A New Cellular Interactome of SARS-CoV-2 Nucleocapsid Protein and Its Biological Implications.

There is still much to uncover regarding the molecular details of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. As the most abundant protein, coronavirus nucleocapsid (N) protein encapsidates viral RNAs, serving as the structural component of ribonucleoprotein and virion, and participates in transcription, replication, and host regulations. Virus-host interaction might give clues to better understand how the virus affects or is affected by its host during infection and identify promising therapeutic candidates. Considering the critical roles of N, we here established a new cellular interactome of SARS-CoV-2 N by using a high-specific affinity purification (S-pulldown) assay coupled with quantitative mass spectrometry and immunoblotting validations, uncovering many N-interacting host proteins unreported previously. Bioinformatics analysis revealed that these host factors are mainly involved in translation regulations, viral transcription, RNA processes, stress responses, protein folding and modification, and inflammatory/immune signaling pathways, in line with the supposed actions of N in viral infection. Existing pharmacological cellular targets and the directing drugs were then mined, generating a drug-host protein network. Accordingly, we experimentally identified several small-molecule compounds as novel inhibitors against SARS-CoV-2 replication. Furthermore, a newly identified host factor, DDX1, was verified to interact and colocalize with N mainly by binding to the N-terminal domain of the viral protein. Importantly, loss/gain/reconstitution-of-function experiments showed that DDX1 acts as a potent anti-SARS-CoV-2 host factor, inhibiting the viral replication and protein expression. The N-targeting and anti-SARS-CoV-2 abilities of DDX1 are consistently independent of its ATPase/helicase activity. Further mechanism studies revealed that DDX1 impedes multiple activities of N, including the N-N interaction, N oligomerization, and N-viral RNA binding, thus likely inhibiting viral propagation. These data provide new clues to better depiction of the N-cell interactions and SARS-CoV-2 infection and may help inform the development of new therapeutic candidates.

Atlas of interactions between SARS-CoV-2 macromolecules and host proteins.

The proteins and RNAs of viruses extensively interact with host proteins after infection. We collected and reanalyzed all available datasets of protein-protein and RNA-protein interactions related to SARS-CoV-2. We investigated the reproducibility of those interactions and made strict filters to identify highly confident interactions. We systematically analyzed the interaction network and identified preferred subcellular localizations of viral proteins, some of which such as ORF8 in ER and ORF7A/B in ER membrane were validated using dual fluorescence imaging. Moreover, we showed that viral proteins frequently interact with host machinery related to protein processing in ER and vesicle-associated processes. Integrating the protein- and RNA-interactomes, we found that SARS-CoV-2 RNA and its N protein closely interacted with stress granules including 40 core factors, of which we specifically validated G3BP1, IGF2BP1, and MOV10 using RIP and Co-IP assays. Combining CRISPR screening results, we further identified 86 antiviral and 62 proviral factors and associated drugs. Using network diffusion, we found additional 44 interacting proteins including two proviral factors previously validated. Furthermore, we showed that this atlas could be applied to identify the complications associated with COVID-19. All data are available in the AIMaP database (https://mvip.whu.edu.cn/aimap/) for users to easily explore the interaction map.

Integrated interactome and transcriptome analysis reveals key host factors critical for SARS-CoV-2 infection.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has seriously threatened global public health and caused huge economic losses. Omics studies of SARS-CoV-2 can help understand the interaction between the virus and host, thereby providing a new perspective in guiding the intervention and treatment of the SARS-CoV-2 infection. Since large amount of SARS-CoV-2 omics data have been accumulated in public databases, this study aimed to identify key host factors involved in SARS-CoV-2 infection through systematic integration of transcriptome and interactome data. By manually curating published studies, we obtained a comprehensive SARS-CoV-2-human protein-protein interactions (PPIs) network, comprising 3591 human proteins interacting with 31 SARS-CoV-2 viral proteins. Using the RobustRankAggregation method, we identified 123 multiple cell line common genes (CLCGs), of which 115 up-regulated CLCGs showed host enhanced innate immunity and chemotactic response signatures. Combined with network analysis, co-expression and functional enrichment analysis, we discovered four key host factors involved in SARS-CoV-2 infection: IFITM1, SERPINE1, DDX60, and TNFAIP2. Furthermore, SERPINE1 was found to facilitate SARS-CoV-2 replication, and can alleviate the endoplasmic reticulum (ER) stress induced by ORF8 protein through interaction with ORF8. Our findings highlight the importance of systematic integration analysis in understanding SARS-CoV-2-human interactions and provide valuable insights for future research on potential therapeutic targets against SARS-CoV-2 infection.

Host-pathogen protein-protein interactions and interactomics in COVID-19

The current pandemic is already a third coronavirus spillover in the span of 20years. This recurrency meant that there was some information available about person-to-person transmission, cell entry, and host-pathogen interactions. The choice to target the Spike viral structural protein was easy, given its importance and specificity, together with a relatively low mutational rate. However, early in the pandemic, before any vaccines were ready, the scientific community was concerned with a lack of effective treatments against COVID-19. As soon as the viral genome was posted online, the race to identify potential host and viral targets gained pace, especially in the form of in silico "multi-omics.” Protein-protein interaction networks help identify key hub genes and proteins, as well as existing approved drugs whose mechanisms of action might be useful against this novel threat. To this end, several approaches have been devised: using influenza virus as a proxy;comparing SARS, MERS, and a common cold coronavirus to SARS-CoV-2;gene ontology matching;and the search for vulnerable proteins (VPs) and their perturbators. The algorithms deployed to perform the task have included a random walk with restart (RWR), degree of centrality, enrichment analysis, and weighted networks, backed up by experimental methods: cloning, tagging, and expressing viral proteins, affinity purification mass spectrometry, biotin labeling, and time-based transcriptomics, among others. Some of the proposed proteins and their genes reveal important pathways. Existing drugs available for repurposing encompass some interesting mechanisms of action: mRNA inhibitors, regulators of sigma receptors, and protease inhibitors. Candidate meds need to be further evaluated in clinical trials. Interestingly, most approaches give different results, and some of the insights and drugs do not make much sense. These studies, however, already make for a wealth of data to be revisited during gene discovery and drug repurposing. © 2023 Elsevier Inc. All rights reserved.

Transcriptional profiles and common genes link lung cancer with the development and severity of COVID-19.

Lung cancer patients with COVID-19 present an increased risk of developing severe disease and, consequently, have poor outcomes. Determining SARS-CoV-2-host interactome in lung cancer cells and tissues, infected or uninfected with SARS-CoV-2, may reveal molecular mechanisms associated with COVID-19 development and severity in lung cancer patients. Here, we integrated transcriptome data of lung tumors from patients with small- or non-small cell lung cancer (SCLC and NSCLC) and normal lung and lung cancer cells infected with SARS-CoV-2. We aimed to characterize molecular mechanisms potentially associated with COVID-19 development and severity in lung cancer patients and to predict the SARS-CoV-2-host cell interactome. We found that the gene expression profiles of lung cell lines infected with SARS-CoV-2 resemble more primary lung tumors than non-malignant lung tissues. In addition, the transcriptomic-based interactome analysis of SCLC and NSCLC revealed increased expression of cancer genes BRCA1 and CENPF, whose proteins are known or predicted to interact with the SARS-CoV-2 spike glycoprotein and helicase, respectively. We also found that TRIB3, a gene coding a putative host-SARS-CoV-2 interacting protein associated with COVID-19 infection, is co-expressed with the up-regulated genes MTHFD2, ADM2, and GPT2 in all tested conditions. Our analysis identified biological processes such as amino acid metabolism and angiogenesis and 22 host mediators of SARS-CoV-2 infection and replication that may contribute to the development and severity of COVID-19 in lung cancers.

Dynamic Interactomics by Cross-Linking Mass Spectrometry: Mapping the Daily Cell Life in Postgenomic Era.

The majority of processes that occur in daily cell life are modulated by hundreds to thousands of dynamic protein-protein interactions (PPI). The resulting protein complexes constitute a tangled network that, with its continuous remodeling, builds up highly organized functional units. Thus, defining the dynamic interactome of one or more proteins allows determining the full range of biological activities these proteins are capable of. This conceptual approach is poised to gain further traction and significance in the current postgenomic era wherein the treatment of severe diseases needs to be tackled at both genomic and PPI levels. This also holds true for COVID-19, a multisystemic disease affecting biological networks across the biological hierarchy from genome to proteome to metabolome. In this overarching context and the current historical moment of the COVID-19 pandemic where systems biology increasingly comes to the fore, cross-linking mass spectrometry (XL-MS) has become highly relevant, emerging as a powerful tool for PPI discovery and characterization. This expert review highlights the advanced XL-MS approaches that provide in vivo insights into the three-dimensional protein complexes, overcoming the static nature of common interactomics data and embracing the dynamics of the cell proteome landscape. Many XL-MS applications based on the use of diverse cross-linkers, MS detection methods, and predictive bioinformatic tools for single proteins or proteome-wide interactions were shown. We conclude with a future outlook on XL-MS applications in the field of structural proteomics and ways to sustain the remarkable flexibility of XL-MS for dynamic interactomics and structural studies in systems biology and planetary health.

Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2.

The ongoing COVID-19 outbreak have posed a significant threat to public health worldwide. Recently Toll-like receptor (TLR) has been proposed to be the drug target of SARS-CoV-2 treatment, the specificity and efficacy of such treatments remain unknown. In the present study we performed the investigation of repurposed drugs via a framework comprising of Search Tool for Interacting Chemicals (STITCH), Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular docking, and virus-host-drug interactome mapping. Chloroquine (CQ) and hydroxychloroquine (HCQ) were utilized as probes to explore the interaction network that is linked to SARS-CoV-2. 47 drug targets were shown to be overlapped with SARS-CoV-2 network and were enriched in TLR signaling pathway. Molecular docking analysis and molecular dynamics simulation determined the direct binding affinity of TLR9 to CQ and HCQ. Furthermore, we established SARS-CoV-2-human-drug protein interaction map and identified the axis of TLR9-ERC1-Nsp13 and TLR9-RIPK1-Nsp12. Therefore, the elucidation of the interactions of SARS-CoV-2 with TLR9 axis will not only provide pivotal insights into SARS-CoV-2 infection and pathogenesis but also improve the treatment against COVID-19.

Identification of phytochemicals in Qingfei Paidu decoction for the treatment of coronavirus disease 2019 by targeting the virus-host interactome.

Qingfei Paidu decoction (QFPDD) has been clinically proven to be effective in the treatment of coronavirus disease 2019 (COVID-19). However, the bioactive components and therapeutic mechanisms remain unclear. This study aimed to explore the effective components and underlying mechanisms of QFPDD in the treatment of COVID-19 by targeting the virus-host interactome and verifying the antiviral activities of its active components in vitro. Key active components and targets were identified by analysing the topological features of a compound-target-pathway-disease regulatory network of QFPDD for the treatment of COVID-19. The antiviral activity of the active components was determined by a live virus infection assay, and possible mechanisms were analysed by pseudotyped virus infection and molecular docking assays. The inhibitory effects of the components tested on the virus-induced release of IL-6, IL-1ß and CXCL-10 were detected by ELISA. Three components of QFPDD, oroxylin A, hesperetin and scutellarin, exhibited potent antiviral activities against live SARS-CoV-2 virus and HCoV-OC43 virus with IC50 values ranging from 18.68 to 63.27 µM. Oroxylin A inhibited the entry of SARS-CoV-2 pseudovirus into target cells and inhibited SARS-CoV-2 S protein-mediated cell-cell fusion by binding with the ACE2 receptor. The active components of QFPDD obviously inhibited the IL-6, IL-1ß and CXCL-10 release induced by the SARS-CoV-2 S protein. This study supports the clinical application of QFPDD and provides an effective analysis method for the in-depth study of the mechanisms of traditional Chinese medicine (TCM) in the prevention and treatment of COVID-19.

The Complexity and Multiplicity of the Specific cAMP Phosphodiesterase Family: PDE4, Open New Adapted Therapeutic Approaches.

Cyclic nucleotides (cAMP, cGMP) play a major role in normal and pathologic signaling. Beyond receptors, cyclic nucleotide phosphodiesterases; (PDEs) rapidly convert the cyclic nucleotide in its respective 5'-nucleotide to control intracellular cAMP and/or cGMP levels to maintain a normal physiological state. However, in many pathologies, dysregulations of various PDEs (PDE1-PDE11) contribute mainly to organs and tissue failures related to uncontrolled phosphorylation cascade. Among these, PDE4 represents the greatest family, since it is constituted by 4 genes with multiple variants differently distributed at tissue, cellular and subcellular levels, allowing different fine-tuned regulations. Since the 1980s, pharmaceutical companies have developed PDE4 inhibitors (PDE4-I) to overcome cardiovascular diseases. Since, they have encountered many undesired problems, (emesis), they focused their research on other PDEs. Today, increases in the knowledge of complex PDE4 regulations in various tissues and pathologies, and the evolution in drug design, resulted in a renewal of PDE4-I development. The present review describes the recent PDE4-I development targeting cardiovascular diseases, obesity, diabetes, ulcerative colitis, and Crohn's disease, malignancies, fatty liver disease, osteoporosis, depression, as well as COVID-19. Today, the direct therapeutic approach of PDE4 is extended by developing allosteric inhibitors and protein/protein interactions allowing to act on the PDE interactome.

Annotating Protein Structures for Understanding SARS-CoV-2 Interactome

Protein Contact Network (PCN) is an emerging paradigm for modelling protein structure. A common approach to interpreting such data is through network-based analyses. It has been shown that clustering analysis may discover allostery in PCN. Nevertheless Network Embedding has shown good performances in discovering hidden communities and structures in network. SARS-CoV-2 proteins, and in particular S protein, have a modular structure that need to be annotated to understand complex mechanism of infections. Such annotations, and in particular the highlighting of regions participating in the binding of human ACE2 and TMPRSS, may help the design of tailored strategy for preventing and blocking infection. In this work, we compare some approaches for graph embedding with respect to some classical clustering approaches for annotating protein structures. Results shows that embedding may reveal interesting structure that constitute the starting point for further analysis. © 2022 CEUR-WS. All rights reserved.

Differences and Similarities between the Lung Transcriptomic Profiles of COVID-19, COPD, and IPF Patients: A Meta-Analysis Study of Pathophysiological Signaling Pathways.

Coronavirus disease 2019 (COVID-19) is a pandemic respiratory disease associated with high morbidity and mortality. Although many patients recover, long-term sequelae after infection have become increasingly recognized and concerning. Among other sequelae, the available data indicate that many patients who recover from COVID-19 could develop fibrotic abnormalities over time. To understand the basic pathophysiology underlying the development of long-term pulmonary fibrosis in COVID-19, as well as the higher mortality rates in patients with pre-existing lung diseases, we compared the transcriptomic fingerprints among patients with COVID-19, idiopathic pulmonary fibrosis (IPF), and chronic obstructive pulmonary disease (COPD) using interactomic analysis. Patients who died of COVID-19 shared some of the molecular biological processes triggered in patients with IPF, such as those related to immune response, airway remodeling, and wound healing, which could explain the radiological images seen in some patients after discharge. However, other aspects of this transcriptomic profile did not resemble the profile associated with irreversible fibrotic processes in IPF. Our mathematical approach instead showed that the molecular processes that were altered in COVID-19 patients more closely resembled those observed in COPD. These data indicate that patients with COPD, who have overcome COVID-19, might experience a faster decline in lung function that will undoubtedly affect global health.

Epigenetic rewiring of pathways related to odour perception in immune cells exposed to SARS-CoV-2 in vivo and in vitro.

A majority of SARS-CoV-2 recoverees develop only mild-to-moderate symptoms, while some remain completely asymptomatic. Although viruses, including SARS-CoV-2, may evade host immune responses by epigenetic mechanisms including DNA methylation, little is known about whether these modifications are important in defence against and healthy recovery from COVID-19 in the host. To this end, epigenome-wide DNA methylation patterns from COVID-19 convalescents were compared to uninfected controls from before and after the pandemic. Peripheral blood mononuclear cell (PBMC) DNA was extracted from uninfected controls, COVID-19 convalescents, and symptom-free individuals with SARS-CoV-2-specific T cell-responses, as well as from PBMCs stimulated in vitro with SARS-CoV-2. Subsequently, the Illumina MethylationEPIC 850K array was performed, and statistical/bioinformatic analyses comprised differential DNA methylation, pathway over-representation, and module identification analyses. Differential DNA methylation patterns distinguished COVID-19 convalescents from uninfected controls, with similar results in an experimental SARS-CoV-2 infection model. A SARS-CoV-2-induced module was identified in vivo, comprising 66 genes of which six (TP53, INS, HSPA4, SP1, ESR1, and FAS) were present in corresponding in vitro analyses. Over-representation analyses revealed involvement in Wnt, muscarinic acetylcholine receptor signalling, and gonadotropin-releasing hormone receptor pathways. Furthermore, numerous differentially methylated and network genes from both settings interacted with the SARS-CoV-2 interactome. Altered DNA methylation patterns of COVID-19 convalescents suggest recovery from mild-to-moderate SARS-CoV-2 infection leaves longstanding epigenetic traces. Both in vitro and in vivo exposure caused epigenetic modulation of pathways thataffect odour perception. Future studies should determine whether this reflects host-induced protective antiviral defense or targeted viral hijacking to evade host defence.

Photocatalytic Chemical Crosslinking for Profiling RNA–Protein Interactions in Living Cells

The dynamic interactions between RNAs and proteins play crucial roles in regulating diverse cellular processes. Proteome‐wide characterization of these interactions in their native cellular context remains desirable but challenging. Herein, we developed a photocatalytic crosslinking (PhotoCAX) strategy coupled with mass spectrometry (PhotoCAX‐MS) and RNA sequencing (PhotoCAX‐seq) for the study of the composition and dynamics of protein‐RNA interactions. By integrating the blue light‐triggered photocatalyst with a dual‐functional RNA–protein crosslinker (RP‐linker) and the phase separation‐based enrichment strategy, PhotoCAX‐MS revealed a total of 2044 RBPs in human HEK293 cells. We further employed PhotoCAX to investigate the dynamic change of RBPome in macrophage cells upon LPS‐stimulation, as well as the identification of RBPs interacting directly with the 5′ untranslated regions of SARS‐CoV‐2 RNA. [ FROM AUTHOR] Copyright of Angewandte Chemie International Edition is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs.

SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the "gene-for-gene and "network for network" concepts as platforms for future directions toward designing efficient anti-viral therapies.

Characterization and functional interrogation of the SARS-CoV-2 RNA interactome.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic, which has led to a devastating global health crisis. The emergence of variants that escape neutralizing responses emphasizes the urgent need to deepen our understanding of SARS-CoV-2 biology. Using a comprehensive identification of RNA-binding proteins (RBPs) by mass spectrometry (ChIRP-MS) approach, we identify 107 high-confidence cellular factors that interact with the SARS-CoV-2 genome during infection. By systematically knocking down their expression in human lung epithelial cells, we find that the majority of the identified RBPs are SARS-CoV-2 proviral factors. In particular, we show that HNRNPA2B1, ILF3, QKI, and SFPQ interact with the SARS-CoV-2 genome and promote viral RNA amplification. Our study provides valuable resources for future investigations into the mechanisms of SARS-CoV-2 replication and the identification of host-centered antiviral therapies.

A BioID-Derived Proximity Interactome for SARS-CoV-2 Proteins.

The novel coronavirus SARS-CoV-2 is responsible for the ongoing COVID-19 pandemic and has caused a major health and economic burden worldwide. Understanding how SARS-CoV-2 viral proteins behave in host cells can reveal underlying mechanisms of pathogenesis and assist in development of antiviral therapies. Here, the cellular impact of expressing SARS-CoV-2 viral proteins was studied by global proteomic analysis, and proximity biotinylation (BioID) was used to map the SARS-CoV-2 virus-host interactome in human lung cancer-derived cells. Functional enrichment analyses revealed previously reported and unreported cellular pathways that are associated with SARS-CoV-2 proteins. We have established a website to host the proteomic data to allow for public access and continued analysis of host-viral protein associations and whole-cell proteomes of cells expressing the viral-BioID fusion proteins. Furthermore, we identified 66 high-confidence interactions by comparing this study with previous reports, providing a strong foundation for future follow-up studies. Finally, we cross-referenced candidate interactors with the CLUE drug library to identify potential therapeutics for drug-repurposing efforts. Collectively, these studies provide a valuable resource to uncover novel SARS-CoV-2 biology and inform development of antivirals.

Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs.

Inhibition of host protein functions using established drugs produces a promising antiviral effect with excellent safety profiles, decreased incidence of resistant variants and favorable balance of costs and risks. Genomic methods have produced a large number of robust host factors, providing candidates for identification of antiviral drug targets. However, there is a lack of global perspectives and systematic prioritization of known virus-targeted host proteins (VTHPs) and drug targets. There is also a need for host-directed repositioned antivirals. Here, we integrated 6140 VTHPs and grouped viral infection modes from a new perspective of enriched pathways of VTHPs. Clarifying the superiority of nonessential membrane and hub VTHPs as potential ideal targets for repositioned antivirals, we proposed 543 candidate VTHPs. We then presented a large-scale drug-virus network (DVN) based on matching these VTHPs and drug targets. We predicted possible indications for 703 approved drugs against 35 viruses and explored their potential as broad-spectrum antivirals. In vitro and in vivo tests validated the efficacy of bosutinib, maraviroc and dextromethorphan against human herpesvirus 1 (HHV-1), hepatitis B virus (HBV) and influenza A virus (IAV). Their drug synergy with clinically used antivirals was evaluated and confirmed. The results proved that low-dose dextromethorphan is better than high-dose in both single and combined treatments. This study provides a comprehensive landscape and optimization strategy for druggable VTHPs, constructing an innovative and potent pipeline to discover novel antiviral host proteins and repositioned drugs, which may facilitate their delivery to clinical application in translational medicine to combat fatal and spreading viral infections.

Uncover New Reactivity of Genetically Encoded Alkyl Bromide Non-Canonical Amino Acids.

Genetically encoded non-canonical amino acids (ncAAs) with electrophilic moieties are excellent tools to investigate protein-protein interactions (PPIs) both in vitro and in vivo. These ncAAs, including a series of alkyl bromide-based ncAAs, mainly target cysteine residues to form protein-protein cross-links. Although some reactivities towards lysine and tyrosine residues have been reported, a comprehensive understanding of their reactivity towards a broad range of nucleophilic amino acids is lacking. Here we used a recently developed OpenUaa search engine to perform an in-depth analysis of mass spec data generated for Thioredoxin and its direct binding proteins cross-linked with an alkyl bromide-based ncAA, BprY. The analysis showed that, besides cysteine residues, BprY also targeted a broad range of nucleophilic amino acids. We validated this broad reactivity of BprY with Affibody/Z protein complex. We then successfully applied BprY to map a binding interface between SUMO2 and SUMO-interacting motifs (SIMs). BprY was further applied to probe SUMO2 interaction partners. We identified 264 SUMO2 binders, including several validated SUMO2 binders and many new binders. Our data demonstrated that BprY can be effectively used to probe protein-protein interaction interfaces even without cysteine residues, which will greatly expand the power of BprY in studying PPIs.

Analysis of SARS-CoV-2 protein interactome map

By calculating the centrality measures of the nodes of the SARS-CoV-2 protein interactome network, we have identified the viral proteins of potential greatest interest for further experimental investigation to understand the mechanisms by which SARS-CoV-2 attacks cells and to identify possible therapeutic targets. The proteins identified in this study including NSP13, NSP7, ORF3a, ORF8a, and ORF8b, were found to be involved in crucial processes of the viral life cycle, and some of them are currently suspected to be antiviral targets. These results thus demonstrate the importance - and the predictive power- of the in silico analysis of the viral interactome to guide and support experimental investigation, which could otherwise be too complex and time-consuming to carry out in clinical and experimental research, given the size and interaction density of the viral protein network and the current still partial knowledge of this new virus. © 2021 IEEE.

PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naïve Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.