Synthesis and bio-evaluation of a novel selective butyrylcholinesterase inhibitor discovered through structure-based virtual screening

In recent years, butyrylcholinesterase (BChE) has gradually gained worldwide interests as a novel target for treating Alzheimer's disease (AD). Here, two pharmacophore models were generated using Schrödinger suite and used to virtually screen ChemDiv database, from which three hits were obtained. Among them, 2513-4169 displayed the highest inhibitory activity and selectivity against BChE (eeAChE IC₅₀ > 10 μM, eqBChE IC₅₀ = 3.73 ± 1.90 μM). Molecular dynamic (MD) simulation validated the binding pattern of 2513-4169 in BChE, and it could form a various of receptor-ligand interactions with adjacent residues. In vitro cytotoxicity assay proved the safety of 2513-4169 on diverse neural cell lines. Moreover, the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) assay performed on SH-SY5Y cells proved the neuroprotective effect of 2513-4169 against toxic Aβ_1-42. In vivo behavioral study further confirmed the great efficacy of 2513-4169 on reversing Aβ_1-42-induced cognitive impairment of mice and clearing the toxic Aβ_1-42 in brains. Moreover, 2513-4169 was proved to be able to cross blood-brain barrier (BBB) through a parallel artificial membrane permeation assay of BBB (PAMPA-BBB). Taken together, 2513-4169 is a promising lead compound for future optimization to discover anti-AD treating agents.

Mitoxantrone, pixantrone and mitoxantrone (2-hydroxyethyl)piperazine are toll-like receptor 4 antagonists, inhibit NF-κB activation, and decrease TNF-alpha secretion in primary microglia

Toll-like receptor 4 (TLR4) recognizes various endogenous and microbial ligands and is an essential part in the innate immune system. TLR4 signaling initiates transcription factor NF-κB and production of proinflammatory cytokines. TLR4 contributes to the development or progression of various diseases including stroke, neuropathic pain, multiple sclerosis, rheumatoid arthritis and cancer, and better therapeutics are currently sought for these conditions. In this study, a library of 140 000 compounds was virtually screened and a resulting hit-list of 1000 compounds was tested using a cellular reporter system. The topoisomerase II inhibitor mitoxantrone and its analogues pixantrone and mitoxantrone (2-hydroxyethyl)piperazine were identified as inhibitors of TLR4 and NF-κB activation. Mitoxantrone was shown to bind directly to the TLR4, and pixantrone and mitoxantrone (2-hydroxyethyl)piperazine were shown to inhibit the production of proinflammatory cytokines such as tumor necrosis factor alpha (TNFα) in primary microglia. The inhibitory effect on NF-κB activation or on TNFα production was not mediated through cytotoxity at ≤ 1 µM concentration for pixantrone and mitoxantrone (2-hydroxyethyl)piperazine treated cells, as assessed by ATP counts. This study thus identifies a new mechanism of action for mitoxantrone, pixantrone, and mitoxantrone (2-hydroxyethyl)piperazine through the TLR4.

In silico assessment of natural products and approved drugs as potential inhibitory scaffolds targeting aminoacyl-tRNA synthetases from Plasmodium

Malaria remains the leading cause of deaths globally, despite significant advancement towards understanding its epidemiology and availability of multiple therapeutic interventions. Poor efficacy of the approved vaccine, and the rapid emergence of antimalarial drug resistance, warrants an urgent need to expedite the process of development of new lead molecules targeting malaria. Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes crucial for ribosomal protein synthesis and are valid antimalarial targets. This study explores the prospects of (re-)positioning the repertoire of approved drugs and natural products as potential malarial aaRS inhibitors. Molecular docking of these two sets of small-molecules to lysyl-, prolyl-, and tyrosyl- synthetases from Plasmodium followed by a comparison of the top-ranking docked compounds against human homologs facilitated identification of promising molecular scaffolds. Raltitrexed and Cefprozil, an anticancer drug and an antibiotic, respectively, showed stronger binding to Plasmodium aaRSs compared to human homologs with > 4 kcal/mol difference in the docking scores. Similarly, a difference of ~ 3 kcal/mol in Glide scores was observed for docked Calcipotriol, a drug used for psoriasis treatment, against the two lysyl-tRNA synthetases. Natural products such as Dihydroxanthohumol and Betmidin, having aromatic rings as a substructure, showed preferential docking to the purine binding pocket in Plasmodium tyrosyl-tRNA synthetase as evident from the calculated change in binding free energies. We present detailed analyses of the calculated intermolecular interaction for all top-scoring docked poses. Overall, this study provides a compelling foundation to design and develop specific antimalarials.

In silico identification of strong binders of the SARS-CoV-2 receptor-binding domain

The world is currently witnessing the spread of the deadly severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the coronavirus disease 2019 (COVID-19). In less than three months since the first cases were reported, the World Health Organization declared it a pandemic disease. Although several treatment and prevention strategies are currently under investigation, a continuous effort to investigate and develop effective cures is urgently needed. Thus, we performed molecular docking and structure-based virtual screening of libraries of approved drugs, antivirals, inhibitors of protein-protein interactions, and one million other small molecules to identify strong binders of the SARS-CoV-2 receptor-binding domain (RBD) that might interfere with the receptor recognition process, so as to inhibit the viral cellular entry. According to our screening and selection criteria, three approved antivirals (elbasvir, grazoprevir, and sovaprevir) and 4 other drugs (hesperidin, pamaqueside, diosmin, and sitogluside) were identified as potent binders of the RBD. The binding of these molecules involved several RBD residues required for the interaction of the virus with its cellular receptor. Furthermore, this study also discussed the pharmacological action of the 4 non-antiviral drugs on hematological and neurological disorders that, in addition to inhibiting the viral entry, could be beneficial against the neurological disorders identified in COVID-19 patients. Besides, six other small-molecules were identified, with no pharmacological description so far, exhibiting strong binding affinities to the RBD that we believe worth being investigated as inhibitors of the SARS-CoV-2-receptor interaction.

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We aimed to identify strong binders of the SARS-CoV-2 RBD to be used as inhibitors of virus-receptor interaction.

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Accordingly, 3 antivirals, 4 other approved drugs and 6 small-molecules have been identified as potential ligands of the RBD.

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The binding of these selected compounds involves several RBD residues crucial for the virus-receptor interaction.

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Besides RBD-binding, we discussed the possible effects of the selected drugs on the COVID-19- related neurological disorders.

Identification of novel αβ-tubulin modulators with antiproliferative activity directed to cancer therapy using ligand and structure-based virtual screening

Among several strategies related to cancer therapy targeting the modulation of αβ-tubulin has shown encouraging findings, more specifically when this is achieved by inhibitors located at the colchicine binding site. In this work, we aim to fish new αβ-tubulin modulators through a diverse and rational VS study, and thus, exhibiting the development of two VS pipelines. This allowed us to identify two compounds 5 and 9 that showed IC₅₀ values of 19.69 and 21.97 μM, respectively, towards possible modulation of αβ-tubulin, such as assessed by in vitro assays in C6 glioma and HEPG2 cell lines. We also evaluated possible mechanisms of action of obtained hits towards the colchicine binding site of αβ-tubulin by using docking approaches. In addition, assessment of the stability of the active (5 and 9) and inactive compounds (3 and 13) within the colchicine binding site was carried out by molecular dynamics (MD) simulations, highlighting the solvent effect and revealing the compound 5 as the most stable in the complex. At last, deep analysis of these results provided some valuable insights on the importance of using mixed ligand- and structure-based strategies in VS campaigns, in order to achieve higher chemical diversity and biological effect as well.

Discovery of small molecule PLpro inhibitor against COVID-19 using structure-based virtual screening, molecular dynamics simulation, and molecular mechanics/Generalized Born surface area (MM/GBSA) calculation

COVID-19 is spreading in a global pandemic that is endangering human life and health. Therefore, there is an urgent need to target COVID-19 to find effective treatments for this emerging acute respiratory infection. Viral Papain-Like cysteine protease (PLpro), similar to papain and the cysteine deubiquitinase enzyme, has been a popular target for coronavirus inhibitors, as an indispensable enzyme in the process of coronavirus replication and infection of the host. Combined structure-based virtual screening, molecular dynamics (MD) simulation, and molecular mechanics/Generalized Born surface area (MM/GBSA) free energy calculation approaches were utilized for identification of PLpro inhibitors. Four compounds (F403_0159, F112_0109, G805_0497, D754_0006) with diverse chemical scaffolds were retrieved as hits based on docking score and clustering analysis. Molecular dynamics simulations indicated that the contribution of van der Waals interaction dominated the binding free energies of these compounds, which may be attributed to the hydrophobicity of active site of PLpro from COVID-19. Moreover, all four compounds formed conservative hydrogen bonds with the residues Asp164, Gln269, and Tyr273. We hoped that these four compounds might represent the promising chemical scaffolds for further development of novel PLpro inhibitors against COVID-19.

In silico identification of drug candidates against COVID-19

The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M pro ), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M pro . Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M pro . Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M pro . Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design.

Application of target repositioning and in silico screening to exploit fatty acid binding proteins (FABPs) from Echinococcus multilocularis as possible drug targets

Fatty acid binding proteins (FABPs) are small intracellular proteins that reversibly bind fatty acids and other hydrophobic ligands. In cestodes, due to their inability to synthesise fatty acids and cholesterol de novo, FABPs, together with other lipid binding proteins, have been proposed as essential, involved in the trafficking and delivery of such lipophilic metabolites. Pharmacological agents that modify specific parasite FABP function may provide control of lipid signalling pathways, inflammatory responses and metabolic regulation that could be of crucial importance for the parasite development and survival. Echinococcus multilocularis and Echinococcus granulosus are, respectively, the causative agents of alveolar and cystic echinococcosis (or hydatidosis). These diseases are included in the World Health Organization's list of priority neglected tropical diseases. Here, we explore the potential of FABPs from cestodes as drug targets. To this end, we have applied a target repurposing approach to identify novel inhibitors of Echinococcus spp. FABPs. An ensemble of computational models was developed and applied in a virtual screening campaign of DrugBank library. 21 hits belonging to the applicability domain of the ensemble models were identified, and 3 of the hits were assayed against purified E. multilocularis FABP, experimentally confirming the model's predictions. Noteworthy, this is to our best knowledge the first report on isolation and purification of such four FABP, for which initial structural and functional characterization is reported here.

Identification of potential drug target in malarial disease using molecular docking analysis

Malaria caused by genus Plasmodium, is a parasite which is the main health issue for humans and about half of the population were suffered. An every year, approximately 1.2–2.7 million people died due to malaria globally. Therefore to prevent the spreading of malaria from the glob novel active drugs with specific activities are necessary. The present study aimed to identify novel drug molecule together with the bioinformatic tools for the development of active malarial drugs. As the search for latest anti malarial compound was developed, this work determined six active blends from various drug databases which possess drug-like characteristics and presents a significant anti malarial actions in in-silico level. Compound ID 300238, 889, 76569, 87324, 45678, and Z185397112are a few of the ligands were got from the Toss lab, Maybridge, Cambridge, Life chem, Bitter, and Examine drug databases and docked against hexokinase 1 protein (PDB: 1CZA) with high throughput practical screening (HTVS) using Glide v6.6. Amid the 6 compounds, compound no: 300238 from Toss lab has the greatest docking score of −9.889 kcal/mol targeting 1CZA protein. The active sites of Hexokinase I of protein were determine by using superimposition of the destination and template structure showed similar structural folds and active sites which were decidedly conserved. The quality of hexokinase I protein was considered to be sterically stable where the protein was prepared by utilizing the software protein preparation execute in the Schrodinger suite. Prepared proteins were evaluated using SAVES and the studies of molecular dynamics of the hexokinase, and the GROMACS were performed for protein–ligand complex. The low HOMO-LUMO energy gaps of the compound verified the greater stability of the molecule. Here, the tested drug candidates have good absorption, distribution, metabolism, and excretion (ADME) properties which were established by using QikProp, version 3.4 of Schrodinger.

Identification of high-affinity inhibitors of SARS-CoV-2 main protease: Towards the development of effective COVID-19 therapy

Coronavirus disease 2019 (COVID-19) is an infectious disease, caused by a newly emerged highly pathogenic virus called novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Targeting the main protease (Mpro, 3CLpro) of SARS-CoV-2 is an appealing approach for drug development because this enzyme plays a significant role in the viral replication and transcription. The available crystal structures of SARS-CoV-2 Mpro determined in the presence of different ligands and inhibitor-like compounds provide a platform for the quick development of selective inhibitors of SARS-CoV-2 Mpro. In this study, we utilized the structural information of co-crystallized SARS-CoV-2 Mpro for the structure-guided drug discovery of high-affinity inhibitors from the PubChem database. The screened compounds were selected on the basis of their physicochemical properties, drug-likeliness, and strength of affinity to the SARS-CoV-2 Mpro. Finally, we have identified 6-Deaminosinefungin (PubChem ID: 10428963) and UNII-O9H5KY11SV (PubChem ID: 71481120) as potential inhibitors of SARS-CoV-2 Mpro which may be further exploited in drug development to address SARS-CoV-2 pathogenesis. Both compounds are structural analogs of known antivirals, having considerable protease inhibitory potential with improved pharmacological properties. All-atom molecular dynamics simulations suggested SARS-CoV-2 Mpro in complex with these compounds is stable during the simulation period with minimal structural changes. This work provides enough evidence for further implementation of the identified compounds in the development of effective therapeutics of COVID-19.

Fenoterol and dobutamine as SARS-CoV-2 main protease inhibitors: A virtual screening study

Global health is under heavy threat by a worldwide pandemic caused by a new type of coronavirus (COVID-19) since its rapid spread in China in 2019 [1]. Currently, there are no approved specific drugs and effective treatment for COVID-19 infection, but several available drugs are known to facilitate tentative treatment. Since drug design, development and testing procedures are time-consuming [2], [1], [2], [3], virtual screening studies with the aid of available drug databases take the initiative at this point and save the time. Besides, drug repurposing strategies promises to identify new agents for the novel diseases in a time-critical fashion. In this study, we used structure based virtual screening method on FDA approved drugs and compounds in clinical trials. As a result of this study we choose three most prominent compounds for further studies. Here we show that these three compounds (dobutamine and its two derivatives) can be considered as promising inhibitors for SARS-CoV-2 main protease and results also demonstrate the possible interactions of dobutamine and its derivatives with SARS-CoV-2 main protease (6W63) [6]. Our efforts in this work directly address current urgency of a new drug discovery against COVID-19.

Structure-Based Design of Novel Peptidomimetics Targeting the SARS-CoV-2 Spike Protein

Purpose

SARS-CoV-2 is a SARS-like novel coronavirus strain first identified in December 2019 in Wuhan, China. The virus has since spread globally, resulting in the current ongoing coronavirus disease 19 (COVID-19) pandemic. SARS-CoV-2 spike protein is a critical factor in the COVID-19 pathogenesis via interactions with the host cell angiotensin-converting enzyme 2 (ACE2) PD domain. Worldwide, numerous efforts are being made to combat COVID19. In the current study, we identified potential peptidomimetics against the SARS-CoV-2 spike protein.

Methods

We utilized the information from ACE2-SARS-CoV-2 binary interactions, and based on crucial interacting interface residues, novel peptidomimetics were designed.

Results

Top scoring peptidomimetics were found to bind at the ACE2 binding site of the receptor-binding domain (RBD) of SARS-CoV-2 spike protein.

Conclusions

The current studies could pave the way for further investigations of these novel and potent compounds against the SARS-CoV-2.

Biophysical and In-Silico Studies of Phytochemicals Targeting Chorismate Synthase from Drug-Resistant Moraxella Catarrhalis

Chorismate serves as a crucial precursor for the synthesis of many aromatic compounds essential for the survival and virulence in various bacteria and protozoans. Chorismate synthase, a vital enzyme in the shikimate pathway, is responsible for the formation of chorismate from enolpyruvylshikimate-3-phosphate (EPSP). Moraxella catarrhalis is reported to be resistant to many beta-lactam antibiotics and causes chronic ailments such as otitis media, sinusitis, laryngitis, and bronchopulmonary infections. Here, we have cloned the aroC gene from Moraxella catarrhalis in pET28c and heterologously produced the chorismate synthase (~ 43 kDa) in Escherichia coli BL21(DE3) cells. We have predicted the three-dimensional structure of this enzyme and used the refined model for ligand-based virtual screening against Supernatural Database using PyRx tool that led to the identification of the top three molecules (caffeic acid, gallic acid, and o-coumaric acid). The resultant protein-ligand complex structures were subjected to 50 ns molecular dynamics (MD) simulation using GROMACS. Further, the binding energy was calculated by MM/PBSA approach using the trajectory obtained from MD simulation. The binding affinities of these compounds were validated with ITC experiments, which suggest that gallic acid has the highest binding affinity amongst these three phytochemicals. Together, these results pave the way for the use of these phytochemicals as potential anti-bacterial compounds.

Rapid discovery of a selective butyrylcholinesterase inhibitor using structure-based virtual screening

Acetylcholinesterase inhibitors are the mainstay of Alzheimer's disease treatments, despite having only short-term symptomatic benefits and severe side effects. Selective butyrylcholinesterase inhibitors (BuChEIs) may be more effective treatments in late-stage Alzheimer's disease with fewer side effects. Virtual screening is a powerful tool for identifying potential inhibitors in large digital compound databases. This study used structure-based virtual screening combined with physicochemical filtering to screen the InterBioScreen and Maybridge databases for novel selective BuChEIs. The workflow rapidly identified 22 potential hits in silico, resulting in the discovery of a human BuChEI with low-micromolar potency in vitro (IC₅₀ 2.4 µM) and high selectivity for butyrylcholinesterase over acetylcholinesterase. The compound was a rapidly reversible BuChEI with mixed-model in vitro inhibition kinetics. The binding interactions were investigated using in silico molecular dynamics and by developing structure-activity relationships using nine analogues. The compound also displayed high permeability in an in vitro model of the blood-brain barrier.

Virtual screening and free energy estimation for identifying Mycobacterium tuberculosis flavoenzyme DprE1 inhibitors

In Mycobacterium tuberculosis (MTB), the cell wall synthesis flavoenzyme decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1) plays a crucial role in host pathogenesis, virulence, lethality and survival under stress. The emergence of different variants of drug resistant MTB are a major threat worldwide which essentially requires more effective new drug molecules with no major side effects. Here, we used structure based virtual screening of bioactive molecules from the ChEMBL database targeting DprE1, having bioactive 78,713 molecules known for anti-tuberculosis activity. An extensive molecular docking, binding affinity and pharmacokinetics profile filtering results in the selection four compounds, C5 (ChEMBL2441313), C6 (ChEMBL2338605), C8 (ChEMBL441373) and C10 (ChEMBL1607606) which may explore as potential drug candidates. The obtained results were validated with thirteen known DprE1 inhibitors. We further estimated the free-binding energy, solvation and entropy terms underlying the binding properties of DprE1-ligand interactions with the implication of MD simulation, MM/GBSA, MM/PBSA and MM/3D-RISM. Interestingly, we find that C6 shows the highest ΔG scores (-41.28 ± 3.51, -22.36 ± 3.17, -10.33 ± 5.70 kcal mol^-1) in MM/GBSA, MM/PBSA and MM/3D-RISM assay, respectively. Whereas, the lowest ΔG scores (-35.31 ± 3.44, -13.67 ± 2.65, -3.40 ± 4.06 kcal mol^-1) observed for CT319, the inhibitor co-crystallized with DprE1. Collectively, the results demonstrated that hit-molecules: C5, C6, C8 and C10 having better binding free energy and molecular affinity as compared to CT319. Thus, we proposed that selected compounds may be explored as lead molecules in MTB therapy.

Identification of novel Epac2 antagonists through in silico and in vitro analyses

cAMP-dependent guanine nucleotide exchange factor (Epac) is a key regulator in signal transduction and represents an excellent drug target to be investigated against various diseases. To date, very few modulators selective for Epac are available; however, there is still an unmet need of isoform-selective inhibitors. In the present study, ligand-based pharmacophores were designed to investigating structurally diverse molecules as Epac2 inhibitors. Pharmacophore models were developed using reported allosteric site inhibitors. The developed models were used to screen 95 thousand compounds from the National Cancer Institute (NCI), Maybride, and our in-house ICCBS Database. The binding mode and efficiency of the screened hits was investigated using molecular docking simulation on the allosteric site of Epac2 apo-protein (PDB ID: 2BYV) followed by ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling Furthermore, obtained in silico screened hits were subjected to in vitro assay for insulin secretion. We identified, three lead molecules RDR02145, AAK-399, and AAD-026 reducing, insulin secretion. Remarkably, a higher inhibitory effect on insulin secretion was observed in AAK-399, and AAD-026 as compared to that of standard Epac2 non-competitive allosteric site inhibitor, MAY0132. Furthermore, Dynamic simulation studies of lead compounds proved the structural stability of the Epac2 auto-inhibited state. These findings underline the potential of these compounds as valuable pharmacological tools for designing future selective probes to inhibit the Epac-mediated signaling pathway.

Computer-aided drug design against spike glycoprotein of SARS-CoV-2 to aid COVID-19 treatment

BACKGROUND

SARS-CoV-2 has the Spike glycoprotein (S) which is crucial in attachment with host receptor and cell entry leading to COVID-19 infection. The current study was conducted to explore drugs against Receptor Binding Domain (RBD) of SARS-CoV-2 using in silico pharmacophore modelling and virtual screening approach to combat COVID-19.

METHODS

All the available sequences of RBD in NCBI were retrieved and multiple aligned to get insight into its diversity. The 3D structure of RBD was modelled and the conserved region was used as a template to design pharmacophore using LigandScout. Lead compounds were screened using Cambridge, Drugbank, ZINC and TIMBLE databases and these identified lead compounds were screened for their toxicity and Lipinski's rule of five. Molecular docking of shortlisted lead compounds was performed using AutoDock Vina and interacting residues were visualized.

RESULTS

Active residues of Receptor Binding Motif (RBM) in S, involved in interaction with receptor, were found to be conserved in all 483 sequences. Using this RBM motif as a pharmacophore a total of 1327 lead compounds were predicted initially from all databases, however, only eight molecules fit the criteria for safe oral drugs. Conclusion: The RBM region of S interacts with Angiotensin Converting Enzyme 2 (ACE2) receptor and Glucose Regulated Protein 78 (GRP78) to mediate viral entry. Based on in silico analysis, the lead compounds scrutinized herewith interact with S, hence, can prevent its internalization in cell using ACE2 and GRP78 receptor.The compounds predicted in this study are based on rigorous computational analysis and the evaluation of predicted lead compounds can be promising in experimental studies.

Anti-HCV and anti-malaria agent, potential candidates to repurpose for coronavirus infection: Virtual screening, molecular docking, and molecular dynamics simulation study

AIMS

Coronavirus disease 2019 (COVID-19) has appeared in Wuhan, China but the fast transmission has led to its widespread prevalence in various countries, which has made it a global concern. Another concern is the lack of definitive treatment for this disease. The researchers tried different treatment options which are not specific. The current study aims to identify potential small molecule inhibitors against the main protease protein of SARS-CoV-2 by the computational approach.

MAIN METHODS

In this study, a virtual screening procedure employing docking of the two different datasets from the ZINC database, including 1615 FDA approved drugs and 4266 world approved drugs were used to identify new potential small molecule inhibitors for the newly released crystal structure of main protease protein of SARS-CoV-2. In the following to validate the docking result, molecular dynamics simulations were applied on selected ligands to identify the behavior and stability of them in the binding pocket of the main protease in 150 nanoseconds (ns). Furthermore, binding energy using the MMPBSA approach was also calculated.

KEY FINDINGS

The result indicates that simeprevir (Hepatitis C virus NS3/4A protease inhibitor) and pyronaridine (antimalarial agent) could fit well to the binding pocket of the main protease and because of some other beneficial features including broad-spectrum antiviral properties and ADME profile, they might be a promising drug candidate for repurposing to the treatment of COVID-19.

SIGNIFICANCE

Simeprevir and pyronaridine were selected by the combination of virtual screening and molecular dynamics simulation approaches as a potential candidate for treatment of COVID-19.

Identification of novel CDK 9 inhibitors based on virtual screening, molecular dynamics simulation, and biological evaluation

AIMS

Cyclin-dependent kinase 9 (CDK9) is a member of the CDK subfamily and plays a major role in the regulation of transcriptional elongation. It has attracted widespread attention as a therapeutic target for cancer. Here, we aimed to explore novel CDK 9 inhibitors by using a hybrid virtual screening strategy.

MAIN METHODS

A hybrid virtual screening strategy was constructed with computer-aided drug design (CADD). First, compounds were filtered in accordance with Lipinski's rule of five and adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Second, a 3D-QSAR pharmacophore model was built and used as a 3D query to screen the obtained hit compounds. Third, the hit compounds were subjected to molecular docking studies. Fourth, molecular dynamics (MD) simulations were performed on CDK9 in complex with the final hits to examine the structural stability. Finally, CDK9 kinase biochemical assay was performed to identify the biological activity of the hit compounds.

KEY FINDINGS

Seven hit compounds were screened out. These hit compounds showed drug-like properties in accordance with Lipinski's rule of five and ADMET. Complexes involving the six hit compounds bound to CDK9 exhibited good structural stability in the MD simulation. Furthermore, these six hit compounds had strong inhibitory activity against CDK9 kinase. In particular, hit 3 showed the most promising activity with the percentage of 71%.

SIGNIFICANCE

The six hit compounds may be promising novel CDK9 inhibitors, and the hybrid virtual screening strategy designed in this study provides an important reference for the design and synthesis of novel CDK9 inhibitors.

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

Design of novel viral attachment inhibitors of the spike glycoprotein (S) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) through virtual screening and dynamics

To date, the global COVID-19 pandemic has been associated with 11.8 million cases and over 545481 deaths. In this study, we have employed virtual screening approaches and selected 415 lead-like compounds from 103 million chemical substances, based on the existing drugs, from PubChem databases as potential candidates for the S protein-mediated viral attachment inhibition. Thereafter, based on drug-likeness and Lipinski's rules, 44 lead-like compounds were docked within the active side pocket of the viral-host attachment site of the S protein. Corresponding ligand properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile were measured. Furthermore, four novel inhibitors were designed and assessed computationally for efficacy. Comparative analysis showed the screened compounds in this study maintain better results than the proposed mother compounds, VE607 and SSAA09E2. The four designed novel lead compounds possessed more fascinating output without deviating from any of Lipinski's rules. They also showed higher bioavailability and the drug-likeness score was 0.56 and 1.81 compared with VE607 and SSAA09E2, respectively. All the screened compounds and novel compounds showed promising ADMET properties. Among them, the compound AMTM-02 was the best candidate, with a docking score of -7.5 kcal/mol. Furthermore, the binding study was verified by molecular dynamics simulation over 100 ns by assessing the stability of the complex. The proposed screened compounds and the novel compounds may give some breakthroughs for the development of a therapeutic drug to treat SARS-CoV-2 proficiently in vitro and in vivo.

Selecting machine-learning scoring functions for structure-based virtual screening

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

Convolutional architectures for virtual screening

Background

A Virtual Screening algorithm has to adapt to the different stages of this process. Early screening needs to ensure that all bioactive compounds are ranked in the first positions despite of the number of false positives, while a second screening round is aimed at increasing the prediction accuracy.

Results

A novel CNN architecture is presented to this aim, which predicts bioactivity of candidate compounds on CDK1 using a combination of molecular fingerprints as their vector representation, and has been trained suitably to achieve good results as regards both enrichment factor and accuracy in different screening modes (98.55% accuracy in active-only selection, and 98.88% in high precision discrimination).

Conclusion

The proposed architecture outperforms state-of-the-art ML approaches, and some interesting insights on molecular fingerprints are devised.

Docking based screening and molecular dynamics simulations to identify potential selective PDE4B inhibitor

Inhibition of phosphodiesterase 4 (PDE4) is a promising therapeutic approach for the treatment of inflammatory pulmonary disorders, i.e. asthma and chronic obstructive pulmonary disease. However, the treatment with non-selective PDE4 inhibitors is associated with side effects such as nausea and vomiting. Among the subtypes of PDE4 inhibited by these inhibitors, PDE4B is expressed in immune, inflammatory and airway smooth muscle cells, whereas, PDE4D is expressed in the area postrema and nucleus of the solitary tract. Thus, PDE4D inhibition is responsible for the emetic response. In this regard, a selective PDE4B inhibitor is expected to be a potential drug candidate for the treatment of inflammatory pulmonary disorders. Therefore, a shared feature pharmacophore model was developed and used as a query for the virtual screening of Maybridge and SPECS databases. A number of filters were applied to ensure only compounds with drug-like properties were selected. Accordingly, nine compounds have been identified as final hits, where HTS04529 showed the highest affinity and selectivity for PDE4B over PDE4D in molecular docking. The docked complexes of HTS04529 with PDE4B and PDE4D were subjected to molecular dynamics simulations for 100ns to assess their binding stability. The results showed that HTS04529 was bound tightly to PDE4B and formed a more stable complex with it than with PDE4D.

Current advances on the development of BET inhibitors: insights from computational methods

Epigenetics was coined almost 70 years ago for the description of heritable phenotype without altering DNA sequences. Research on the field has uncovered significant roles of such mechanisms, that account for the biogenesis of several diseases. Further studies have led the way for drug development which targets epi-enzymes, mainly for cancer treatment. Of the numerous epi-targets involved with histone acetylation, bromodomains have captured the spotlight of drug discovery focused on novel therapies. However, due to high sequence identity, the development of potent and selective inhibitors poses a significant challenge. Herein, we discuss recent computational developments on BET inhibitors and other methods that may be applied for drug discovery in general. As a proof-of-concept, we discuss a virtual screening to identify novel BET inhibitors based on coumarin derivatives. From public data, we identified putative structure-activity relationships of coumarin scaffold and propose R-group modifications for BET selectivity. Results showed that the optimization and design of novel coumarins could be further explored.