Protein Interaction Sites Research Articles

Regulation of FcεRI signaling by lipid phosphatases.

Mast cells (MCs) are tissue-resident sentinels of hematopoietic origin that play a prominent role in allergic diseases. They express the high-affinity receptor for IgE (FcεRI), which when cross-linked by multivalent antigens triggers the release of preformed mediators, generation of arachidonic acid metabolites, and the synthesis of cytokines and chemokines. Stimulation of the FcεRI with increasing antigen concentrations follows a characteristic bell-shaped dose-responses curve. At high antigen concentrations, the so-called supra-optimal conditions, repression of FcεRI-induced responses is facilitated by activation and incorporation of negative signaling regulators. In this context, the SH2-containing inositol-5'-phosphatase, SHIP1, has been demonstrated to be of particular importance. SHIP1 with its catalytic and multiple protein interaction sites provides several layers of control for FcεRI signaling. Regulation of SHIP1 function occurs on various levels, e.g., protein expression, receptor and membrane recruitment, competition for protein-protein interaction sites, and activating modifications enhancing the phosphatase function. Apart from FcεRI-mediated signaling, SHIP1 can be activated by diverse unrelated receptor systems indicating its involvement in the regulation of antigen-dependent cellular responses by autocrine feedback mechanisms or tissue-specific and/or (patho-) physiologically determined factors. Thus, pharmacologic engagement of SHIP1 may represent a beneficial strategy for patients suffering from acute or chronic inflammation or allergies.

Chapter Two - Photoreactive Stapled Peptides to Identify and Characterize BCL-2 Family Interaction Sites by Mass Spectrometry

Protein interactions dictate a myriad of cellular activities that maintain health or cause disease. Dissecting these binding partnerships, and especially their sites of interaction, fuels the discovery of signaling pathways, disease mechanisms, and next-generation therapeutics. We previously applied all-hydrocarbon peptide stapling to chemically restore α-helical shape to bioactive motifs that become unfolded when taken out of context from native signaling proteins. For example, we developed stabilized alpha-helices of BCL-2 domains (SAHBs) to dissect and target protein interactions of the BCL-2 family, a critical network that regulates the apoptotic pathway. SAHBs are α-helical surrogates that bind both stable and transient physiologic interactors and have effectively uncovered novel sites of BCL-2 family protein interaction. To leverage stapled peptides for proteomic discovery, we describe our conversion of SAHBs into photoreactive agents that irreversibly capture their protein targets and facilitate rapid identification of the peptide helix binding sites. We envision that the development of photoreactive stapled peptides will accelerate the discovery of novel and unanticipated protein interactions and how they impact health and disease.

Multiple Dynamic DNA Rearrangements are Tightly Coupled to Distinct Stages of Human Telomerase Catalysis

Telomerase is essential for telomere homeostasis in most rapidly dividing cells, including the majority of human cancers. Telomeres are specialized chromatin structures which distinguish the natural ends of eukaryotic chromosomes from sites of DNA damage. Catalytically active human telomerase is minimally comprised of the telomerase RNA (hTR) and the protein telomerase reverse transcriptase (hTERT). DNA binding by telomerase is mediated by both hTR and hTERT, which align the 3' end of a single-stranded DNA substrate with a region of hTR that templates the reverse transcription reaction. After synthesis of a single telomere DNA repeat (GGTTAG in humans), the nascent DNA strand must dissociate from the RNA template and translocate to realign with the template for the next round of DNA repeat synthesis. Here, we present a novel single molecule assay that permits direct detection of individual human telomerase enzymes bound to a DNA substrate. Conformational dynamics within the enzyme-substrate complex are directly monitored as a distance-dependent change in the energy transfer efficiency between two dye molecules - a phenomenon known as Förster Resonance Energy Transfer (FRET). We have found that human telomerase, like other telomerases, actively manipulates the DNA:RNA hybrid length during DNA synthesis. When template synthesis nears completion, we observe structural dynamics throughout the entire primer that suggest concurrent manipulation of the hybrid and reorientation distal protein-DNA contacts. Through both FRET and a nuclease protection assays, we have shown that this rearrangement induces motion of the entire DNA primer, threading the 5' DNA out of the telomerase complex. Threading of the DNA allows telomerase to maintain contact with the DNA for processive repeat addition while simultaneously providing potential interaction sites for other telomeric binding proteins.

The Prediction of Protein-Protein Interaction Sites Based on RBF Classifier Improved by SMOTE

Protein-protein interaction sites are the basis of biomolecule interactions, which are widely used in drug target identification and new drug discovery. Traditional site predictors of protein-protein interaction mostly based on unbalanced datasets, the classification results tend to negative class, resulting in a lower predictive accuracy for positive class. A method called RBFIS (radial basis function improved by SMOTE) is presented in the paper to address the problem. The intelligent algorithm SMOTE is used to artificially synthesize the imbalanced datasets of negative sample classes. Simultaneously, KNN algorithm is utilized to interpolate values between the minority class samples to generate new samples, making the sample data tend to balance as much as possible. Then, RBF classifier is used to construct the site predictor of protein-protein interaction based on the processed quasi-equilibrium sample sets. The results of experiments indicated that the method had an improvement on recall andf-measure of positive class compared with traditional methods by 12% and 25%. Moreover, many rounds of experiments were performed for different combinations of features. It was observed that the key combination of different multiple features can better efficiently improve the prediction performance. In conclusion, the studies we have performed show that the proposed method is better for dealing with the imbalanced protein interaction sites.

Conformational Biases of Linear Motifs

Linear motifs (LMs) are protein-protein interaction sites, typically consisting of ~4-20 amino acid residues that are often found in disordered proteins or regions, and function largely independent from other parts of the proteins they are found in. These short sequence patterns are involved in a wide spectrum of biological functions including cell cycle control, transcriptional regulation, enzymatic catalysis, cell signaling, protein trafficking, etc. Even though LMs may adopt defined structures in complexes with targets, which can be determined by conventional methods, their uncomplexed states can be highly dynamic and difficult to characterize. This hinders our understanding of the structure-function relationship of LMs. Here, the uncomplexed states of 6 different LMs are investigated using atomistic molecular dynamics (MD) simulations. The total simulation time was about 63 μs. The results show that LMs can have distinct conformational propensities, which often resemble their complexed state. As a result, the free state structure and dynamics of LMs may hold important clues regarding binding mechanisms, affinities and specificities. The findings should be helpful in advancing our understanding of the mechanisms whereby disordered amino acid sequences bind targets, modeling disordered proteins/regions, and computational prediction of binding affinities.

Fragment based discovery of arginine isosteres through REPLACE: Towards non-ATP competitive CDK inhibitors

In order to develop non-ATP competitive CDK2/cyclin A inhibitors, the REPLACE strategy has been applied to generate fragment alternatives for the N-terminal tetrapeptide of the cyclin binding motif (HAKRRLIF) involved in substrate recruitment prior to phosphotransfer. The docking approach used for the prediction of small molecule mimics for peptide determinants was validated through reproduction of experimental binding modes of known inhibitors and provides useful information for evaluating binding to protein–protein interaction sites. Further to this, potential arginine isosteres predicted using the validated LigandFit docking method were ligated to the truncated C-terminal peptide, RLIF using solid phase synthesis and evaluated in a competitive binding assay. After testing, identified fragments were shown to represent not only appropriate mimics for a critical arginine residue but also to interact effectively with a minor hydrophobic pocket present in the binding groove. Further evaluation of binding modes was undertaken to optimize the potency of these compounds. Through further application of the REPLACE strategy in this study, peptide-small molecule hybrid CDK2 inhibitors were identified that are more drug-like and suitable for further optimization as anti-tumor therapeutics.

Abstract B3: Anti-angiogenic properties of FAK-VEGFR3 scaffold inhibitors, C9 and C10.

Abstract Introduction: Focal adhesion kinase (FAK) and vascular endothelial growth factor receptor 3 (VEGFR3) are tyrosine kinases, which function as key modulators of survival and metastasis signals in cancer cells. We have previously shown that VEGFR3 binds to the FAT domain of FAK and this interaction promotes the survival of tumor cells. We developed novel small molecule compounds C9 and C10 which specifically bind and inhibit the FAK-VEGFR3 interaction. Both these compounds inhibit cancer cell proliferation, induce apoptosis, disrupt the FAK-VEGFR3 signaling axis, and significantly inhibit tumor growth in xenograft mouse models of breast and pancreatic cancer. Since FAK and VEGFR3 have been implicated in tumor angiogenesis, we investigated the potential anti-angiogenic effects of C9 and C10. Methods: Binding of C9 and C10 to the FAT domain of FAK was tested using fluorescence polarization. Effects of C9 and C10 were tested in vitro by MTS assay (viability) and tube formation assay using HUVEC cells. Directed in vivo angiogenesis assay (DIVAA) kit was used to determine anti-angiogenic effects of both compounds. Single agent efficacy of C9 and C10 was tested in a pancreatic xenograft mouse model for 30 days. Mouse tumor tissues were analyzed for markers of proliferation (Ki67), endothelial vessels (CD31), and lymphangiogenic vessels (LYVE1) using immunohistochemistry. Results: Fluorescence polarization confirmed that C9 and C10 effectively bind to the FAT domain of FAK. C9 and C10 reduced cell viability of HUVEC cells and dramatically decreased tube formation by reducing total branching points, total loops and total tube length in a dose and time dependent manner. DIVAA assay confirmed that C9 and C10 significantly reduced the formation of endothelial blood vessels as compared to untreated groups. In vivo, 30 days treatment with low doses of C9 and C10 as single agents led to 40% to 45% reduction in tumor volume (P<0.05). Further analyses of these mouse tumors confirmed the anti-angiogenic effects of C9 and C10. Conclusions: We have shown that targeting the FAK-VEGFR3 protein protein interaction site allows for dual inhibition of tumor proliferation as well as tumor angiogenesis. This study highlights the importance of oral-based small molecule inhibitors that might impact the therapeutic management of solid tumors. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B3. Citation Format: Priyanka A. Agharkar, Manivannan Ethirajan, Jianqun Liao, Michael Yemma, Brian Buckley, Mikhail Chernov, William Cance, Ravindra Pandey, Elena Kurenova. Anti-angiogenic properties of FAK-VEGFR3 scaffold inhibitors, C9 and C10. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B3.

Fast prediction of protein–protein interaction sites based on Extreme Learning Machines

Conventional machine learning methods can be used to identify protein–protein interaction sites and study the gene regulatory networks and functions. However, when applied to large datasets, the computational complexities of these methods become a major drawback. With a significantly reduced computational complexity, the Extreme Learning Machines provide an attractive balance between computational time and generalization performance. In the method proposed in this paper, after searching for interfacial residues using a dynamic strategy and extracting spatially neighboring residue profiles for a set of 563 non-redundant protein chains, we implement the interface prediction either on multi-chain sets or on single-chain sets, using the two methods Extreme Learning Machines and support vector machines for a comparable study. As a consequence, in both multi-chain and single-chain cases Extreme Learning Machines tend to obtain higher Recall values than support vector machines, and in the multi-chain case Extreme Learning Machines as well show a remarkable advantage in the computational speed.

In silico design and construction of metal‐binding hybrid proteins for specific removal of cadmium based on CS3 pili display on the surface of Escherichia coli

In this study, through a combination of bioinformatics and genetic engineering procedures, high-affinity metal-binding peptides were designed and expressed on the surface of Escherichia coli for selective Cd(2+) adsorption. Putative cadmium-binding motifs were identified by searches against the Prosite database and permissive sites in the major subunit (CstH) of the enterotoxigenic E. coli pili were predicted based on the data derived from modeling of 3D structures, secondary structure prediction and assignment, inspection of protein hydropathy and exposed regions, and also protein interaction sites. The metal-binding motifs were inserted into one permissive site of the CstH (amino acid 38) with the aid of the SOEing PCR technique. The capacity and selectivity of the recombinant bacteria displaying hybrid pili to adsorb cadmium were evaluated with the atomic absorption procedure. The levels of Cd(2+) accumulation in the recombinant E. coli strains were 13.9- and 11.33-fold higher than those in the control strain. Cd(2+) was selectively absorbed from a solution containing equal concentrations of four metals, resulting in more than 90% of the total adsorbed metals being Cd(2+) , showing a relatively high affinity for Cd(2+) over other coexisting metal ions.

RIPiT-Seq: A high-throughput approach for footprinting RNA:protein complexes

Development of high-throughput approaches to map the RNA interaction sites of individual RNA binding proteins (RBPs) transcriptome-wide is rapidly transforming our understanding of post-transcriptional gene regulatory mechanisms. Here we describe a ribonucleoprotein (RNP) footprinting approach we recently developed for identifying occupancy sites of both individual RBPs and multi-subunit RNP complexes. RNA:protein immunoprecipitation in tandem (RIPiT) yields highly specific RNA footprints of cellular RNPs isolated via two sequential purifications; the resulting RNA footprints can then be identified by high-throughput sequencing (Seq). RIPiT-Seq is broadly applicable to all RBPs regardless of their RNA binding mode and thus provides a means to map the RNA binding sites of RBPs with poor inherent ultraviolet (UV) crosslinkability. Further, among current high-throughput approaches, RIPiT has the unique capacity to differentiate binding sites of RNPs with overlapping protein composition. It is therefore particularly suited for studying dynamic RNP assemblages whose composition evolves as gene expression proceeds.

Genome-scale sequence data processing and epigenetic analysis of DNA methylation

A new approach recently developed for detecting cytosine DNA methylation (mC) and analyzing the genome-scale DNA methylation profiling, is called BS-Seq which is based on bisulfite conversion of genomic DNA combined with next-generation sequencing. The method can not only provide an insight into the difference of genome-scale DNA methylation among different organisms, but also reveal the conservation of DNA methylation in all contexts and nucleotide preference for different genomic regions, including genes, exons, and repetitive DNA sequences. It will be helpful to under-stand the epigenetic impacts of cytosine DNA methylation on the regulation of gene expression and maintaining silence of repetitive sequences, such as transposable elements. In this paper, we introduce the preprocessing steps of DNA methylation data, by which cytosine (C) and guanine (G) in the reference sequence are transferred to thymine (T) and adenine (A), and cytosine in reads is transferred to thymine, respectively. We also comprehensively review the main content of the DNA methylation analysis on the genomic scale: (1) the cytosine methylation under the context of different sequences; (2) the distribution of genomic methylcytosine; (3) DNA methylation context and the preference for the nucleotides; (4) DNA- protein interaction sites of DNA methylation; (5) degree of methylation of cytosine in the different structural elements of genes. DNA methylation analysis technique provides a powerful tool for the epigenome study in human and other species, and genes and environment interaction, and founds the theoretical basis for further development of disease diagnostics and therapeutics in human.

Structural Properties of Non-Traditional Drug Targets Present New Challenges for Virtual Screening

Traditional drug targets have historically included signaling proteins that respond to small molecules and enzymes that use small molecules as substrates. Increasing attention is now being directed toward other types of protein targets, in particular those that exert their function by interacting with nucleic acids or other proteins rather than small-molecule ligands. Here, we systematically compare existing examples of inhibitors of protein-protein interactions to inhibitors of traditional drug targets. While both sets of inhibitors bind with similar potency, we find that the inhibitors of protein-protein interactions typically bury a smaller fraction of their surface area upon binding to their protein targets. The fact that an average atom is less buried suggests that more atoms are needed to achieve a given potency, explaining the observation that ligand efficiency is typically poor for inhibitors of protein-protein interactions. We then carried out a series of docking experiments and found a further consequence of these relatively exposed binding modes is that structure-based virtual screening may be more difficult: such binding modes do not provide sufficient clues to pick out active compounds from decoy compounds. Collectively, these results suggest that the challenges associated with such non-traditional drug targets may not lie with identifying compounds that potently bind to the target protein surface, but rather with identifying compounds that bind in a sufficiently buried manner to achieve good ligand efficiency and, thus, good oral bioavailability. While the number of available crystal structures of distinct protein interaction sites bound to small-molecule inhibitors is relatively small at present (only 21 such complexes were included in this study), these are sufficient to draw conclusions based on the current state of the field; as additional data accumulate it will be exciting to refine the viewpoint presented here. Even with this limited perspective however, we anticipate that these insights, together with new methods for exploring protein conformational fluctuations, may prove useful for identifying the "low-hanging fruit" among non-traditional targets for therapeutic intervention.

DBSI: DNA-binding site identifier

In this study, we present the DNA-Binding Site Identifier (DBSI), a new structure-based method for predicting protein interaction sites for DNA binding. DBSI was trained and validated on a data set of 263 proteins (TRAIN-263), tested on an independent set of protein-DNA complexes (TEST-206) and data sets of 29 unbound (APO-29) and 30 bound (HOLO-30) protein structures distinct from the training data. We computed 480 candidate features for identifying protein residues that bind DNA, including new features that capture the electrostatic microenvironment within shells near the protein surface. Our iterative feature selection process identified features important in other models, as well as features unique to the DBSI model, such as a banded electrostatic feature with spatial separation comparable with the canonical width of the DNA minor groove. Validations and comparisons with established methods using a range of performance metrics clearly demonstrate the predictive advantage of DBSI, and its comparable performance on unbound (APO-29) and bound (HOLO-30) conformations demonstrates robustness to binding-induced protein conformational changes. Finally, we offer our feature data table to others for integration into their own models or for testing improved feature selection and model training strategies based on DBSI.

The Antiprion Compound 6-Aminophenanthridine Inhibits the Protein Folding Activity of the Ribosome by Direct Competition

Domain V of the 23S/25S/28S rRNA of the large ribosomal subunit constitutes the active center for the protein folding activity of the ribosome (PFAR). Using in vitro transcribed domain V rRNAs from Escherichia coli and Saccharomyces cerevisiae as the folding modulators and human carbonic anhydrase as a model protein, we demonstrate that PFAR is conserved from prokaryotes to eukaryotes. It was shown previously that 6-aminophenanthridine (6AP), an antiprion compound, inhibits PFAR. Here, using UV cross-linking followed by primer extension, we show that the protein substrates and 6AP interact with a common set of nucleotides on domain V of 23S rRNA. Mutations at the interaction sites decreased PFAR and resulted in loss or change of the binding pattern for both the protein substrates and 6AP. Moreover, kinetic analysis of human carbonic anhydrase refolding showed that 6AP decreased the yield of the refolded protein but did not affect the rate of refolding. Thus, we conclude that 6AP competitively occludes the protein substrates from binding to rRNA and thereby inhibits PFAR. Finally, we propose a scheme clarifying the mechanism by which 6AP inhibits PFAR.

Mycobacterium tuberculosis RNA Polymerase-binding Protein A (RbpA) and Its Interactions with Sigma Factors

RNA polymerase-binding protein A (RbpA), encoded by Rv2050, is specific to the actinomycetes, where it is highly conserved. In the pathogen Mycobacterium tuberculosis, RbpA is essential for growth and survival. RbpA binds to the β subunit of the RNA polymerase where it activates transcription by unknown mechanisms, and it may also influence the response of M. tuberculosis to the current frontline anti-tuberculosis drug rifampicin. Here we report the solution structure of RbpA and identify the principle sigma factor σ(A) and the stress-induced σ(B) as interaction partners. The protein has a central ordered domain with a conserved hydrophobic surface that may be a potential protein interaction site. The N and C termini are highly dynamic and are involved in the interaction with the sigma factors. RbpA forms a tight complex with the N-terminal domain of σ(B) via its N- and C-terminal regions. The interaction with sigma factors may explain how RbpA stabilizes sigma subunit binding to the core RNA polymerase and thereby promotes initiation complex formation. RbpA could therefore influence the competition between principal and alternative sigma factors and hence the transcription profile of the cell.

The interplay between RPGR, PDEδ and Arl2/3 regulate the ciliary targeting of farnesylated cargo

Defects in primary cilia result in human diseases known as ciliopathies. The retinitis pigmentosa GTPase regulator (RPGR), mutated in the most severe form of the eye disease, is located at the transition zone of the ciliary organelle. The RPGR-interacting partner PDEδ is involved in trafficking of farnesylated ciliary cargo, but the significance of this interaction is unknown. The crystal structure of the propeller domain of RPGR shows the location of patient mutations and how they perturb the structure. The RPGR·PDEδ complex structure shows PDEδ on a highly conserved surface patch of RPGR. Biochemical experiments and structural considerations show that RPGR can bind with high affinity to cargo-loaded PDEδ and exposes the Arl2/Arl3-binding site on PDEδ. On the basis of these results, we propose a model where RPGR is acting as a scaffold protein recruiting cargo-loaded PDEδ and Arl3 to release lipidated cargo into cilia.

Druggable Protein Interaction Sites Are More Predisposed to Surface Pocket Formation than the Rest of the Protein Surface

Despite intense interest and considerable effort via high-throughput screening, there are few examples of small molecules that directly inhibit protein-protein interactions. This suggests that many protein interaction surfaces may not be intrinsically “druggable” by small molecules, and elevates in importance the few successful examples as model systems for improving our fundamental understanding of druggability. Here we describe an approach for exploring protein fluctuations enriched in conformations containing surface pockets suitable for small molecule binding. Starting from a set of seven unbound protein structures, we find that the presence of low-energy pocket-containing conformations is indeed a signature of druggable protein interaction sites and that analogous surface pockets are not formed elsewhere on the protein. We further find that ensembles of conformations generated with this biased approach structurally resemble known inhibitor-bound structures more closely than equivalent ensembles of unbiased conformations. Collectively these results suggest that “druggability” is a property encoded on a protein surface through its propensity to form pockets, and inspire a model in which the crude features of the predisposed pocket(s) restrict the range of complementary ligands; additional smaller conformational changes then respond to details of a particular ligand. We anticipate that the insights described here will prove useful in selecting protein targets for therapeutic intervention.

Full-length Gαq–phospholipase C-β3 structure reveals interfaces of the C-terminal coiled-coil domain

Phospholipase C-β (PLCβ) is directly activated by Gαq, but the molecular basis for how its distal C-terminal domain (CTD) contributes to maximal activity is poorly understood. Herein we present both the crystal structure and cryo-EM 3D reconstructions of human full-length PLCβ3 in complex with murine Gαq. The distal CTD forms an extended, monomeric helical bundle consisting of three anti-parallel segments with structural similarity to membrane-binding bin–amphiphysin–Rvs (BAR) domains. Sequence conservation of the distal CTD identifies putative membrane and protein interaction sites, the latter of which bind the N-terminal helix of Gαq in both the crystal structure and cryo-EM reconstructions. Functional analysis suggests the distal CTD plays roles in membrane targeting and in optimizing the orientation of the catalytic core at the membrane for maximal rates of lipid hydrolysis.

Current Status of Machine Learning-Based Methods for Identifying Protein-Protein Interaction Sites

High-throughput experimental technologies continue to alter the study of current system biology. Investigators are understandably eager to harness the power of these new technologies. Protein-protein interactions on these platforms, however, present numerous production and bioinformatics challenges. Some issues like feature extraction, feature representation, prediction algorithm and results analysis have become increasingly problematic in the prediction of protein-protein interaction sites. The development of powerful, efficient prediction methods for inferring protein interface residues based on protein primary sequence or/and 3D structure is critical for the research community to accelerate research and publications. Currently, machine learning-based approaches are drawing the most attention in predicting protein interaction sites. This review aims to describe the state of the whole pipeline when machine learning strategies are applied to infer protein interaction sites.

MetaPIS: A Sequence-based Meta-server for Protein Interaction Site Prediction

The identification of interfaces in protein complexes is effective for the elucidation of protein function and helps us to understand their roles in biological processes. With the exponentially growing amount of protein sequence data, an exploration of new methods that predict protein interaction sites based solely on sequence information is becoming increasingly urgent. Because a combination of different methods could produce better results than a single method, interaction site prediction can be improved through the utilization of different methods. This paper describes a new method that predicts interaction sites based on protein sequences by integrating five different algorithms employing meta-method, Majority Vote and SVMhmm Regression techniques. The 'metaPIS' web-server was implemented for meta-prediction. An evaluation of the meta-methods using independent datasets revealed that Majority Vote achieved the highest average Matthews correlation coefficient (0.181) among all the methods assessed. SVMhmm Regression achieved a lower score but provided a more stable result. The metaPIS server allows experimental biologists to speculate regarding protein function by identifying potential interaction sites based on protein sequence. As a web server, metaPIS is freely accessible to the public at http://202.116.74.5:84/metapis.