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.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19.

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

Computational modeling of human-nCoV protein-protein interaction network.

Novel coronavirus(SARS-CoV2) replicates the host cell's genome by interacting with the host proteins. Due to this fact, the identification of virus and host protein-protein interactions could be beneficial in understanding the disease transmission behavior of the virus as well as in potential COVID-19 drug identification. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to the SARS-CoV epidemic in 2003 (∼89% similarity). With this hypothesis, the present work focuses on developing a computational model for the nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions. Initially, level-1 and level-2 human spreader proteins are identified in the SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered potential human targets for nCoV bait proteins. A gene-ontology-based fuzzy affinity function has been used to construct the nCoV-Human protein interaction network at a ∼99.98% specificity threshold. This also identifies 37 level-1 human spreaders for COVID-19 in the human protein-interaction network. 2474 level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using six potential FDA-listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins.

The viral phoenix: enhanced infectivity and immunity evasion of SARS-CoV-2 variants.

SARS-CoV-2 pandemic continues with emergence of new variants of concerns. These variants are fueling the third and fourth waves of pandemic across many nations. Here we describe the new emerging variants of SARS-CoV-2 and why they have enhanced infectivity and possess the ability to evade immunity.

Detection of spreader nodes in human-SARS-CoV protein-protein interaction network.

The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein-protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein-protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art.

Drug repurposing for COVID-19 using computational screening: Is Fostamatinib/R406 a potential candidate?

With the gradual increase in the COVID-19 mortality rate, there is an urgent need for an effective drug/vaccine. Several drugs like Remdesivir, Azithromycin, Favirapir, Ritonavir, Darunavir, etc., are put under evaluation in more than 300 clinical trials to treat COVID-19. On the other hand, several vaccines like Pfizer-BioNTech, Moderna, Johnson & Johnson's Janssen, Sputnik V, Covishield, Covaxin, etc., also evolved from the research study. While few of them already gets approved, others show encouraging results and are still under assessment. In parallel, there are also significant developments in new drug development. But, since the approval of new molecules takes substantial time, drug repurposing studies have also gained considerable momentum. The primary agent of the disease progression of COVID-19 is SARS-CoV2/nCoV, which is believed to have ~89% genetic resemblance with SARS-CoV, a coronavirus responsible for the massive outbreak in 2003. With this hypothesis, Human-SARS-CoV protein interactions are used to develop an in-silico Human-nCoV network by identifying potential COVID-19 human spreader proteins by applying the SIS model and fuzzy thresholding by a possible COVID-19 FDA drugs target-based validation. At first, the complete list of FDA drugs is identified for the level-1 and level-2 spreader proteins in this network, followed by applying a drug consensus scoring strategy. The same consensus strategy is involved in the second analysis but on a curated overlapping set of key genes/proteins identified from COVID-19 symptoms. Validation using subsequent docking study has also been performed on COVID-19 potential drugs with the available major COVID-19 crystal structures whose PDB IDs are: 6LU7, 6M2Q, 6W9C, 6M0J, 6M71 and 6VXX. Our computational study and docking results suggest that Fostamatinib (R406 as its active promoiety) may also be considered as one of the potential candidates for further clinical trials in pursuit to counter the spread of COVID-19.

Transmission of SARS-CoV-2 in Inpatient and Outpatient Settings in a Veterans Affairs Health Care System.

BACKGROUND: Health care personnel and patients are at risk to acquire severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in health care settings, including in outpatient clinics and ancillary care areas. METHODS: Between May 1, 2020, and January 31, 2021, we identified clusters of 3 or more coronavirus disease 2019 (COVID-19) cases in which nosocomial transmission was suspected in a Veterans Affairs health care system. Asymptomatic employees and patients were tested for SARS-CoV-2 if they were identified as being at risk through contact tracing investigations; for 7 clusters, all personnel and/or patients in a shared work area were tested regardless of exposure history. Whole-genome sequencing was performed to determine the relatedness of SARS-CoV-2 samples from the clusters and from control employees and patients. RESULTS: Of 14 clusters investigated, 7 occurred in community-based outpatient clinics, 1 in the emergency department, 3 in ancillary care areas, and 3 on hospital medical/surgical wards that did not provide care for patients with known COVID-19 infection. Eighty-one of 82 (99%) symptomatic COVID-19 cases and 31 of 35 (89%) asymptomatic cases occurred in health care personnel. Sequencing analysis provided support for several transmission events between coworkers and in 2 cases supported transmission from health care personnel to patients. There were no documented transmissions from patients to personnel. CONCLUSIONS: Clusters of COVID-19 with nosocomial transmission predominantly involved health care personnel and often occurred in outpatient clinics and ancillary care areas. There is a need for improved measures to prevent transmission of SARS-CoV-2 by health care personnel in inpatient and outpatient settings.

Current understanding of the surface contamination and contact transmission of SARS-CoV-2 in healthcare settings.

The novel coronavirus disease (COVID-19) has rapidly spread across the world and was subsequently declared as a pandemic in 2020. To overcome this public health challenge, comprehensive understanding of the disease transmission is urgently needed. Recent evidences suggest that the most common route of transmission for SARS-CoV-2 is likely via droplet, aerosol, or direct contact in a person-to-person encounter, although the possibility of transmission via fomites from surfaces cannot be ruled out entirely. Environmental contamination in COVID-19 patient rooms is widely observed due to viral shedding from both asymptomatic and symptomatic patients, and SARS-CoV-2 can survive on hospital surfaces for extended periods. Sequence of contact events can spread the virus from one surface to the other in a hospital setting. Here, we review the studies related to viral shedding by COVID-19 patients that can contaminate surfaces and survival of SARS-CoV-2 on different types of surfaces commonly found in healthcare settings, as well as evaluating the importance of surface to person transmission characteristics. Based on recent evidences from the literature, decontamination of hospital surfaces should constitute an important part of the infection control and prevention of COVID-19.

Classical and alternative disinfection strategies to control the COVID-19 virus in healthcare facilities: a review.

The coronavirus disease COVID-19 has spread throughout the world and has been declared as a pandemic by the World Health Organization on March 11th, 2020. The COVID-19 is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). One possible mode of virus transmission is through surfaces in the healthcare settings. This paper reviews currently used disinfection strategies to control SARS-CoV-2 at the healthcare facilities. Chemical disinfectants include hypochlorite, peroxymonosulfate, alcohols, quaternary ammonium compounds, and hydrogen peroxide. Advanced strategies include no-touch techniques such as engineered antimicrobial surfaces and automated room disinfection systems using hydrogen peroxide vapor or ultraviolet light.