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.

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100-250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.

Repositioning liothyronine for cancer immunotherapy by blocking the interaction of immune checkpoint TIGIT/PVR

Background

Inhibitors targeting immune checkpoint were proved effective in cancer immunotherapy, such as PD-1/PD-L1 blockade. The novel immune checkpoint TIGIT/PVR plays critical roles in suppressing the anti-tumor effects of CD8⁺ T and NK cells, and dual blockade of TIGIT/PVR and PD-1/PD-L1 by antibody can elicit synergistic effects in tumor models and clinical trials. However, small molecules for TIGIT/PVR blockade have not been investigated.

Methods

The expression of PVR in tumors were analyzed by using TCGA, Oncomine and GEO database, and in cancer cell lines examined by flow cytometry. Natural product compounds were docked to PVR for virtual screening by using the software Molecular Operating Environment (MOE). Candidate compounds were further tested by biolayer interferometry-based binding assay, microscale thermophoresis assay and cell based blocking assay. The in vitro activity of the candidate compound was determined by MTT, peripheral blood mononuclear cells (PBMCs) activation assay and coculture assay. The anti-tumor effects and mechanism were also investigated by using MC38 tumor-bearing mice model and immune cell depletion tumor model.

Results

PVR was over-expressed in many tumor tissues and cancer cell lines, making it a promising therapeutic target. Through virtual screening, binding, and blocking assay, liothyronine was discovered to bind PVR and block the interaction of TIGIT/PVR. Liothyronine could enhance the function of CD4⁺ and CD8⁺ T cells in PBMCs. Besides, in the Jurkat-hTIGIT and CHOK1-hPVR coculture assay, liothyronine could reverse the IL-2 secretion inhibition resulted by TIGIT/PVR ligation. Although had no influence on the proliferation of tumor cells in vitro, liothyronine could significantly inhibit tumor growth when administrated in vivo, by enhancing CD8⁺ T cell infiltration and immune responses in the tumor bearing mice. The immune cell depletion model showed that the anti-tumor effects of liothyronine depends on CD4⁺ T cells, CD8⁺ T cells and NK cells.

Conclusions

A small molecule liothyronine was discovered to serve as a potential candidate for cancer immunotherapy by blocking the immune checkpoint TIGIT/PVR.

Structural-based virtual screening and in vitro assays for small molecules inhibiting the feline coronavirus 3CL protease as a surrogate platform for coronaviruses

Feline infectious peritonitis (FIP) which is caused by feline infectious peritonitis virus (FIPV), a variant of feline coronavirus (FCoV), is a member of family Coronaviridae, together with severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and SARS-CoV-2. So far, neither effective vaccines nor approved antiviral therapeutics are currently available for the treatment of FIPV infection. Both human and animal CoVs shares similar functional proteins, particularly the 3CL protease (3CL^pro), which plays the pivotal role on viral replication. We investigated the potential drug-liked compounds and their inhibitory interaction on the 3CL^pro active sites of CoVs by the structural-bases virtual screening. Fluorescence resonance energy transfer (FRET) assay revealed that three out of twenty-eight compounds could hamper FIPV 3CL^pro activities with IC₅₀ of 3.57 ± 0.36 μM to 25.90 ± 1.40 μM, and Ki values of 2.04 ± 0.08 to 15.21 ± 1.76 μM, respectively. Evaluation of antiviral activity using cell-based assay showed that NSC629301 and NSC71097 could strongly inhibit the cytopathic effect and also reduced replication of FIPV in CRFK cells in all examined conditions with the low range of EC₅₀ (6.11 ± 1.90 to 7.75 ± 0.48 μM and 1.99 ± 0.30 to 4.03 ± 0.60 μM, respectively), less than those of ribavirin and lopinavir. Analysis of FIPV 3CL^pro-ligand interaction demonstrated that the selected compounds reacted to the crucial residues (His41 and Cys144) of catalytic dyad. Our investigations provide a fundamental knowledge for the further development of antiviral agents and increase the number of anti-CoV agent pools for feline coronavirus and other related CoVs.

•

Virtual screening and molecular docking revealed three lead compounds bound to FIPV 3CL^pro active site.

•

The 3D structures of 3CL^pro of coronaviruses including SARS-CoV-2 are highly conserved.

•

These compounds showed inhibitory effects on the proteases of FIPV, PEDV, SARS-CoV and SARS-CoV-2.

•

Their antiviral activities are better than Ribavirin and Lopinavir while comparable to GC376.

Discovery of HIV entry inhibitors via a hybrid CXCR4 and CCR5 receptor pharmacophore-based virtual screening approach

Chemokine receptors are key regulators of cell migration in terms of immunity and inflammation. Among these, CCR5 and CXCR4 play pivotal roles in cancer metastasis and HIV-1 transmission and infection. They act as essential co-receptors for HIV and furnish a route to the cell entry. In particular, inhibition of either CCR5 or CXCR4 leads very often the virus to shift to a more virulent dual-tropic strain. Therefore, dual receptor inhibition might improve the therapeutic strategies against HIV. In this study, we aimed to discover selective CCR5, CXCR4, and dual CCR5/CXCR4 antagonists using both receptor- and ligand-based computational methods. We employed this approach to fully incorporate the interaction attributes of the binding pocket together with molecular dynamics (MD) simulations and binding free energy calculations. The best hits were evaluated for their anti-HIV-1 activity against CXCR4- and CCR5-specific NL4.3 and BaL strains. Moreover, the Ca²⁺ mobilization assay was used to evaluate their antagonistic activity. From the 27 tested compounds, three were identified as inhibitors: compounds 27 (CCR5), 6 (CXCR4) and 3 (dual) with IC₅₀ values ranging from 10.64 to 64.56 μM. The binding mode analysis suggests that the active compounds form a salt bridge with the glutamates and π-stacking interactions with the aromatic side chains binding site residues of the respective co-receptor. The presented hierarchical virtual screening approach provides essential aspects in identifying potential antagonists in terms of selectivity against a specific co-receptor. The compounds having multiple heterocyclic nitrogen atoms proved to be relatively more specific towards CXCR4 inhibition as compared to CCR5. The identified compounds serve as a starting point for further development of HIV entry inhibitors through synthesis and quantitative structure-activity relationship studies.

Ferrihydrite nanoparticles insights: Structural characterization, lactate dehydrogenase binding and virtual screening assay

The binding between the enzyme lactate dehydrogenase (LDH) and ferrihydrite nanoparticles (Fh-NPs) was investigated by means of small-angle neutron scattering (SANS), Fourier-transform infrared (FTIR) spectroscopy, fluorescence and Förster resonance energy transfer (FRET) and molecular docking. Fh-NPs - LDH compounds of dimensions under 100 nm are formed. The conformational changes and the mechanism of interaction between LDH and Fh-NPs simple and doped with Cu and Co, and the effect of these NPs on the thermal denaturation of LDH were monitored. The quenching mechanism is static, the binding occurring with moderate affinity, being mainly driven by hydrogen bonding and van der Waals forces. FRET occurs at a minimal distance of 2.55 nm. Thermal denaturation of LDH in the presence of simple and doped Fh-NPs shows that the thermodynamic parameters of protein unfolding are significantly changed with temperature. The denaturation temperature of LDH shifts to higher values in the presence of all Fh-NPs, than in the case of simple LDH. The docking approach estimates the energy corresponding to the best fit of the ferrihydrite in the LDH binding site near Trp. These results have direct implications on the uses of the complex of LDH with Fh-NPs in various biochemical, biological, or clinical applications.

Structure-based virtual screening of phytochemicals and repurposing of FDA approved antiviral drugs unravels lead molecules as potential inhibitors of coronavirus 3C-like protease enzyme

Coronaviruses are enveloped positive-strand RNA viruses belonging to family Coronaviridae and order Nidovirales which cause infections in birds and mammals. Among the human coronaviruses, highly pathogenic ones are Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome coronavirus (MERS-CoV) which have been implicated in severe respiratory syndrome in humans. There are no approved antiviral drugs or vaccines for the treatment of human CoV infection to date. The recent outbreak of new coronavirus pandemic, coronavirus disease 2019 (COVID-19) has caused a high mortality rate and infections around the world which necessitates the need for the discovery of novel anti-coronaviral drugs. Among the coronaviruses proteins, 3C-like protease (3CL^pro) is an important drug target against coronaviral infection as the auto-cleavage process catalysed by the enzyme is crucial for viral maturation and replication. The present work is aimed at the identification of suitable lead molecules for the inhibition of 3CL^pro enzyme via a computational screening of the Food and Drug Administration (FDA) approved antiviral drugs and phytochemicals. Based on binding energies and molecular interaction studies, we shortlisted five lead molecules (both FDA approved drugs and phytochemicals) for each enzyme targets (SARS-CoV-2 3CL^pro, SARS-CoV 3CL^pro and MERS-CoV 3CL^pro). The lead molecules showed higher binding affinity compared to the standard inhibitors and exhibited favourable hydrophobic interactions and a good number of hydrogen bonds with their respective targets. A few promising leads with dual inhibition potential were identified among FDA approved antiviral drugs which include DB13879 (Glecaprevir), DB09102 (Daclatasvir), molecule DB09297 (Paritaprevir) and DB01072 (Atazanavir). Among the phytochemicals, 11,646,359 (Vincapusine), 120,716 (Alloyohimbine) and 10,308,017 (Gummadiol) showed triple inhibition potential against all the three targets and 102,004,710 (18-Hydroxy-3-epi-alpha-yohimbine) exhibited dual inhibition potential. Hence, the proposed lead molecules from our findings can be further investigated through in vitro and in vivo studies to develop into potential drug candidates against human coronaviral infections.

Structure-based virtual screening and molecular dynamics of phytochemicals derived from Saudi medicinal plants to identify potential COVID-19 therapeutics

Coronavirus disease 2019 (COVID-19) has affected almost every country in the world by causing a global pandemic with a high mortality rate. Lack of an effective vaccine and/or antiviral drugs against SARS-CoV-2, the causative agent, has severely hampered the response to this novel coronavirus. Natural products have long been used in traditional medicines to treat various diseases, and purified phytochemicals from medicinal plants provide a valuable scaffold for the discovery of new drug leads. In the present study, we performed a computational screening of an in-house database composed of ~1000 phytochemicals derived from traditional Saudi medicinal plants with recognised antiviral activity. Structure-based virtual screening was carried out against three druggable SARS-CoV-2 targets, viral RNA-dependent RNA polymerase (RdRp), 3-chymotrypsin-like cysteine protease (3CL^pro) and papain like protease (PL^pro) to identify putative inhibitors that could facilitate the development of potential anti-COVID-19 drug candidates. Computational analyses identified three compounds inhibiting each target, with binding affinity scores ranging from -9.9 to -6.5 kcal/mol. Among these, luteolin 7-rutinoside, chrysophanol 8-(6-galloylglucoside) and kaempferol 7-(6″-galloylglucoside) bound efficiently to RdRp, while chrysophanol 8-(6-galloylglucoside), 3,4,5-tri-O-galloylquinic acid and mulberrofuran G interacted strongly with 3CL^pro, and withanolide A, isocodonocarpine and calonysterone bound tightly to PL^pro. These potential drug candidates will be subjected to further in vitro and in vivo studies and may assist the development of effective anti-COVID-19 drugs.

Repurposing old drugs to fight multidrug resistant cancers

Overcoming multidrug resistance represents a major challenge for cancer treatment. In the search for new chemotherapeutics to treat malignant diseases, drug repurposing gained a tremendous interest during the past years. Repositioning candidates have often emerged through several stages of clinical drug development, and may even be marketed, thus attracting the attention and interest of pharmaceutical companies as well as regulatory agencies. Typically, drug repositioning has been serendipitous, using undesired side effects of small molecule drugs to exploit new disease indications. As bioinformatics gain increasing popularity as an integral component of drug discovery, more rational approaches are needed. Herein, we show some practical examples of in silico approaches such as pharmacophore modelling, as well as pharmacophore- and docking-based virtual screening for a fast and cost-effective repurposing of small molecule drugs against multidrug resistant cancers. We provide a timely and comprehensive overview of compounds with considerable potential to be repositioned for cancer therapeutics. These drugs are from diverse chemotherapeutic classes. We emphasize the scope and limitations of anthelmintics, antibiotics, antifungals, antivirals, antimalarials, antihypertensives, psychopharmaceuticals and antidiabetics that have shown extensive immunomodulatory, antiproliferative, pro-apoptotic, and antimetastatic potential. These drugs, either used alone or in combination with existing anticancer chemotherapeutics, represent strong candidates to prevent or overcome drug resistance. We particularly focus on outcomes and future perspectives of drug repositioning for the treatment of multidrug resistant tumors and discuss current possibilities and limitations of preclinical and clinical investigations.

Benchmarking the performance of MM/PBSA in virtual screening enrichment using the GPCR-Bench dataset

Recent breakthroughs in G protein-coupled receptor (GPCR) crystallography and the subsequent increase in number of solved GPCR structures has allowed for the unprecedented opportunity to utilize their experimental structures for structure-based drug discovery applications. As virtual screening represents one of the primary computational methods used for the discovery of novel leads, the GPCR-Bench dataset was created to facilitate comparison among various virtual screening protocols. In this study, we have benchmarked the performance of Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) in improving virtual screening enrichment in comparison to docking with Glide, using the entire GPCR-Bench dataset of 24 GPCR targets and 254,646 actives and decoys. Reranking the top 10% of the docked dataset using MM/PBSA resulted in improvements for six targets at EF_1% and nine targets at EF_5%, with the gains in enrichment being more pronounced at the EF_1% level. We additionally assessed the utility of rescoring the top ten poses from docking and the ability of short MD simulations to refine the binding poses prior to MM/PBSA calculations. There was no clear trend of the benefit observed in both cases, suggesting that utilizing a single energy minimized structure for MM/PBSA calculations may be the most computationally efficient approach in virtual screening. Overall, the performance of MM/PBSA rescoring in improving virtual screening enrichment obtained from docking of the GPCR-Bench dataset was found to be relatively modest and target-specific, highlighting the need for validation of MM/PBSA-based protocols prior to prospective use.