Protein-protein interactions of human viruses

Role of two modules controlling the interaction between SKAP1 and SRC kinases comparison with SKAP2 architecture and consequences for evolution

SRC kinase associated phosphoprotein 1 (SKAP1), an adaptor for protein assembly, plays an important role in the immune system such as stabilizing immune synapses. Understanding how these functions are controlled at the level of the protein-protein interactions is necessary to describe these processes and to develop therapeutics. Here, we dissected the SKAP1 modular organization to recognize SRC kinases and compared it to that of its paralog SRC kinase associated phosphoprotein 2 (SKAP2). Different conserved motifs common to either both proteins or specific to SKAP2 were found using this comparison. Two modules harboring different binding properties between SKAP1 and SKAP2 were identified: one composed of two conserved motifs located in the second interdomain interacting at least with the SH2 domain of SRC kinases and a second one composed of the DIM domain modulated by the SH3 domain and the activation of SRC kinases. This work suggests a convergent evolution of the binding properties of some SRC kinases interacting specifically with either SKAP1 or SKAP2.

Measuring criticality in control of complex biological networks

Recent controllability analyses have demonstrated that driver nodes tend to be associated to genes related to important biological functions as well as human diseases. While researchers have focused on identifying critical nodes, intermittent nodes have received much less attention. Here, we propose a new efficient algorithm based on the Hamming distance for computing the importance of intermittent nodes using a Minimum Dominating Set (MDS)-based control model. We refer to this metric as criticality. The application of the proposed algorithm to compute criticality under the MDS control framework allows us to unveil the biological importance and roles of the intermittent nodes in different network systems, from cellular level such as signaling pathways and cell-cell interactions such as cytokine networks, to the complete nervous system of the nematode worm C. elegans. Taken together, the developed computational tools may open new avenues for investigating the role of intermittent nodes in many biological systems of interest in the context of network control.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.

A Pan-Respiratory Antiviral Chemotype Targeting a Host Multi-Protein Complex

We present a novel small molecule antiviral chemotype that was identified by an unconventional cell-free protein synthesis and assembly-based phenotypic screen for modulation of viral capsid assembly. Activity of PAV-431, a representative compound from the series, has been validated against infectious virus in multiple cell culture models for all six families of viruses causing most respiratory disease in humans. In animals this chemotype has been demonstrated efficacious for Porcine Epidemic Diarrhea Virus (a coronavirus) and Respiratory Syncytial Virus (a paramyxovirus). PAV-431 is shown to bind to the protein 14-3-3, a known allosteric modulator. However, it only appears to target the small subset of 14-3-3 which is present in a dynamic multi-protein complex whose components include proteins implicated in viral lifecycles and in innate immunity. The composition of this target multi-protein complex appears to be modified upon viral infection and largely restored by PAV-431 treatment. Our findings suggest a new paradigm for understanding, and drugging, the host-virus interface, which leads to a new clinical therapeutic strategy for treatment of respiratory viral disease.

Role of molecular mimicry in the SARS-CoV-2-human interactome for pathogenesis of cardiovascular diseases: An update to ImitateDB

Mimicry of host proteins is a strategy employed by pathogens to hijack host functions. Domain and motif mimicry was explored in the experimental and predicted SARS-CoV-2-human interactome. The host first interactor proteins were also added to capture the continuum of the interactions. The domains and motifs of the proteins were annotated using NCBI CD Search and ScanProsite, respectively. Host and pathogen proteins with a common host interactor and similar domain/motif constitute a mimicry pair indicating global structural similarity (domain mimicry pair; DMP) or local sequence similarity (motif mimicry pair; MMP). 593 DMPs and 7,02,472 MMPs were determined. AAA, DEXDc and Macro domains were frequent among DMPs whereas glycosylation, myristoylation and RGD motifs were abundant among MMP. The proteins involved in mimicry were visualised as a SARS-CoV-2 mimicry interaction network. The host proteins were enriched in multiple CVD pathways indicating the role of mimicry in COVID-19 associated CVDs. Bridging nodes were identified as potential drug targets. Approved antihypertensive and anti-inflammatory drugs are proposed for repurposing against COVID-19 associated CVDs. The SARS-CoV-2 mimicry data has been updated in ImitateDB (http://imitatedb.sblab-nsit.net/SARSCoV2Mimicry). Determination of key mechanisms, proteins, pathways, drug targets and repurposing candidates is critical for developing therapeutics for SARS CoV-2 associated CVDs.

A comprehensive protein interaction map and druggability investigation prioritized dengue virus NS1 protein as promising therapeutic candidate

Dengue Virus (DENV) is a serious threat to human life worldwide and is one of the most dangerous vector-borne diseases, causing thousands of deaths annually. We constructed a comprehensive PPI map of DENV with its host Homo sapiens and performed various bioinformatics analyses. We found 1195 interactions between 858 human and 10 DENV proteins. Pathway enrichment analysis was performed on the two sets of gene products, and the top 5 human proteins with the maximum number of interactions with dengue viral proteins revealed noticeable results. The non-structural protein NS1 in DENV had the maximum number of interactions with the host protein, followed by NS5 and NS3. Among the human proteins, HBA1 and UBE2I were associated with 7 viral proteins, and 3 human proteins (CSNK2A1, RRP12, and HSP90AB1) were found to interact with 6 viral proteins. Pharmacophore-based virtual screening of millions of compounds in the public databases was performed to identify potential DENV-NS1 inhibitors. The lead compounds were selected based on RMSD values, docking scores, and strong binding affinities. The top ten hit compounds were subjected to ADME profiling which identified compounds C2 (MolPort-044-180-163) and C6 (MolPort-001-742-737) as lead inhibitors against DENV-NS1. Molecular dynamics trajectory analysis and intermolecular interactions between NS1 and the ligands displayed the molecular stability of the complexes in the cellular environment. The in-silico approaches used in this study could pave the way for the development of potential specie-specific drugs and help in eliminating deadly viral infections. Therefore, experimental and clinical assays are required to validate the results of this study.

Proteomic applications in identifying protein-protein interactions

There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.

Occurrences of similar viral diversity in campus wastewater and reclaimed water of a university dormitory

Water reuse from wastewater sources still remain some critical safety concerns associated with treacherous contaminants like pathogenic viruses. In this study, viral diversities in campus wastewater (CWW) and its reclaimed water (RCW) recycled for toilet flushing and garden irrigation of a university dormitory were assessed using metagenomic sequencing for acquisition of more background data. Results suggested majority (>80%) of gene sequences within assembled contigs predicted by open reading frame (ORF) finder were no-hit yet believed to be novel/unrevealed viral genomic information whereas hits matched bacteriophages (i.e., mainly Myoviridae, Podoviridae, and Siphoviridae families) were predominant in both CWW and RCW samples. Moreover, few pathogenic viruses (<1%) related to infections of human skin (e.g., Molluscum contagiosum virus, MCV), digestion system (e.g., hepatitis C virus, HCV), and gastrointestinal tract (e.g., human norovirus, HuNoV) were also noticed raising safety concerns about application of reclaimed waters. Low-affinity interactions of particular viral exterior proteins (e.g., envelope glycoproteins or spike proteins) for disinfectant ligand (e.g., chlorite) elucidated treatment limitations of current sewage processing systems even with membrane bioreactor and disinfectant contactor. Revolutionary disinfection approaches together with routine monitoring and new regulations are prerequisite to secure pathogen-correlated water quality for safer reuse of reclaimed waters.

Mechanism of cell cycle regulation and cell proliferation during human viral infection

Over the history of the coevolution of Host viral interaction, viruses have customized the host cellular machinery into their use for viral genome replication, causing effective infection and ultimately aiming for survival. They do so by inducing subversions to the host cellular pathways like cell cycle via dysregulation of important cell cycle checkpoints by viral encoded proteins, arresting the cell cycle machinery, blocking cytokinesis as well as targeting subnuclear bodies, thus ultimately disorienting the cell proliferation. Both DNA and RNA viruses have been active participants in such manipulation resulting in serious outcomes of cancer. They achieve this by employing different mechanisms-Protein-protein interaction, protein-phosphorylation, degradation, redistribution, viral homolog, and viral regulation of APC at different stages of cell cycle events. Several DNA viruses cause the quiescent staged cells to undergo cell cycle which increases nucleotide pools logistically significantly persuading viral replication whereas few other viruses arrest a particular stage of cell cycle. This allows the latter group to sustain the infection which allows them to escape host immune response and support viral multiplication. Mechanical study of signaling such viral mediated pathways could give insight into understanding the etiology of tumorigenesis and progression. Overall this chapter highlights the possible strategies employed by DNA/RNA viral families which impact the normal cell cycle but facilitate viral infected cell replication. Such information could contribute to comprehending viral infection-associated disorders to further depth.

Bioinformatics approaches for unveiling virus-host interactions

The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.

Viral hijacking mechanism in humans through protein-protein interactions

Numerous viruses have evolved mechanisms to inhibit or alter the host cell's apoptotic response as part of their coevolution with their hosts. The analysis of virus-host protein interactions require an in-depth understanding of both the viral and host protein structures and repertoires, as well as evolutionary mechanisms and pertinent biological facts. Throughout the course of a viral infection, there is constant battle for binding between virus and cellular proteins. Exogenous interfaces facilitating viral-host interactions are well known for constantly targeting and suppressing endogenous interfaces mediating intraspecific interactions, such as viral-viral and host-host connections. In these interactions, the protein-protein interactions (PPIs), are mostly shown as networks (protein interaction networks, PINs), with proteins represented as nodes and their interactions represented as edges. Host proteins with a higher degree of connectivity are more likely to interact with viral proteins. Due to technical advancements, three-dimensional interactions may now be visualized computationally utilizing molecular modeling and cryo-EM approaches. The uniqueness of viral domain repertoires, their evolution, and their activities during viral infection make viruses fascinating models for research. This chapter aims to provide readers a complete picture of the viral hijacking mechanism in protein-protein interactions.

An Ultraviolet/Visible Light Regulated Protein Transport Gate Constructed by Pillar[6]arene-based Host-Guest System

Protein transport is an interesting and intrinsic life feature that is highly relevant to physiology and disease in living beings. Herein, inspired by nature, based on the supramolecular host-guest interaction, we have introduced the classical azobenzene light switches and L-phenylalanine derived pillar[6]arene (L-Phe-P6) into the artificial nanochannel to construct light-responsive nanochannels that could regulate protein transport effectively under the control of ultraviolet (UV) and visible (Vis) light. The light-controlled distribution of L-Phe-P6 in the channel led to the difference in surface charges in the nanochannel, which eventually brought the difference in protein transport. This research may not only provide a convenient theoretical model for biological research, but also a flexible light-responsive protein transport model, which will play a crucial role in light-controlled release of protein drugs and so on.

The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses

Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.

Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences

BACKGROUND AND OBJECTIVE

The world is currently facing a global emergency due to COVID-19, which requires immediate strategies to strengthen healthcare facilities and prevent further deaths. To achieve effective remedies and solutions, research on different aspects, including the genomic and proteomic level characterizations of SARS-CoV-2, are critical. In this work, the spatial representation/composition and distribution frequency of 20 amino acids across the primary protein sequences of SARS-CoV-2 were examined according to different parameters.

METHOD

To identify the spatial distribution of amino acids over the primary protein sequences of SARS-CoV-2, the Hurst exponent and Shannon entropy were applied as parameters to fetch the autocorrelation and amount of information over the spatial representations. The frequency distribution of each amino acid over the protein sequences was also evaluated. In the case of a one-dimensional sequence, the Hurst exponent (HE) was utilized due to its linear relationship with the fractal dimension (D), i.e. D+HE=2, to characterize fractality. Moreover, binary Shannon entropy was considered to measure the uncertainty in a binary sequence then further applied to calculate amino acid conservation in the primary protein sequences.

RESULTS AND CONCLUSION

Fourteen (14) SARS-CoV protein sequences were evaluated and compared with 105 SARS-CoV-2 proteins. The simulation results demonstrate the differences in the collected information about the amino acid spatial distribution in the SARS-CoV-2 and SARS-CoV proteins, enabling researchers to distinguish between the two types of CoV. The spatial arrangement of amino acids also reveals similarities and dissimilarities among the important structural proteins, E, M, N and S, which is pivotal to establish an evolutionary tree with other CoV strains.

Bimolecular Fluorescence Complementation to Visualize Protein-Protein Interactions in Cells

Examining protein-protein interactions provides critical insight into numerous human diseases and infections. Here we describe a protocol for bimolecular fluorescence complementation, which can be used to directly visualize and characterize intracellular protein-protein interactions and ascertain their localization using fluorescence microscopy.

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

virusMED: your travel guide to the virus world

As we respond to viral epidemics and accelerate the discovery of new viruses, sifting through vast volumes of structural virology data could rapidly become an impossible task. virusMED is a curated atlas of metal/drug-binding and immunological hotspots in viral protein structures that provides a navigation guide for structure-function analysis and the development of antiviral strategies.

In silico predictions of protein interactions between Zika virus and human host

Background

The ZIKA virus (ZIKV) belongs to the Flaviviridae family, was first isolated in the 1940s, and remained underreported until its global threat in 2016, where drastic consequences were reported as Guillan-Barre syndrome and microcephaly in newborns. Understanding molecular interactions of ZIKV proteins during the host infection is important to develop treatments and prophylactic measures; however, large-scale experimental approaches normally used to detect protein-protein interaction (PPI) are onerous and labor-intensive. On the other hand, computational methods may overcome these challenges and guide traditional approaches on one or few protein molecules. The prediction of PPIs can be used to study host-parasite interactions at the protein level and reveal key pathways that allow viral infection.

Results

Applying Random Forest and Support Vector Machine (SVM) algorithms, we performed predictions of PPI between two ZIKV strains and human proteomes. The consensus number of predictions of both algorithms was 17,223 pairs of proteins. Functional enrichment analyses were executed with the predicted networks to access the biological meanings of the protein interactions. Some pathways related to viral infection and neurological development were found for both ZIKV strains in the enrichment analysis, but the JAK-STAT pathway was observed only for strain PE243 when compared with the FSS13025 strain.

Conclusions

The consensus network of PPI predictions made by Random Forest and SVM algorithms allowed an enrichment analysis that corroborates many aspects of ZIKV infection. The enrichment results are mainly related to viral infection, neuronal development, and immune response, and presented differences among the two compared ZIKV strains. Strain PE243 presented more predicted interactions between proteins from the JAK-STAT signaling pathway, which could lead to a more inflammatory immune response when compared with the FSS13025 strain. These results show that the methodology employed in this study can potentially reveal new interactions between the ZIKV and human cells.

Computational Analysis of Domains Vulnerable to HPV-16 E6 Oncoprotein and Corresponding Hot Spot Residues

BACKGROUND

Human Papilloma Virus (HPV) is the primary cause of cancers in cervix, head and neck regions. Oncoprotein E6 of HPV-16, after infecting human body, alters host protein- protein interaction networks. E6 interacts with several proteins, causing the infection to progress into cervical cancer. The molecular basis for these interactions is the presence of short linear peptide motifs on E6 identical to those on human proteins.

METHODS

Motifs of LXXLL and E/DLLL/V-G after identification on E6, were analyzed for their dynamic fluctuations by use of elastic network models. Correlation analysis of amino acid residues of E6 was also performed in specific regions of motifs.

RESULTS

Arginine, Leucine, Glutamine, Threonine and Glutamic acid have been identified as hot spot residues of E6 which can subsequently provide a platform for drug designing and understanding of pathogenesis of cervical cancer. These amino acids play a significant role in stabilizing interactions with host proteins, ultimately causing infections and cancers.

CONCLUSION

Our study validates the role of linear binding motifs of E6 of HPV in interacting with these proteins as an important event in the propagation of HPV in human cells and its transformation into cervical cancer. The study further predicts the domains of protein kinase and armadillo as part of the regions involved in the interaction of E6AP, Paxillin and TNF R1, with viral E6.

Insights into the Dynamic Fluctuations of the Protein HPV16 E1 and Identification of Motifs by Using Elastic Network Modeling

BACKGROUND

Cancers of cervix, head and neck regions have been found to be associated with Human Papilloma Virus (HPV) infection. E1 protein makes an important papillomavirus replication factor. Among the ORFs of papillomaviruses, the most conserved sequence is that of the E1 ORF. It is the viral helicase with being a member of class of ATPases associated with diverse cellular activities (AAA+) helicases. The interactions of E1 with human DNA and proteins occurs in the presence of short linear peptide motifs on E1 identical to those on human proteins.

METHODS

Different Motifs were identified on HPV16 E1 by using ELMs. Elastic network models were generated by using 3D structures of E1. Their dynamic fluctuations were analyzed on the basis of B factors, correlation analysis and deformation energies.

RESULTS

3 motifs were identified on E1 which can interact with Cdk and Cyclin domains of human proteins. 11 motifs identified on E1 have their CDs of Pkinase on human proteins. LIG_MYND_2 has been identified as involved in stabilizing interaction of E1 with Hsp40 and Hsp70. These motifs and amino acids comprising these motifs play a major role in maintaining interactions with human proteins, ultimately causing infections leading to cancers.

CONCLUSION

Our study identified various motifs on E1 which interact with specific counter domains found in human proteins, already reported having the interactions with E1. We also validated the involvement of these specific motifs containing regions of E1 by modeling elastic networks of E1. These motif involving interactions could be used as drug targets.

Mapping the SARS-CoV-2-Host Protein-Protein Interactome by Affinity Purification Mass Spectrometry and Proximity-Dependent Biotin Labeling: A Rational and Straightforward Route to Discover Host-Directed Anti-SARS-CoV-2 Therapeutics

Protein-protein interactions (PPIs) are the vital engine of cellular machinery. After virus entry in host cells the global organization of the viral life cycle is strongly regulated by the formation of virus-host protein interactions. With the advent of high-throughput -omics platforms, the mirage to obtain a "high resolution" view of virus-host interactions has come true. In fact, the rapidly expanding approaches of mass spectrometry (MS)-based proteomics in the study of PPIs provide efficient tools to identify a significant number of potential drug targets. Generation of PPIs maps by affinity purification-MS and by the more recent proximity labeling-MS may help to uncover cellular processes hijacked and/or altered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), providing promising therapeutic targets. The possibility to further validate putative key targets from high-confidence interactions between viral bait and host protein through follow-up MS-based multi-omics experiments offers an unprecedented opportunity in the drug discovery pipeline. In particular, drug repurposing, making use of already existing approved drugs directly targeting these identified and validated host interactors, might shorten the time and reduce the costs in comparison to the traditional drug discovery process. This route might be promising for finding effective antiviral therapeutic options providing a turning point in the fight against the coronavirus disease-2019 (COVID-19) outbreak.

Chicken bromodomain-containing protein 2 interacts with the Newcastle disease virus matrix protein and promotes viral replication

Bromodomain-containing protein 2 (BRD2) is a nucleus-localized serine-threonine kinase that plays pivotal roles in the transcriptional control of diverse genes. In our previous study, the chicken BRD2 (chBRD2) protein was found to interact with the Newcastle disease virus (NDV) matrix (M) protein using a yeast two-hybrid screening system, but the role of the chBRD2 protein in the replication of NDV remains unclear. In this study, we first confirmed the interaction between the M protein and chBRD2 protein using fluorescence co-localization, co-immunoprecipitation and pull-down assays. Intracellular binding studies indicated that the C-terminus (aa 264-313) of the M protein and the extra-terminal (ET) domain (aa 619-683) of the chBRD2 protein were responsible for interactions with each other. Interestingly, although two amino acids (T621 and S649) found in the chBRD2/ET domain were different from those in the human BRD2/ET domain and in that of other mammals, they did not disrupt the BRD2-M interaction or the chBRD2-M interaction. In addition, we found that the transcription of the chBRD2 gene was obviously decreased in both NDV-infected cells and pEGFP-M-transfected cells in a dose-dependent manner. Moreover, small interfering RNA-mediated knockdown of chBRD2 or overexpression of chBRD2 remarkably enhanced or reduced NDV replication by upregulating or downregulating viral RNA synthesis and transcription, respectively. Overall, we demonstrate for the first time that the interaction of the M protein with the chBRD2 protein in the nucleus promotes NDV replication by downregulating chBRD2 expression and facilitating viral RNA synthesis and transcription. These results will provide further insight into the biological functions of the M protein in the replication of NDV.

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV–host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV–host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV–human protein–protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies represent a basis for developing new anti-HIV therapeutic strategies such as new drugs preventing infection of CD4+ cells and viral replication, effective vaccines, “shock and kill” and “block and lock” approaches to cure latent infection.

Inferring Virus-Host relationship between HPV and its host Homo sapiens using protein interaction network

Human papilloma virus (HPV) is a serious threat to human life globally with over 100 genotypes including cancer causing high risk HPVs. Study on protein interaction maps of pathogens with their host is a recent trend in ‘omics’ era and has been practiced by researchers to find novel drug targets. In current study, we construct an integrated protein interaction map of HPV with its host human in Cytoscape and analyze it further by using various bioinformatics tools. We found out 2988 interactions between 12 HPV and 2061 human proteins among which we identified MYLK, CDK7, CDK1, CDK2, JAK1 and 6 other human proteins associated with multiple viral oncoproteins. The functional enrichment analysis of these top-notch key genes is performed using KEGG pathway and Gene Ontology analysis, which reveals that the gene set is enriched in cell cycle a crucial cellular process, and the second most important pathway in which the gene set is involved is viral carcinogenesis. Among the viral proteins, E7 has the highest number of associations in the network followed by E6, E2 and E5. We found out a group of genes which is not targeted by the existing drugs available for HPV infections. It can be concluded that the molecules found in this study could be potential targets and could be used by scientists in their drug design studies.

Proximity-dependent biotinylation by TurboID to identify protein-protein interaction networks in yeast

The use of proximity-dependent biotinylation assays coupled to mass spectrometry (PDB-MS) has changed the field of protein-protein interaction studies. Yet, despite the recurrent and successful use of BioID-based protein-protein interactions screening in mammalian cells, the implementation of PDB-MS in yeast has not been effective. Here we report a simple and rapid approach in yeast to effectively screen for proximal and interacting proteins in their natural cellular environment by using TurboID, a recently described version of the BirA biotin ligase. Using the protein arginine methyltransferase Rmt3 and the RNA exosome subunits, Rrp6 and Dis3, the application of PDB-MS in yeast by using TurboID was able to recover protein-protein interactions previously identified using other biochemical approaches and provided new complementary information for a given protein bait. The development of a rapid and effective PDB assay that can systematically analyze protein-protein interactions in living yeast cells opens the way for large-scale proteomics studies in this powerful model organism.