Structure-based virtual screening,in silico docking,ADME properties prediction and molecular dynamics studies for the identification of potential inhibitors against SARS-CoV-2 Mpro

COVID-19 is a viral pandemic caused by SARS-CoV-2. Due to its highly contagious nature, millions of people are getting affected worldwide knocking down the delicate global socio-economic equilibrium. According to the World Health Organization, COVID-19 has affected over 186 million people with a mortality of around 4 million as of July 09, 2021. Currently, there are few therapeutic options available for COVID-19 control. The rapid mutations in SARS-CoV-2 genome and development of new virulent strains with increased infection and mortality among COVID-19 patients, there is a great need to discover more potential drugs for SARS-CoV-2 on a priority basis. One of the key viral enzymes responsible for the replication and maturation of SARS-CoV-2 is M^pro protein. In the current study, structure-based virtual screening was used to identify four potential ligands against SARS-CoV-2 M^pro from a set of 8,722 ASINEX library compounds. These four compounds were evaluated using ADME filter to check their ADME profile and druggability, and all the four compounds were found to be within the current pharmacological acceptable range. They were individually docked to SARS-CoV-2 M^pro protein to assess their molecular interactions. Further, molecular dynamics (MD) simulations was carried out on protein–ligand complex using Desmond at 100 ns to explore their binding conformational stability. Based on RMSD, RMSF and hydrogen bond interactions, it was found that the stability of protein–ligand complex was maintained throughout the entire 100 ns simulations for all the four compounds. Some of the key ligand amino acid residues participated in stabilizing the protein–ligand interactions includes GLN 189, SER 10, GLU 166, ASN 142 with PHE 66 and TRP 132 of SARS-CoV-2 M^pro. Further optimization of these compounds could lead to promising drug candidates for SARS-CoV-2 M^pro target.

     

Identification of dual Acetyl-CoA carboxylases 1 and 2 inhibitors by pharmacophore based virtual screening and molecular docking approach

Acetyl-CoA carboxylase (ACC) is a crucial metabolic enzyme that plays a vital role in obesity-induced type 2 diabetes and fatty acid metabolism. To identify dual inhibitors of Acetyl-CoA carboxylase1 and Acetyl-CoA carboxylase2, a pharmacophore modelling approach has been employed. The best HypoGen pharmacophore model for ACC2 inhibitors (Hypo1_ACC2) consists of one hydrogen bond acceptor, one hydrophobic aliphatic and one hydrophobic aromatic feature, whereas the best pharmacophore (Hypo1_ACC1) for ACC1 consists of one additional hydrogen-bond donor (HBD) features. The best pharmacophore hypotheses were validated by various methods such as test set, decoy set and Cat-Scramble methodology. The validated pharmacophore models were used to screen several small-molecule databases, including Specs, NCI, ChemDiv and Natural product databases to identify the potential dual ACC inhibitors. The virtual hits were then subjected to several filters such as estimated $\text{ IC}_{50}$ value, quantitative estimation of drug-likeness and molecular docking analysis. Finally, three novel compounds with diverse scaffolds were selected as potential starting points for the design of novel dual ACC inhibitors.     

Identification of novel potential HIF-prolyl hydroxylase inhibitors by in silico screening

Suppression of HIF-prolyl hydroxylase (PHD) activity by small-molecule inhibitors leads to the stabilization of hypoxia inducible factor and has been recognized as promising drug target for the treatment of ischemic diseases. In this study, pharmacophore-based virtual screening and molecular docking approaches were concurrently used with suitable modifications to identify target-specific PHD inhibitors with better absorption, distribution, metabolism, and excretion properties and to readily minimize false positives and false negatives. A customized method based on the active site information of the enzyme was used to generate a pharmacophore hypothesis (AAANR). The hypothesis was validated and utilized for chemical database screening and the retrieved hit compounds were subjected to molecular docking for further refinement. AAANR hypothesis comprised three H-bond acceptor, one negative ionizable group and one aromatic ring feature. The hypothesis was validated using decoy set with a goodness of fit score of 2 and was used as a 3D query for database screening. After manual selection, molecular docking and further refinement based on the molecular interactions of inhibitors with the essential amino acid residues, 18 hits with good absorption, distribution, metabolism, and excretion (ADME) properties were selected as excellent PHD inhibitors. The best pharmacophore hypothesis, AAANR, contains chemical features required for the effective inhibition of PHD. Using AAANR, we have identified 18 potential and diverse virtual leads, which can be readily evaluated for their potency as novel inhibitors of PHD. It can be concluded that the combination of pharmacophore, molecular docking, and manual interpretation approaches can be more successful than the traditional approach alone for discovering more effective inhibitors.     

Discovery of inhibitors targeting protein tyrosine phosphatase 1B using a combined virtual screening approach

Molecular Diversity - Protein tyrosine phosphatase 1B (PTP1B) acts as a therapeutic target for type 2 diabetes. However, the major challenges of PTP1B drug discovery are the poor selectivity and...     

Identifying potential inhibitors of biofilm-antagonistic proteins to promote biofilm formation: a virtual screening and molecular dynamics simulations approach

Molecular Diversity - Microbial biofilms play a critical role in environmental biotechnology and associated applications. Biofilm production can be enhanced by inhibiting the function of proteins...     

Identification of potential glutaminyl cyclase inhibitors from lead-like libraries by in silico and in vitro fragment-based screening

A glutaminyl cyclase (QC) fragment library was in silico selected by disconnection of the structure of known QC inhibitors and by lead-like 2D virtual screening of the same set. The resulting fragment library (204 compounds) was acquired from commercial suppliers and pre-screened by differential scanning fluorimetry followed by functional in vitro assays. In this way, 10 fragment hits were identified (\(\sim \)5 % hit rate, best inhibitory activity: 16 \(\upmu \hbox {M}\)). The in vitro hits were then docked to the active site of QC, and the best scoring compounds were analyzed for binding interactions. Two fragments bound to different regions in a complementary manner, and thus, linking those fragments offered a rational strategy to generate novel QC inhibitors. Based on the structure of the virtual linked fragment, a 77-membered QC target focused library was selected from vendor databases and docked to the active site of QC. A PubChem search confirmed that the best scoring analogues are novel, potential QC inhibitors.     

Identification of new novel scaffold for Aurora A inhibition by pharmacophore modeling and virtual screening

Aurora kinases belong to family of highly conserved serine/threonine protein kinases that are involved in diverse cell cycle events and play a major role in regulation of cell division. Abnormal expression of Aurora kinases may lead to cancer; hence, these are considered as a potential target in cancer treatment. In this research article, we identified three novel Aurora A inhibitors using modern computational tools. A four-point common 3D pharmacophore hypothesis of Aurora A (AurA) inhibitors was developed using a diverse set of 55 thienopyrimidine derivatives. A three-dimensional quantitative structure–activity relationship (3D-QSAR) study was carried out using atom-based alignment of diverse set of 55 molecules to evaluate the structure– activity relationships. Docking and 3D-QSAR studies were performed with the 3D structure of AurA to evaluate the generated pharmacophore. The pharmacophore model and 3D-QSAR results complemented the results of our docking study. The pharmacophore hypothesis, which yields the best results, was used to screen the Zinc ‘clean drug-like’ database. Various database filters such as 3D-arrangement of pharmacophoric features, predicted activity and binding interaction score were used to retrieve hits having potential AurA inhibition activity.     

An efficient multistep ligand-based virtual screening approach for GPR40 agonists

G protein-coupled receptor 40/free fatty acid receptor 1 (GPR40/FFAR1) is a member of the GPCR superfamily, and GPR40 agonists have therapeutic potential for type 2 diabetes. With the crystal structure of GPR40 currently unavailable, various ligand-based virtual screening approaches can be applied to identify novel agonists of GPR40. It is known that each ligand-based method has its own advantages and limitations. To improve the efficiency of individual ligand-based methods, an efficient multistep ligand-based virtual screening approach is presented in this study, including the pharmacophore-based screening, physicochemical property filtering, protein–ligand interaction fingerprint similarity analysis, and 2D-fingerprint structural similarity search. A focused decoy library was generated and used to evaluate the efficiency of this virtual screening protocol. This multistep workflow not only significantly improved the hit rate compared with each individual ligand-based method, but also identified diverse known actives from decoys. This protocol may serve as an efficient virtual screening tool for the targets without crystal structures available to discover novel active compounds.     

Dual inhibitors of Janus kinase 2 and 3 (JAK2/3): designing by pharmacophore- and docking-based virtual screening approach

JAK2 and JAK3 are non-receptor protein tyrosine kinases implicated in B-cell- and T-cell-mediated diseases. Both enzymes work via different pathways but are involved in the pathogenesis of common lymphoid-derived diseases. Hence, targeting both Janus kinases together can be a potential strategy for the treatment of these diseases. In the present study, two separate pharmacophore-based 3D-QSAR models ADRR.92 ( $Q_{\mathrm{test}}^{2} 0.663, R^{2}_{\mathrm{train}} 0.849$ , F value 219.3) for JAK2 and ADDRR.142 ( $Q^{2}_{\mathrm{test}}0.655, R_{\mathrm{train}}^{2}$ 0.869, F value 206.9) for JAK3 were developed. These models were employed for the screening of a PHASE database of approximately 1.5 million compounds; subsequently, the retrieved hits were screened employing docking simulations with JAK2 and JAK3 proteins. Finally, ADME properties of screened dual inhibitors displaying essential interactions with both proteins were calculated to filter candidates with poor pharmacokinetic profiles. These candidates could serve as novel therapeutic agents in the treatment of lymphoid-related diseases.     

Selection of antileishmanial sesquiterpene lactones from SistematX database using a combined ligand-/structure-based virtual screening approach

Molecular Diversity - Leishmaniasis refers to a complex of diseases, caused by the intracellular parasitic protozoans belonging to the genus Leishmania. Among the three types of disease...     

LBVS: an online platform for ligand-based virtual screening using publicly accessible databases

Abundant data on compound bioactivity and publicly accessible chemical databases increase opportunities for ligand-based drug discovery. In order to make full use of the data, an online platform for ligand-based virtual screening (LBVS) using publicly accessible databases has been developed. LBVS adopts Bayesian learning approach to create virtual screening models because of its noise tolerance, speed, and efficiency in extracting knowledge from data. LBVS currently includes data derived from BindingDB and ChEMBL. Three validation approaches have been employed to evaluate the virtual screening models created from LBVS. The tenfold cross validation results of twenty different LBVS models demonstrate that LBVS achieves an average AUC value of 0.86. Our internal and external testing results indicate that LBVS is predictive for lead identifications. LBVS can be publicly accessed at http://rcdd.sysu.edu.cn/lbvs.     

Ligand-based virtual screening,molecular docking,and molecular dynamics of eugenol analogs as potential acetylcholinesterase inhibitors with biological activity against Spodoptera frugiperda

Molecular Diversity - The development of new, more selective, environmental-friendly insecticide alternatives is in high demand for the control of Spodoptera frugiperda (S. frugiperda). The major...     

Ambiguities in chiral-symmetry breaking using the effective potential approach

Using a form of the effective potential for composite operators with a variational approach we show that it is possible to get different directions of the chiral phase transition in QCD. Which one occurs depends on the way the Schwinger-Dyson equation for the fermion self-energy is used in the 2-loop term of the effective potential. We must choose the 2-loop term which agrees with phenomenology in each form of the effective potential.     

Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking,molecular dynamics simulation and binding free energy calculation

Molecular Diversity - 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...     

Discovery and identification of PIM-1 kinase inhibitors through a hybrid screening approach

PIM-1 kinase is an important therapeutic target in the treatment of cancer. Discovery and identification of PIM-1 Inhibitors with novel scaffolds are an effective way for developing potent therapeutic agents for the treatment of cancers. Here we proposed a hybrid screening approach which combines an optimal structure-based drug design strategy and a simple pharmacophore model to discover PIM-1 kinase inhibitors. With the proposed hybrid screening approach, the SPECS database containing 204,580 molecules was screened. In total, 89 hits were obtained. Forty three of them were purchased and tested in bioassays. Finally, 5 lead compounds with novel scaffolds were identified to exhibit promising antitumor activities against human leukemia cell line MV4-11, K-562 and human prostate cancer cell line PC-3 and DU145. Their $\hbox {IC}_{50}$ values range from 4.40 to $37.96 \,\upmu \hbox {M}$ . Three hits with 3 different scaffolds were selected from these five hits for binding mode analysis. It was demonstrated that the subtle differences in the interactions of the representatives with PIM-1 kinase contribute to the different inhibitory activities. It was also demonstrated that the suggested hybrid screening approach is an effective method to discover PIM-1 inhibitors possessing different scaffolds. These leads have a strong likelihood to act as further starting points for us in the optimization and development of potent PIM-1 inhibitors.     

Identification of potential photovoltaic absorbers based on first-principles spectroscopic screening of materials

There are numerous inorganic materials that may qualify as good photovoltaic (PV) absorbers, except that the currently available selection principle-focusing on materials with a direct band gap of ～1.3 eV (the Shockley-Queisser criteria)-does not provide compelling design principles even for the initial material screening. Here we offer a calculable selection metric of "spectroscopic limited maximum efficiency (SLME)" that can be used for initial screening based on intrinsic properties alone. It takes into account the band gap, the shape of absorption spectra, and the material-dependent nonradiative recombination losses. This is illustrated here via high-throughput first-principles quasiparticle calculations of SLME for ～260 generalized I(p)III(q)VI(r) chalcopyrite materials. It identifies over 20 high-SLME materials, including the best known as well as previously unrecognized PV absorbers.