Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology.

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.

Chemical cross-linking coupled with mass spectrometry, an emerging approach for protein topology and interaction studies, has gained increasing interest in the past few years. A number of recent proof-of-principle studies on model proteins or protein complex systems with improved cross-linking strategies have shown great promise. However, the heterogeneity and low abundance of the cross-linked products as well as data complexity continue to pose enormous challenges for large-scale application of cross-linking approaches. A novel mass spectrometry-cleavable cross-linking strategy embodied in Protein Interaction Reporter (PIR) technology, first reported in 2005, was recently successfully applied for in vivo identification of protein-protein interactions as well as actual regions of the interacting proteins that share close proximity while present within cells. PIR technology holds great promise for achieving the ultimate goal of mapping protein interaction network at systems level using chemical cross-linking. In this review, we will briefly describe the recent progress in the field of chemical cross-linking development with an emphasis on the PIR concepts, its applications and future directions.

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.

The four divergent serotypes of dengue virus are the causative agents of dengue fever, dengue hemorrhagic fever and dengue shock syndrome. About two-fifths of the world's population live in areas where dengue is prevalent, and thousands of deaths are caused by the viruses every year. Dengue virus is transmitted from one person to another primarily by the yellow fever mosquito, Aedes aegypti. Recent studies have begun to define how the dengue viral proteins interact with host proteins to mediate viral replication and pathogenesis. A combined analysis of these studies, however, suggests that many virus-host protein interactions remain to be identified, especially for the mosquito host. In this study, we used high-throughput yeast two-hybrid screening to identify mosquito and human proteins that physically interact with dengue proteins. We tested each identified host protein against the proteins from all four serotypes of dengue to identify interactions that are conserved across serotypes. We further confirmed many of the interactions using co-affinity purification assays. As in other large-scale screens, we identified some previously detected interactions and many new ones, moving us closer to a complete host – dengue protein interactome. To help summarize and prioritize the data for further study, we combined our interactions with other published data and identified a subset of the host-dengue interactions that are now supported by multiple forms of evidence. These data should be useful for understanding the interplay between dengue and its hosts and may provide candidates for drug targets and vector control strategies.

Introduction: The objective of this study is to investigate the interaction between Candida albicans and human proteins during oral candidiasis, with the aim of identifying pathways through which the pathogen subverts host cells. Methods: A comprehensive list of interactions between human proteins and C. albicans was obtained from the Human Protein Interaction Database using specific screening criteria. Then, the genes that exhibit differential expression during oral candidiasis in C. albicans were mapped with the list of human-Candida interactions to identify the corresponding host proteins. The identified host proteins were further compared with proteins specific to the tongue, resulting in a final list of 99 host proteins implicated in oral candidiasis. The interactions between host proteins and C. albicans proteins were analyzed using the STRING database, enabling the construction of protein-protein interaction networks. Similarly, the gene regulatory network of Candida proteins was reconstructed using data from the PathoYeastract and STRING databases. Core module proteins within the targeted host protein-protein interaction network were identified using ModuLand, a Cytoscape plugin. The expression levels of the core module proteins under diseased conditions were assessed using data from the GSE169278 dataset. To gain insights into the functional characteristics of both host and pathogen proteins, ontology analysis was conducted using Enrichr and YeastEnrichr, respectively. Result: The analysis revealed that three Candida proteins, HHT21, CYP5, and KAR2, interact with three core host proteins, namely, ING4 (in the DNMT1 module), SGTA, and TOR1A. These interactions potentially impair the immediate immune response of the host against the pathogen. Additionally, differential expression analysis of fungal proteins and their transcription factors in Candida-infected oral cell lines indicated that Rob1p, Tye7p, and Ume6p could be considered candidate transcription factors involved in instigating the pathogenesis of oral candidiasis during host infection. Conclusion: Our study provides a molecular map of the host-pathogen interaction during oral candidiasis, along with potential targets for designing regimens to overcome oral candidiasis, particularly in immunocompromised individuals.

Deep-HPI-pred: an R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions

The Impact of Gene Expression Regulation on Evolution of Extracellular Signaling Pathways

Hepatocellular carcinoma (HCC) is the second leading cause of cancer death worldwide. Advanced HCC has proven particularly difficult to treat because of a scarcity of clear genetic drivers of cancer progression; thus, there are currently no predictive markers that guide HCC therapy. HCC arises in the context of co-morbid hepatitis due to hepatitis B virus (HBV), hepatitis C (HCV) or fatty liver disease. We hypothesize that protein-protein interactions (PPIs) between viral proteins and HCC genes may contribute to tumor initiation and maintenance. In order to characterize these PPIs, we performed affinity purification - mass spectrometry (APMS), defining 145 HBV/host PPIs including known and novel interacting partners. We next used a network propagation algorithm to identify host genes and protein complexes that were preferentially mutated in the absence of HBV infection. HBV is a small DNA virus, with 4 genes of which only one has enzymatic activity, raising a question as to how HBV interaction modifies host behavior. Using AP-MS of host proteins, we found that the HBV X protein (HBx) remodels multiple host protein complexes through direct interaction. These physical effects on complex components result in distinct biochemical behavior from the CRL4 E3 ubiquitin ligase complex as well as the phosphatase PP2A, as determined through global phosphoproteomics and ubiquitin analysis. We show that this remodeling driven by HBx substantially changes cellular protein turnover and downstream signaling dynamics. We followed this up with assessments of cellular viability and proliferation in response to pharmacological inhibition or CRISPRi-based knockdown of HBx effectors. Our data support a model where HBV proteins alter the components and behavior of key regulatory protein complexes in the cell, altering tumor behavior and raising the possibility of precision therapeutics for HCC. Citation Format: John D. Gordan, Adriana Pitea, Manon Eckhardt, Gwendolyn Jang, Rigney E. Turnham, Alex L M. Choi, John Von Dollen, Huat C. Lim, Elizabeth F. Thayer, R. Katie Kelley, Danielle L. Swaney, Wei Zhang, Fabian J. Theis, Trey Ideker, Nevan J. Krogan. Hepatitis B virus remodels host protein interaction networks to generate distinct cellular dependencies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4891.

Secondary structure elements often mediate protein-protein interactions. Despite their low abundance in folded proteins, polyproline II (PPII) and its variant, the triple helix, are frequently involved in protein-protein interactions, likely due to their peculiar propensity to be solvent-exposed. We here review the role of PPII and triple helix in mediating host-pathogen interactions, with a particular emphasis to the structural aspects of these processes. After a brief description of the basic structural features of these elements, examples of host-pathogen interactions involving these motifs are illustrated. Literature data suggest that the role played by PPII motif in these processes is twofold. Indeed, PPII regions may directly mediate interactions between proteins of the host and the pathogen. Alternatively, PPII may act as structural spacers needed for the correct positioning of the elements needed for adhesion and infectivity. Recent investigations have highlighted that collagen triple helix is also a common target for bacterial adhesins. Although structural data on complexes between adhesins and collagen models are rather limited, experimental and theoretical studies have unveiled some interesting clues of the recognition process. Interestingly, very recent data show that not only is the triple helix used by pathogens as a target in the host-pathogen interaction but it may also act as a bait in these processes since bacterial proteins containing triple helix regions have been shown to interact with host proteins. As both PPII and triple helix expose several main chain non-satisfied hydrogen bond acceptors and donors, both elements are highly solvated. The preservation of the solvation state of both PPII and triple helix upon protein-protein interaction is an emerging aspect that will be here thoroughly discussed.

Protein kinase RAF is strategically located in the "Ras-MAP-kinase signal transduction pathway", a principle system which transmits signals from growth factor receptors to the nucleus, resulting in cell proliferation. Growth factor responses are mediated in part by activation of Ras, which in turn activates RAF to phosphorylate MEK, its downstream substrate. MEK activates MAP-kinase to influence nuclear events. It is clear, however, that a network of signals other than those carried by Ras plays a role in RAF regulation. These orthogonal influences are mediated by: serine/threonine kinases, tyrosine kinases, and protein-protein interactions. As a further complication to the RAF network, three isoforms of RAF have been established which have divergent N-terminal regulatory domains. Whereas these divergent regulatory domains implicate isoform-specific functions, no clear evidence or hypothesis for distinct functions for individual isoforms has been presented. Recently, "isoform-specific protein interactions" have been identified among numerous proteins interacting with RAF. These studies may serve to delineate independent functions for RAF isoforms.

RNA-dependent RNA polymerases (RdRps) play a key role in the life cycle of RNA viruses and impact their immunobiology. The arenavirus lymphocytic choriomeningitis virus (LCMV) strain Clone 13 provides a benchmark model for studying chronic infection. A major genetic determinant for its ability to persist maps to a single amino acid exchange in the viral L protein, which exhibits RdRp activity, yet its functional consequences remain elusive. To unravel the L protein interactions with the host proteome, we engineered infectious L protein-tagged LCMV virions by reverse genetics. A subsequent mass-spectrometric analysis of L protein pulldowns from infected human cells revealed a comprehensive network of interacting host proteins. The obtained LCMV L protein interactome was bioinformatically integrated with known host protein interactors of RdRps from other RNA viruses, emphasizing interconnected modules of human proteins. Functional characterization of selected interactors highlighted proviral (DDX3X) as well as antiviral (NKRF, TRIM21) host factors. To corroborate these findings, we infected Trim21-/- mice with LCMV and found impaired virus control in chronic infection. These results provide insights into the complex interactions of the arenavirus LCMV and other viral RdRps with the host proteome and contribute to a better molecular understanding of how chronic viruses interact with their host.

Protein-protein interactions (PPI) are essential in every step of the HIV replication cycle. Mapping the interactions between viral and host proteins is a fundamental target for the design and development of new therapeutics. In this review, we focus on rational development of anti-HIV-1 peptides based on mapping viral-host and viral-viral protein interactions all across the HIV-1 replication cycle. We also discuss the mechanism of action, specificity and stability of these peptides, which are designed to inhibit PPI. Some of these peptides are excellent tools to study the mechanisms of PPI in HIV-1 replication cycle and for the development of anti-HIV-1 drug leads that modulate PPI.

Proteins interact in complex protein–protein interaction (PPI) networks whose topological properties—such as scale-free topology, hierarchical modularity, and dissortativity—have suggested models of network evolution. Currently preferred models invoke preferential attachment or gene duplication and divergence to produce networks whose topology matches that observed for real PPIs, thus supporting these as likely models for network evolution. Here, we show that the interaction density and homodimeric frequency are highly protein age–dependent in real PPI networks in a manner which does not agree with these canonical models. In light of these results, we propose an alternative stochastic model, which adds each protein sequentially to a growing network in a manner analogous to protein crystal growth (CG) in solution. The key ideas are (1) interaction probability increases with availability of unoccupied interaction surface, thus following an anti-preferential attachment rule, (2) as a network grows, highly connected sub-networks emerge into protein modules or complexes, and (3) once a new protein is committed to a module, further connections tend to be localized within that module. The CG model produces PPI networks consistent in both topology and age distributions with real PPI networks and is well supported by the spatial arrangement of protein complexes of known 3-D structure, suggesting a plausible physical mechanism for network evolution.

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.