Identifying 14-3-3 interactome binding sites with deep learning

Signal flow direction is one of the most important features of the protein-protein interactions in signaling networks. However, almost all the outcomes of current high-throughout techniques for protein-protein interactions mapping are usually supposed to be non-directional. Based on the pairwise interaction domains, here we defined a novel parameter protein interaction directional score and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08%, and error ratio 16.91%. As an application, we established an integrated human directional protein interaction network, including 2,237 proteins and 5,530 interactions, and inferred a large amount of novel signaling pathways. Directional protein interaction network was strongly supported by the known signaling pathways literature (with the 87.5% accuracy) and further analyses on the biological annotation, subcellular localization, and network topology property. Thus, this study provided an effective method to define the upstream/downstream relations of interacting protein pairs and a powerful tool to unravel the unknown signaling pathways.

In Silico Analysis of Potential Stabilizer Binding Sites at Protein-RNA Interfaces.

The solvent of membrane proteins is the membrane lipids in which they are embedded. Therefore, the nature of the lipids that surround membrane proteins impacts their dynamics and interactions. Unfortunately, how membrane proteins dynamically interact is difficult to study, and little is experimentally known how membrane proteins interplay in a membrane at the molecular scale. Herein, high‐speed atomic force microscopy (HS‐AFM) is used to dynamically image a well‐controlled bottom‐up system consisting of two aquaporin‐fold membrane proteins, pentameric FocA and tetrameric GlpF, that interact in membranes composed of varying amounts of 1,2‐dioleoyl‐sn‐glycero‐3‐phosphocholine (DOPC) and E. coli lipids. It is found that the lipid environment significantly influences membrane protein mobility and interaction, where increased E. coli lipid content reduces protein movement, while DOPC‐rich environments promote mobility. Furthermore, the supramolecular structures of the membrane proteins and protomer interactions in clusters are also lipid modulated, where E. coli lipids favor specific protein–protein interactions, whereas greater interaction variability is found in DOPC. These findings highlight the role of lipids in regulating protein diffusion and interactions and suggest that lipid–protein interaction energetics play a significant role in controlling membrane protein interactions and supramolecular assembly.

Demonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus [PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in the Luteoviridae and with unrelated viruses in the Herpesviridae and Adenoviridae. Functional analysis of three PLRV-interacting host proteins in planta using a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection-hallmarks of host-pathogen interactions-were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies. The exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy.

Protein:protein interactions are fundamental in living organism homeostasis. Here we introduce VHH6, a junctional epitope antibody capable of specifically recognizing a neo-epitope when two proteins interact, albeit transiently, to form a complex. Orthogonal biophysical techniques have been used to prove the “junctional epitope” nature of VHH6, a camelid single domain antibody recognizing the IL-6–gp80 complex but not the individual components alone. X-ray crystallography, HDX-MS and SPR analysis confirmed that the CDR regions of VHH6 interact simultaneously with IL-6 and gp80, locking the two proteins together. At the cellular level, VHH6 was able to alter the response of endothelial cells to exogenous IL-6, promoting a sustained STAT3 phosphorylation signal, an accumulation of IL-6 in vesicles and an overall pro-inflammatory phenotype supported further by transcriptomic analysis. Junctional epitope antibodies, like VHH6, not only offer new opportunities in screening and structure-aided drug discovery, but could also be exploited as therapeutics to modulate complex protein:protein interactions.

BackgroundProtein binding site prediction by computational means can yield valuable information that complements and guides experimental approaches to determine the structure of protein complexes. Predictions become even more relevant and timely given the current resolution of protein interaction maps, where there is a very large and still expanding gap between the available information on: (i) which proteins interact and (ii) how proteins interact. Proteins interact through exposed residues that present differential physicochemical properties, and these can be exploited to identify protein interfaces.ResultsHere we present VORFFIP, a novel method for protein binding site prediction. The method makes use of broad set of heterogeneous data and defined of residue environment, by means of Voronoi Diagrams that are integrated by a two-steps Random Forest ensemble classifier. Four sets of residue features (structural, energy terms, sequence conservation, and crystallographic B-factors) used in different combinations together with three definitions of residue environment (Voronoi Diagrams, sequence sliding window, and Euclidian distance) have been analyzed in order to maximize the performance of the method.ConclusionsThe integration of different forms information such as structural features, energy term, evolutionary conservation and crystallographic B-factors, improves the performance of binding site prediction. Including the information of neighbouring residues also improves the prediction of protein interfaces. Among the different approaches that can be used to define the environment of exposed residues, Voronoi Diagrams provide the most accurate description. Finally, VORFFIP compares favourably to other methods reported in the recent literature.

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

AlgK Is a TPR-Containing Protein and the Periplasmic Component of a Novel Exopolysaccharide Secretin

The interferon antiviral system is a primary barrier to virus replication triggered upon recognition of nonself RNAs by the cytoplasmic sensors encoded by retinoic acid-inducible gene I (RIG-I), melanoma differentiation-associated gene 5 (MDA5), and laboratory of genetics and physiology gene 2 (LGP2). Paramyxovirus V proteins are interferon antagonists that can selectively interact with MDA5 and LGP2 through contact with a discrete helicase domain region. Interaction with MDA5, an activator of antiviral signaling, disrupts interferon gene expression and antiviral responses. LGP2 has more diverse reported roles as both a coactivator of MDA5 and a negative regulator of both RIG-I and MDA5. This functional dichotomy, along with the concurrent interference with both cellular targets, has made it difficult to assess the unique consequences of V protein interaction with LGP2. To directly evaluate the impact of LGP2 interference, MDA5 and LGP2 variants unable to be recognized by measles virus and parainfluenza virus 5 (PIV5) V proteins were tested in signaling assays. Results indicate that interaction with LGP2 specifically prevents coactivation of MDA5 signaling and that LGP2's negative regulatory capacity was not affected. V proteins only partially antagonize RIG-I at high concentrations, and their expression had no additive effects on LGP2-mediated negative regulation. However, conversion of RIG-I to a direct V protein target was accomplished by only two amino acid substitutions that allowed both V protein interaction and efficient interference. These results clarify the unique consequences of MDA5 and LGP2 interference by paramyxovirus V proteins and help resolve the distinct roles of LGP2 in both activation and inhibition of antiviral signal transduction. Importance: Paramyxovirus V proteins interact with two innate immune receptors, MDA5 and LGP2, but not RIG-I. V proteins prevent MDA5 from signaling to the beta interferon promoter, but the consequences of LGP2 targeting are poorly understood. As the V protein targets MDA5 and LGP2 simultaneously, and LGP2 is both a positive and negative regulator of both MDA5 and RIG-I, it has been difficult to evaluate the specific advantages conferred by LGP2 targeting. Experiments with V-insensitive proteins revealed that the primary outcome of LGP2 interference is suppression of its ability to synergize with MDA5. LGP2's negative regulation of MDA5 and RIG-I remains intact irrespective of V protein interaction. Complementary experiments demonstrate that RIG-I can be converted to V protein sensitivity by two amino acid substitutions. These findings clarify the functions of LGP2 as a positive regulator of MDA5 signaling, demonstrate the basis for V-mediated LGP2 targeting, and broaden our understanding of paramyxovirus-host interactions.

Some of the protein interactions are still unidentified. Thus, many research about protein interactions had been held. HIV-1 is a dangerous virus that has no medicine yet. The research about HIV-1 proteins and human proteins interactions leads to the insight of drug target prediction. Biclustering technique is the beginning step before the prediction step. Biclustering is the process to cluster the dataset through two perspectives. The result of biclustering can be applied to predict unidentified protein interactions. Currently, this technique is more efficiently and effectively than the experimental method. The LCM-MBC is one of the biclustering algorithms to find biclusters from protein interactions dataset. This algorithm uses graph theory as the basic to obtain the maximal biclique. The algorithm can represent as enumeration tree. Every subtree result from the bicliques which are the biclusters. This algorithm performs quickly and efficiently in the term of memory consumptions. In this research, we apply the LCM-MBC algorithm for 16215 types of interactions between HIV-1 proteins and human proteins. We find 852 biclusters which the maximal bicluster has a size of 4 rows and 204 columns.

Stresses increasing the load of unfolded proteins that enter the endoplasmic reticulum (ER) trigger a protective response termed the unfolded protein response (UPR). Stromal cell-derived factor2 (SDF2)-type proteins are highly conserved throughout the plant and animal kingdoms. In this study we have characterized AtSDF2 as crucial component of the UPR in Arabidopsis thaliana. Using a combination of biochemical and cell biological methods, we demonstrate that SDF2 is induced in response to ER stress conditions causing the accumulation of unfolded proteins. Transgenic reporter plants confirmed induction of SDF2 during ER stress. Under normal growth conditions SDF2 is highly expressed in fast growing, differentiating cells and meristematic tissues. The increased production of SDF2 due to ER stress and in tissues that require enhanced protein biosynthesis and secretion, and its association with the ER membrane qualifies SDF2 as a downstream target of the UPR. Determination of the SDF2 three-dimensional crystal structure at 1.95 A resolution revealed the typical beta-trefoil fold with potential carbohydrate binding sites. Hence, SDF2 might be involved in the quality control of glycoproteins. Arabidopsis sdf2 mutants display strong defects and morphological phenotypes during seedling development specifically under ER stress conditions, thus establishing that SDF2-type proteins play a key role in the UPR.

Cdc25 phosphatase B, a potential target for cancer therapy, is inhibited by a series of quinones. The binding site and mode of quinone inhibitors to Cdc25B remains unclear, whereas this information is important for structure-based drug design. We investigated the potential binding site of NSC663284 [DA3003-1 or 6-chloro-7-(2-morpholin-4-yl-ethylamino)-quinoline-5, 8-dione] through docking and molecular dynamics simulations. Of the two main binding sites suggested by docking, the molecular dynamics simulations only support one site for stable binding of the inhibitor. Binding sites in and near the Cdc25B catalytic site that have been suggested previously do not lead to stable binding in 50ns molecular dynamics (MD) simulations. In contrast, a shallow pocket between the C-terminal helix and the catalytic site provides a favourable binding site that shows high stability. Two similar binding modes featuring protein-inhibitor interactions involving Tyr428, Arg482, Thr547 and Ser549 are identified by clustering analysis of all stable MD trajectories. The relatively flexible C-terminal region of Cdc25B contributes to inhibitor binding. The binding mode of NSC663284, identified through MD simulation, likely prevents the binding of protein substrates to Cdc25B. The present results provide useful information for the design of quinone inhibitors and their mechanism of inhibition.

Thermodynamic and kinetic origins of Alzheimer's and related diseases: A chemical engineer's perspective

Protein-protein interactions (PPIs) play a pivotal role in the regulation of many physiological processes. The dysfunction of some PPIs interactions led to the alteration of different biological pathways causing various diseases including cancer. In this context, the inhibition of PPIs represents an attractive strategy for the design of new antitumoral agents. In recent years, computational approaches were successfully used to study the interactions between proteins, providing useful hints for the design of small molecules able to modulate PPIs. Targeting PPIs presents several challenges mainly due to the large and flat binding surface that lack the typical binding pockets of traditional drug targets. Despite these hurdles, substantial progress has been made in the last decade resulting in the identification of PPI modulators where some of them even found clinical use. This study focuses on MUC1-CIN85 PPI which is involved in the migration and invasion of cancer cells. Particularly, we investigated the presence of druggable binding sites on the CIN85 surface which provided new insights for the structure-based design of novel MUC1-CIN85 PPI inhibitors as anti-metastatic agents.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.