Improving virtual screening results with MM/GBSA and MM/PBSA rescoring

Virtual screening (VS) based on molecular docking is one of the most useful methods in computer-aided drug design. By allowing to identify computationally putative ligands binding to the proteins of interest, VS dramatically reduces the time and expense of the development of novel therapeutics. Among the limitations of the VS approaches is the low accuracy of scoring functions implemented in docking methods for assessing binding affinity. Many such scoring functions are developed for rapid, high-throughput evaluation of binding energy of multiple conformations generated by a searching algorithm. The methods for more rigorous calculation of binding affinity calculation are generally time-consuming. Even so, in many studies more accurate methods were used for rescoring of the final poses and false-positive hits evaluation. We performed VS for three benchmark sets and used energy minimization with MM/PB(GB)SA methods (molecular mechanics energies combined with the Poisson-Boltzmann or generalized Born and surface area) to rescore binding affinities. The comparison of the area under the curve (AUC), enrichment factor (EF), and Boltzmann-enhanced discrimination of receiver operating characteristics (BEDROC) showed essential improvements in the binding energy prediction after the rescoring. Finally, we provide a program for minimization and rescoring VS results based on freely available AmberTools. The code requires just the final binding poses of the ligand as the input and can be used with any docking program.

Analysis of natural compounds against the activity of SARS-CoV-2 NSP15 protein towards an effective treatment against COVID-19: a theoretical and computational biology approach

Coronavirus infectious disease 2019 (COVID-19), a viral infection caused by a novel coronavirus (nCoV), continues to emerge as a serious threat to public health. This pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2) has infected globally with 1,550,000 plus deaths to date, representing a high risk to public health. No effective drug or vaccine is available to curb down this deadly virus. The expedition for searching for a potential drug or vaccine against COVID-19 is of massive potential and favour to the community. This study is focused on finding an effective natural compound that can be processed further into a potential inhibitor to check the activity of SARS-CoV-2 with minimal side effects targeting NSP15 protein, which belongs to the EndoU enzyme family. The natural screening suggested two efficient compounds (PubChem ID: 95372568 and 1776037) with dihydroxyphenyl region of the compound, found to be important in the interaction with the viral protein showing promising activity which may act as a potent lead inhibitory molecule against the virus. In combination with virtual screening, modelling, drug likeliness, molecular docking, and 500 ns cumulative molecular dynamics simulations (100 ns for each complex) along with the decomposition analysis to calculate and confirm the stability and fold, we propose 95372568 and 1776037 as novel compounds of natural origin capable of getting developed into potent lead molecules against SARS-CoV-2 target protein NSP15.

In silico and in vitro screening of licensed antimalarial drugs for repurposing as inhibitors of hepatitis E virus

ABSTRACT

Hepatitis E virus (HEV) infection is emerging in Cameroon and represents one of the most common causes of acute hepatitis and jaundice. Moreover, earlier reports showed evidence of falciparum malaria/HEVcoexistence. Although the Sofosbuvir/Ribavirin combination was recently proposed in the treatment of HEV-infected patients, no specific antiviral drug has been approved so far, thereby urging the search for new therapies. Fortunately, drug repurposing offers a good alternative to this end. In this study, we report the in silico and in vitro activities of 8 licensed antimalarial drugs and two anti-hepatitis C virus agents used as references (Sofosbuvir, and Ribavirin), for repurposing as antiviral inhibitors against HEV. Compounds were docked against five HEV-specific targets including the Zinc-binding non-structural protein (6NU9), RNA-dependent RNA polymerase (RdRp), cryoEM structure of HEV VLP, genotype 1 (6LAT), capsid protein ORF-2, genotype 3 (2ZTN), and the E2s domain of genotype 1 (3GGQ) using the iGEMDOCK software and their pharmacokinetic profiles and toxicities were predicted using ADMETlab2.0. Their in vitro effects were also assessed on a gt 3 p6Gluc replicon system using the luciferase reporter assay. The docking results showed that Sofosbuvir had the best binding affinities with 6NU9 (- 98.22 kcal/mol), RdRp (- 113.86 kcal/mol), 2ZTN (- 106.96 kcal/mol), while Ribavirin better collided with 6LAT (- 99.33 kcal/mol). Interestingly, Lumefantrine showed the best affinity with 3GGQ (-106.05 kcal/mol). N-desethylamodiaquine and Amodiaquine presented higher binding scores with 6NU9 (- 93.5 and - 89.9 kcal/mol respectively vs - 80.83 kcal/mol), while Lumefantrine had the greatest energies with RdRp (- 102 vs - 84.58), and Pyrimethamine and N-desethylamodiaquine had stronger affinities with 2ZTN compared to Ribavirin (- 105.17 and - 102.65 kcal/mol vs - 96.04 kcal/mol). The biological screening demonstrated a significant (P < 0.001) antiviral effect on replication with 1 µM N-desethylamodiaquine, the major metabolite of Amodiaquine. However, Lumefantrine showed no effect at the tested concentrations (1, 5, and 10 µM). The biocomputational analysis of the pharmacokinetic profile of both drugs revealed a low permeability of Lumefantrine and a specific inactivation by CYP3A2 which might partly contribute to the short half-time of this drug. In conclusion, Amodiaquine and Lumefantrine may be good antimalarial drug candidates for repurposing against HEV. Further in vitro and in vivo experiments are necessary to validate these predictions.

GRAPHIC ABSTRACT

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40203-021-00093-y.

High-throughput screening of natural compounds and inhibition of a major therapeutic target HsGSK-3β for Alzheimer's disease using computational approaches

BACKGROUND

Alzheimer's disease is a leading neurodegenerative disease worldwide and is the 6th leading cause of death in the USA. AD is a very complex disease and the drugs available in the market cannot fully cure it. The glycogen synthase kinase 3 beta plays a major role in the hyperphosphorylation of tau protein which forms the neurofibrillary tangles which is a major hallmark of AD. In this study, we have used a series of computational approaches to find novel inhibitors against GSK-3β to reduce the TAU hyperphosphorylation.

RESULTS

We have retrieved a set of compounds (n=167,741) and screened against GSK-3β in four sequential steps. The resulting analysis of virtual screening suggested that 404 compounds show good binding affinity and can be employed for pharmacokinetic analysis. From here, we have selected 20 compounds those were good in terms of pharmacokinetic parameters. All these compounds were re-docked by using Autodock Vina followed by Autodock. Four best compounds were employed for MDS and here predicted RMSD, RMSF, Rg, hydrogen bonds, SASA, PCA, and binding-free energy. From all these analyses, we have concluded that out of 167,741 compounds, the ZINC15968620, ZINC15968622, and ZINC70707119 can act as lead compounds against HsGSK-3β to reduce the hyperphosphorylation.

CONCLUSION

The study suggested three compounds (ZINC15968620, ZINC15968622, and ZINC70707119) have great potential to be a drug candidate and can be tested using in vitro and in vivo experiments for further characterization and applications.

Detection of Natural Inhibitors against Human Liver Cancer Cell Lines through QSAR, Molecular Docking and ADMET Studies

BACKGROUND

Liver cancer is ranked as the fifth most prevalent and third most lethal cancer worldwide. The incidence rates of this cancer are on the rise, and only limited treatment options are available.

METHODS

To identify and optimize the inhibitors of liver cancer cell-lines, a QSAR model was developed by using multiple linear regression methods. The robustness of the model was validated through statistical methods and wet-lab experiments.

RESULTS

The developed QSAR models yielded high activity descriptor relationship accuracy of 91%, referred to by regression coefficient (r²= 0.91), and a high activity prediction accuracy of 89%. The external predicted (pred_r²) ability of the model was found to be 90%.

CONCLUSION

The QSAR study indicates that chemical descriptors such as to measure of electronegative atom count (Epsilon3), atom type count descriptors (MMFF_10), number of a carbon atom connected with four single bonds (SssssCE- index), molecular weight and, number of oxygen atom connected with two aromatic bonds (SaaOE-index) are significantly correlated with anticancer activity. The model, which was validated statistically and through wet-lab experiments, was further used in the virtual screening of potential inhibitors against the liver cancer cell line WRL68. ADMET risk screening, synthetic accessibility, and Lipinski's rule of five are used to filter false positive hits. AfterwardS, to achieve a set of aligned ligand poses and rank the predicted active compounds, docking studies were carried out. The studied compounds and their metabolites were also analyzed for different pharmacokinetics parameters. Finally, a series of compounds was proposed as anticancer agents.

Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases

In the context of the recently emerging COVID-19 pandemic, we developed a deep learning model that can be used to predict the inhibitory activity of 3CLpro in severe acute respiratory syndrome coronavirus (SARS-CoV) for unknown compounds during the virtual screening process. This paper proposes a novel deep learning-based method to implement virtual screening with convolutional neural network (CNN) architecture. The descriptors represent chemical molecules, and these descriptors are input into the CNN framework to train a model and predict active compounds. When compared to other machine learning methods, including random forest, naive Bayes, decision tree, and support vector machine, the proposed CNN model's evaluation of the test set showed an accuracy of 0.86, a sensitivity of 0.45, a specificity of 0.96, a precision of 0.73, a recall of 0.45, an F-measure of 0.55, and a ROC of 0.71. The CNN model screened 17 out of 918 phytochemical compounds; 60 out of 423 from the natural product NCI divset IV; 17,831 out of 112,267 from the ZINC natural product database; and 315 out of 1556 FDA-approved drugs as anti-SARS-CoV agents. Further, to prioritize drug-like compounds, Lipinski's rule of five was applied to screen anti-SARS-CoV compounds (excluding FDA-approved drugs), resulting in 10, 59, and 14,025 hit molecules. Out of 10 phytochemical compounds, 9 anti-SARS-CoV agents belonged to the flavonoid group. In conclusion, the proposed CNN model can prove useful for developing novel target-specific anti-SARS-CoV compounds.

Identification of high affinity and low molecular alternatives of boceprevir against SARS-CoV-2 main protease: A virtual screening approach

SARS-CoV-2 has posed global challenge for healthcare due to COVID-19. The main protease (M^pro) of this virus is considered as a major target for drug development efforts. In this work, we have used virtual screening approach with molecular dynamics simulations to identify high affinity and low molecular weight alternatives of boceprevir, a repurposed drug currently being evaluated against M^pro. Out of 180 compounds screened, two boceprevir analogs (PubChem ID: 57841991 and 58606278) were reported as potential alternatives with comparable predicted protease inhibitor potential and pharmacological properties. Further experimental validation of the reported compounds may contribute to the ongoing investigation of boceprevir.

Screening of drug databank against WT and mutant main protease of SARS-CoV-2: Towards finding potential compound for repurposing against COVID-19

Although several pharmacological agents are under investigation to be repurposed as therapeutic against COVID-19, not much success has been achieved yet. So, the search for an effective and active option for the treatment of COVID-19 is still a big challenge. The Spike protein (S), RNA-dependent RNA polymerase (RdRp), and Main protease (Mpro) are considered to be the primary therapeutic drug target for COVID-19. In this study we have screened the drugbank compound library against the Main Protease. But our search was not limited to just Mpro. Like other viruses, SARS-CoV-2, have also acquired unique mutations. These mutations within the active site of these target proteins may be an important factor hindering effective drug candidate development. In the present study we identified important active site mutations within the SARS-CoV-2 Mpro (Y54C, N142S, T190I and A191V). Further the drugbank database was computationally screened against Mpro and the selected mutants. Finally, we came up with the common molecules effective against the wild type (WT) and all the selected Mpro. The study found Imiglitazar, was found to be the most active compound against the wild type of Mpro. While PF-03715455 (Y54C), Salvianolic acid A (N142S and T190I), and Montelukast (A191V) were found to be most active against the other selected mutants. It was also found that some other compounds such as Acteoside, 4-Amino-N- {4-[2-(2,6-Dimethyl-Phenoxy)-Acetylamino]-3-Hydroxy-1-Isobutyl-5-Phenyl-Pentyl}-Benzamide, PF-00610355, 4-Amino-N-4-[2-(2,6-Dimethyl-Phenoxy)-Acetylamino]-3-Hydroxy-1-Isobutyl-5-Phenyl-Pentyl}-Benzamide and Atorvastatin were showing high efficacy against the WT as well as other selected mutants. We believe that these molecules will provide a better and effective option for the treatment of COVID-19 clinical manifestations.

Exploring the endogenous potential of Hemidesmus indicus against breast cancer using in silico studies and quantification of 2-hydroxy-4-methoxy benzaldehyde through RP-HPLC

Being a woman and getting older are the main risk factors for breast cancer. While admitting the increasing prevalence of breast cancer among females globally, there is an increasing urge for widening the range of chemical compounds that can act as potential inhibitors for certain cancer target receptors. Current investigation involves virtually screening of 19 protein receptors having major role in signal transduction pathway of breast cancer development against 47 compounds present in Hemidesmus indicus. Virtual screening and supplementary analysis were performed using freely available softwares, tools and online servers. To obtain meaningful results, a comparative scenario was created by screening FDA-approved drugs/drug analogues against the same 19 receptors by keeping all the parameters same as to that of ligands. Two ligands namely Taraxasteryl acetate and Rutin were found to be the best ligands with high binding affinity towards six protein receptors establishing strong receptor ligand interactions. Furthermore, the major volatile compound, a high demand flavouring agent and an isomer of vanillin, namely 2-hydroxy-4-methoxy benzaldehyde (MBALD) specifically found in the roots of Hemidesmus, was quantified by RP-HPLC using a reverse phase C-18 column. The methanolic extract of fresh roots was found to contain 0.221 mg of MBALD/gram of tissue. From the current investigation, it could be surmised that Hemidesmus indicus had demonstrated its potential in both pharmaceuticals and the food industry.

Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the first and rate-limiting step in catabolism of tryptophan via the kynurenine pathway, which plays a pivotal role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for many diseases, such as breast cancer, lung cancer, colon cancer, prostate cancer, etc. In this study, docking-based virtual screening and bioassays were conducted to identify novel inhibitors of IDO1. The cellular assay demonstrated that 24 compounds exhibited potent inhibitory activity against IDO1 at micromolar level, including 8 compounds with IC₅₀ values below 10 μM and the most potent one (compound 1) with IC₅₀ of 1.18 ± 0.04 μM. Further lead optimization based on similarity searching strategy led to the discovery of compound 28 as an excellent inhibitor with IC₅₀ of 0.27 ± 0.02 μM. Then, the structure-activity relationship of compounds 1, 2, 8 and 14 analogues is discussed. The interaction modes of two compounds against IDO1 were further explored through a Python Based Metal Center Parameter Builder (MCPB.py) molecular dynamics simulation, binding free energy calculation and electrostatic potential analysis. The novel IDO1 inhibitors of compound 1 and its analogues could be considered as promising scaffold for further development of IDO1 inhibitors.

Identification of novel inhibitors of GLUT1 by virtual screening and cell-based assays

In order to fuel the uncontrolled cell proliferation and division, tumor cells reprogram the energy metabolism to Warburg effect, where glucose is preferably converted by glycolysis even in the presence of oxygen. However, the high energetic demand of tumor cells require upregulating the expression of glucose transporters, notably GLUT1, which substantially increases glucose uptake into cytoplasm. GLUT1 is overexpressed in a variety of tumor cells and is likely to be a potential drug target in the treatment of pan-cancers. Although many small molecules were reported to inhibit the glucose uptake function by various measurements, several shortcomings such as weak binding affinity, low specificity of the known inhibitors demand the identification of alternative inhibitors with novel scaffolds. In this study, we performed a virtual screening campaign by docking each compound from Chemdiv database to the glucose binding pocket based on the crystal structure of GLUT1 (PDB ID 4PYP) and four small molecules with novel scaffolds were identified to inhibit the glucose uptake of cancer cells at the sub-micromole level. The identified compounds may serve as starting points for the development of anti-cancer drugs via the manipulation of the energy metabolism.

Discovery of a Novel Antimicrobial Agent by the Virtual Screening of a Library of Small Molecules

A virtual screening approach based upon a combination of docking and pharmacophore methods was utilized on a library of 1.4 million molecules to identify novel antimicrobial agents, which may potentially act via inhibition of the caseinolytic protease. Using this method, compound 6 was found to be bactericidal against three staphylococcal species (minimum inhibitory concentration (MIC)=4-16 μg/mL). Further, subsequent structural optimization of 6 led to the identification of compound 24, which was shown to be the most potent analog within the series (MIC=4 μg/mL) and outperforming the antibiotic controls for two of the staphylococcal species. The newly discovered antimicrobial agent (24) demonstrated excellent in silico ADME properties and was non-toxic when tested on two human skin cell lines. As such, compound 24 has the potential for use as a lead molecule in the development of a novel class of antimicrobial agents.

A molecular dynamics simulation study of the ACE2 receptor with screened natural inhibitors to identify novel drug candidate against COVID-19

Background & Objectives

The massive outbreak of Novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has turned out to be a serious global health issue worldwide. Currently, no drugs or vaccines are available for the treatment of COVID-19. The current computational study was attempted to identify a novel therapeutic inhibitor against novel SARS-CoV-2 using in silico drug discovery pipeline.

Methods

In the present study, the human angiotensin-converting enzyme 2 (ACE2) receptor was the target for the designing of drugs against the deadly virus. The 3D structure of the receptor was modeled & validated using a Swiss-model, Procheck & Errat server. A molecular docking study was performed between a group of natural & synthetic compounds having proven anti-viral activity with ACE2 receptor using Autodock tool 1.5.6. The molecular dynamics simulation study was performed using Desmond v 12 to evaluate the stability and interaction of the ACE2 receptor with a ligand.

Results

Based on the lowest binding energy, confirmation, and H-bond interaction, cinnamic acid (−5.20 kcal/mol), thymoquinone (−4.71 kcal/mol), and andrographolide (Kalmegh) (−4.00 kcal/mol) were screened out showing strong binding affinity to the active site of ACE2 receptor. MD simulations suggest that cinnamic acid, thymoquinone, and andrographolide (Kalmegh) could efficiently activate the biological pathway without changing the conformation in the binding site of the ACE2 receptor. The bioactivity and drug-likeness properties of compounds show their better pharmacological property and safer to use.

Interpretation & Conclusions

The study concludes the high potential of cinnamic acid, thymoquinone, and andrographolide against the SARS-CoV-2 ACE2 receptor protein. Thus, the molecular docking and MD simulation study will aid in understanding the molecular interaction between ligand and receptor binding site, thereby leading to novel therapeutic intervention.

Natural products for treatment of Plasmodium falciparum malaria: An integrated computational approach

Malaria is a life-threatening infectious disease with an estimated 229 million cases in the year 2019 worldwide. Plasmodium falciparum 1-deoxy-d-xylulose-5-phosphate reductoisomerase (PfDXR) is one of the key enzymes in the biosynthetic pathway of isoprenoid, (required for parasite growth and survival) and considered as an attractive target for designing anti-malarial drugs. Fosmidomycin is an effective DXR inhibitor and has been proven effective and safe against P. falciparum in clinical trials. However, due to low bioavailability and inappropriate drug attributes, it is not a preferred option. The present study was performed to identify PfDXR inhibitors with improved pharmacology/safety. For this purpose, an integrated computational framework, comprising of pharmacophore modeling, virtual screening, molecular docking, molecular dynamics (MD) simulation and MM/PBSA, was used. The binding free energy analysis was performed using a focused library of phytochemicals established from medicinal plants. The study identified four bioactive compounds namely, Myricetin 3-rhamnoside, 7-O-Galloyltricetiflavan, (25S)-5-beta-spirostan-3-beta-ol 3-O-beta-d-glucopyranosyl-(1->2)-beta-d-glucopyranoside, and Oleanolic acid 28-O-beta-d-glucopyranoside as potential inhibitors of PfDXR. The selection of these four compounds was based on pharmacophore mapping, docking score, binding stability, molecular interactions with the residues of PfDXR active site, binding stability and free energy estimation. In conclusion, medicinal plant-based scaffolds were predicted with enhanced efficacy and adequate physiochemical/pharmacokinetic profile that might be helpful in controlling malaria.

Modelling studies reveal the importance of the C-terminal inter motif loop of NSP1 as a promising target site for drug discovery and screening of potential phytochemicals to combat SARS-CoV-2

COVID-19 pandemic causative SARS-CoV-2 coronavirus is still rapid in progression and transmission even after a year. Understanding the viral transmission and impeding the replication process within human cells are considered as the vital point to control and overcome COVID-19 infection. Non-structural Protein 1, one among the proteins initially produced upon viral entry into human cells, instantly binds with the human ribosome and inhibit the host translation process by preventing the mRNA attachment. However, the formation of NSP1 bound Ribosome complex does not affect the viral replication process. NSP1 plays an indispensable role in modulating the host gene expression and completely steals the host cellular machinery. The full-length structure of NSP1 is essential for the activity in the host cell and importantly the loop connecting N and C-terminal domains are reported to play a role in ribosome binding. Due to the unavailability of the experimentally determined full-length structure of NSP1, we have modelled the complete structure using comparative modelling and the stability and conformational behaviour of the modelled structure was evaluated through molecular dynamics simulation. Interestingly, the present study reveals the significance of the inter motif loop to serves as a potential binding site for drug discovery experiments. Further, we have screened the phytochemicals from medicinal plant sources since they were used for several hundred years that minimizes the traditional drug development time. Among the 5638 phytochemicals screened against the functionally associated binding site of NSP1, the best five phytochemicals shown high docking score of −9.63 to −8.75 kcal/mol were further evaluated through molecular dynamics simulations to understand the binding affinity and stability of the complex. Prime MM-GBSA analysis gave the relative binding free energies for the top five compounds (dihydromyricetin, 10-demethylcephaeline, dihydroquercetin, pseudolycorine and tricetin) in the range of −45.17 kcal/mol to −37.23 kcal/mol, indicating its binding efficacy in the predicted binding site of NSP1. The density functional theory calculations were performed for the selected five phytochemicals to determine the complex stability and chemical reactivity. Thus, the identified phytochemicals could further be used as effective anti-viral agents to overcome COVID-19 and as well as several other viral infections.

Multi-conformation representation of Mpro identifies promising candidates for drug repurposing against COVID-19

The COVID-19 main protease (Mpro), one of the conserved proteins of the novel coronavirus is crucial for its replication and so is a very lucrative drug target. Till now, there is no drug molecule that has been convincingly identified as the inhibitor of the function of this protein. The current pandemic situation demands a shortcut to quickly reach to a lead compound or a drug, which may not be the best but might serve as an interim solution at least. Following this notion, the present investigation uses virtual screening to find a molecule which is alraedy approved as a drug for some other disease but could be repurposed to inhibit Mpro. The potential of the present method of work to identify such a molecule, which otherwise would have been missed out, lies in the fact that instead of just using the crystallographically identified conformation of the receptor’s ligand binding pocket, molecular dynamics generated ensemble of conformations has been used. It implicitly included the possibilities of “induced-fit” and/or “population shift” mechanisms of ligand fitting. As a result, the investigation has not only identified antiviral drugs like ribavirin, ritonavir, etc., but it has also captured a wide variety of drugs for various other diseases like amrubicin, cangrelor, desmopressin, diosmin, etc. as the potent possibilities. Some of these ligands are versatile to form stable interactions with various different conformations of the receptor and therefore have been statistically surfaced in the investigation. Overall the investigation offers a wide range of compounds for further testing to confirm their scopes of applications to combat the COVID-19 pandemic.

Discovery of a new series of PI3K-δ inhibitors from Virtual Screening

PI3K-δ mediates key immune cell signaling pathways and is a target of interest for treatment of oncological and immunological disorders. Here we describe the discovery and optimization of a novel series of PI3K-δ selective inhibitors. We first identified hits containing an isoindolinone scaffold using a combined ligand- and receptor-based virtual screening workflow, and then improved potency and selectivity guided by structural data and modeling. Careful optimization of molecular properties led to compounds with improved permeability and pharmacokinetic profile, and high potency in a whole blood assay.

Machine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics

De novo design of mini-proteins (4-12 kDa) has recently been shown to produce new candidates for protein therapeutics. They are temperature stable molecules that bind to the drug target with high affinity for inhibiting its interactions. The development of mini-protein binders requires laboratory screening of tens of thousands of molecules for effective target binding. In this study we trained machine learning classifiers which can distinguish, with 90% accuracy and 80% precision, mini-protein binders from non-binding molecules designed for a particular target; this significantly reduces the number of mini protein candidates for experimental screening. Further, on the basis of our results we propose a multi-stage protocol where a small dataset (few hundred experimentally verified target-specific mini-proteins) can be used to train classifiers for improving the efficiency of mini-protein design for any specific target.

Discovery of novel Hsp90 C-terminal domain inhibitors that disrupt co-chaperone binding

Heat shock protein 90 (Hsp90) is an essential molecular chaperone that performs vital stress-related and housekeeping functions in cells and is a current therapeutic target for diseases such as cancers. Particularly, the development of Hsp90 C-terminal domain (CTD) inhibitors is highly desirable as inhibitors that target the N-terminal nucleotide-binding domain may cause unwanted biological effects. Herein, we report on the discovery of two drug-like novel Hsp90 CTD inhibitors by using virtual screening and intrinsic protein fluorescence quenching binding assays, paving the way for future development of new therapies that employ molecular chaperone inhibitors.

Discovery of novel HBV capsid assembly modulators by structure-based virtual screening and bioassays

HBV capsid assembly has been regarded as an attractive potential target for anti-HBV therapy. In this study, we discovery the Novel HBV capsid assembly modulators (CAMs) through structure-based virtual screening and bioassays. A total of 16 structurally diverse compounds were purchased and assayed, including three compounds with inhibition rate > 50% at 20 μM. Further lead optimization based on the most potent compound II-1-7 (EC₅₀ = 5.6 ± 0.1 µM) were performed by using substructure searching strategy, resulting in compound II-2-9 with an EC₅₀ value of 1.8 ± 0.6 μM. In bimolecular fluorescence complementation (BiFC) assay, compound II-2-9 inhibited the HBV by disrupting the HBV capsid interactions. In summary, this study provides a highly efficient way to discover novel CAMs, and 2-aryl-4-quinolyl amide derivatives could serve as the starting point for development of novel anti-HBV drugs.

Investigation of the Click-Chemical Space for Drug Design Using ZINClick

This chapter provides a brief overview of the applications of ZINClick virtual library. In the last years, we have investigated the click-chemical space covered by molecules containing the triazole ring and generated a database of 1,2,3-triazoles called ZINClick, starting from literature reported alkynes and azides synthesizable in no more than three synthetic steps from commercially available products. This combinatorial database contains millions of 1,4-disubstituted 1,2,3-triazoles that are easily synthesizable. The library is regularly updated and can be freely downloaded from http://www.ZINClick.org . This virtual library is a good starting point to explore a new portion of chemical space.

Structure-based identification of SARS-CoV-2 main protease inhibitors from anti-viral specific chemical libraries: an exhaustive computational screening approach

Abstract

Worldwide coronavirus disease 2019 (COVID-19) outbreak is still threatening global health since its outbreak first reported in the late 2019. The causative novel virus has been designated as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although COVID-19 emergent with significant mortality, there is no availability of definite treatment measures. It is now extremely desirable to identify potential chemical entities against SARS-CoV-2 for the treatment of COVID-19. In the present study, a state-of-art virtual screening protocol was implemented on three anti-viral specific chemical libraries against SARS-CoV-2 main protease (M^pro). Particularly, viewing the large-scale biological role of M^pro in the viral replication process it has been considered as a prospective anti-viral drug target. Herein, on collected 79,892 compounds, hierarchical multistep docking followed by relative binding free energy estimation has been performed. Thereafter, implying a user-defined XP-dock and MM-GBSA cut-off scores as −8.00 and −45.00 kcal/mol, chemical space has been further reduced. Exhaustive molecular binding interactions analyses and various pharmacokinetics profiles assessment suggested four compounds (ChemDiv_D658-0159, ChemDiv_F431-0433, Enamine_Z3019991843 and Asinex_LAS_51389260) as potent inhibitors/modulators of SARS-CoV-2 M^pro. In-depth protein–ligand interactions stability in the dynamic state has been evaluated by 100 ns molecular dynamics (MD) simulation studies along with MM-GBSA-based binding free energy estimations of entire simulation trajectories that have revealed strong binding affinity of all identified compounds towards M^pro. Hence, all four identified compounds might be considered as promising candidates for future drug development specifically targeting the SARS-CoV-2 M^pro; however, they also need experimental assessment for a better understanding of molecular interaction mechanisms.

Graphic abstract

Design, synthesis and biological evaluation of dihydrofurocoumarin derivatives as potent neuraminidase inhibitors

Neuraminidase (NA) is a promising target for development of anti-influenza drugs. In this study a dihydrofurocoumarin derivative ZINC05577497 was discovered as a lead NA inhibitor based on docking-based virtual screening technique. The optimization of lead ZINC05577497 led to the discovery of a series of novel NA inhibitors 5a-5j. Compound 5b has the most potent activity against NA with IC₅₀ = 0.02 µM, which is lower than those of the reference oseltamivir carboxylate (OSC) (IC₅₀ = 0.04 µM) and ZINC05577497 (IC₅₀ = 0.11 µM). Other target compounds also show potential inhibition of NA activity. Molecular docking results indicate that the good potency of 5b may be attributed to the elongation of the dihydrofurocoumarin ring to the 150-cavity. The results of this paper will be useful to discover more potent NA inhibitors.

Discovery of novel Staphylococcus aureus penicillin binding protein 2a inhibitors by multistep virtual screening and biological evaluation

Penicillin-binding protein 2a (PBP2a) is an essential protein involved in the resistance to β-lactam antibiotics acquired by methicillin-resistant Staphylococcus aureus (MRSA) and is a potential antibacterial target. In the current study, we employed a strategy that combined virtual screening with biological evaluation to discover novel inhibitors of PBP2a. In this investigation, a hybrid virtual screening method, consisting of drug-likeness evaluation (Lipinski's Rule of Five and ADMET) and rigid (LibDock) and semi-flexible (CDOCKER) docking-based virtual screenings, was used for retrieving novel PBP2a inhibitors from commercially available chemical databases. 11 compounds were selected from the final hits and subsequently shifted to experimental studies. Among them, Hit 2, Hit 3, and Hit 10 exhibited excellent anti-MRSA ATCC 33591 activity and weak toxicity in vitro. The affinity of the three compounds to bind to PBP2a was further confirmed by surface plasmon resonance (SPR) experiments and molecular dynamics (MD) simulation. An inter-complex interaction study showed that all hit compounds adapted well to the allosteric site of the PBP2a protein. In addition, Hit 2 (with best binding affinity to PBP2a, K_D = 1.29 × 10^-7 M) significantly inhibits proliferation of MRSA clinical isolates. Together, the 3 hit compounds, especially Hit 2, may be potential non-β-lactam antibiotics against MRSA and the work will provide clues for the future development of specific compounds that block the interaction of PBP2a with their targets.

Pharmacophore model, docking, QSAR, and molecular dynamics simulation studies of substituted cyclic imides and herbal medicines as COX-2 inhibitors

Cyclooxygenase-2 (COX-2) enzyme inhibitors have not eliminated the necessity for developed drugs not only in the nonsteroidal anti-inflammatory drug (NSAIDs) area, but also in other therapeutic applications including prevention of cancer and Alzheimer's disease. A series of novel substituted cyclic imides have been reported as selective COX-2 inhibitors. To understand the structural features responsible for their activity, a 3D validated pharmacophore and quantitative structure−activity relationship (QSAR) model have been developed. The values of enrichment factor (EF), goodness of hit score (GH), area under the ROC curve (AUC), sensitivity, and specificity refer to the good ability of the pharmacophore model to identify active compounds. Multiple linear regression (MLR) produced statistically significant QSAR model with (R²_training = 0.763, R²_test = 0.96) and predictability (Q²_training = 0.66, Q²_test = 0.84). Then, using the pharmacophore and QSAR models, eight authenticated botanicals in two herbal medicines and the ZINC compounds database, were virtually screened for ligands to COX-2. The retrieved hits which also obey lipinski's rule of five (RO5) were docked in the COX-2 3D structure to investigate their binding mode and affinity. Finally, based on the docking results, nine molecules were prioritized as promising hits that could be used as leads to discover novel COX-2 inhibitors. COX-2 inhibition of most of these hits has not been reported previously. Ten-nanosecond molecular dynamics simulation (10-ns MD) was performed on the initial structure COX-2 complex with ZINC000113253375 and ZINC000043170560 resulted from the docking. Our utilization of the 3D pharmacophore model, QSAR, molecular docking, and molecular dynamics simulation trials can be a potent strategy to successfully predict activity, efficiently design drugs, and screen large numbers of new compounds as active drug candidates.