Repurposing FDA-approved Drugs Targeting SARS-CoV2 3CLpro: a study by applying Virtual Screening, Molecular Dynamics, MM-PBSA Calculations and Covalent Docking

Background: Since the end of 2019, the etiologic agent SAR-CoV-2 responsible for one of the most significant epidemics in history has caused severe global economic, social, and health damages. The drug repurposing approach and application of Structure-based Drug Discovery (SBDD) using in silico techniques are increasingly frequent, leading to the identification of several molecules that may represent promising potential. Method: In this context, here we use in silico methods of virtual screening (VS), pharmacophore modeling (PM), and fragment-based drug design (FBDD), in addition to molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM -PBSA) calculations, and covalent docking (CD) for the identification of potential treatments against SARS-CoV-2. We initially validated the docking protocol followed by VS in 1,613 FDA-approved drugs obtained from the ZINC database. Thus, we identified 15 top hits, of which three of them were selected for further simulations. In parallel, for the compounds with a fit score value ≤ of 30, we performed the FBDD protocol, where we designed 12 compounds Result: By applying a PM protocol in the ZINC database, we identified three promising drug candidates. Then, the 9 top hits were evaluated in simulations of MD, MM-PBSA, and CD. Subsequently, MD showed that all identified hits showed stability at the active site without significant changes in the protein's structural integrity, as evidenced by the RMSD, RMSF, Rg, SASA graphics. They also showed interactions with the catalytic dyad (His41 and Cys145) and other essential residues for activity (Glu166 and Gln189) and high affinity for MM-PBSA, with possible covalent inhibition mechanism. Conclution: Finally, our protocol helped identify potential compounds wherein ZINC896717 (Zafirlukast), ZINC1546066 (Erlotinib), and ZINC1554274 (Rilpivirine) were more promising and could be explored in vitro, in vivo, and clinical trials to prove their potential as antiviral agents.

Structure-Based Virtual Screening, Insilico Docking, ADME Prediction, and Molecular Dynamics Studies for Identification of Potential NMT Inhibitors Against Malaria

One of the major public health problems globally, malaria, is mainly caused protozoan parasites from the genus Plasmodium, and commonly spreads to people through the bites of infected female mosquitoes of the genus Anopheles. Strategies for treatment, prevention, and control are available for malaria but the eradication of malaria still poses great challenge due to plasmodium’s drug resistance over the past decades. Development of novel antimalarial drugs remains a significant task to protect people from malaria. N-Myristoyl transferase is responsible for the N-Myristoylation catalysis process and the survival of Plasmodium species. Thus, it is considered a therapeutic drug target in protozoans and was recently validated as a significant target for Plasmodium vivax. In this present scenario, we endeavour to identify effective NMT inhibitors to prevent the onset of malaria in the human species. Initially, the structure-based virtual screening was executed against ZINC database and four potential candidates for NMT were identified. Furthermore, the four identified compounds were subjected to ADME prediction and all the four compounds found within adequate range with predicted ADME properties. Eventually, we conducted the molecular dynamics simulation to investigate the binding stability of top three protein-ligand complexes at different time scale by employing the tool Desmond. The molecular dynamics simulation studies revealed the protein-ligand complexes were stable throughout the entire simulation. Besides, we noticed that the residues ASN 365, PHE 103 and HIS 213 of NMT were crucially involved in the formation of various intermolecular interactions, significantly contributing to the stability of protein-ligand complexes. From this computational investigation, we suggest that the three identified potential compounds are extremely useful for further lead optimization and drug development.

Working Mechanism of Remdesivir and Discovering New Potential Inhibitors to SARS-CoV-2 RNA-Dependent RNA Polymerase (RdRp) using High-Throughput Virtual Screening and Molecular Dynamics Simulations .

RNA-dependent RNA polymerase (RdRp), is an enzyme essential component in the RNA replication within the life cycle of the severely acute respiratory coronavirus-2 (SARS-CoV-2), causing the deadly respiratory induced sickness COVID-19. Remdesivir is a prodrug that has seen some success in inhibiting this enzyme, however there is still the pressing need for effective alternatives. In this study, we present the discovery of four non-nucleoside small molecules that bind favorably to RdRp over adenosine-triphosphate (ATP) and active-form remdesivir-triphosphate (RTP) using high-throughput virtual screening (HTVS) coupled with extensive (total 4800 ns) molecular dynamics (MD) simulations with using the ZINC compounds database against SARS-CoV-2 RdRp (PDB: 7BV2). We found that the simulations with both ATP and RTP remained stable for the duration of their trajectories, and it was revealed that the phosphate tail of RTP was stabilized by a positive amino acid pocket near the entry channel of RTP and magnesium ions containing residues K551, R553, R555 and K621. It was also found that residues D623, D760, and N691 further stabilized the ribose portion of RTP with U10 on the template RNA strand forming hydrogen pairs with the adenosine motif. Using these models of RdRp, we employed them to screen the ZINC database of ~17 million molecules. Using docking and drug properties scoring, we narrowed down our selection to fourteen candidates. These were subjected to 200 ns simulations each underwent free energy calculations. We identified four hit compounds from the ZINC database that have similar binding poses to RTP while possessing lower overall binding free energies, with ZINC097971592 having a binding free energy two times lower than RTP.

Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination (R2) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules.

Glycosidic vs. Aglycol Form of Natural Products as Putative Tyrosinase Inhibitors

Numerous natural products and designed molecules have been evaluated as tyrosinase inhibitors that impede enzymes’ oxidation activity. In the present study, new potent natural inhibitors were retrieved from the ZINC database by the similarity-screening of 37 previously reported tyrosinase inhibitors. The screening resulted in 42 candidate inhibitory molecules that were categorized into five groups. Molecular-docking analysis for these compounds, as well as for three others known for their inhibition activity (caffeic acid, naringenin, and gallic acid), was carried out against the tyrosinase structure from Agaricus bisporus (AbTYR). The top-scoring compounds were used for further comparative analysis with their corresponding naturally occurring glycosides. The results suggested that the glycosylated inhibitors could interact better with the enzyme than their aglycon forms. In order to further examine the role of the sugar side group of potent tyrosinase inhibitors, the dynamic behavior of two such pairs of glycosidic/aglycol forms (naringin–naringenin and icariin–icaritin) in their complexes with the enzyme were studied by means of 20-ns MD simulations. The increased number of intermolecular hydrogen bonds and their augmented lifetime between AbTYR and the glycosidic analogues showed that the naringin and icariin molecules form more stable complexes than naringenin and icaritin with tyrosinase, and thus are more potent inhibitors.

MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning

The hit-to-lead process makes the physicochemical properties of the hit molecules that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process. The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process. In this study, we have developed a SMILES-based generative model that can be generated starting from a certain molecule. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network. We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization. The source code is available at https://github.com/sekijima-lab/mermaid.

Proteome wide screening for identification of putative drug target gene in Nautella italica and structure-based ligand screening for therapeutic candidates

In silico based subtractive genomic approaches were employed to identify the key drug targets for an opportunistic pathogen Nautella italica, a member of the marine Roseobacter clade that causes bleaching disease in the temperate-marine red macro algae, Delisea pulchra. The aim of this study is to propose new active ligands against bleaching disease seen in algae. Using comparative and subtractive genomic approach, a set of 21 proteins were identified as the therapeutic drug target proteins for algal bleaching. This core set of drug targets has been analyzed for network topology using string network analysis and major hub gene identified by CytoHubba was rpoB (DNA directed RNA Polymerase subunit beta). The three-dimensional structure of rpoB was built by comparative modelling and used to perform a virtual screening of Zinc database by DOCK Blaster server. The 50 top scored compounds were screened for toxicity analysis by OSIRIS Data Warrior and ECOSAR tool. Further refinement by autodock program revealed two compounds ZINC49821385 and ZINC97218938 with the best binding energy of -7.07 and -6.79 respectively. These results indicated that 5-(4- isopropylphenyl)furan-2-carboxamide (ZINC ID 49821385) could be one of the potential ligand to treat bleaching disease in algae.

Selection of SARS-CoV-2 main protease inhibitor using structure-based virtual screening

Background: Conserved domains within SARS coronavirus 2 nonstructural proteins represent key targets for the design of novel inhibitors. Methods: The authors aimed to identify potential SARS coronavirus 2 NSP5 inhibitors using the ZINC database along with structure-based virtual screening and molecular dynamics simulation. Results: Of 13,840 compounds, 353 with robust docking scores were initially chosen, of which ten hit compounds were selected as candidates for detailed analyses. Three compounds were selected as coronavirus NSP5 inhibitors after passing absorption, distribution, metabolism, excretion and toxicity study; root and mean square deviation; and radius of gyration calculations. Conclusion: ZINC000049899562, ZINC000169336666 and ZINC000095542577 are potential NSP5 protease inhibitors that warrant further experimental studies.

AN OVERVIEW OF EPIGENETIC DRUGS, AND THEIR VIRTUAL SCREENING STUDY RETRIEVED FROM ZINC DATABASE ALONG WITH AN AUTODOCK STUDY OF THE BEST INHIBITOR

Objective: Over the last 30 y cancer epigenetics research has grown extensively. It is note-worthy to recognize that epigenetic misregulation could substantiate the development of cancer and we need to continue to look for anti-neoplastic epi-drugs. Taking into consideration this phenomenon, our first aim is to search for an effective epi-drugs by virtual screening from ZINC database and to explore the validity of the virtual screening. The second aim is to explore a binding conformation of the top affinity ligands against macromolecules, by docking experiment. Methods: The virtual screening was conducted by our Virtual Screening by Docking (VSDK) algorithm and procedure. Small molecules were randomly downloaded by ZINC database. For docking experiment, AutoDock 4.2.6 and AutoDock Tool were used. Results: It took eight to ten hours for the successful virtual screening of the 2778 small compounds retrieved at random from ZINC database. Among histone H2B E76K mutant (HHEM) inhibitors and DNA methyltransferase (DNMT) inhibitors, the first ranked inhibitors were 1H-1,2,4-triazole-3,5-diamine and 2-ethyl-1,3,4-oxadiazole respectively. Conclusion: As for the molecular structures obtained from virtual screening, most of the top ten HHEM and DNMT inhibitors contained 5-member rings. More than two times in affinity difference between the top and bottom ten compounds would indicate a successful virtual screening experiment. The histogram chart of AutoDock4 runs appeared in the lowest affinity region with two or three hydrogen bonds indicating a reliable conformation docking.

Screening of Zinc Database against Streptococcal Cysteine Protease Enzyme for Identification of Novel Group A Streptococcus Inhibitors

Inhibition of streptococcal cysteine protease has recently emerged as quite a promising target to treat severe cases of Group A Streptococcus infections. For the identification of streptococcal cysteine protease inhibitors, structure-based virtual screening (SBVS) of the ZINC Database was performed. The docking protocol was performed with the help of AutoDock Tools and AutoDock Vina software. Based on binding affinity and similarity of interactions with our target receptor streptococcal cysteine protease, 4 hit compounds were identified, which were further subjected to ADMET (Adsorption, Distribution, Metabolism, Excretion, Toxicity) and Drug-likeness to identify the best hit compound. The most potent compound showed binding of -7.7 KJ/mol with receptor streptococcal cysteine protease. It also showed 6 similar amino acid interactions with the receptor’s native ligand along with good ADME and Drug-likeness properties. Furthermore, the molecular dynamics simulation analysis revealed that the complex formed between the protein streptococcal cysteine protease and the hit compound ZINC000205429716 had good structural stability. The current study reveals the successful use of in silico SBVS methods for the identification of novel and possible streptococcal cysteine protease inhibitors, with compound ZINC000205429716 serving as a potential lead for the creation of Group A Streptococcus inhibitors.