Exploring the protein–protein interaction landscape in plants

Protein–protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.     

Systematic protein–protein interaction mapping for clinically relevant human GPCRs

G‐protein‐coupled receptors (GPCRs) are the largest family of integral membrane receptors with key roles in regulating signaling pathways targeted by therapeutics, but are difficult to study using existing proteomics technologies due to their complex biochemical features. To obtain a global view of GPCR‐mediated signaling and to identify novel components of their pathways, we used a modified membrane yeast two‐hybrid (MYTH) approach and identified interacting partners for 48 selected full‐length human ligand‐unoccupied GPCRs in their native membrane environment. The resulting GPCR interactome connects 686 proteins by 987 unique interactions, including 299 membrane proteins involved in a diverse range of cellular functions. To demonstrate the biological relevance of the GPCR interactome, we validated novel interactions of the GPR37, serotonin 5‐HT4d, and adenosine ADORA2A receptors. Our data represent the first large‐scale interactome mapping for human GPCRs and provide a valuable resource for the analysis of signaling pathways involving this druggable family of integral membrane proteins.     

One‐plasmid tunable coexpression for mycobacterial protein–protein interaction studies

A single plasmid that allows controlled coexpression has been developed for use in mycobacteria. The tetracycline inducible promoter, PtetO, was used to provide tetracycline‐dependent induction of one gene, while the Psmyc, Pimyc, or Phsp promoters were used to provide three different levels of constitutive expression of a second gene. The functions of these four individual promoters were established using green fluorescent protein (GFP) and a newly identified red fluorescence inducible protein from Geobacillus sterothermophilus strain G1.13 (RFIP) as reporters. The tandem use of GFP and RFIP as reporter genes allowed optimization of the tunable coexpression in Mycobacterium smegmatis; either time at a fixed inducer concentration or changes in inducer concentration could be used to control the protein:protein ratio. This single vector system was used to coexpress the two‐protein Mycobacterium tuberculosis stearoyl‐CoA Δ⁹ desaturase complex (integral membrane desaturase Rv3229c and NADPH oxidoreductase Rv3230c) in M. smegmatis. The catalytic activity was found to increase in a manner corresponding to increasing the level of Rv3230c relative to a fixed level of Rv3229c. This system, which can yield finely tuned coexpression of the fatty acid desaturase complex in mycobacteria, may be useful for study of other multicomponent complexes. Furthermore, the tunable coexpression strategy used herein should also be applicable in other species with minor modifications.     

Thrombopoietin receptor-based protein–protein interaction screening (THROPPIS)

As protein–protein interactions (PPIs) are involved in many cellular events, development of mammalian cytosolic PPI detection systems is important for drug discovery as well as understanding biological phenomena. We have previously reported a c-kit-based PPI screening (KIPPIS) system, in which proteins of interest were fused with a receptor tyrosine kinase c-kit, leading to intracellular PPI-dependent cell growth. However, it has not been investigated whether PPI can be detected using other receptors. In this study, we employed a thrombopoietin receptor, which belongs to the Type I cytokine receptor family, to develop a thrombopoietin receptor-based PPI screening (THROPPIS) system. To improve the sensitivity of THROPPIS, we examined two strategies of (i) localization of the chimeric receptors on the cell membrane, and (ii) addition of a helper module to the chimeric receptors. Intriguingly, the nonlocalized chimeric receptor showed the best performance of THROPPIS. Furthermore, the addition of the helper module dramatically improved the detection sensitivity. In total, 5 peptide–domain interactions were detected successfully, demonstrating the versatility of THROPPIS. In addition, a peptide–domain interaction was detected even when insulin receptor or epidermal growth factor receptor was used as a signaling domain, demonstrating that this PPI detection system can be extended to other receptors.     

Organic dyes as small molecule protein–protein interaction inhibitors for the CD40–CD154 costimulatory interaction

It is becoming increasingly clear that small molecules can often act as effective protein–protein interaction (PPI) inhibitors, an area of increasing interest for its many possible therapeutic applications. We have identified several organic dyes and related small molecules that (i) concentration‐dependently inhibit the important CD40–CD154 costimulatory interaction with activities in the low micromolar (µM) range, (ii) show selectivity toward this particular PPI, (iii) seem to bind on the surface of CD154, and (iv) concentration‐dependently inhibit the CD154‐induced B cell proliferation. They were identified through an iterative activity screening/structural similarity search procedure starting with suramin as lead, and the best smaller compounds, the main focus of the present work, achieved an almost 3‐fold increase in ligand efficiency (ΔG⁰/nonhydrogen atom = 0.8 kJ/N_nHa) approaching the average of known promising small‐molecule PPI inhibitors (～1.0 kJ/N_nHa). Since CD154 is a member of the tumor necrosis factor (TNF) superfamily of cell surface interaction molecules, inhibitory activities on the TNF‐R1–TNF‐α interactions were also determined to test for specificity, and the compounds selected here all showed more than 30‐fold selectivity toward the CD40–CD154 interaction. Because of their easy availability in various structural scaffolds and because of their good protein‐binding ability, often explored for tissue‐specific staining and other purposes, such organic dyes can provide a valuable addition to the chemical space searched to identify small molecule PPI inhibitors in general. Copyright © 2009 John Wiley & Sons, Ltd.     

Optimization of protein–protein docking for predicting Fc–protein interactions

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc–protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc‐binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc–protein complexes are available, numerous others have not yet been determined. Protein–protein docking could be used to investigate Fc–protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking‐based structural bioinformatics approach is developed for predicting the structures of Fc–protein complexes. Based on the available set of X‐ray structures of Fc–protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc–protein complexes, as well as in the design of peptides and proteins that target Fc.     

Clusia: Holy Grail and enigma

Clusia is the only genus with bona fide dicotyledonous trees performing Crassulacean acid metabolism (CAM). Clusia minor L. is extraordinarily flexible, being C(3)/CAM intermediate and expressing the photosynthetic modes C(3), CAM, CAM-cycling, and CAM-idling. C(3) photosynthesis and CAM can be observed simultaneously in two opposite leaves on a node and possibly even within the same leaf in the interveinal lamina and the major vein tissue, respectively. The relative activity of photosystem II (PhiPSII) indicating photosynthetic energy use, is larger under photorespiratory than under non-photorespiratory conditions due to the particular energy demand of photorespiration. The heterogeneity of PhiPSII over the leaves as visualized by chlorophyll fluorescence imaging in the C(3) mode is larger under non-photorespiratory conditions than under photorespiratory conditions. These observations indicate that photorespiration, presumably by its particular energy demand, synchronizes photosynthetic activity over the leaves. In the CAM mode, the heterogeneity of PhiPSII is more dependent on the transitions between CAM phases. Free-running circadian oscillations of photosynthesis are strongly dampened in both the C(3) and the CAM mode. Photorespiration is under circadian clock control in both the C(3) and the CAM mode. PhiPSII and the heterogeneity of PhiPSII oscillate in phase with CO(2) uptake and photorespiration only under non-photorespiratory conditions. Under photorespiratory conditions, PhiPSII does not oscillate and there is no heterogeneity, again indicating the stabilizing function of photorespiration. Plants acclimatized to perform CAM switch to C(3) photosynthesis during free-running oscillations while subjected to constant illumination.     

Using the clustered circular layout as an informative method for visualizing protein–protein interaction networks

The force‐directed layout is commonly used in computer‐generated visualizations of protein–protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein–protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.     

An inter‐species protein–protein interaction network across vast evolutionary distance

In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical‐versus‐functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an “inter‐interactome” approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter‐interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra‐species networks. Although substantially reduced relative to intra‐species networks, the levels of functional overlap in the yeast–human inter‐interactome network uncover significant remnants of co‐functionality widely preserved in the two proteomes beyond human–yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co‐functionality. Such non‐functional interactions, however, represent a reservoir from which nascent functional interactions may arise.     

PLIM: A protein–ligand interaction modeller

A Computer program is presented that models the binding of selected chemical groups to a protein surface. The groups are successively incorporated at energetically favourable positions to build up a pharmacophore pattern that may be used as the basis for a database search for possible ligands. The ability to predict known binding points in a trypsin–inhibitor complex is demonstrated, and the results from a run on dihydrofolate reductase are shown to be usable as a pharmacophore pattern for a database search.     

Stabilization of collagen through bioconversion: An insight in protein–protein interaction

Collagen is a natural protein, which is used as a vital biomaterial in tissue engineering. The major concern about native collagen is lack of its thermal stability and weak resistance to proteolytic degradation. In this scenario, the crosslinking compounds used for stabilization of collagen are mostly of chemical nature and exhibit toxicity. The enzyme mediated crosslinking of collagen provides a novel alternative, nontoxic method for stabilization. In this study, aldehyde forming enzyme (AFE) is used in the bioconversion of hydroxylmethyl groups of collagen to formyl groups that results in the formation of peptidyl aldehyde. The resulted peptidyl aldehyde interacts with bipolar ions of basic amino acid residues of collagen. Further interaction leads to the formation of conjugated double bonds (aldol condensation involving the aldehyde group of peptidyl aldehyde) within the collagen. The enzyme modified collagen matrices have shown an increase in the denaturation temperature, when compared with native collagen. Enzyme modified collagen membranes exhibit resistance toward collagenolytic activity. Moreover, they exhibited a nontoxic nature. The catalytic activity of AFE on collagen as a substrate establishes an efficient modification, which enhances the structural stability of collagen. This finds new avenues in the context of protein–protein stabilization and discovers paramount application in tissue engineering. © 2014 Wiley Periodicals, Inc. Biopolymers 101: 903–911, 2014.     

On the protein–protein diffusional encounter complex

When two proteins diffuse together to form a bound complex, an intermediate is formed at the end‐point of diffusional association which is called the encounter complex. Its characteristics are important in determining association rates, yet its structure cannot be directly observed experimentally. Here, we address the problem of how to construct the ensemble of three‐dimensional structures which constitute the protein–protein diffusional encounter complex using available experimental data describing the dependence of protein association rates on mutation and on solvent ionic strength and viscosity. The magnitude of the association rates is fitted well using a variety of definitions of encounter complexes in which the two proteins are located at up to about 17 Å root‐mean‐squared distance from their relative arrangement in the bound complex. Analysis of the ionic strength dependence of bimolecular association rates shows that this is determined to a greater extent by the (protein charge) – (salt ion) separation distance than by the protein–protein charge separation distance. Consequently, ionic strength dependence of association rates provides little information about the geometry of the encounter complex. On the other hand, experimental data on electrostatic rate enhancement, mutation and viscosity dependence suggest a model of the encounter complex in which the two proteins form a subset of the contacts present in the bound complex and are significantly desolvated. Copyright © 1999 John Wiley & Sons, Ltd.     

Complex structure of cytochrome c–cytochrome c oxidase reveals a novel protein–protein interaction mode

Mitochondrial cytochrome c oxidase (CcO) transfers electrons from cytochrome c (Cyt.c) to O₂ to generate H₂O, a process coupled to proton pumping. To elucidate the mechanism of electron transfer, we determined the structure of the mammalian Cyt.c–CcO complex at 2.0‐Å resolution and identified an electron transfer pathway from Cyt.c to CcO. The specific interaction between Cyt.c and CcO is stabilized by a few electrostatic interactions between side chains within a small contact surface area. Between the two proteins are three water layers with a long inter‐molecular span, one of which lies between the other two layers without significant direct interaction with either protein. Cyt.c undergoes large structural fluctuations, using the interacting regions with CcO as a fulcrum. These features of the protein–protein interaction at the docking interface represent the first known example of a new class of protein–protein interaction, which we term “soft and specific”. This interaction is likely to contribute to the rapid association/dissociation of the Cyt.c–CcO complex, which facilitates the sequential supply of four electrons for the O₂ reduction reaction.     

The structural analysis of protein–protein interactions by NMR spectroscopy

A comprehensive understanding of protein–protein interactions is an important next step in our quest to understand how the information contained in a genome is put into action. Although a number of experimental techniques can report on the existence of a protein– protein interaction, very few can provide detailed structural information. NMR spectroscopy is one of these, and in recent years several complementary NMR approaches, including residual dipolar couplings and the use of paramagnetic effects, have been developed that can provide insight into the structure of protein–protein complexes. In this article, we review these approaches and comment on their strengths and weaknesses.     

Predicting permanent and transient protein–protein interfaces

Protein–protein interactions (PPIs) are involved in diverse functions in a cell. To optimize functional roles of interactions, proteins interact with a spectrum of binding affinities. Interactions are conventionally classified into permanent and transient, where the former denotes tight binding between proteins that result in strong complexes, whereas the latter compose of relatively weak interactions that can dissociate after binding to regulate functional activity at specific time point. Knowing the type of interactions has significant implications for understanding the nature and function of PPIs. In this study, we constructed amino acid substitution models that capture mutation patterns at permanent and transient type of protein interfaces, which were found to be different with statistical significance. Using the substitution models, we developed a novel computational method that predicts permanent and transient protein binding interfaces (PBIs) in protein surfaces. Without knowledge of the interacting partner, the method uses a single query protein structure and a multiple sequence alignment of the sequence family. Using a large dataset of permanent and transient proteins, we show that our method, BindML+, performs very well in protein interface classification. A very high area under the curve (AUC) value of 0.957 was observed when predicted protein binding sites were classified. Remarkably, near prefect accuracy was achieved with an AUC of 0.991 when actual binding sites were classified. The developed method will be also useful for protein design of permanent and transient PBIs. © Proteins 2013. © 2012 Wiley Periodicals, Inc.