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.

Effect of p-sulfonatocalix[4]resorcinarene (PSC[4]R) on the solubility and bioavailability of a poorly water soluble drug lamotrigine (LMN) and computational investigation3

Acetylcholinesterase (AChE) is an important drug target for the treatment of Alzheimer's disease. A novel series of coumarin-piperazine derivatives were synthesized and their potency to inhibit human AChE enzyme (hAChE) was studied. All the final compounds were characterized by infrared, (1)H NMR, (13)C NMR, and elemental analysis. Docking experiments of the designed coumarin-piperazine derivatives were carried out in order to compare the theoretical and experimental binding affinities toward hAChE, to delineate the inhibitory mechanism. Subsequently, a structure-activity relationship (SAR) study using the molecular field method showed that the hydrophobic field and positive charge center conferred by the coumarin and piperazine moieties demonstrated an inhibitory mechanism. Among the compounds tested, 3f, 3j, and 3m were found to be the most potent inhibitors of hAChE.

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.

Receptor‐based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein‐ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the Energetic CONTributions of Amino acid residues and its possible Cross‐Talk (ECONTACT) to study ligand binding using per‐residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross‐talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein‐ligand interaction energies deduced from multiple structures are decomposed into per‐residue energy terms, which are employed as variables to principal component analysis and generated cross‐terms. Out of 16 cross‐talks derived from eight datasets of protein‐ligand systems, the ECONTACT approach is able to associate 10 potential cross‐talks with site‐directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross‐talks in protein structures. The top two cross‐talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross‐talks in any protein‐ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact/.