set of protein complexes suggests that large conformational changes upon binding are common. In addition, we observe considerable enrichment of intrinsically disordered sequences in proteins predicted to undergo large conformational changes. Finally, we demonstrate that the relative solvent accessible surface area of monomeric proteins can be used as a simple proxy for protein flexibility. This reveals a powerful connection between the flexibility of unbound proteins and their binding-induced conformational changes, consistent with the conformational selection model of molecular recognition.     

Detection of protein complexes using a protein ranking algorithm

Detecting protein complexes from protein‐protein interaction (PPI) network is becoming a difficult challenge in computational biology. There is ample evidence that many disease mechanisms involve protein complexes, and being able to predict these complexes is important to the characterization of the relevant disease for diagnostic and treatment purposes. This article introduces a novel method for detecting protein complexes from PPI by using a protein ranking algorithm (ProRank). ProRank quantifies the importance of each protein based on the interaction structure and the evolutionarily relationships between proteins in the network. A novel way of identifying essential proteins which are known for their critical role in mediating cellular processes and constructing protein complexes is proposed and analyzed. We evaluate the performance of ProRank using two PPI networks on two reference sets of protein complexes created from Munich Information Center for Protein Sequence, containing 81 and 162 known complexes, respectively. We compare the performance of ProRank to some of the well known protein complex prediction methods (ClusterONE, CMC, CFinder, MCL, MCode and Core) in terms of precision and recall. We show that ProRank predicts more complexes correctly at a competitive level of precision and recall. The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of the proposed method. Proteins 2012;. © 2012 Wiley Periodicals, Inc.