A Phage-Assisted Continuous Selection Approach for Deep Mutational Scanning of Protein-Protein Interactions

Protein-protein interactions (PPIs) are critical for organizing molecules in a cell and mediating signaling pathways. Dysregulation of PPIs is often a key driver of disease. To better understand the biophysical basis of such disease processes-and to potentially target them-it is critical to understand the molecular determinants of PPIs. Deep mutational scanning (DMS) facilitates the acquisition of large amounts of biochemical data by coupling selection with high throughput sequencing (HTS). The challenging and labor-intensive design and optimization of a relevant selection platform for DMS, however, limits the use of powerful directed evolution and selection approaches. To address this limitation, we designed a versatile new phage-assisted continuous selection (PACS) system using our previously reported proximity-dependent split RNA polymerase (RNAP) biosensors, with the aim of greatly simplifying and streamlining the design of a new selection platform for PPIs. After characterization and validation using the model KRAS/RAF PPI, we generated a library of RAF variants and subjected them to PACS and DMS. Our HTS data revealed positions along the binding interface that are both tolerant and intolerant to mutations, as well as which substitutions are tolerated at each position. Critically, the "functional scores" obtained from enrichment data through continuous selection for individual variants correlated with K_D values measured in vitro, indicating that biochemical data can be extrapolated from sequencing using our new system. Due to the plug and play nature of RNAP biosensors, this method can likely be extended to a variety of other PPIs. More broadly, this, and other methods under development support the continued development of evolutionary and high-throughput approaches to address biochemical problems, moving toward a more comprehensive understanding of sequence-function relationships in proteins.

Reversible biotinylation of purified proteins for measuring protein-protein interactions

Measuring protein-protein interactions using purified proteins in vitro is one of the most frequently used approach to understand the biochemical and mechanistic details of cellular signaling pathways. Typically, affinity tags are genetically fused to proteins of interest, and they are used to capture and detect them. However, in some cases, fusion of bulky affinity tags might present a significant limitation in these experiments, especially if the regions in close proximity of tags are involved in protein-protein interactions. Here, we present a step-by-step protocol for an alternative approach that involves reversible biotinylation of purified proteins using a simple chemical-conjugation of cleavable biotin moiety. Biotinylated proteins can be directly used as bait for selective immobilization on solid support for measuring protein-protein interactions. Furthermore, biotinylation of protein of interest also allows specific detection in standard biochemical assays. This simple, straightforward and modular protocol can be directly adapted and applied to facilitate the detection of novel protein-protein interactions as well as measuring apparent affinities of such interactions.

Evaluation of core serous epithelial ovarian cancer genes as potential prognostic markers and indicators of the underlying molecular mechanisms using an integrated bioinformatics analysis

Ovarian cancer is a major cause of mortality in women. However, the molecular events underlying the pathogenesis of the disease are yet to be fully elucidated. In the present study, an integrated bioinformatics analysis was performed to identify core genes involved in serous epithelial ovarian cancer. A total of three expression datasets were downloaded from the Gene Expression Omnibus database, and included 46 serous epithelial ovarian cancer and 30 ovarian surface epithelium samples. The three datasets were merged, and batch normalization was performed. The normalized merged data were subsequently analyzed for differentially expressed genes (DEGs). In total, 2,212 DEGs were identified, including 1,300 upregulated and 912 downregulated genes. Gene Ontology analysis revealed that these DEGs were primarily involved in ‘regulation of cell cycle’, ‘mitosis’, ‘DNA packaging’ and ‘nucleosome assembly’. The main cellular components included ‘extracellular region part’, ‘chromosome’, ‘extracellular matrix’ and ‘condensed chromosome kinetochore’, whereas the molecular functions included ‘Calcium ion binding’, ‘polysaccharide binding’, ‘enzyme inhibitor activity’, ‘growth factor activity’, ‘cyclin-dependent protein kinase regulator activity’, ‘microtubule motor activity’ and ‘Wnt receptor activity’. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that these DEGs were predominantly involved in ‘Wnt signaling pathway’, ‘pathways in cancer’, ‘PI3K-Akt signaling pathway’, ‘cell cycle’, ‘ECM-receptor interaction’, ‘p53 signaling pathway’ and ‘focal adhesion’. The 20 most significant DEGs were identified from the protein-protein interaction network, and Oncomine analysis of these core genes revealed that 13 were upregulated and two were downregulated in serous epithelial ovarian cancer. Survival analysis revealed that cyclin B1, polo like kinase 1, G protein subunit γ transducin 1 and G protein subunit γ 12 are key molecules that may be involved in the prognosis of serous epithelial ovarian cancer. These core genes may provide novel treatment targets, although their roles in the carcinogenesis and prognosis of serous epithelial ovarian cancer require further study.

Skin Protein Profile after Major Weight Loss and Its Role in Body Contouring Surgery

Chronic inflammation during morbid obesity significantly alters cutaneous tissue. Large weight loss achieved after bariatric surgery minimizes or halts damage caused by metabolic syndrome, but further deteriorates the clinical condition of skin. Postbariatric skin flaccidity produces major difficulties to plastic surgery. In this study, we analyzed differences in protein composition of the skin between patients with morbid obesity and those after large weight loss and established correlations between differentially expressed proteins and clinical characteristics of postbariatric skin tissue, to improve body contouring surgery techniques.

Protein-Protein Interactions of Phosphodiesterases

BACKGROUND

Phosphodiesterases (PDEs) are enzymes that play a key role in terminating cyclic nucleotides signalling by catalysing the hydrolysis of 3', 5'- cyclic adenosine monophosphate (cAMP) and/or 3', 5' cyclic guanosine monophosphate (cGMP), the second messengers within the cell that transport the signals produced by extracellular signalling molecules which are unable to get into the cells. However, PDEs are proteins which do not operate alone but in complexes that made up of a many proteins.

OBJECTIVE

This review highlights some of the general characteristics of PDEs and focuses mainly on the Protein-Protein Interactions (PPIs) of selected PDE enzymes. The objective is to review the role of PPIs in the specific mechanism for activation and thereby regulation of certain biological functions of PDEs.

METHODS

The article discusses some of the PPIs of selected PDEs as reported in recent scientific literature. These interactions are critical for understanding the biological role of the target PDE.

RESULTS

The PPIs have shown that each PDE has a specific mechanism for activation and thereby regulation a certain biological function.

CONCLUSION

Targeting of PDEs to specific regions of the cell is based on the interaction with other proteins where each PDE enzyme binds with specific protein(s) via PPIs.

Rational modulator design by exploitation of protein-protein complex structures

The horizon of drug discovery is currently expanding to target and modulate protein-protein interactions (PPIs) in globular proteins and intrinsically disordered proteins that are involved in various diseases. To either interrupt or stabilize PPIs, the 3D structure of target protein-protein (or protein-peptide) complexes can be exploited to rationally design PPI modulators (inhibitors or stabilizers) through structure-based molecular design. In this review, we present an overview of experimental and computational methods that can be used to determine 3D structures of protein-protein complexes. Several approaches including rational and in silico methods that can be applied to design peptides, peptidomimetics and small compounds by utilization of determined 3D protein-protein/peptide complexes are summarized and illustrated.

In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery

BACKGROUND

Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis.

MATERIALS & METHOD

In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.

CONCLUSION

Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.

Miniaturised interaction proteomics on a microfluidic platform with ultra-low input requirements

Essentially all cellular processes are orchestrated by protein-protein interactions (PPIs). In recent years, affinity purification coupled to mass spectrometry (AP-MS) has been the preferred method to identify cellular PPIs. Here we present a microfluidic-based AP-MS workflow, called on-chip AP-MS, to identify PPIs using minute amounts of input material. By using this automated platform we purify the human Cohesin, CCC and Mediator complexes from as little as 4 micrograms of input lysate, representing a 50─100-fold downscaling compared to regular microcentrifuge tube-based protocols. We show that our platform can be used to affinity purify tagged baits as well as native cellular proteins and their interaction partners. As such, our method holds great promise for future biological and clinical AP-MS applications in which sample amounts are limited.

Is protein context responsible for peptide-mediated interactions?

Many cell signaling pathways are orchestrated by the weak, transient, and reversible peptide-mediated interactions (PMIs). Here, the role of protein context in contributing to the stability and specificity of PMIs is investigated systematically.

Global Identification of Protein Complexes within the Membrane Proteome of Arabidopsis Roots Using a SEC-MS Approach

Biological processes consist of several consecutive and interacting steps as, for example, in signal transduction cascades or metabolic reaction chains. These processes are regulated by protein-protein interactions and the formation of larger protein complexes, which also occur within biological membranes. To gain a large-scale overview of complex-forming proteins and the composition of such complexes within the cellular membranes of Arabidopsis roots, we use the combination of size-exclusion chromatography and mass spectrometry. First, we identified complex-forming proteins by a retention shift analysis relative to expected retention times of monomeric proteins during size-exclusion chromatography. In a second step we predicted complex composition through pairwise correlation of elution profiles. As result we present an interactome of 963 proteins within cellular membranes of Arabidopsis roots. Identification of complex-forming proteins was highly robust between two independently grown root proteomes. The protein complex composition derived from pairwise correlations of coeluting proteins reproducibly identified stable protein complexes (ribosomes, proteasome, mitochondrial respiratory chain supercomplexes) but showed higher variance between replicates regarding transient interactions (e.g., interactions with kinases) within membrane protein complexes.

Proximity Ligation Assay (PLA)

Proximity ligation assay (PLA), also referred to as Duolink® PLA technology, permits detection of protein-protein interactions in situ (at distances <40 nm) at endogenous protein levels. It exploits specific antibodies identifying (either directly or indirectly) the two proteins of interest and utilizes specific DNA primers covalently linked to the antibodies. A hybridization step followed by DNA amplification with fluorescent probes permit visualization of spots of proximity by fluorescence microscopy. Since the development of PLA in 2002, it has been increasingly used to detect the interaction between two proteins with high sensitivity and specificity. It is a simple and sensitive technique to study protein-protein interaction in cells. © 2018 by John Wiley & Sons, Inc.

Pull-down Assay on Streptavidin Beads and Surface Plasmon Resonance Chips for SWATH-MS-based Interactomics

BACKGROUND/AIM

Pul-down assay is a popular in vitro method for identification of physical interactors of selected proteins. Here, for the first time, we compared three conventional variants of pull-down assay with the streptavidin-modified surface plasmon resonance (SPR) chips for the detection of PDZ and LIM domain protein 2 (PDLIM2) interaction partners.

MATERIALS AND METHODS

PDLIM2 protein-protein interactions were analysed by three variants of pull-down assay on streptavidin beads using LC-MS/MS in "Sequential Window Acquisition of all Theoretical fragment ion spectra (SWATH)" mode and compared with LC-SWATH-MS/MS data from SPR chips.

RESULTS

The results showed that (i) the use of SPR chip led to comparable data compared to on-column streptavidin beads, (ii) gravity flow and microflow in wash and elution steps provided better results than centrifugation, and (iii) type and concentration of detergent did not significantly affect the interactome data of cancer-associated PDLIM2.

CONCLUSION

Our study supports further application of SPR-based affinity purification with SWATH mass spectrometry for reproducible and controlled characterization of cancer-associated interactomes.

Reciprocal Perspective for Improved Protein-Protein Interaction Prediction

All protein-protein interaction (PPI) predictors require the determination of an operational decision threshold when differentiating positive PPIs from negatives. Historically, a single global threshold, typically optimized via cross-validation testing, is applied to all protein pairs. However, we here use data visualization techniques to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles. The recent development of high throughput PPI predictors has enabled the comprehensive scoring of all possible protein-protein pairs. This, in turn, has given rise to context, enabling us now to evaluate a PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective (RP), which estimates a localized threshold on a per-protein basis using several rank order metrics. By considering a putative PPI from the perspective of each of the proteins within the pair, RP rescores the predicted PPI and applies a cascaded Random Forest classifier leading to improvements in recall and precision. We here validate RP using two state-of-the-art PPI predictors, the Protein-protein Interaction Prediction Engine and the Scoring PRotein INTeractions methods, over five organisms: Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus. Results demonstrate the application of a post hoc RP rescoring layer significantly improves classification (p < 0.001) in all cases over all organisms and this new rescoring approach can apply to any PPI prediction method.

Probing Protein-Protein Interactions Using Asymmetric Labeling and Carbonyl-Carbon Selective Heteronuclear NMR Spectroscopy

Protein-protein interactions (PPIs) regulate a plethora of cellular processes and NMR spectroscopy has been a leading technique for characterizing them at the atomic resolution. Technically, however, PPIs characterization has been challenging due to multiple samples required to characterize the hot spots at the protein interface. In this paper, we review our recently developed methods that greatly simplify PPI studies, which minimize the number of samples required to fully characterize residues involved in the protein-protein binding interface. This original strategy combines asymmetric labeling of two binding partners and the carbonyl-carbon label selective (CCLS) pulse sequence element implemented into the heteronuclear single quantum correlation (¹H-15N HSQC) spectra. The CCLS scheme removes signals of the J-coupled 15N⁻13C resonances and records simultaneously two individual amide fingerprints for each binding partner. We show the application to the measurements of chemical shift correlations, residual dipolar couplings (RDCs), and paramagnetic relaxation enhancements (PRE). These experiments open an avenue for further modifications of existing experiments facilitating the NMR analysis of PPIs.

Coil conversion to β-strand induced by dimerization

Most molecular processes in living organisms rely on protein-protein interactions, many of which are mediated by β-sheet interfaces; this study investigates the formation of β-sheet interfaces through the conversion of coils into β-strands. Following an exhaustive search in the Protein Data Bank, the corresponding structural dimorphic fragments were extracted, characterized, and analyzed. Their short strand lengths and specific amino acid profiles indicate that dimorphic β-strand interfaces are likely to be less stable than standard ones and could even convert to coil interfaces if their environment changes. Moreover, the construction of a simple classifier able to discriminate between the sequences of dimorphic and standard β-strand interfaces suggests that the nature of those dimorphic sequences could be predicted, providing a novel means of identifying proteins capable of forming dimers.

Training host-pathogen protein-protein interaction predictors

Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, pathogenic infections are caused by interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI detection techniques have limitations in terms of cost and large-scale application. Hence, computational approaches are developed to predict PPIs. This study aims to develop machine learning models to predict inter-species PPIs with a special interest in HPIs. Specifically, we focus on seeking answers to three questions that arise while developing an HPI predictor: (1) How should negative training examples be selected? (2) Does assigning sample weights to individual negative examples based on their similarity to positive examples improve generalization performance? and, (3) What should be the size of negative samples as compared to the positive samples during training and evaluation? We compare two available methods for negative sampling: random versus DeNovo sampling and our experiments show that DeNovo sampling offers better accuracy. However, our experiments also show that generalization performance can be improved further by using a soft DeNovo approach that assigns sample weights to negative examples inversely proportional to their similarity to known positive examples during training. Based on our findings, we have also developed an HPI predictor called HOPITOR (Host-Pathogen Interaction Predictor) that can predict interactions between human and viral proteins. The HOPITOR web server can be accessed at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor .

SPR Biosensors in Direct Molecular Fishing: Implications for Protein Interactomics

We have developed an original experimental approach based on the use of surface plasmon resonance (SPR) biosensors, applicable for investigation of potential partners involved in protein⁻protein interactions (PPI) as well as protein⁻peptide or protein⁻small molecule interactions. It is based on combining a SPR biosensor, size exclusion chromatography (SEC), mass spectrometric identification of proteins (LC-MS/MS) and direct molecular fishing employing principles of affinity chromatography for isolation of potential partner proteins from the total lysate of biological samples using immobilized target proteins (or small non-peptide compounds) as ligands. Applicability of this approach has been demonstrated within the frame of the Human Proteome Project (HPP) and PPI regulation by a small non-peptide biologically active compound, isatin.

Fragment-based quantum mechanical calculation of protein-protein binding affinities

The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method has been successfully utilized for efficient linear-scaling quantum mechanical (QM) calculation of protein energies. In this work, we applied the EE-GMFCC method for calculation of binding affinity of Endonuclease colicin-immunity protein complex. The binding free energy changes between the wild-type and mutants of the complex calculated by EE-GMFCC are in good agreement with experimental results. The correlation coefficient (R) between the predicted binding energy changes and experimental values is 0.906 at the B3LYP/6-31G*-D level, based on the snapshot whose binding affinity is closest to the average result from the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculation. The inclusion of the QM effects is important for accurate prediction of protein-protein binding affinities. Moreover, the self-consistent calculation of PB solvation energy is required for accurate calculations of protein-protein binding free energies. This study demonstrates that the EE-GMFCC method is capable of providing reliable prediction of relative binding affinities for protein-protein complexes. © 2018 Wiley Periodicals, Inc.

Application of the biotin-labeled toxin mutant for affinity isolation of associated proteins in the mammalian cells

Cholera toxin (CT), one of the AB₅ bacterial toxin families, is produced by Vibrio cholerae, breeches the intestinal epithelial barrier and enters host epithelial cells to cause the massive secretory diarrhea. This study focused on understanding the retro-translocation machinery of the bacterial toxin using biotin-avidin technology to explain toxin trafficking from the endoplasmic reticulum (ER) to the cytosol. Because the association between the A1 chain of CT and other components of the retro-translocation machinery is likely transient or very weak, the successful bioengineering of such a mutant to be trapped as an intermediate in ER is essential for affinity isolation and further analysis. Here, we prepared a mutant toxin that 15 amino acid Biotin Acceptor Peptide (BAP) was fused to the C-terminal of A1 chain of CT. Biotinylation efficiency of the BAP-inserted cholera toxin (BT) was nearly 100%. Moreover, BT was functionally toxic and successfully pulled down by NeutrAvidin in vitro and in vivo. However, NeutrAvidin-bound biotinylated BT was not toxic. These results suggest the possibility of a plug effect of the biotin-NeutrAvidin-BT complex stuck in the ER without retro-translocation to the cytosol. Therefore, this model might identify the interacting proteins with A1 chain of CT in the host cells by holding the moment of retro-translocation of the bacterial toxin. In conclusion, this study established the model using biotin-avidin technology to elucidate the molecular basis for retro-translocation of bacterial toxin from within the lumen of ER to the cytosol.

Rbfox family proteins make the homo- and hetero-oligomeric complexes

Rbfox family of proteins that consists of Rbfox1, Rbfox2, and Rbfox3 in mammals regulates alternative pre-mRNA splicing in various tissues via direct binding to their RNA binding element. Although many studies have indicated the splicing activity of each member of the Rbfox family, the interactions of Rbfox family proteins are largely unknown. Here, we have investigated interactions among Rbfox family proteins. Co-immunoprecipitation (Co-IP) and GST-pull down assays confirmed that Rbfox proteins form homo and hetero complexes. Moreover, in vivo crosslinking using disuccinimidyl suberate treatment indicated that the Rbfox proteins form a dimer which then assembles with other proteins to form a large multiprotein complex. Duolink in situ proximity ligation (PLA) assay revealed that neuron specific Rbfox3 protein interacts with other Rbfox family proteins. This study is the first to provide an evidence that Rbfox family proteins form homo- and hetero-oligomeric complexes in vivo.

Visualization of RMRs (Receptor Membrane RING-H2) Dimerization in Nicotiana benthamiana Leaves Using a Bimolecular Fluorescence Complementation (BiFC) Assay

The bimolecular fluorescent complementation (BiFC) is a fluorescent complementation method largely used to investigate protein-protein interaction in living cells. This method is based on the ability of two nonfluorescent fragments to assemble forming a native fluorescent reporter with the same spectral properties of the native reporter. Such fragments are fused to putative protein partners that in case of interaction will bring the two halves in close proximity, allowing for the reconstitution of an active fluorescent reporter. The BiFC has been used to investigate protein-protein interaction in a number of different organisms, including plants. In plant cells, many essential pathways of protein trafficking and subcellular localization necessitate the intervention of several protein units organized in multisubunit complexes. It is well known that vacuolar sorting of many secretory soluble proteins require the intervention of specific transmembrane cargo receptors able to interact forming dimers. In this chapter we describe a BiFC method for the efficient visualization of RMR (Receptor Membrane RING-H2) type 2 dimerization in agro-infiltrated Nicotiana benthamiana leaves. Furthermore, this relatively simple method represents an optimal strategy to test protein-protein interaction using any other putative protein partners of interest in plant cells.

Analysis of Protein-Protein Interaction by Co-IP in Human Cells

While there are various approaches available to analyze protein-protein interactions, coimmunoprecipitation (co-IP) remains one of the most classic and commonly used methods to discover novel protein interactions or to determine the physical association of proteins. The assay begins with the preparation of total cell or tissue lysate in an appropriate lysis buffer. Protein of interest in the lysate is captured using a specific antibody and precipitated along with its binding proteins using a resin. After a series of washes to remove nonbound proteins in the lysate, the resultant immune complexes are subjected to immunoblotting, in-gel protein staining, or mass spectrometry to determine the protein-protein interaction of interest. In this chapter, a standard IP/co-IP protocol is described and potential problems and troubleshooting are discussed.

Different protein-protein interface patterns predicted by different machine learning methods

Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

Specific Unbinding Forces Between Mutated Human P-Selectin Glycoprotein Ligand-1 and Viral Protein-1 Measured Using Force Spectroscopy

Protein tyrosine sulfation (PTS) is a key modulator of extracellular protein-protein interaction (PPI), which regulates principal biological processes. For example, the capsid protein VP1 of enterovirus 71 (EV71) specifically interacts with sulfated P-selectin glycoprotein ligand-1 (PSGL-1) to facilitate virus invasion. Currently available methods cannot be used to directly observe PTS-induced PPI. In this study, atomic force microscopy was used to measure the interaction between sulfated or mutated PSGL-1 and VP1. We found that the binding strength increased by 6.7-fold following PTS treatment on PSGL-1 with a specific antisulfotyrosine antibody. Similar results were obtained when the antisulfotyrosine antibody was replaced with the VP1 protein of EV71; however, the interaction forces of VP1 were only approximately one-third of those of the antisulfotyrosine antibody. We also found that PTS on the tyrosine-51 residue of glutathione S-transferases fusion-PSGL-1 was mainly responsible for the PTS-induced PPI. Our results contribute to the fundamental understanding of PPI regulated through PTS.

Aquaporin Protein-Protein Interactions

Aquaporins are tetrameric membrane-bound channels that facilitate transport of water and other small solutes across cell membranes. In eukaryotes, they are frequently regulated by gating or trafficking, allowing for the cell to control membrane permeability in a specific manner. Protein–protein interactions play crucial roles in both regulatory processes and also mediate alternative functions such as cell adhesion. In this review, we summarize recent knowledge about aquaporin protein–protein interactions; dividing the interactions into three types: (1) interactions between aquaporin tetramers; (2) interactions between aquaporin monomers within a tetramer (hetero-tetramerization); and (3) transient interactions with regulatory proteins. We particularly focus on the structural aspects of the interactions, discussing the small differences within a conserved overall fold that allow for aquaporins to be differentially regulated in an organism-, tissue- and trigger-specific manner. A deep knowledge about these differences is needed to fully understand aquaporin function and regulation in many physiological processes, and may enable design of compounds targeting specific aquaporins for treatment of human disease.

Methylation of protein phosphatase 2A-Influence of regulators and environmental stress factors

Protein phosphatase 2A catalytic subunit (PP2A-C) has a terminal leucine subjected to methylation, a regulatory mechanism conserved from yeast to mammals and plants. Two enzymes, LCMT1 and PME1, methylate and demethylate PP2A-C, respectively. The physiological importance of these posttranslational modifications is still enigmatic. We investigated these processes in Arabidopsis thaliana by mutant phenotyping, by global expression analysis, and by monitoring methylation status of PP2A-C under different environmental conditions. The lcmt1 mutant, possessing essentially only unmethylated PP2A-C, had less dense rosettes, and earlier flowering than wild type (WT). The pme1 mutant, with 30% reduction in unmethylated PP2A-C, was phenotypically comparable with WT. Approximately 200 overlapping genes were twofold upregulated, and 200 overlapping genes were twofold downregulated in both lcmt1 and pme1 relative to WT. Differences between the 2 mutants were also striking; 97 genes were twofold upregulated in pme1 compared with lcmt1, indicating that PME1 acts as a negative regulator for these genes. Analysis of enriched GO terms revealed categories of both abiotic and biotic stress genes. Furthermore, methylation status of PP2A-C was influenced by environmental stress, especially by hypoxia and salt stress, which led to increased levels of unmethylated PP2A-C, and highlights the importance of PP2A-C methylation/demethylation in environmental responses.

A New Methodology for Incorporating Chiral Linkers into Stapled Peptides

Stapled peptides have arisen as a new class of chemical probe and potential therapeutic agents for modulating protein–protein interactions. Here, we report the first two‐component i,i+7 stapling methodology that makes use of two orthogonal, on‐resin stapling reactions to incorporate linkers bearing a chiral centre into a p53‐derived stapled peptide. Post‐stapling modifications to the chain were performed on‐resin and enabled rapid access to various peptide derivatives from a single staple. The stapled peptides have increased helicity, protease stability and in vitro binding affinities to MDM2 compared to the equivalent unstapled peptide. This approach can be used to generate a library of diverse stapled peptides with different properties starting from a single stapled peptide, with scope for much greater functional diversity than that provided by existing stapling methodologies.

Impact of the amino acid sequence on the conformation of side chain lactam-bridged octapeptides

Synthetic helical peptides are valuable scaffolds for the development of modulators of protein-protein interactions involving helical motifs. Backbone-to-side chain or side chain-to-side chain constraints have been and still are intensively exploited to stabilize short α-helices. Very often, these constraints have been combined with backbone modifications induced by Cα-tetrasubstituted, β-, or γ-amino acids, which facilitate the α-peptide or α/β/γ-peptide adopting an α-helical conformation. In this work, we investigated the helical character of octapeptides that were cyclized by a Lys-Asp-(i,i + 4)-lactam bridge. We started with two sequences extracted from the helix-loop-helix region of the Id proteins, which are inhibitors of cell differentiation during development and in cancer. Nineteen analogs containing the lactam bridge at different positions and displaying different amino acid core triads (i + 1,2,3) as well as outer residues were prepared by solid-phase methodology. Their conformation in water and water/2,2,2-trifluoroethanol mixtures was investigated by circular dichroism (CD) spectroscopy. The cyclopeptides could be grouped in helix-prone and non-helix-prone structures. Both the amino acid core triad (i + 1,2,3) and the pendant residues positively or negatively affected the formation of a helical structure. Computational studies based on the NMR-derived helical structure of a cyclopeptide containing Aib at position (i + 2) of the triad were generally in agreement with the secondary structure propensity of the cyclopeptides observed by CD spectroscopy. In conclusion, the Lys-Asp-(i,i + 4)-lactam bridge may succeed or fail in the stabilization of short helices, depending on the primary structure. Moreover, computational methods may be valuable tools to discriminate helix-prone from non-helix-prone peptide-based macrolactams. Copyright © 2017 European Peptide Society and John Wiley & Sons, Ltd.

Proteomic analysis of the cullin 4B interactome using proximity-dependent biotinylation in living cells

Cullin 4B (CUL4B) mutations have been implicated in mental retardation and dopamine-related behaviors due to disruptions in their interaction with cullin-RING E3 ligases (CRLs). Thus, further identification of CUL4B substrates can increase the knowledge of protein homeostasis and illuminate the role of CUL4B in neuropsychiatric disease. However, the transient nature of the coupling between CUL4B and its substrates is difficult to detect in vivo using current approaches, thus hampers efforts to investigate functions of CRLs within unperturbed living systems. In this study, we sought to discover CUL4B interactants with or without dopamine stimulation. BirA (118G) proximity-dependent biotin labeling combined with LC-MS was employed to biotinylate and identify transient and weak interactants of CUL4B. After purification with streptavidin beads and identified by LC-MS, a total of 150 biotinylated proteins were identified at baseline condition, 53 of which are well-known CUL4B interactants. After dopamine stimulation, 29 proteins disappeared and were replaced by 21 different protein interactants. The altered CUL4B interactants suggest that CUL4B regulates protein turnover and homeostasis in response to dopamine stimulation. Our results demonstrate the potential of this approach to identify novel CUL4B-related molecules in respond to cellular stimuli, which may be applied to other types of signaling pathways.

An ensemble approach for large-scale identification of protein- protein interactions using the alignments of multiple sequences

Protein-Protein Interactions (PPI) is not only the critical component of various biological processes in cells, but also the key to understand the mechanisms leading to healthy and diseased states in organisms. However, it is time-consuming and cost-intensive to identify the interactions among proteins using biological experiments. Hence, how to develop a more efficient computational method rapidly became an attractive topic in the post-genomic era. In this paper, we propose a novel method for inference of protein-protein interactions from protein amino acids sequences only. Specifically, protein amino acids sequence is firstly transformed into Position-Specific Scoring Matrix (PSSM) generated by multiple sequences alignments; then the Pseudo PSSM is used to extract feature descriptors. Finally, ensemble Rotation Forest (RF) learning system is trained to predict and recognize PPIs based solely on protein sequence feature. When performed the proposed method on the three benchmark data sets (Yeast, H. pylori, and independent dataset) for predicting PPIs, our method can achieve good average accuracies of 98.38%, 89.75%, and 96.25%, respectively. In order to further evaluate the prediction performance, we also compare the proposed method with other methods using same benchmark data sets. The experiment results demonstrate that the proposed method consistently outperforms other state-of-the-art method. Therefore, our method is effective and robust and can be taken as a useful tool in exploring and discovering new relationships between proteins. A web server is made publicly available at the URL http://202.119.201.126:8888/PsePSSM/ for academic use.

Overcoming T. gondii infection and intracellular protein nanocapsules as biomaterials for ultrasonically controlled drug release

One of the pivotal matters of concern in intracellular drug delivery is the preparation of biomaterials containing drugs that are compatible with the host target.

Protein-protein interactions can be predicted using coiled coil co-evolution patterns

Protein-protein interactions are sometimes mediated by coiled coil structures. The evolutionary conservation of interacting orthologs in different species, along with the presence or absence of coiled coils in them, may help in the prediction of interacting pairs. Here, we illustrate how the presence of coiled coils in a protein can be exploited as a potential indicator for its interaction with another protein with coiled coils. The prediction capability of our strategy improves when restricting our dataset to highly reliable, known protein-protein interactions. Our study of the co-evolution of coiled coils demonstrates that pairs of interacting proteins can be distinguished from not interacting pairs by means of their structural information. This hints at the potential of our strategy to predict new protein-protein interactions.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

Background

Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis.

Results

Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous.

Conclusions

The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0346-4) contains supplementary material, which is available to authorized users.

Applying bimolecular fluorescence complementation to screen and purify aquaporin protein:protein complexes

Protein:protein interactions play key functional roles in the molecular machinery of the cell. A major challenge for structural biology is to gain high‐resolution structural insight into how membrane protein function is regulated by protein:protein interactions. To this end we present a method to express, detect, and purify stable membrane protein complexes that are suitable for further structural characterization. Our approach utilizes bimolecular fluorescence complementation (BiFC), whereby each protein of an interaction pair is fused to nonfluorescent fragments of yellow fluorescent protein (YFP) that combine and mature as the complex is formed. YFP thus facilitates the visualization of protein:protein interactions in vivo, stabilizes the assembled complex, and provides a fluorescent marker during purification. This technique is validated by observing the formation of stable homotetramers of human aquaporin 0 (AQP0). The method's broader applicability is demonstrated by visualizing the interactions of AQP0 and human aquaporin 1 (AQP1) with the cytoplasmic regulatory protein calmodulin (CaM). The dependence of the AQP0‐CaM complex on the AQP0 C‐terminus is also demonstrated since the C‐terminal truncated construct provides a negative control. This screening approach may therefore facilitate the production and purification of membrane protein:protein complexes for later structural studies by X‐ray crystallography or single particle electron microscopy.

Integration of multiple biological features yields high confidence human protein interactome

The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level.

Current Experimental Methods for Characterizing Protein-Protein Interactions

Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method.

Current Experimental Methods for Characterizing Protein-Protein Interactions

Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method.

Structure-Based Design of Peptidic Inhibitors of the Interaction between CC Chemokine Ligand 5 (CCL5) and Human Neutrophil Peptides 1 (HNP1)

Protein-protein interactions (PPIs) are receiving increasing interest, much sparked by the realization that they represent druggable targets. Recently, we successfully developed a peptidic inhibitor, RRYGTSKYQ ("SKY" peptide), that shows high potential in vitro and in vivo to interrupt a PPI between the platelet-borne chemokine CCL5 and the neutrophil-derived granule protein HNP1. This PPI plays a vital role in monocyte adhesion, representing a key mechanism in acute and chronic inflammatory diseases. Here, we present extensive and detailed computational methods applied to develop the SKY peptide. We combined experimentally determined binding affinities (KD) of several orthologs of CCL5 with HNP1 with in silico studies to identify the most likely heterodimeric CCL5-HNP1 complex which was subsequently used as a starting structure to rationally design peptidic inhibitors. Our method represents a fast and simple approach that can be widely applied to determine other protein-protein complexes and moreover to design inhibitors or stabilizers of protein-protein interaction.

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

Protein-protein interactions (PPIs) are key molecular events to biology. However, it remains a challenge to visualize PPIs with sufficient resolution and sensitivity in cells because the resolution of conventional light microscopy is diffraction-limited to ~250 nm. By combining bimolecular fluorescence complementation (BiFC) with photoactivated localization microscopy (PALM), PPIs can be visualized in cells with single molecule sensitivity and nanometer spatial resolution. BiFC is a commonly used technique for visualizing PPIs with fluorescence contrast, which involves splitting of a fluorescent protein into two non-fluorescent fragments. PALM is a recent superresolution microscopy technique for imaging biological samples at the nanometer and single molecule scales, which uses phototransformable fluorescent probes such as photoactivatable fluorescent proteins (PA-FPs). BiFC-PALM was demonstrated by splitting PAmCherry1, a PA-FP compatible with PALM, for its monomeric nature, good single molecule brightness, high contrast ratio, and utility for stoichiometry measurements. When split between amino acids 159 and 160, PAmCherry1 can be made into a BiFC probe that reconstitutes efficiently at 37 °C with high specificity to PPIs and low non-specific reconstitution. Ras-Raf interaction is used as an example to show how BiFC-PALM helps to probe interactions at the nanometer scale and with single molecule resolution. Their diffusion can also be tracked in live cells using single molecule tracking (smt-) PALM. In this protocol, factors to consider when designing the fusion proteins for BiFC-PALM are discussed, sample preparation, image acquisition, and data analysis steps are explained, and a few exemplary results are showcased. Providing high spatial resolution, specificity, and sensitivity, BiFC-PALM is a useful tool for studying PPIs in intact biological samples.

Multi-scale perturbations of protein interactomes reveal their mechanisms of regulation, robustness and insights into genotype-phenotype maps

Cellular architectures and signaling machineries are organized through protein-protein interactions (PPIs). High-throughput methods to study PPIs in yeast have opened a new perspective on the organization of the cell by allowing the study of whole protein interactomes. Recent investigations have moved from the description of this organization to the analysis of its dynamics by experimenting how protein interaction networks (PINs) are rewired in response to perturbations. Here we review studies that have used the budding yeast as an experimental system to explore these altered networks. Given the large space of possible PPIs and the diversity of potential genetic and environmental perturbations, high-throughput methods are an essential requirement to survey PIN perturbations on a large scale. Network perturbations are typically conceptualized as the removal of entire proteins (nodes), the modification of single PPIs (edges) or changes in growth conditions. These studies have revealed mechanisms of PPI regulation, PIN architectural organization, robustness and sensitivity to perturbations. Despite these major advances, there are still inherent limits to current technologies that lead to a trade-off between the number of perturbations and the number of PPIs that can be considered simultaneously. Nevertheless, as we exemplify here, targeted approaches combined with the existing resources remain extremely powerful to explore the inner organization of cells and their responses to perturbations.

DOCKSCORE: a webserver for ranking protein-protein docked poses

Background

Proteins interact with a variety of other molecules such as nucleic acids, small molecules and other proteins inside the cell. Structure-determination of protein-protein complexes is challenging due to several reasons such as the large molecular weights of these macromolecular complexes, their dynamic nature, difficulty in purification and sample preparation. Computational docking permits an early understanding of the feasibility and mode of protein-protein interactions. However, docking algorithms propose a number of solutions and it is a challenging task to select the native or near native pose(s) from this pool. DockScore is an objective scoring scheme that can be used to rank protein-protein docked poses. It considers several interface parameters, namely, surface area, evolutionary conservation, hydrophobicity, short contacts and spatial clustering at the interface for scoring.

Results

We have implemented DockScore in form of a webserver for its use by the scientific community. DockScore webserver can be employed, subsequent to docking, to perform scoring of the docked solutions, starting from multiple poses as inputs. The results, on scores and ranks for all the poses, can be downloaded as a csv file and graphical view of the interface of best ranking poses is possible.

Conclusions

The webserver for DockScore is made freely available for the scientific community at: http://caps.ncbs.res.in/dockscore/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0572-6) contains supplementary material, which is available to authorized users.

Stressor-induced proteome alterations in zebrafish: a meta-analysis of response patterns

Proteomics approaches are being increasingly applied in ecotoxicology on the premise that the identification of specific protein expression changes in response to a particular chemical would allow elucidation of the underlying molecular pathways leading to an adverse effect. This in turn is expected to promote the development of focused testing strategies for specific groups of toxicants. Although both gel-based and gel-free global characterization techniques provide limited proteome coverage, the conclusions regarding the cellular processes affected are still being drawn based on the few changes detected. To investigate how specific the detected responses are, we analyzed a set of studies that characterized proteome alterations induced by various physiological, chemical and biological stressors in zebrafish, a popular model organism. Our analysis highlights several proteins and protein groups, including heat shock and oxidative stress defense proteins, energy metabolism enzymes and cytoskeletal proteins, to be most frequently identified as responding to diverse stressors. In contrast, other potentially more specifically responding protein groups are detected much less frequently. Thus, zebrafish proteome responses to stress reported by different studies appear to depend mostly on the level of stress rather than on the specific stressor itself. This suggests that the most broadly used current proteomics technologies do not provide sufficient proteome coverage to allow in-depth investigation of specific mechanisms of toxicant action. We suggest that the results of any differential proteomics experiment performed with zebrafish should be interpreted keeping in mind the list of the most frequent responders that we have identified. Similar reservations should apply to any other species where proteome responses are analyzed by global proteomics methods. Careful consideration of the reliability and significance of observed changes is necessary in order not to over-interpret the experimental results and to prevent the proliferation of false positive linkages between the chemical and the cellular functions it perturbs. We further discuss the implications of the identified "top lists" of frequently responding proteins and protein families, and suggest further directions for proteomics research in ecotoxicology. Apart from improving the proteome coverage, further research should focus on defining the significance of the observed stress response patterns for organism phenotypes and on searching for common upstream regulators that can be targeted by specific assays.