High-sensitivity profiling of SARS-CoV-2 noncoding region-host protein interactome reveals the potential regulatory role of negative-sense viral RNA.

A deep understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-host interactions is crucial to developing effective therapeutics and addressing the threat of emerging coronaviruses. The role of noncoding regions of viral RNA (ncrRNAs) has yet to be systematically scrutinized. We developed a method using MS2 affinity purification coupled with liquid chromatography-mass spectrometry and designed a diverse set of bait ncrRNAs to systematically map the interactome of SARS-CoV-2 ncrRNA in Calu-3, Huh7, and HEK293T cells. Integration of the results defined the core ncrRNA-host protein interactomes among cell lines. The 5' UTR interactome is enriched with proteins in the small nuclear ribonucleoproteins family and is a target for the regulation of viral replication and transcription. The 3' UTR interactome is enriched with proteins involved in the stress granules and heterogeneous nuclear ribonucleoproteins family. Intriguingly, compared with the positive-sense ncrRNAs, the negative-sense ncrRNAs, especially the negative-sense of 3' UTR, interacted with a large array of host proteins across all cell lines. These proteins are involved in the regulation of the viral production process, host cell apoptosis, and immune response. Taken together, our study depicts the comprehensive landscape of the SARS-CoV-2 ncrRNA-host protein interactome and unveils the potential regulatory role of the negative-sense ncrRNAs, providing a new perspective on virus-host interactions and the design of future therapeutics. Given the highly conserved nature of UTRs in positive-strand viruses, the regulatory role of negative-sense ncrRNAs should not be exclusive to SARS-CoV-2. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a pandemic affecting millions of lives. During replication and transcription, noncoding regions of the viral RNA (ncrRNAs) may play an important role in the virus-host interactions. Understanding which and how these ncrRNAs interact with host proteins is crucial for understanding the mechanism of SARS-CoV-2 pathogenesis. We developed the MS2 affinity purification coupled with liquid chromatography-mass spectrometry method and designed a diverse set of ncrRNAs to identify the SARS-CoV-2 ncrRNA interactome comprehensively in different cell lines and found that the 5' UTR binds to proteins involved in U1 small nuclear ribonucleoprotein, while the 3' UTR interacts with proteins involved in stress granules and the heterogeneous nuclear ribonucleoprotein family. Interestingly, negative-sense ncrRNAs showed interactions with a large number of diverse host proteins, indicating a crucial role in infection. The results demonstrate that ncrRNAs could serve diverse regulatory functions.

Monkeypox virus: phylogenomics, host-pathogen interactome and mutational cascade.

While the world is still recovering from the Covid-19 pandemic, monkeypox virus (MPXV) awaits to cause another global outbreak as a challenge to all of mankind. However, the Covid-19 pandemic has taught us a lesson to speed up the pace of viral genomic research for the implementation of preventive and treatment strategies. One of the important aspects of MPXV that needs immediate insight is its evolutionary lineage based on genomic studies. Utilizing high-quality isolates from the GISAID (Global Initiative on Sharing All Influenza Data) database, primarily sourced from Europe and North America, we employed a SNP-based whole-genome phylogeny method and identified four major clusters among 628 MPXV isolates. Our findings indicate a distinct evolutionary lineage for the first MPXV isolate, and a complex epidemiology and evolution of MPXV strains across various countries. Further analysis of the host-pathogen interaction network revealed key viral proteins, such as E3, SPI-2, K7 and CrmB, that play a significant role in regulating the network and inhibiting the host's cellular innate immune system. Our structural analysis of proteins E3 and CrmB revealed potential disruption of stability due to certain mutations. While this study identified a large number of mutations within the new outbreak clade, it also reflected that we need to move fast with the genomic analysis of newly detected strains from around the world to develop better prevention and treatment methods.

Molecular insight into the specific interactions of the SARS-Coronavirus-2 nucleocapsid with RNA and host protein.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) nucleocapsid protein is the most abundantly expressed viral protein during infection where it targets both RNA and host proteins. However, identifying how a single viral protein interacts with so many different targets remains a challenge, providing the impetus here for identifying the interaction sites through multiple methods. Through a combination of nuclear magnetic resonance (NMR), electron microscopy, and biochemical methods, we have characterized nucleocapsid interactions with RNA and with three host proteins, which include human cyclophilin-A, Pin1, and 14-3-3τ. Regarding RNA interactions, the nucleocapsid protein N-terminal folded domain preferentially interacts with smaller RNA fragments relative to the C-terminal region, suggesting an initial RNA engagement is largely dictated by this N-terminal region followed by weaker interactions to the C-terminal region. The nucleocapsid protein forms 10 nm ribonuclear complexes with larger RNA fragments that include 200 and 354 nucleic acids, revealing its potential diversity in sequestering different viral genomic regions during viral packaging. Regarding host protein interactions, while the nucleocapsid targets all three host proteins through its serine-arginine-rich region, unstructured termini of the nucleocapsid protein also engage host cyclophilin-A and host 14-3-3τ. Considering these host proteins play roles in innate immunity, the SARS-CoV-2 nucleocapsid protein may block the host response by competing interactions. Finally, phosphorylation of the nucleocapsid protein quenches an inherent dynamic exchange process within its serine-arginine-rich region. Our studies identify many of the diverse interactions that may be important for SARS-CoV-2 pathology during infection.

ChikvInt: a Chikungunya virus-host protein-protein interaction database.

Chikungunya is a fast-mutating virus causing Chikungunya virus disease (ChikvD) with a significant load of disability-adjusted life years (DALY) around the world. The outbreak of this virus is significantly higher in the tropical countries. Several experiments have identified crucial viral-host protein-protein interactions (PPIs) between Chikungunya Virus (Chikv) and the human host. However, no standard database that catalogs this PPI information exists. Here we develop a Chikv-Human PPI database, ChikvInt, to facilitate understanding ChikvD disease pathogenesis and the progress of vaccine studies. ChikvInt consists of 109 interactions and is available at www.chikvint.com.

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.

Identification of SARS-CoV-2 Main Protease (Mpro) Cleavage Sites Using Two-Dimensional Electrophoresis and In Silico Cleavage Site Prediction.

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays a crucial role in its life cycle. The Mpro-mediated limited proteolysis of the viral polyproteins is necessary for the replication of the virus, and cleavage of the host proteins of the infected cells may also contribute to viral pathogenesis, such as evading the immune responses or triggering cell toxicity. Therefore, the identification of host substrates of the viral protease is of special interest. To identify cleavage sites in cellular substrates of SARS-CoV-2 Mpro, we determined changes in the HEK293T cellular proteome upon expression of the Mpro using two-dimensional gel electrophoresis. The candidate cellular substrates of Mpro were identified by mass spectrometry, and then potential cleavage sites were predicted in silico using NetCorona 1.0 and 3CLP web servers. The existence of the predicted cleavage sites was investigated by in vitro cleavage reactions using recombinant protein substrates containing the candidate target sequences, followed by the determination of cleavage positions using mass spectrometry. Unknown and previously described SARS-CoV-2 Mpro cleavage sites and cellular substrates were also identified. Identification of target sequences is important to understand the specificity of the enzyme, as well as aiding the improvement and development of computational methods for cleavage site prediction.

Molecular insights on bioactive compounds againstCovid-19: A Network pharmacological and computational study.

BACKGROUND: Network pharmacology based identification of phytochemicals in the form of cocktails against off-targets can play a significant role in inhibition of SARS_CoV2 viral entry and its propagation. This study includes network pharmacology, virtual screening, docking and molecular dynamics to investigate the distinct antiviral mechanisms of effective phytochemicals against SARS_CoV2. METHODS: SARS_CoV2 human-protein interaction network was explored from the BioGRID database and analysed using Cytoscape. Further analysis was performed to explore biological function, protein-phytochemical/drugs network and up-down regulation of pathological host target proteins. This lead to understand the antiviral mechanism of phytochemicals against SARS_CoV2. The network was explored through g:Profiler, EnrichR, CTD, SwissTarget, STITCH, DrugBank, BindingDB, STRING and SuperPred. Virtual screening of phytochemicals against potential antiviral targets such as M-Pro, NSP1, Receptor binding domain, RNA binding domain, and ACE2 discloses the effective interaction between them. Further, the binding energy calculations through simulation of the docked complex explains the efficiency and stability of the interactions. RESULTS: The network analysis identified quercetin, genistein, luteolin, eugenol, berberine, isorhamnetin and cinnamaldehyde to be interacting with host proteins ACE2, DPP4, COMT, TUBGCP3, CENPF, BRD2 and HMOX1 which are involved in antiviral mechanisms such as viral entry, viral replication, host immune response, and antioxidant activity. Thus indicating that herbal cocktails can effectively tackle the viral hijacking of the crucial biological functions of human host. Further exploration through Virtual screening, docking and molecular dynamics recognizes the effective interaction of phytochemicals such as punicalagin, scutellarin, and solamargine with their respective potential targets. CONCLUSION: This work illustrates probable strategy for identification of phytochemical based cocktails and off-targets which are effective against SARS_CoV 2.

Bioinformatics-Based Approaches to Study Virus-Host Interactions During SARS-CoV-2 Infection.

As the knowledge of biomolecules is increasing from the last decades, it is helping the researchers to understand the unsolved issues regarding virology. Recent technologies in high-throughput sequencing are providing the swift generation of SARS-CoV-2 genomic data with the basic inside of viral infection. Owing to various virus-host protein interactions, high-throughput technologies are unable to provide complete details of viral pathogenesis. Identifying the virus-host protein interactions using bioinformatics approaches can assist in understanding the mechanism of SARS-CoV-2 infection and pathogenesis. In this chapter, recent integrative bioinformatics approaches are discussed to help the virologists and computational biologists in the identification of structurally similar proteins of human and SARS-CoV-2 virus, and to predict the potential of virus-host interactions. Considering experimental and time limitations for effective viral drug development, computational aided drug design (CADD) can reduce the gap between drug prediction and development. More research with respect to evolutionary solutions could be helpful to make a new pipeline for virus-host protein-protein interactions and provide more understanding to disclose the cases of host switch, and also expand the virulence of the pathogen and host range in developing viral infections.

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.

Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2.

BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.

Dynamic, but Not Necessarily Disordered, Human-Virus Interactions Mediated through SLiMs in Viral Proteins.

Most viruses have small genomes that encode proteins needed to perform essential enzymatic functions. Across virus families, primary enzyme functions are under functional constraint; however, secondary functions mediated by exposed protein surfaces that promote interactions with the host proteins may be less constrained. Viruses often form transient interactions with host proteins through conformationally flexible interfaces. Exposed flexible amino acid residues are known to evolve rapidly suggesting that secondary functions may generate diverse interaction potentials between viruses within the same viral family. One mechanism of interaction is viral mimicry through short linear motifs (SLiMs) that act as functional signatures in host proteins. Viral SLiMs display specific patterns of adjacent amino acids that resemble their host SLiMs and may occur by chance numerous times in viral proteins due to mutational and selective processes. Through mimicry of SLiMs in the host cell proteome, viruses can interfere with the protein interaction network of the host and utilize the host-cell machinery to their benefit. The overlap between rapidly evolving protein regions and the location of functionally critical SLiMs suggest that these motifs and their functional potential may be rapidly rewired causing variation in pathogenicity, infectivity, and virulence of related viruses. The following review provides an overview of known viral SLiMs with select examples of their role in the life cycle of a virus, and a discussion of the structural properties of experimentally validated SLiMs highlighting that a large portion of known viral SLiMs are devoid of predicted intrinsic disorder based on the viral SLiMs from the ELM database.

Analysis of the SARS-CoV-2-host protein interaction network reveals new biology and drug candidates: focus on the spike surface glycoprotein and RNA polymerase.

Introduction: The COVID-19 pandemic originated from the emergence of anovel coronavirus, SARS-CoV-2, which has been intensively studied since its discovery in order to generate the knowledge necessary to accelerate the development of vaccines and antivirals. Of note, many researchers believe there is great potential in systematically identifying host interactors of viral factors already targeted by existing drugs.Areas Covered: Herein, the authors discuss in detail the only available large-scale systematic study of the SARS-CoV-2-host protein-protein interaction network. More specifically, the authors review the literature on two key SARS-CoV-2 drug targets, the Spike surface glycoprotein, and the RNA polymerase. The authors also provide the reader with their expert opinion and future perspectives.Expert opinion: Interactions made by viral proteins with host factors reveal key functions that are likely usurped by the virus and, as aconsequence, points to known drugs that can be repurposed to fight viral infection and collateral damages that can exacerbate various disease conditions in COVID-19.

Computational approach to decipher cellular interactors and drug targets during co-infection of SARS-CoV-2, Dengue, and Chikungunya virus.

The world is reeling under severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, and it will be frightening if compounded by other co-existing infections. The co-occurrence of the Dengue virus (DENV) and Chikungunya virus (CHIKV) has been into existence, but recently the co-infection of DENV and SARS-CoV-2 has been reported. Thus, the possibility of DENV, CHIKV, and SARS-CoV-2 co-infection could be predicted in the future with enhanced vulnerability. It is essential to elucidate the host interactors and the connected pathways to understand the biological insights. The in silico approach using Cytoscape was exploited to elucidate the common human proteins interacting with DENV, CHIKV, and SARS-CoV-2 during their probable co-infection. In total, 17 interacting host proteins were identified showing association with envelope, structural, non-structural, and accessory proteins. Investigating the functional and biological behaviour using PANTHER, UniProtKB, and KEGG databases uncovered their association with several cellular pathways including, signaling pathways, RNA processing and transport, cell cycle, ubiquitination, and protein trafficking. Withal, exploring the DrugBank and Therapeutic Target Database, total seven druggable host proteins were predicted. Among all integrin beta-1, histone deacetylase-2 (HDAC2) and microtubule affinity-regulating kinase-3 were targeted by FDA approved molecules/ drugs. Furthermore, HDAC2 was predicted to be the most significant target, and some approved drugs are available against it. The predicted druggable targets and approved drugs could be investigated to obliterate the identified interactions that could assist in inhibiting viral infection.

Brief review on repurposed drugs and vaccines for possible treatment of COVID-19.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of the pandemic coronavirus disease 2019 (Covid-19) has claimed more than a million lives. Various in silico, in vitro, and in vivo studies are being conducted to understand the effect of SARS-CoV-2 on the cellular metabolism of humans and the various drugs and drug-targets that may be used. In this review, we discuss protein-protein interactions (PPIs) between viral and human proteins as well as viral targets like proteases. We try to understand the molecular mechanism of various repurposed antiviral drugs against SARS-CoV-2, their combination therapies, drug dosage regimens, and their adverse effects along with possible alternatives like non-toxic antiviral phytochemicals. Ultimately, randomized controlled trials are needed to identify which of these compounds has the required balance of efficacy and safety. We also focus on the recent advancements in diagnostic methods and vaccine candidates developed around the world to fight against Covid-19.

Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-19 (COVID-19) being associated with severe pneumonia. Like with other viruses, the interaction of SARS-CoV-2 with host cell proteins is necessary for successful replication, and cleavage of cellular targets by the viral protease also may contribute to the pathogenesis, but knowledge about the human proteins that are processed by the main protease (3CLpro) of SARS-CoV-2 is still limited. We tested the prediction potentials of two different in silico methods for the identification of SARS-CoV-2 3CLpro cleavage sites in human proteins. Short stretches of homologous host-pathogen protein sequences (SSHHPS) that are present in SARS-CoV-2 polyprotein and human proteins were identified using BLAST analysis, and the NetCorona 1.0 webserver was used to successfully predict cleavage sites, although this method was primarily developed for SARS-CoV. Human C-terminal-binding protein 1 (CTBP1) was found to be cleaved in vitro by SARS-CoV-2 3CLpro, the existence of the cleavage site was proved experimentally by using a His6-MBP-mEYFP recombinant substrate containing the predicted target sequence. Our results highlight both potentials and limitations of the tested algorithms. The identification of candidate host substrates of 3CLpro may help better develop an understanding of the molecular mechanisms behind the replication and pathogenesis of SARS-CoV-2.

SARS-CoV-2 protein drug targets landscape: a potential pharmacological insight view for the new drug development.

Introduction: Protein drug targets play a significant choice in different stages of the drug discovery process. There is an urgent need to understand the drug discovery approaches and protein drug targets (PDT) of SARS-CoV-2, with structural insights for the development of SARS-CoV-2 drugs through targeted therapeutic approach.Areas covered: We have described the protein as a drug target class and also discussed various drug discovery approaches for SARS-CoV-2 involving the protein drug targets such as drug repurposing study, designing of viral entry inhibitors, viral replication inhibitors, and different enzymes of the virus. We have performed comprehensive literature search from the popular databases such as PubMed Google scholar, Web of Science, and Scopus. Finally, we have illustrated the structural landscape of different significant viral proteins (3 CLpro or Mpro, PLpro, RdRp, helicase, S protein) and host proteins as drug targets (cathepsin L, furin, TMPRSS2, ACE2).Expert opinion: The structural landscape of PDT with their binding pockets, and significant residues involved in binding has been discussed further to better understand the PDT and the structure-based drug discovery for SARS-CoV-2. This attempt will increase more therapeutic options, and combination therapies with a multi-target strategy.