Direct Comparison of N-Glycans and Their Isomers Derived from Spike Glycoprotein 1 of MERS-CoV, SARS-CoV-1, and SARS-CoV-2.

The emergence of COVID-19 pandemic has engaged the scientific community around the globe in the rapid development of effective therapeutics and vaccines. Owing to its crucial role in the invasion of the host cell, spike (S) glycoprotein is one of the major targets in these studies. The S1 subunit of the S protein (S1 protein) accommodates the receptor-binding domain, which enables the initial binding of the virus to the host cell. Being a heavily glycosylated protein, numerous studies have investigated its glycan composition. However, none of the studies have explored the isomeric glycan distribution of this protein. Furthermore, this isomeric glycan distribution has never been compared to that in S1 proteins of other coronaviruses, severe acute respiratory syndrome coronavirus 1 and Middle East respiratory syndrome coronavirus, which were responsible for past epidemics. This study explores the uncharted territory of the isomeric glycan distribution in the coronaviruses' S1 protein using liquid chromatography coupled to tandem mass spectrometry. We believe that our data would facilitate future investigations to study the role of isomeric glycans in coronavirus viral pathogenesis.

Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning.

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.