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The pandemic outbreak of the Corona viral infection has become a critical global health issue. Biophysical and structural evidence shows that spike protein possesses a high binding affinity towards host angiotensin-converting enzyme 2 and viral hemagglutinin-acetylesterase (HE) glycoprotein receptor. We selected HE as a target in this study to identify potential inhibitors using a combination of various computational approaches such as molecular docking, ADMET analysis, dynamics simulations and binding free energy calculations. Virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin as potential HE inhibitors with better binding energy. Furthermore, molecular dynamics simulations for 100 ns time scale revealed that most of the key HE contacts were retained throughout the simulations trajectories. Binding free energy calculations using MM/PBSA approach ranked the top-five potential NPACT compounds which can act as effective HE inhibitors.

Understanding how MHC class II (MHC‐II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC‐II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC‐II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC‐II structures and the binding affinity (BA) (IC50). We present here a machine‐learning approach to calculate the BA of the peptides within the MHC‐II pocket for HLA‐DRA1, HLA‐DRB1, HLA‐DP, and HLA‐DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC‐II complexes as the templates, followed by site‐directed peptide docking. The structural interaction fingerprints generated from such docked pMHC‐II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC‐II peptide lengths (9–19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC‐II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of.612–.898) than MHCII3D (.03–.594) and NetMHCIIpan‐3.2 (.289–.692) programs in the HLA‐DRA1, HLA‐DRB1 types. Similarly, the MHC2AffyPred program achieved CC between.91 and.98 for HLA‐DP and HLA‐DQ peptides (13‐mer to 17‐mer). Further, a case study on MHC‐II binding 15‐mer peptides of severe acute respiratory syndrome coronavirus‐2 showed very close competency in computing the IC50 values compared to the sequence‐based NetMHCIIpan v3.2 and v4.0 programs with a correlation of.998 and.570, respectively. [ABSTRACT FROM AUTHOR]

Novel SARS-CoV-2, an etiological factor of Coronavirus disease 2019 (COVID-19), poses a great challenge to the public health care system. Among other druggable targets of SARS-Cov-2, the main protease (Mpro) is regarded as a prominent enzyme target for drug developments owing to its crucial role in virus replication and transcription. We pursued a computational investigation to identify Mpro inhibitors from a compiled library of natural compounds with proven antiviral activities using a hierarchical workflow of molecular docking, ADMET assessment, dynamic simulations and binding free-energy calculations. Five natural compounds, Withanosides V and VI, Racemosides A and B, and Shatavarin IX, obtained better binding affinity and attained stable interactions with Mpro key pocket residues. These intermolecular key interactions were also retained profoundly in the simulation trajectory of 100 ns time scale indicating tight receptor binding. Free energy calculations prioritized Withanosides V and VI as the top candidates that can act as effective SARS-CoV-2 Mpro inhibitors. [ABSTRACT FROM AUTHOR]

SARS-CoV-2, the viral particle, is responsible for triggering the 2019 Coronavirus disease outbreak (COVID-19). To tackle this situation, a number of strategies are being devised to either create an antidote, a vaccine, or agents capable of preventing its infection. To enable research on these strategies, numerous target proteins are identified where Spike (S) protein is presumed to be of immense potential. S-protein interacts with human angiotensin-converting-enzyme-2 (ACE2) for cell entry. The key region of S-protein that interacts with ACE2 is a portion of it designated as a receptor-binding domain (RBD), following whereby the viral membrane fuses with the alveolar membrane to enter the human cell. The proposition is to recognize molecules from the bundle of phytochemicals of medicinal plants known to possess antiviral potentials as a lead that could interact and mask RBD, rendering them unavailable to form ACE2 interactions. Such a molecule is called the 'S-protein blocker'. A total of 110 phytochemicals from Withania somnifera , Asparagus racemosus, Zinziber officinalis , Allium sativum , Curcuma longa and Adhatoda vasica were used in the study, of which Racemoside A, Ashwagandhanolide, Withanoside VI, Withanoside IV and Racemoside C were identified as top five hits using molecular docking. Further, essential Pharmacophore features and their ADMET profiles of these compounds were studied following to which the best three hits were analyzed for their interaction with RBD using Molecular Dynamics (MD) simulation. Binding free energy calculations were performed using MM/GBSA, proving these phytochemicals can serve as S-protein blocker. [Display omitted] • Constructing the library of antiviral compounds from medicinal plants. • Assessing the potency of these compounds for masking S-protein of SARS-CoV-2. • Molecular docking, dynamics simulation and pharmacophore mapping. [ABSTRACT FROM AUTHOR]