SARS-CoV-2-human protein-protein interaction network

A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction

Protein-protein interaction is one of the ways viruses interact with their hosts. Therefore, identifying protein interactions between viruses and hosts helps explain how virus proteins work, how they replicate, and how they cause disease. SARS-CoV-2 is a new type of virus that emerged from the coronavirus family in 2019 and caused a worldwide pandemic. Detection of human proteins interacting with this novel virus strain plays an important role in monitoring the cellular process of virus-associated infection. Within the scope of the study, a natural language processing-based collective learning method is proposed for the prediction of potential SARS-CoV-2-human PPIs. Protein language models were obtained with the prediction-based word2Vec and doc2Vec embedding methods and the frequency-based tf-idf method. Known interactions were represented by proposed language models and traditional feature extraction methods (conjoint triad and repeat pattern), and their performances were compared. The interaction data were trained with support vector machine, artificial neural network (ANN), k-nearest neighbor (KNN), naive Bayes (NB), decision tree (DT), and ensemble algorithms. Experimental results show that protein language models are a promising protein representation method for protein-protein interaction prediction. The term frequency-inverse document frequency-based language model performed the SARS-CoV-2 protein-protein interaction estimation with an error of 1.4%. Additionally, the decisions of high-performing learning models for different feature extraction methods were combined with a collective voting approach to make new interaction predictions. For 10,000 human proteins, 285 new potential interactions were predicted, with models combining decisions.

Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19

COVID-19, the disease caused by SARS-CoV-2, has been disrupting our lives for more than two years now. SARS-CoV-2 interacts with human proteins to pave its way into the human body, thereby wreaking havoc. Moreover, the mutating variants of the virus that take place in the SARS-CoV-2 genome are also a cause of concern among the masses. Thus, it is very important to understand human-spike protein-protein interactions (PPIs) in order to predict new PPIs and consequently propose drugs for the human proteins in order to fight the virus and its different mutated variants, with the mutations occurring in the spike protein. This fact motivated us to develop a complete pipeline where PPIs and drug-protein interactions can be predicted for human-SARS-CoV-2 interactions. In this regard, initially interacting data sets are collected from the literature, and noninteracting data sets are subsequently created for human-SARS-CoV-2 by considering only spike glycoprotein. On the other hand, for drug-protein interactions both interacting and noninteracting data sets are considered from DrugBank and ChEMBL databases. Thereafter, a model based on a sequence-based feature is used to code the protein sequences of human and spike proteins using the well-known Moran autocorrelation technique, while the drugs are coded using another well-known technique, viz., PaDEL descriptors, to predict new human-spike PPIs and eventually new drug-protein interactions for the top 20 predicted human proteins interacting with the original spike protein and its different mutated variants like Alpha, Beta, Delta, Gamma, and Omicron. Such predictions are carried out by random forest as it is found to perform better than other predictors, providing an accuracy of 90.53% for human-spike PPI and 96.15% for drug-protein interactions. Finally, 40 unique drugs like eicosapentaenoic acid, doxercalciferol, ciclesonide, dexamethasone, methylprednisolone, etc. are identified that target 32 human proteins like ACACA, DST, DYNC1H1, etc.

Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human–Coronavirus Family Interactome

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host–pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host–pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host–pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host–pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.

An insight into SARS-CoV-2 membrane protein interaction with spike, envelope, and nucleocapsid proteins

Intraviral protein-protein interactions are crucial for replication, pathogenicity, and viral assembly. Among these, virus assembly is a critical step as it regulates the arrangements of viral structural proteins and helps in the encapsulation of genomic material. SARS-CoV-2 structural proteins play an essential role in the self-rearrangement, RNA encapsulation, and mature virus particle formation. In SARS-CoV, the membrane protein interacts with the envelope and spike protein in Endoplasmic Reticulum Golgi Intermediate Complex (ERGIC) to form an assembly in the lipid bilayer, followed by membrane-ribonucleoprotein (nucleocapsid) interaction. In this study, we tried to understand the interaction of membrane protein's interaction with envelope, spike, and nucleocapsid proteins using protein-protein docking. Further, simulation studies were performed up to 100 ns to examine the stability of protein-protein complexes of Membrane-Envelope, Membrane-Spike, and Membrane-Nucleocapsid proteins. Prime MM-GBSA showed high binding energy calculations for the simulated structures than the docked complex. The interactions identified in our study will be of great importance, as it provides valuable insight into the protein-protein complex, which could be the potential drug targets for future studies.Communicated by Ramaswamy H. Sarma.

Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach

Background

Drug resistance in breast cancer is an unsolved problem in treating patients. It has been recently discussed that lysosomes contribute to the invasion and angiogenesis of cancer cells. There is evidence that lysosomes can also cause multi-drug resistance. We analyzed this emerging concept in breast cancer through computational and systems biology approaches.

Objectives

We aimed to identify the key lysosome-related genes associated with drug-resistant breast cancer.

Methods

All genes contributing to the structure and function of lysosomes were inquired through the Human Lysosome Gene Database. The prioritized top 51 genes from the provided lists of Endeavour, ToppGene, and GPSy as prioritization tools were selected. All lysosomal genes and 12 breast cancer-related genes aligned to identify the most similar genes to breast cancer-related genes. Different centralities were applied to score each human protein to calculate the most central lysosomal genes in the human protein-protein interaction (PPI) network. Common genes were extracted from the results of the mentioned methods as a selected gene set. For Gene Ontology enrichment, the selected gene set was analyzed by WebGestalt, DAVID, and KOBAS. The PPI network was constructed via the STRING database. The PPI network was analyzed utilizing Cytoscape for topology network interaction and CytoHubba to extract hub genes.

Results

Based on biological studies, literature reviews, and comparing all mentioned analyzing methods, six genes were introduced as essential in breast cancer. This computational approach to all lysosome-related genes suggested that candidate genes include PRF1, TLR9, CLTC, GJA1, AP3B1, and RPTOR. The analyses of these six genes suggest that they may have a crucial role in breast cancer development, which has rarely been evaluated. These genes have a potential therapeutic implication for new drug discovery for chemo-resistant breast cancer.

Conclusions

The present work focused on all the functional and structural lysosome-related genes associated with breast cancer. It revealed the top six lysosome hub genes that might serve as therapeutic targets in drug-resistant breast cancer. Since these genes play a pivotal role in the structure and function of lysosomes, targeting them can effectively overcome drug resistance.

The use of integrated text mining and protein-protein interaction approach to evaluate the effects of combined chemotherapeutic and chemopreventive agents in cancer therapy

Combining chemotherapeutic (CT) and chemopreventive (CP) agents for cancer treatment is controversial, and the issue has not yet been conclusively resolved. In this study, by integrating text mining and protein-protein interaction (PPI), the combined effects of these two kinds of agents in cancer treatment were investigated. First, text mining was performed by the Pathway Studio database to study the effects of various agents (CP and CT) on cancer-related processes. Then, each group’s most important hub genes were obtained by calculating different centralities. Finally, the results of in silico analysis were validated by examining the combined effects of hesperetin (Hst) and vincristine (VCR) on MCF-7 cells. In general, the results of the in silico analysis revealed that the combination of these two kinds of agents could be useful for treating cancer. However, the PPI analysis revealed that there were a few important proteins that could be targeted for intelligent therapy while giving treatment with these agents. In vitro experiments confirmed the results of the in silico analysis. Also, Hst and VCR had good harmony in modulating the hub genes obtained from the in silico analysis and inducing apoptosis in the MCF-7 cell line.

Discovering Common Pathogenic Mechanisms of COVID-19 and Parkinson Disease: An Integrated Bioinformatics Analysis

Coronavirus disease 2019 (COVID-19) has emerged since December 2019 and was later characterized as a pandemic by WHO, imposing a major public health threat globally. Our study aimed to identify common signatures from different biological levels to enlighten the current unclear association between COVID-19 and Parkinson's disease (PD) as a number of possible links, and hypotheses were reported in the literature. We have analyzed transcriptome data from peripheral blood mononuclear cells (PBMCs) of both COVID-19 and PD patients, resulting in a total of 81 common differentially expressed genes (DEGs). The functional enrichment analysis of common DEGs are mostly involved in the complement system, type II interferon gamma (IFNG) signaling pathway, oxidative damage, microglia pathogen phagocytosis pathway, and GABAergic synapse. The protein-protein interaction network (PPIN) construction was carried out followed by hub detection, revealing 10 hub genes (MX1, IFI27, C1QC, C1QA, IFI6, NFIX, C1S, XAF1, IFI35, and ELANE). Some of the hub genes were associated with molecular mechanisms such as Lewy bodies-induced inflammation, microglia activation, and cytokine storm. We investigated regulatory elements of hub genes at transcription factor and miRNA levels. The major transcription factors regulating hub genes are SOX2, XAF1, RUNX1, MITF, and SPI1. We propose that these events may have important roles in the onset or progression of PD. To sum up, our analysis describes possible mechanisms linking COVID-19 and PD, elucidating some unknown clues in between.

CovInter: interaction data between coronavirus RNAs and host proteins

Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.

Drug-Disease Severity and Target-Disease Severity Interaction Networks in COVID-19 Patients

Drug interactions with other drugs are a well-known phenomenon. Similarly, however, pre-existing drug therapy can alter the course of diseases for which it has not been prescribed. We performed network analysis on drugs and their respective targets to investigate whether there are drugs or targets with protective effects in COVID-19, making them candidates for repurposing. These networks of drug-disease interactions (DDSIs) and target-disease interactions (TDSIs) revealed a greater share of patients with diabetes and cardiac co-morbidities in the non-severe cohort treated with dipeptidyl peptidase-4 (DPP4) inhibitors. A possible protective effect of DPP4 inhibitors is also plausible on pathophysiological grounds, and our results support repositioning efforts of DPP4 inhibitors against SARS-CoV-2. At target level, we observed that the target location might have an influence on disease progression. This could potentially be attributed to disruption of functional membrane micro-domains (lipid rafts), which in turn could decrease viral entry and thus disease severity.

CoVM2: Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method

The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding states of SARS-CoV-2 and human proteins. Experimental research on SARS-CoV-2 has accumulated a large amount of structural data and protein-protein interactions (PPIs), but the studies on the SARS-CoV-2–human PPI networks lack integration of physical associations with possible protein docking information. In addition, the docking structures of variant viral proteins with human receptor proteins are still insufficient. This study constructed SARS-CoV-2–human protein–protein interaction network with data integration methods. Crystal structures were collected to map the interaction pairs. The pairs of direct interactions and physical associations were selected and analyzed for variant docking calculations. The study examined the structures of spike (S) glycoprotein of variants Delta B.1.617.2, Omicron BA.1, and Omicron BA.2. The calculated docking structures of S proteins and potential human receptors were obtained. The study integrated binary protein interactions with 3D docking structures to fulfill an extended view of SARS-CoV-2 proteins from a macro- to micro-scale.

The SARS-CoV-2 targeted human RNA binding proteins network biology to investigate COVID-19 associated manifestations

The global coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has had unprecedented social and economic ramifications. Identifying targets for drug repurposing could be an effective means to present new and fast treatments. Furthermore, the risk of morbidity and mortality from COVID-19 goes up when there are coexisting medical conditions, however, the underlying mechanisms remain unclear. In the current study, we have adopted a network-based systems biology approach to investigate the RNA binding proteins (RBPs)-based molecular interplay between COVID-19, various human cancers, and neurological disorders. The network based on RBPs commonly involved in the three disease conditions consisted of nine RBPs connecting 10 different cancer types, 22 brain disorders, and COVID-19 infection, ultimately hinting at the comorbidities and complexity of COVID-19. Further, we underscored five miRNAs with reported antiviral properties that target all of the nine shared RBPs and are thus therapeutically valuable. As a strategy to improve the clinical conditions in comorbidities associated with COVID-19, we propose perturbing the shared RBPs by drug repurposing. The network-based analysis presented hereby contributes to a better knowledge of the molecular underpinnings of the comorbidities associated with COVID-19.

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.

A virus–target host proteins recognition method based on integrated complexes data and seed extension

Background

Target drugs play an important role in the clinical treatment of virus diseases. Virus-encoded proteins are widely used as targets for target drugs. However, they cannot cope with the drug resistance caused by a mutated virus and ignore the importance of host proteins for virus replication. Some methods use interactions between viruses and their host proteins to predict potential virus–target host proteins, which are less susceptible to mutated viruses. However, these methods only consider the network topology between the virus and the host proteins, ignoring the influences of protein complexes. Therefore, we introduce protein complexes that are less susceptible to drug resistance of mutated viruses, which helps recognize the unknown virus–target host proteins and reduce the cost of disease treatment.

Results

Since protein complexes contain virus–target host proteins, it is reasonable to predict virus–target human proteins from the perspective of the protein complexes. We propose a coverage clustering-core-subsidiary protein complex recognition method named CCA-SE that integrates the known virus–target host proteins, the human protein–protein interaction network, and the known human protein complexes. The proposed method aims to obtain the potential unknown virus–target human host proteins. We list part of the targets after proving our results effectively in enrichment experiments.

Conclusions

Our proposed CCA-SE method consists of two parts: one is CCA, which is to recognize protein complexes, and the other is SE, which is to select seed nodes as the core of protein complexes by using seed expansion. The experimental results validate that CCA-SE achieves efficient recognition of the virus–target host proteins.

An approach to cellular tropism of SARS-CoV-2 through protein-protein interaction and enrichment analysis

COVID-19, caused by SARS-CoV-2, is a primarily pulmonary disease that can affect several organs, directly or indirectly. To date, there are many questions about the different pathological mechanisms. Here, we generate an approach to identify the cellular-level tropism of SARS-CoV-2 using human proteomics, virus-host interactions, and enrichment analysis. Through a network-based approach, the molecular context was visualized and analyzed. This procedure was also performed for SARS-CoV-1. We obtained proteomes and interactomes from 145 different cells corresponding to 57 different tissues. We discarded the cells without the proteins known for interacting with the virus, such as ACE2 or TMPRSS2. Of the remaining cells, a gradient of susceptibility to infection was observed. In addition, we identified proteins associated with the coagulation cascade that can be directly or indirectly affected by viral proteins. As a whole we identified 55 cells that could be potentially controlled by the virus, with different susceptibilities, mainly being pneumocytes, heart, kidney, liver, or small intestine cells. These results help to explain the molecular context and provide elements for possible treatments in the current situation. This strategy may be useful for other viruses, especially those with limited reported PPI, such as a new virus.

Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features

SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein–protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.

Integrative COVID-19 biological network inference with probabilistic core decomposition

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

Understanding COVID-19 Pathogenesis: A Drug-Repurposing Effort to Disrupt Nsp-1 Binding to Export Machinery Receptor Complex

Non-structural protein 1 (Nsp1) is a virulence factor found in all beta coronaviruses (b-CoVs). Recent studies have shown that Nsp1 of SARS-CoV-2 virus interacts with the nuclear export receptor complex, which includes nuclear RNA export factor 1 (NXF1) and nuclear transport factor 2-like export factor 1 (NXT1). The NXF1-NXT1 complex plays a crucial role in the transport of host messenger RNA (mRNA). Nsp1 interferes with the proper binding of NXF1 to mRNA export adaptors and its docking to the nuclear pore complex. We propose that drugs targeting the binding surface between Nsp1 and NXF1-NXT1 may be a useful strategy to restore host antiviral gene expression. Exploring this strategy forms the main goals of this paper. Crystal structures of Nsp1 and the heterodimer of NXF1-NXT1 have been determined. We modeled the docking of Nsp1 to the NXF1-NXT1 complex, and discovered repurposed drugs that may interfere with this binding. To our knowledge, this is the first attempt at drug-repurposing of this complex. We used structural analysis to screen 1993 FDA-approved drugs for docking to the NXF1-NXT1 complex. The top hit was ganirelix, with a docking score of -14.49. Ganirelix competitively antagonizes the gonadotropin releasing hormone receptor (GNRHR) on pituitary gonadotrophs, and induces rapid, reversible suppression of gonadotropin secretion. The conformations of Nsp1 and GNRHR make it unlikely that they interact with each other. Additional drug leads were inferred from the structural analysis of this complex, which are discussed in the paper. These drugs offer several options for therapeutically blocking Nsp1 binding to NFX1-NXT1, which may normalize nuclear export in COVID-19 infection.

Finding Prediction of Interaction Between SARS-CoV-2 and Human Protein: A Data-Driven Approach

COVID-19 pandemic defined a worldwide health crisis into a humanitarian crisis. Amid this global emergency, human civilization is under enormous strain since no proper therapeutic method is discovered yet. A wave of research effort has been put toward the invention of therapeutics and vaccines against COVID-19. Contrarily, the spread of this fatal virus has already infected millions of people and claimed many lives all over the world. Computational biology can attempt to understand the protein–protein interactions between the viral protein and host protein. Therefore, potential viral–host protein interactions can be identified which is known as crucial information toward the discovery of drugs. In this study, an approach was presented for predicting novel interactions from maximal biclusters. Additionally, the predicted interactions are verified from biological perspectives. For this, a study was conducted on the gene ontology and KEGG-pathway in relation to the newly predicted interactions.

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.

Insights into the SARS-CoV-2-Mediated Alteration in the Stress Granule Protein Regulatory Networks in Humans

The rapidly and constantly evolving coronavirus, SARS-CoV-2, imposes a great threat to human health causing severe lung disease and significant mortality. Cytoplasmic stress granules (SGs) exert anti-viral activities due to their involvement in translation inhibition and innate immune signaling. SARS-CoV-2 sequesters important SG nucleator proteins and impairs SG formation, thus evading the host response for efficient viral replication. However, the significance of SGs in COVID-19 infection remains elusive. In this study, we utilize a protein-protein interaction network approach to systematically dissect the crosstalk of human post-translational regulatory networks governed by SG proteins due to SARS-CoV-2 infection. We uncovered that 116 human SG proteins directly interact with SARS-CoV-2 proteins and are involved in 430 different brain disorders including COVID-19. Further, we performed gene set enrichment analysis to identify the drugs against three important key SG proteins (DYNC1H1, DCTN1, and LMNA) and also looked for potential microRNAs (miRNAs) targeting these proteins. We identified bexarotene as a potential drug molecule and miRNAs, hsa-miR-615-3p, hsa-miR-221-3p, and hsa-miR-124-3p as potential candidates for the treatment of COVID-19 and associated manifestations.

Alveolar Regeneration in COVID-19 Patients: A Network Perspective

A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients.

Interactome of human and SARS-CoV-2 proteins to identify human hub proteins associated with comorbidities

SARS-CoV-2 has a higher chance of progression in adults of any age with certain underlying health conditions or comorbidities like cancer, neurological diseases and in certain cases may even lead to death. Like other viruses, SARS-CoV-2 also interacts with host proteins to pave its entry into host cells. Therefore, to understand the behaviour of SARS-CoV-2 and design of effective antiviral drugs, host-virus protein-protein interactions (PPIs) can be very useful. In this regard, we have initially created a human-SARS-CoV-2 PPI database from existing works in the literature which has resulted in 7085 unique PPIs. Subsequently, we have identified at most 10 proteins with highest degrees viz. hub proteins from interacting human proteins for individual virus protein. The identification of these hub proteins is important as they are connected to most of the other human proteins. Consequently, when they get affected, the potential diseases are triggered in the corresponding pathways, thereby leading to comorbidities. Furthermore, the biological significance of the identified hub proteins is shown using KEGG pathway and GO enrichment analysis. KEGG pathway analysis is also essential for identifying the pathways leading to comorbidities. Among others, SARS-CoV-2 proteins viz. NSP2, NSP5, Envelope and ORF10 interacting with human hub proteins like COX4I1, COX5A, COX5B, NDUFS1, CANX, HSP90AA1 and TP53 lead to comorbidities. Such comorbidities are Alzheimer, Parkinson, Huntington, HTLV-1 infection, prostate cancer and viral carcinogenesis. Subsequently, using Enrichr tool possible repurposable drugs which target the human hub proteins are reported in this paper as well. Therefore, this work provides a consolidated study for human-SARS-CoV-2 protein interactions to understand the relationship between comorbidity and hub proteins so that it may pave the way for the development of anti-viral drugs.

Mutational Landscape and Interaction of SARS-CoV-2 with Host Cellular Components

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid evolution has led to a global health crisis. Increasing mutations across the SARS-CoV-2 genome have severely impacted the development of effective therapeutics and vaccines to combat the virus. However, the new SARS-CoV-2 variants and their evolutionary characteristics are not fully understood. Host cellular components such as the ACE2 receptor, RNA-binding proteins (RBPs), microRNAs, small nuclear RNA (snRNA), 18s rRNA, and the 7SL RNA component of the signal recognition particle (SRP) interact with various structural and non-structural proteins of the SARS-CoV-2. Several of these viral proteins are currently being examined for designing antiviral therapeutics. In this review, we discuss current advances in our understanding of various host cellular components targeted by the virus during SARS-CoV-2 infection. We also summarize the mutations across the SARS-CoV-2 genome that directs the evolution of new viral strains. Considering coronaviruses are rapidly evolving in humans, this enables them to escape therapeutic therapies and vaccine-induced immunity. In order to understand the virus's evolution, it is essential to study its mutational patterns and their impact on host cellular machinery. Finally, we present a comprehensive survey of currently available databases and tools to study viral-host interactions that stand as crucial resources for developing novel therapeutic strategies for combating SARS-CoV-2 infection.

SARS CoV-2 Nucleoprotein Enhances the Infectivity of Lentiviral Spike Particles

The establishment of SARS CoV-2 spike-pseudotyped lentiviral (LV) systems has enabled the rapid identification of entry inhibitors and neutralizing agents, alongside allowing for the study of this emerging pathogen in BSL-2 level facilities. While such frameworks recapitulate the cellular entry process in ACE2+ cells, they are largely unable to factor in supplemental contributions by other SARS CoV-2 genes. To address this, we performed an unbiased ORF screen and identified the nucleoprotein (N) as a potent enhancer of spike-pseudotyped LV particle infectivity. We further demonstrate that the spike protein is better enriched in virions when the particles are produced in the presence of N protein. This enrichment of spike renders LV particles more infectious as well as less vulnerable to the neutralizing effects of a human IgG-Fc fused ACE2 microbody. Importantly, this improvement in infectivity is observed with both wild-type spike protein as well as the D614G mutant. Our results hold important implications for the design and interpretation of similar LV pseudotyping-based studies.

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.

A SARS-CoV-2 -human metalloproteome interaction map

The recent pandemic caused by the novel coronavirus resulted in the greatest global health crisis since the Spanish flu pandemic of 1918. There is limited knowledge of whether SARS-CoV-2 is physically associated with human metalloproteins. Recently, high-confidence, experimentally supported protein-protein interactions between SARS-CoV-2 and human proteins were reported. In this work, 58 metalloproteins among these human targets have been identified by a structure-based approach. This study reveals that most human metalloproteins interact with the recently discovered SARS-CoV-2 orf8 protein, whose antibodies are one of the principal markers of SARS-CoV-2 infections. Furthermore, this work provides sufficient evidence to conclude that Zn²⁺ plays an important role in the interplay between the novel coronavirus and humans. First, the content of Zn-binding proteins in the involved human metalloproteome is significantly higher than that of the other metal ions. Second, a molecular linkage between the identified human Zn-binding proteome with underlying medical conditions, that might increase the risk of severe illness from the SARS-CoV-2 virus, has been found. Likely perturbations of host cellular metal homeostasis by SARS-CoV-2 infection are highlighted.

Current status and future perspectives of computational studies on human-virus protein-protein interactions

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.

The analysis on the human protein domain targets and host-like interacting motifs for the MERS-CoV and SARS-CoV/CoV-2 infers the molecular mimicry of coronavirus

The MERS-CoV, SARS-CoV, and SARS-CoV-2 are highly pathogenic viruses that can cause severe pneumonic diseases in humans. Unfortunately, there is a non-available effective treatment to combat these viruses. Domain-motif interactions (DMIs) are an essential means by which viruses mimic and hijack the biological processes of host cells. To disentangle how viruses achieve this process can help to develop new rational therapies. Data mining was performed to obtain DMIs stored as regular expressions (regexp) in 3DID and ELM databases. The mined regexp information was mapped on the coronaviruses' proteomes. Most motifs on viral protein that could interact with human proteins are shared across the coronavirus species, indicating that molecular mimicry is a common strategy for coronavirus infection. Enrichment ontology analysis for protein domains showed a shared biological process and molecular function terms related to carbon source utilization and potassium channel regulation. Some of the mapped motifs were nested on B, and T cell epitopes, suggesting that it could be as an alternative way for reverse vaccinology. The information obtained in this study could be used for further theoretic and experimental explorations on coronavirus infection mechanism and development of medicines for treatment.

Perspectives About Modulating Host Immune System in Targeting SARS-CoV-2 in India

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of coronavirus induced disease-2019 (COVID-19), is a type of common cold virus responsible for a global pandemic which requires immediate measures for its containment. India has the world's largest population aged between 10 and 40 years. At the same time, India has a large number of individuals with diabetes, hypertension and kidney diseases, who are at a high risk of developing COVID-19. A vaccine against the SARS-CoV-2, may offer immediate protection from the causative agent of COVID-19, however, the protective memory may be short-lived. Even if vaccination is broadly successful in the world, India has a large and diverse population with over one-third being below the poverty line. Therefore, the success of a vaccine, even when one becomes available, is uncertain, making it necessary to focus on alternate approaches of tackling the disease. In this review, we discuss the differences in COVID-19 death/infection ratio between urban and rural India; and the probable role of the immune system, co-morbidities and associated nutritional status in dictating the death rate of COVID-19 patients in rural and urban India. Also, we focus on strategies for developing masks, vaccines, diagnostics and the role of drugs targeting host-virus protein-protein interactions in enhancing host immunity. We also discuss India's strengths including the resources of medicinal plants, good food habits and the role of information technology in combating COVID-19. We focus on the Government of India's measures and strategies for creating awareness in the containment of COVID-19 infection across the country.