Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs

Discovery and characterization of novel pyridone and furan substituted ligands of choline acetyltransferase

The key to the management of two devastating diseases, namely Alzheimer's Disease (AD) and Amyotrophic Lateral Sclerosis (ALS) lies in an early diagnosis, which is difficult due to its multifactorial nature. However, a common hallmark of AD and ALS is degeneration of cholinergic system. Choline acetyltransferase (ChAT) has been proposed as a potential target for development of cholinergic-specific biomarker. However, lack of selective, potent, brain permeable molecular probes of ChAT hinder development of ChAT biomarkers. In this study, we have successfully utilised structure-based virtual screening approach and identified two ChAT inhibitors from a database of 1.4 million compounds. The compounds were then subjected to rigorous in vitro characterization. Compound V6 showed Ki value of 11 μM and IC50 value of 21.73 μM, while V15 showed Ki and IC50 values of 4.5 and 9.42 μM, respectively for ChAT enzyme. V6 and V15 showed good solubility of 0.21 mg/mL and 0.17 mg/mL respectively and cytotoxicity analysis indicated no toxicity. We also performed a 200 ns molecular dynamics simulation, which revealed the intricate interaction dynamics for V6 and V15 with ChAT binding pocket. Moreover, the Tanimoto similarity analysis indicated the novelty and structural diversity of the hits. In conclusion, these validated hits provide a platform to develop potent, selective, blood-brain barrier permeable small molecules as chemical probes of ChAT or as Positron Emission Tomography tracer for early diagnosis and/or in vivo monitoring of the effect of new therapeutic candidates in spectrum of neurodegenerative disorders, in which cholinergic deficit is one of the hallmarks.

In silico evaluation of bisphosphonates identifies leading candidates for SARS-CoV-2 RdRp inhibition

The novel coronavirus disease (COVID-19) pandemic has resulted in 777 million confirmed cases and over 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Post Acute Sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well. A growing list of drugs are being suggested by various in silico, in vitro and ex vivo models, however currently only two treatment options are widely used: the RNA-dependent RNA polymerase (RdRp) inhibitor remdesivir, and the main protease inhibitor nirmatrelvir in combination with ritonavir. Computational drug development tools and in silico studies involving molecular docking, molecular dynamics, entropy calculations and pharmacokinetics can be useful to identify new targets to treat COVID-19 and PASC, as shown in this work and our recent paper that identified alendronate as a promising candidate. In this study, we have investigated all bisphosphonates (BPs) on the ChEMBL database which can bind competitively to nidovirus RdRp-associated nucleotidyl (NiRAN) transferase domain, and systematically down selected seven candidates (CHEMBL608526, CHEMBL196676, CHEMBL164344, CHEMBL4291724, CHEMBL4569308, CHEMBL387132, CHEMBL98211), two of which closely resemble the approved drugs minodronate and zoledronate. This work and our recent paper together provide an in silico mechanistic explanation for alendronate and zoledronate users having dramatically reduced odds of SARS-CoV-2 testing, COVID-19 diagnosis, and COVID-19-related hospitalizations, and indicate that similar observational studies in Japan with minodronate could be valuable.

In vitro enzymatic and cell culture assays for SARS-CoV-2 main protease interaction with ambenonium

The 2019 pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 led to millions of deaths worldwide since its emergence. The viral genomic material can code structural and non-structural proteins including the main protease or 3CLpro, a cysteine protease that cleavages the viral polyprotein generating 11 proteins that participate in viral pre-replication. Thus, 3CLpro is a promising therapeutic target for SARS-CoV-2 inhibition by new drugs or drug repositioning because 3CLpro is dissimilar to human proteases. We conducted in vitro assays demonstrating the modulation activity of ambenonium, a drug already used in Myasthenia gravis that acts by inhibiting the action of acetylcholinesterase, and had its potential inhibitory activity against viral replication pointed out in a previous in silico study. In concentrations of 100 µM, 50 µM, 25 µM, 10 µM, and 1 µM there was no inhibition in the formation of lysis plates, with a slight increase in the genome copy number at the higher concentrations evaluated. However, in the concentrations of 0,1 µM and 0,01 µM, there was a reduction in the number of lysis plates. This behavior suggests that the ambenonium acts as a modulator of viral activity in vitro. To investigate potential conformational changes in the protein between dimeric and monomeric forms in the presence of the compound, a local docking analysis was performed. Results indicated this conformational shift is possible, though further studies are needed to confirm these findings.

Harnessing Structure Prediction of Polo-Like Kinase 4 for Drug Repurposing

Polo-like kinase 4 (PLK4) is a centrosome-specific kinase aberrantly expressed in cancers. Drugs inhibiting its catalytic kinase domain are under clinical phase-1/2 trials in patients with different leukemia types. However, the kinase domain of PLK4 shows structural similarity with other kinases. Therefore, drugs targeting the unique C-terminal polo-box domain (PBD) of PLK4 could provide better specificity. The knowledge of domain orientation in a full-length PLK4 structure is imperative for drug discovery. In this work, we utilized ab initio and threading approaches to predict the full-length structure of human PLK4, which was employed for virtually screening the ChEMBL library. Among the hit compounds targeting the unique regions in PLK4, we identified Alectinib, which affects centrosome numbers corresponding to PLK4 levels at centrosomes. The FT-IR analysis also confirmed Alectinib interaction with the PBD. Therefore, this work identifies a chemical scaffold that could be repurposed to target the unique regions of PLK4.

Synthesis, characterization, quantum mechanical calculations and biomedical docking studies on curcumin analogs: 2, 6-(Difurfurylidene) cyclohexanone and 2, 6 - Bis (2,6-Dichloro Benzylidene) Cyclohexanone

The initiation of colorectal cancer is controlled by various factors, including random occurrences and genetic alterations affecting oncogenes and tumor suppressor genes.Curcumin, a significant compound extracted from turmeric, has attracted interest for its robust anticancer properties, particularly regarding its analogs, 2, 6-bisdifurfurylidene cyclohexanone (DFC) and 2, 6-bis (2, 6-dichlorobenzylidene) cyclohexanone (DCC), which were synthesized and assessed for their anticancer efficacy. A combination of spectroscopic techniques and molecular docking methods was utilized to comprehensively evaluate the interaction behaviors of DFC and DCC. The application of density functional theory (DFT) using the B3LYP/6-311G (d, p) basis set facilitated the prediction of spectroscopic properties. The molecular docking investigations conducted using the Glide docking program from Schrodinger Maestro elucidated the interactions of these drugs at the molecular level. In vitro investigations were performed to evaluate the cytotoxic efficacy of the synthesized curcumin analogs. The determined IC50 values revealed that DFC displayed an IC50 of approximately 82 μM, and DCC exhibited a significantly lower IC50 of around 10 μM. This notable disparity highlights the potential of DFC and DCC as a more efficacious cytotoxic agent and further research be conducted on the produced chemicals in the future.

MP-13, a novel chimeric peptide of morphiceptin and pepcan-9, produces potent antinociception with limited side effects

Pharmacological investigations have substantiated the potential of bifunctional opioid/cannabinoid agonists in delivering potent analgesia while minimizing adverse reactions. Peptide modulators of cannabinoid receptors, known as pepcans, have been investigated before. In this study, we designed a series of chimeric peptides based on pepcans and morphiceptin (YPFP-NH2). Here, we combined injections of pepcans and morphiceptin to investigate the combination treatment of opioids and cannabis and compared the analgesic effect with chimeric compounds. Subsequently, we employed computational docking to screen the compounds against opioid and cannabinoid receptors, along with an acute pain model, to identify the most promising peptide. Among these peptides, MP-13, a morphiceptin and pepcan-9 (PVNFKLLSH) construct, exhibited superior supraspinal analgesic efficacy in the tail-flick test, with an ED50 value at 1.43 nmol/mouse, outperforming its parent peptides and other chimeric analogs. Additionally, MP-13 displayed potent analgesic activity mediated by mu-opioid receptor (MOR), delta-opioid receptor (DOR), and cannabinoid type 1 (CB1) receptor pathways. Furthermore, MP-13 did not induce psychological dependence and gastrointestinal motility inhibition at the effective analgesic doses, and it maintained non-tolerance-forming antinociception throughout a 7-day treatment regimen, with an unaltered count of microglial cells in the periaqueductal gray region, supporting this observation. Moreover, intracerebroventricular administration of MP-13 demonstrated dose-dependent antinociception in murine models of neuropathic, inflammatory, and visceral pain. Our findings provide promising insights for the development of opioid/cannabinoid peptide agonists, addressing a crucial gap in the field and holding significant potential for future research and development. PERSPECTIVE: This article offers insights into the combination treatment of pepcans with morphiceptin. Among the chimeric peptides, MP-13 exhibited potent analgesic effects in a series of preclinical pain models with a favorable side-effect profile.

In silico fragment-based design and pharmacophore modelling of therapeutics against dengue virus envelope protein

Dengue virus, an arbovirus of genus Flavivirus, is an infectious disease causing organisms in the tropical environment leading to numerous deaths every year. No therapeutic is available against the virus till date with only symptomatic relief available. Here, we have tried to design therapeutic compounds from scratch by fragment based method followed by pharmacophore based modelling to find suitable similar structure molecules and validated the same by MD simulation, followed by binding energy calculations and ADMET analysis. The receptor binding region of the dengue envelope protein was considered as the target for prevention of viral host cell entry and thus infection. This resulted in the final selection of kanamycin as a stable binding molecule against the Dengue virus envelope protein receptor binding domain. This study results in selection of a single molecule having high binding energy and prominent stable interactions as determined by post simulation analyses. This study aims to provide a direction for development of small molecule therapeutics against the dengue virus in order to control infection. This study may open a new avenue in the arena of structure based and fragment based therapeutic design to obtain novel molecules with therapeutic potential.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00262-9.

In silico screening and evaluation of antiviral peptides as inhibitors against ORF9b protein of SARS-CoV-2

The present study investigated antiviral peptides (AVPs) as inhibitors of SARS-CoV-2 Orf9b protein, a novel target for disrupting the Orf9b-TOM70 complex crucial for viral infection. In silico screening via molecular docking and MD simulations identified AVP1442 and AVP1896 with high binding affinities to Orf9b (- 846.3 kcal mol-1 and - 820 kcal mol-1, respectively), comparable to the Orf9b-TOM70 complex (- 810.99 kcal mol-1). These AVPs interacted with key amino acid residues in Orf9b, including phosphorylation sites. In addition, AVPs also closely interacted with conserved regions in Orf9b. AVP1896 formed a hydrogen bond with Orf9b's threonine at position 84. AVP1442 interacted with Orf9b's leucine at position 15. Favorable Ramachandran plots and compactness during MD simulations for up to 100 ns suggest good stability of formed complexes. These non-toxic AVPs warrant further in vitro and in vivo evaluation, potentially as components of drug cocktails with small molecules or interferon-based therapies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04032-4.

Elucidation of furanone as ergosterol pathway inhibitor in Cryptococcus neoformans

In the era of antiretroviral therapy, the prevalence of Cryptococcal infection among HIV patients in developed countries has decreased considerably. However, C. neoformans ranks top among the critical priority pathogen that affects a wide range of immunocompromised individuals. The threat of C. neoformans is because of its incredibly multifaceted intracellular survival capabilities. Cell membrane sterols especially ergosterol and enzymes of its biosynthetic pathway are considered fascinating drug targets because of their structural stability. In this study, the ergosterol biosynthetic enzymes were modeled and docked with furanone derivatives. Among the tested ligands Compound 6 has shown a potential interaction with Lanosterol 14 α-demethylase. This best-docked protein-ligand complex was taken further to molecular dynamics simulation. In addition, Compound 6 was synthesized and an in vitro study was conducted to quantify the ergosterol in Compound 6 treated cells. Altogether the computational and in vitro study demonstrates that Compound 6 has anticryptococcal activity by targeting the biosynthetic pathway of ergosterol.Communicated by Ramaswamy H. Sarma.

Enhancing generalizability and performance in drug-target interaction identification by integrating pharmacophore and pre-trained models

MOTIVATION

In drug discovery, it is crucial to assess the drug-target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its application in large-scale virtual screening. Deep learning-based methods learn virtual scoring functions from labeled datasets and can quickly predict affinity. However, there are three limitations. First, existing methods only consider the atom-bond graph or one-dimensional sequence representations of compounds, ignoring the information about functional groups (pharmacophores) with specific biological activities. Second, relying on limited labeled datasets fails to learn comprehensive embedding representations of compounds and proteins, resulting in poor generalization performance in complex scenarios. Third, existing feature fusion methods cannot adequately capture contextual interaction information.

RESULTS

Therefore, we propose a novel DTA prediction method named HeteroDTA. Specifically, a multi-view compound feature extraction module is constructed to model the atom-bond graph and pharmacophore graph. The residue concat graph and protein sequence are also utilized to model protein structure and function. Moreover, to enhance the generalization capability and reduce the dependence on task-specific labeled data, pre-trained models are utilized to initialize the atomic features of the compounds and the embedding representations of the protein sequence. A context-aware nonlinear feature fusion method is also proposed to learn interaction patterns between compounds and proteins. Experimental results on public benchmark datasets show that HeteroDTA significantly outperforms existing methods. In addition, HeteroDTA shows excellent generalization performance in cold-start experiments and superiority in the representation learning ability of drug-target pairs. Finally, the effectiveness of HeteroDTA is demonstrated in a real-world drug discovery study.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/daydayupzzl/HeteroDTA.

In silico validation of hyaluronic acid - drug conjugates based targeted drug delivery for the treatment of COVID-19

The impact of COVID-19 urges scientists to develop targeted drug delivery to manage Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral infections with a fast recovery rate. The aim of the study is to develop Hyaluronic Acid (HA) drug conjugates of viral drugs to target two important enzymes (Mpro and PLpro) of SARS-CoV-2. Three antiviral drugs, namely Dexamethasone (DEX), Favipiravir (FAV), and Remdesivir (REM), were chosen for HA conjugation due to their reactive functional groups. Free forms of drugs (DEX, FAV, REM) and HA drug conjugates (HA-DEX, HA-FAV, HA-REM, HA-RHA, HA-RHE) were validated against Mpro (PDB ID 6LU7) and PLpro (PDB 7LLZ), which play an essential role in the replication and reproduction of the SARS-CoV-2 virus. The results of the present study revealed that HA-drug conjugates possess higher binding affinity and the best docking score towards the Mpro and PLpro target proteins of SARS-CoV-2 than their free forms of drugs. ADMET screening resulted that HA-drug conjugates exhibited better pharmacokinetic profiles than their pure forms of drugs. Further, molecular dynamic simulation studies, essential dynamics and free energy landscape analyses show that HA antiviral drug conjugates possess good trajectories and energy status, with the PLpro target protein (PDB 7LLZ) of SARS-CoV-2 through long-distance (500 ns) simulation screening. The research work recorded the best drug candidate for Cell-Targeted Drug Delivery (CTDD) for SARS-CoV-2-infected cells through hyaluronic acid conjugates of antiviral drugs.

Consensus Pharmacophore Strategy For Identifying Novel SARS-Cov-2 Mpro Inhibitors from Large Chemical Libraries

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome and is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development, and multiple Mpro crystals complexed with competitive inhibitors have been reported. In this study, we aimed to develop an Mpro consensus pharmacophore as a tool to expand the search for inhibitors. We generated a consensus model by aligning and summarizing pharmacophoric points from 152 bioactive conformers of SARS-CoV-2 Mpro inhibitors. Validation against a library of conformers from a subset of ligands showed that our model retrieved poses that reproduced the crystal-binding mode in 77% of the cases. Using models derived from a consensus pharmacophore, we screened >340 million compounds. Pharmacophore-matching and chemoinformatics analyses identified new potential Mpro inhibitors. The candidate compounds were chemically dissimilar to the reference set, and among them, demonstrating the relevance of our model. We evaluated the effect of 16 candidates on Mpro enzymatic activity finding that seven have inhibitory activity. Three compounds (1, 4, and 5) had IC50 values in the midmicromolar range. The Mpro consensus pharmacophore reported herein can be used to identify compounds with improved activity and novel chemical scaffolds against Mpro. The method developed for its generation is provided as an open-access code (https://github.com/AngelRuizMoreno/ConcensusPharmacophore) and can be applied to other pharmacological targets.

Targeting CD38/ ADP-ribosyl cyclase as a novel therapeutic strategy for identification of three potent agonists for leukopenia treatment

Leukopenia is the most common side effect of chemotherapy and radiotherapy. It potentially deteriorates into a life-threatening complication in cancer patients. Despite several agents being approved for clinical administration, there are still high incidences of pathogen-related disease due to a lack of functional immune cells. ADP-ribosyl cyclase of CD38 displays a regulatory effect on leukopoiesis and the immune system. To explore whether the ADP-ribosyl cyclase was a potential therapeutic target of leukopenia. We established a drug screening model based on an ADP-ribosyl cyclase-based pharmacophore generation algorithm and discovered three novel ADP-ribosyl cyclase agonists: ziyuglycoside II (ZGSII), brevifolincarboxylic acid (BA), and 3,4-dihydroxy-5-methoxybenzoic acid (DMA). Then, in vitro experiments demonstrated that these three natural compounds significantly promoted myeloid differentiation and antibacterial activity in NB4 cells. In vivo, experiments confirmed that the compounds also stimulated the recovery of leukocytes in irradiation-induced mice and zebrafish. The mechanism was investigated by network pharmacology, and the top 12 biological processes and the top 20 signaling pathways were obtained by intersecting target genes among ZGSII, BA, DMA, and leukopenia. The potential signaling molecules involved were further explored through experiments. Finally, the ADP-ribosyl cyclase agonists (ZGSII, BA, and DMA) has been found to regenerate microbicidal myeloid cells to effectively ameliorate leukopenia-associated infection by activating CD38/ADP-ribosyl cyclase-Ca2+-NFAT. In summary, this study constructs a drug screening model to discover active compounds against leukopenia, reveals the critical roles of ADP-ribosyl cyclase in promoting myeloid differentiation and the immune response, and provides a promising strategy for the treatment of radiation-induced leukopenia.

Computational drug development for membrane protein targets

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Revolutionizing Vaccine Development for COVID-19: A Review of AI-Based Approaches

The evolvement of COVID-19 vaccines is rapidly being revolutionized using artificial intelligence-based technologies. Small compounds, peptides, and epitopes are collected to develop new therapeutics. These substances can also guide artificial intelligence-based modeling, screening, or creation. Machine learning techniques are used to leverage pre-existing data for COVID-19 drug detection and vaccine advancement, while artificial intelligence-based models are used for these purposes. Models based on artificial intelligence are used to evaluate and recognize the best candidate targets for future therapeutic development. Artificial intelligence-based strategies can be used to address issues with the safety and efficacy of COVID-19 vaccine candidates, as well as issues with manufacturing, storage, and logistics. Because antigenic peptides are effective at eliciting immune responses, artificial intelligence algorithms can assist in identifying the most promising COVID-19 vaccine candidates. Following COVID-19 vaccination, the first phase of the vaccine-induced immune response occurs when major histocompatibility complex (MHC) class II molecules (typically bind peptides of 12–25 amino acids) recognize antigenic peptides. Therefore, AI-based models are used to identify the best COVID-19 vaccine candidates and ensure the efficacy and safety of vaccine-induced immune responses. This study explores the use of artificial intelligence-based approaches to address logistics, manufacturing, storage, safety, and effectiveness issues associated with several COVID-19 vaccine candidates. Additionally, we will evaluate potential targets for next-generation treatments and examine the role that artificial intelligence-based models can play in identifying the most promising COVID-19 vaccine candidates, while also considering the effectiveness of antigenic peptides in triggering immune responses. The aim of this project is to gain insights into how artificial intelligence-based approaches could revolutionize the development of COVID-19 vaccines and how they can be leveraged to address challenges associated with vaccine development. In this work, we highlight potential barriers and solutions and focus on recent improvements in using artificial intelligence to produce COVID-19 drugs and vaccines, as well as the prospects for intelligent training in COVID-19 treatment discovery.

Synthesis, characterization, DNA binding interactions, DFT calculations, and Covid-19 molecular docking of novel bioactive copper(I) complexes developed via unexpected reduction of azo-hydrazo ligands

In this work, we focused on the 3rd goal of the sustainable development plan: achieving good health and supporting well-being. Two redox-active hydrazo ligands namely, phenylcarbonohydrazonoyldicyanide (PCHD) and pyridin-4-ylcarbonohydrazonoyl-dicyanide (PyCHD), and their copper(I) complexes have been synthesized and characterized. The analytical data indicates the formation of copper(I) complexes despite starting with copper(II) perchlorate salt. The 1H-NMR and UV-visible spectral studies in DMSO revealed that PyCHD mainly exists in its azo-form, while PCHD exists in azo ↔ hydrazo equilibrium form, and confirmed the copper(I) oxidation state. XPS, spectral and electrochemistry data indicated the existence of copper(I) valence of both complexes. Cyclic voltammetry of PCHD and its copper(I) complex supported the reduction power of the ligand. The antimicrobial activity, cytotoxicity against the mammalian breast carcinoma cell line (MCF7), and DNA interaction of the compounds are investigated. All compounds showed high antimicrobial, and cytotoxic activities, relative to the standard drugs. Upon studying the wheat DNA binding, PCHD and PyCHD were found to bind through external contacts, while both [Cu(PCHD)2]ClO4.H2O and [Cu(PyCHD)2]ClO4.H2O were intercalated binding. In-silico molecular docking simulations against Estrogen Receptor Alpha Ligand Binding Domain (ID: 6CBZ) were performed on all produced compounds and confirmed the invitro experimentally best anticancer activity of [Cu(PyCHD)2]ClO4.H2O. The molecular docking tests against SARS-CoV-2 main protease (ID: 6 WTT) showed promising activity in the order of total binding energy values: [Cu(PCHD)2]ClO4.H2O > [Cu(PyCHD)2]ClO4.H2O > PCHD > PyCHD.

Structure-based virtual screening of ROCK1 inhibitors for the discovery of Enterovirus-A71 antivirals

Human enterovirus A71 (EV-A71) is the major causative agent of hand, foot, and mouth disease (HFMD), which may lead to neurological sequelae and even death. Although EV-A71 seriously threatens public health, there remains no efficient drug for the treatment of EV-A71 infection. We previously demonstrated that ROCK1 is a novel host dependency factor for EV-A71 replication and can serve as a target for the development of anti-EV-A71 therapeutics. In this study, we identified a subset of inhibitors with potential anti-EV-A71 activity by virtual screening using ROCK1 as a target. Among the hits, Dasabuvir, an HCV polymerase inhibitor, was found to have the best antiviral activity which is consistent with the ranking scores in Autodock Vina and iGEMDOCK. We found that Dasabuvir efficiently suppressed EV-A71 replication in a dose-dependent manner. Moreover, Dasabuvir not only efficiently suppressed the replication of EV-A71 in RD cells, but also in multiple cell lines, including HEK-293T, Caco-2, HT-29, HepG2, and Huh7. Besides, Dasabuvir alleviated the release of proinflammatory cytokines caused by EV-A71 infection. Notably, Dasabuvir also exhibited antiviral activity of CVA10, indicating it may have broad-spectrum antiviral activity against species Enteroviruses A. Hence, our results further confirm that ROCK1 can be a potential drug target and suggest Dasabuvir could be a clinical candidate for the treatment of EV-A71 infection.

Repurposing and computational design of PARP inhibitors as SARS-CoV-2 inhibitors

Coronavirus disease 2019 (COVID-19) is a recent pandemic that caused serious global emergency. To identify new and effective therapeutics, we employed a drug repurposing approach. The poly (ADP ribose) polymerase inhibitors were used for this purpose and were repurposed against the main protease (Mpro) target of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The results from these studies were used to design compounds using the 'Grow Scaffold' modules available on Discovery Studio v2018. The three designed compounds, olaparib 1826 and olaparib 1885, and rucaparib 184 demonstrated better CDOCKER docking scores for Mpro than their parent compounds. Moreover, the compounds adhered to Lipinski's rule of five and demonstrated a synthetic accessibility score of 3.55, 3.63, and 4.30 for olaparib 1826, olaparib 1885, and rucaparib 184, respectively. The short-range Coulombic and Lennard-Jones potentials also support the potential binding of the modified compounds to Mpro. Therefore, we propose these three compounds as novel SARS-CoV-2 inhibitors.

Bias-force guided simulations combined with experimental validations towards GPR17 modulators identification

Glioblastoma Multiforme (GBM) is known to be by far the most aggressive brain tumor to affect adults. The median survival rate of GBM patient's is < 15 months, while the GBM cells aggressively develop resistance to chemo- and radiotherapy with their self-renewal capacity which suggests the pressing need to develop novel preventative measures. We have recently proved that GPR17 -an orphan G protein-coupled receptor- is highly expressed on the GBM cell surface and it has a vital role to play in the disease progression. Despite the progress made on GBM downregulation, there still remain difficulties in developing a promising modulator for GPR17, till date. Here, we have performed robust virtual screening combined with biased-force pulling molecular dynamic (MD) simulations to predict high-affinity GPR17 modulators followed by experimental validation. Initially, the database containing 1379 FDA-approved drugs were screened against the orthosteric binding pocket of the GPR17. The external bias-potentials were then applied to the screened hits during the MD simulations which enabled to predict a spectrum of rupture peak force values that were used to select four approved drugs -ZINC000003792417 (Sacubitril), ZINC000014210457 (Victrelis), ZINC000001536109 (Pralatrexate) and ZINC000003925861 (Vorapaxar)- as top hits. The hits selected turns out to demonstrate unique dissociation pathways, interaction pattern, and change in polar network over time. Subsequently the selected hits with GPR17 were measured by inhibiting the forskolin-stimulated cAMP accumulation in GBM cell lines, LN229 and SNB19. The ex vivo validations shows that Sacubitril drug can act as a full agonist, while Vorapaxar functions as a partial agonist for GPR17. The pEC₅₀ of Sacubitril was identified as 4.841 and 4.661 for LN229 and SNB19, respectively. Small interference of the RNA (siRNA)- silenced the GPR17 to further validate the targeted binding of Sacubitril with GPR17. In the current investigation, we have identified new repurposable GPR17 specific drugs which are likely to increase the opportunity to treat orphan deadly diseases.

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

Phytochemical rich Himalayan Rhododendron arboreum petals inhibit SARS-CoV-2 infection in vitro

Phytochemicals with potential to competitively bind to the host receptors or inhibit SARS-CoV-2 replication, may prove to be useful as adjunct therapeutics for COVID-19. We profiled and investigated the phytochemicals of Rhododendron arboreum petals sourced from Himalayan flora, undertook in vitro studies and found it as a promising candidate against SARS-CoV-2. The phytochemicals were reported in various scientific investigations to act against a range of virus in vitro and in vivo, which prompted us to test against SARS-CoV-2. In vitro assays of R. arboreum petals hot aqueous extract confirmed dose dependent reduction in SARS-CoV-2 viral load in infected Vero E6 cells (80% inhibition at 1 mg/ml; IC50 = 173 µg/ml) and phytochemicals profiled were subjected to molecular docking studies against SARS CoV-2 target proteins. The molecules 5-O-Feruloyl-quinic acid, 3-Caffeoyl-quinic acid, 5-O-Coumaroyl-D-quinic acid, Epicatechin and Catechin showed promising binding affinity with SARS-CoV-2 Main protease (M^Pro; PDB ID: 6LU7; responsible for viral replication) and Human Angiotensin Converting Enzyme-2 (ACE2; PDB ID: 1R4L; mediate viral entry in the host). Molecular dynamics (MD) simulation of 5-O-Feruloyl-quinic acid, an abundant molecule in the extract complexed with the target proteins showed stable interactions. Taken together, the phytochemical profiling, in silico analysis and in vitro anti-viral assay revealed that the petals extract act upon M^Pro and may be inhibiting SARS-CoV-2 replication. This is the first report highlighting R. arboreum petals as a reservoir of antiviral phytochemicals with potential anti-SARS-CoV-2 activity using an in vitro system.

Computational approaches to define poncirin from Magnolia champaka leaves as a novel multi-target inhibitor of SARS-CoV-2

Phytochemical-based drug discovery against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been the focus of the current scenario. In this context, we aimed to perform the phytochemical profiling of Magnolia champaka, an evergreen tree from the Magnoliaceae family, in order to perform a virtual screening of its phytoconstituents against different biological targets of SARS-CoV-2. The phytochemicals identified from the ethanol extract of M. champaka leaves using liquid chromatography-mass spectroscopy (LC-MS) technique were screened against SARS-CoV-2 spike glycoprotein (PDB ID: 6M0J), main protease/Mpro (PDB ID: 6LU7), and papain-like protease/PLpro (PDB ID: 7CMD) through computational tools. The experimentation design included molecular docking simulation, molecular dynamics simulation, and binding free energy calculations. Through molecular docking simulation, we identified poncirin as a common potential inhibitor of all the above-mentioned target proteins. In addition, molecular dynamics simulations, binding free energy calculations, and PCA analysis also supported the outcomes of the virtual screening. By the virtue of all the in silico results obtained, poncirin could be taken for in vitro and in vivo studies in near future.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening and in-silico study of natural products as potential DENV-2 RdRp inhibitors

Dengue fever is a significant public health concern throughout the world, causing an estimated 500,000 hospitalizations and 20,000 deaths each year, despite the lack of effective therapies. The DENV-2 RdRp has been identified as a potential target for the development of new and effective dengue therapies. This research's primary objective was to discover an anti-DENV inhibitor using in silico ligand- and structure-based approaches. To begin, a ligand-based pharmacophore model was developed, and 130 distinct natural products (NPs) were screened. Docking of the pharmacophore-matched compounds were performed to the active site of DENV-2 RdRp protease . Eleven compounds were identified as potential DENV-2 RdRp inhibitors based on docking energy and binding interactions. ADMET and drug-likeness were done to predict their pharmacologic, pharmacokinetic, and drug-likeproperties . Compounds ranked highest in terms of pharmacokinetics and drug-like appearances were then subjected to additional toxicity testing to determine the leading compound. Additionally, MD simulation of the lead compound was performed to confirm the docked complex's stability and the binding site determined by docking. As a result, the lead compound (compound-108) demonstrated an excellent match to the pharmacophore, a strong binding contact and affinity for the RdRp enzyme, favourable pharmacokinetics, and drug-like characteristics. In summary, the lead compound identified in this study could be a possible DENV-2 RdRp inhibitor that may be further studied on in vitro and in vivo models to develop as a drug candidate.Communicated by Ramaswamy H. Sarma.

Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings

Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, M^pro. The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules in silico. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant M^pro, with half-maximal inhibitory concentration values (IC₅₀) in the range 0.18–18.82 μM. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus M^pro was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

Arjunetin as a promising drug candidate against SARS-CoV-2: molecular dynamics simulation studies

Stem and bark of the tree Terminalia arjuna Wight & Arn. (Combretaceae) has been documented to exhibit therapeutic properties like cardiotonic, anticancer, antiviral, antibacterial, antifungal, hypercholesterolemia, hypolipidemic, and anti-coagulant. Our previous studies have shown that, ethanolic extract of T. arjuna bark exhibits radical scavenging anti-oxidant activity and also effectively inhibited catalase activity. In this study, oleanane triterpenoids type compounds viz., oleanolic acid, arjunolic acid, arjunolitin, arjunetin were isolated from ethanolic bark extract as bio-active compound and their structures were elucidated using ¹H, ¹³C NMR, HR-ESIMS, IR. Of the various compounds, Arjunetin showed significant inhibition of catalase activity as compared to the other compounds. Based on the structural similarity between arjunetin and current antiviral drugs, we propose that arjunetin might exhibit antiviral activity. Molecular docking and molecular dynamics studies showed that arjunetin binds to the binds to key targets of SARS-CoV-2 namely, 3CLpro, PLpro, and RdRp) with a higher binding energy values (3CLpro, -8.4 kcal/mol; PLpro, -7.6 kcal/mol and RdRp, -8.1 kcal/mol) as compared with FDA approved protease inhibitor drugs to Lopinavir (3CLpro, -7.2 kcal/mole and PLpro -7.7 kcal/mole) and Remdesivir (RdRp -7.6 kcal/mole). To further investigate this, we performed 200-500 ns molecular dynamics simulation studies. The results transpired that the binding affinity of Arjunetin is higher than Remdesivir in the RNA binding cavity of RdRp. Based on structural similarity between arjunetin and Saikosaponin (a known antiviral agents) and based on our molecular docking and molecular dynamic simulation studies, we propose that arjunetin can be a promising drug candidate against Covid-19.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 main protease inhibitors: What is moving in the field of peptides and peptidomimetics?

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still affecting people worldwide. Despite the good degree of immunological protection achieved through vaccination, there are still severe cases that require effective antivirals. In this sense, two specific pharmaceutical preparations have been marketed already, the RdRp polymerase inhibitor molnupiravir and the main viral protease inhibitor nirmatrelvir (commercialized as Paxlovid, a combination with ritonavir). Nirmatrelvir is a peptidomimetic acting as orally available, covalent, and reversible inhibitor of SARS-CoV-2 main viral protease. The success of this compound has revitalized the search for new peptide and peptidomimetic protease inhibitors. This highlight collects some selected examples among those recently published in the field of SARS-CoV-2.

An Integrated Analysis of Mechanistic Insights into Biomolecular Interactions and Molecular Dynamics of Bio-Inspired Cu(II) and Zn(II) Complexes towards DNA/BSA/SARS-CoV-2 3CLpro by Molecular Docking-Based Virtual Screening and FRET Detection

Novel constructed bioactive mixed-ligand complexes (1b) [Cu^II(L)₂(phen)] and (2b) [Zn^II(L)₂(phen)] {where, L = 2-(4-morpholinobenzylideneamino)phenol), phen = 1,10-phenanthroline} have been structurally analysed by various analytical and spectroscopic techniques, including, magnetic moments, thermogravimetric analysis, and X-ray crystallography. Various analytical and spectral measurements assigned showed that all complexes appear to have an octahedral geometry. Agar gel electrophoresis's output demonstrated that the Cu(II) complex (1b) had efficient deoxyribonucleic cleavage and complex (2b) demonstrated the partial cleavage accomplished with an oxidation agent, which generates spreadable OH^● through the Fenton type mechanism. The DNA binding constants observed from viscosity, UV-Vis spectral, fluorometric, and electrochemical titrations were in the following sequence: (1b) > (2b) > (HL), which suggests that the complexes (1b-2b) might intercalate DNA, a possibility that is supported by the biothermodynamic measurements. In addition, the observed binding constant results of BSA by electronic absorption and fluorometric titrations indicate that complex (1b) revealed the best binding efficacy as compared to complex (2b) and free ligand. Interestingly, all compounds are found to interact with BSA through a static approach, as further attested by FRET detection. The DFT and molecular docking calculations were also performed to realize the electronic structure, reactivity, and binding capability of all test samples with CT-DNA, BSA, and the SARS-CoV-2 3CL^Pro, which revealed the binding energies were in a range of -8.1 to -8.9, -7.5 to -10.5 and -6.7--8.8 kcal/mol, respectively. The higher reactivity of the complexes than the free ligand is supported by the FMO theory. Among all the observed data for antioxidant properties against DPPH^᛫, ^᛫OH, O₂^-• and NO^᛫ free radicals, complex (1a) had the best biological efficacy. The antimicrobial and cytotoxic characteristics of all test compounds have been studied by screening against certain selected microorganisms as well as against A549, HepG2, MCF-7, and NHDF cell lines, respectively. The observed findings revealed that the activity enhances coordination as compared to free ligand via Overtone's and Tweedy's chelation mechanisms. This is especially encouraging given that in every case, the experimental findings and theoretical detections were in perfect accord.

Molecular docking-based virtual screening: Challenges in hits identification for Anti-SARS-Cov-2 activity

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) requires finding new drugs or repurposing drugs for clinical use. Molecular docking belongs to structure-based drug design providing a fast method for identifying the hit compounds with antiviral activity against SARS-Cov-2. However, the weakness of the docking method is compounded by the limited crystallographic information and comparison drugs due to the novelty of this virus can present challenges in identifying hits of anti-SARS-Cov-2. In the current review, we highlighted several aspects, especially those related to the target structure, docking validation, and virtual hit selection, that need to be considered to obtain reliable docking results. Here, we discussed several cases pertaining to the issue highlighted and approaches that could be used to solve them.

Identification of Active Compounds against Melanoma Growth by Virtual Screening for Non-Classical Human DHFR Inhibitors

Antifolates such as methotrexate (MTX) have been largely known as anticancer agents because of their role in blocking nucleic acid synthesis and cell proliferation. Their mechanism of action lies in their ability to inhibit enzymes involved in the folic acid cycle, especially human dihydrofolate reductase (hDHFR). However, most of them have a classical structure that has proven ineffective against melanoma, and, therefore, inhibitors with a non-classical lipophilic structure are increasingly becoming an attractive alternative to circumvent this clinical resistance. In this study, we conducted a protocol combining virtual screening (VS) and cell-based assays to identify new potential non-classical hDHFR inhibitors. Among 173 hit compounds identified (average logP = 3.68; average MW = 378.34 Da), two-herein, called C1 and C2-exhibited activity against melanoma cell lines B16 and A375 by MTT and Trypan-Blue assays. C1 showed cell growth arrest (39% and 56%) and C2 showed potent cytotoxic activity (77% and 51%) in a dose-dependent manner. The effects of C2 on A375 cell viability were greater than MTX (98% vs 60%) at equivalent concentrations and times. Our results indicate that the integrated in silico/in vitro approach provided a benchmark to identify novel promising non-classical DHFR inhibitors showing activity against melanoma cells.

Use of Human Lung Tissue Models for Screening of Drugs against SARS-CoV-2 Infection

The repurposing of licenced drugs for use against COVID-19 is one of the most rapid ways to develop new and alternative therapeutic options to manage the ongoing pandemic. Given circa 7817 licenced compounds available from Compounds Australia that can be screened, this paper demonstrates the utility of commercially available ex vivo/3D airway and alveolar tissue models. These models are a closer representation of in vivo studies than in vitro models, but retain the benefits of rapid in vitro screening for drug efficacy. We demonstrate that several existing drugs appear to show anti-SARS-CoV-2 activity against both SARS-CoV-2 Delta and Omicron Variants of Concern in the airway model. In particular, fluvoxamine, as well as aprepitant, everolimus, and sirolimus, has virus reduction efficacy comparable to the current standard of care (remdesivir, molnupiravir, nirmatrelvir). Whilst these results are encouraging, further testing and efficacy studies are required before clinical use can be considered.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

Molecular modeling identification of potential drug candidates from selected African plants against SARS-CoV-2 key druggable proteins

Coronavirus disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is one of the major health threats the world has experienced. In order to stem the tide of the virus and its associated disease, rapid efforts have been dedicated to identifying credible anti-SARS-CoV-2 drugs. This study forms part of the continuing efforts to develop anti-SARS-CoV-2 molecules and employed a computational structure-activity relationship approach with emphasis on 99 plant secondary metabolites from eight selected African medicinal plants with proven therapeutic benefits against respiratory diseases focusing on the viral protein targets [Spike protein (Sgp), Main protease (Mpro), and RNA-dependent RNA polymerase (RdRp)]. The results of the molecular dynamics simulation of the best docked compounds presented as binding free energy revealed that three compounds each against the Sgp (VBS, COG and ABA), and Mpro (COR, QOR and ABG) had higher and better affinity for the proteins than the respective reference drugs, cefoperazone (CSP) and Nelfinavir (NEF), while four compounds (HDG, VBS, COR and KOR) had higher and favorable binding affinity towards RdRp than the reference standard, ramdesivir (RDS). Analysis of interaction with the receptor binding domain amino acid residues of Sgp showed that VBS had the highest number of interactions (17) relative to 14 and 13 for COG and ABA, respectively. For Mpro, COR showed interactions with catalytic dyad residues (His172 and Cys145). Compared to RDS, COR, HDG, VBS and KOR formed 19, 18, 17 and 12 H-bond and Van der Waal bonds, respectively, with RdRp. Furthermore, structural examination of the three proteins after binding to the lead compounds revealed that the compounds formed stable complexes. These observations suggest that the identified compounds might be beneficial in the fight against COVID-19 and are suggested for further in vitro and in vivo experimental validation.

Molecular docking of ethanol extracts of katuk leaf (Sauropus androgynus) on functional proteins of severe acute respiratory syndrome coronavirus 2

The COVID-19 pandemic has become a major health crisis globally. Alternative treatment approaches include using food sources rich in flavonoid compounds, such as the leaves of katuk plant (Sauropus androgynus). The purpose of this study was to analyze the characteristics of the flavonoid group present in active compounds of katuk leaves (Sauropus androgynus) and to study the mechanism underlying interactions (molecular docking) of these compounds with 3CLpro, Nsp1, Nsp3, RdRp, Nsp7_Nsp8 complex, and PLpro in SARS-CoV-2, and ACE2 in humans. In silico analysis was performed using Hex 8.0. software, which is primary tool of docking analysis. Interaction between the ligand and its receptors were analyzed using the software Discovery studio 4.1. The results of this study indicated that ABCD chains of 3CLpro had the highest bond energy with afzelin (-42.77 Kcal/mol), RdRp Nsp7_Nsp8 complex had the highest bond energy with trifolin (-310.87 Kcal/mol), PLpro had the highest bond energy with afzelin (-190.23 Kcal/mol), Nsp1 had the highest bond energy with afzelin (-286.89 Kcal/mol), Nsp3 had the highest bond energy with trifolin (-334.97 Kcal/mol), and ACE2 had the highest bond energy with trifolin (-307.96 Kcal/mol). Thus, on comparison with conventionally used drugs, the active flavonoid compounds in katuk leaves (Sauropus androgynus) showed specific affinity for 3CLpro, Nsp1, Nsp3, RdRp Nsp7_Nsp8 complex, and PLpro in SARS-CoV-2 and ACE2 in humans. Thus, katuk leaves a potential herbal candidates to derive new drugs or complementary medicines for COVID-19.

Z-Guggulsterone Is a Potential Lead Molecule of Dawa-ul-Kurkum against Hepatocellular Carcinoma

An ancient saffron-based polyherbal formulation, Dawa-ul-Kurkum (DuK), has been used to treat liver ailments and other diseases and was recently evaluated for its anticancer potential against hepatocellular carcinoma (HCC) by our research team. To gain further insight into the lead molecule of DuK, we selected ten active constituents belonging to its seven herbal constituents (crocin, crocetin, safranal, jatamansone, isovaleric acid, cinnamaldehyde, coumaric acid, citral, guggulsterone and dehydrocostus lactone). We docked them with 32 prominent proteins that play important roles in the development, progression and suppression of HCC and those involved in endoplasmic reticulum (ER) stress to identify the binding interactions between them. Three reference drugs for HCC (sorafenib, regorafenib, and nivolumab) were also examined for comparison. The in silico studies revealed that, out of the ten compounds, three of them—viz., Z-guggulsterone, dehydrocostus lactone and crocin—showed good binding efficiency with the HCC and ER stress proteins. Comparison of binding affinity with standard drugs was followed by preliminary in vitro screening of these selected compounds in human liver cancer cell lines. The results provided the basis for selecting Z-guggulsterone as the best-acting phytoconstituent amongst the 10 studied. Further validation of the binding efficiency of Z-guggulsterone was undertaking using molecular dynamics (MD) simulation studies. The effects of Z-guggulsterone on clone formation and cell cycle progression were also assessed. The anti-oxidant potential of Z-guggulsterone was analyzed through DPPH and FRAP assays. qRTPCR was utilized to check the results at the in vitro level. These results indicate that Z-guggulsterone should be considered as the main constituent of DuK instead of the crocin in saffron, as previously hypothesized.

In silico anti-SARS-CoV-2 activities of five-membered heterocycle-substituted benzimidazoles

The manuscript deals with cost-effective synthesis, structural characterization and in silico SARS-CoV-2 screening activity of 5-membered heterocycle-substituted benzimidazole derivatives, 1-((1H-pyrrol-2-yl)methyl)-2-(1H-pyrrol-2-yl)-1H-benzo[d]imidazole (L1), 2-(furan-2-yl)-1-(furan-2-ylmethyl)-1H-benzo[d]imidazole (L2), 2-(thiophen-2-yl)-1-(thiophen-2-ylmethyl)-1H-benzo[d]imidazole (L3). The benzimidazole compounds were synthesized through a green-synthetic approach by coupling of 5-membered heterocyclic-carboxaldehyde and o-phenylenediamine in water under an aerobic condition. The compounds were characterized by various spectroscopic methods and X-ray structural analysis. The suitable single-crystals of the methyl derivative of L3 were grown as L3' which crystallized in a monoclinic system and the thiophene groups co-existed in a nearly a perpendicular orientation. Further, in silico anti-SARS-CoV-2 proficiency of the synthetic derivatives is evaluated against main protease (M^pro) and non-structural proteins (nsp2 and nsp7) of SARS-CoV-2. Molecular docking and molecular dynamics analysis of the ligands (L1-L3) against M^pro and nsp2 and nsp7 for 50 ns reveal that L3 turns out to be the superlative antiviral candidate against M^pro, nsp2 and nsp7 of SARS-CoV-2 as evident from the binding score and stability of the ligand-docked complexes with considerable binding energy changes.

Molecular modelling and simulation techniques to investigate the effects of fungal metabolites on the SARS-CoV-2 RdRp protein inhibition

Various protein/receptor targets have been discovered through in-silico research. They are expanding rapidly due to their extensive advantage of delivering new drug candidates more quickly, efficiently, and at a lower cost. The automation of organic synthesis and biochemical screening will lead to a revolution in the entire research arena in drug discovery. In this research article, a few fungal metabolites were examined through an in-silico approach which involves major steps such as (a) Molecular Docking Analysis, (b) Drug likeness and ADMET studies, and (c) Molecular Dynamics Simulation. Fungal metabolites were taken from Antibiotic Database which showed antiviral effects on severe viral diseases such as HIV. Docking, Lipinski's, and ADMET analyses investigated the binding affinity and toxicity of five metabolites: Chromophilone I, iso; F13459; Stachyflin, acetyl; A-108836; Integracide A (A-108835). Chromophilone I, iso was subjected to additional analysis, including a 50 ns MD simulation of the protein to assess the occurring alterations. This molecule's docking data shows that it had the highest binding affinity. ADMET research revealed that the ligand might be employed as an oral medication. MD simulation revealed that the ligand–protein interaction was stable. Finally, this ligand can be exploited to develop SARS-CoV-2 therapeutic options. Fungal metabolites that have been studied could be a potential source for future lead candidates. Further study of these molecules may result in creating an antiviral drug to battle the SARS-CoV-2 virus.

Molecular docking and identification of G-protein-coupled receptor 120 (GPR120) agonists as SARS COVID-19 MPro inhibitors

COVID-19 has become a pandemic, and any new drug for treating the disease could save millions of lives. Several drugs already in use for other diseases and medical conditions are repurposed for treating COVID-19 in an attempt to find treatment for the disease without spending research time on ADME TOX and other studies on side effects. In this exercise, the drugs repurposed are from antiviral, antibiotics, antiviral for HIV and HCV, anti-cancer, natural medicines, etc. Possible repurposing anti-diabetic GPR-120 agonists used as for SAR-CoV-2 is attempted in the study by carrying out docking of 68 GPR-120 agonists. Ten of these compounds were found to have docking scores −8.3 to −8.0, and the best docking score was observed for an arylsulfonamide and a biarylpropanoic acid belonging to GPR120 agonists previously evaluated for the treatment of type II diabetes. These GPR120 agonists could serve as start point for novel inhibitors for the discovery of drugs to treat COVID-19.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Prediction of CIAPIN1 (Cytokine-Induced Apoptosis Inhibitor 1) Signaling Pathway and Its Role in Cholangiocarcinoma Metastasis

Cholangiocarcinoma (CCA), a malignancy of the biliary epithelium, can arise at any point in the biliary system. We previously reported that CIAPIN1 is detectable in the sera and that its overexpression was associated with poor prognosis and metastasis of CCA patients. In this study, we investigated further its expression in CCA tissues, biological functions, and related signaling pathways in CCA cells. First, we examined CIAPIN1 expression in CCA tissues of 39 CCA patients using immunohistochemistry (IHC). Then, CIAPIN1-related proteins expressed in CCA cells were identified using RNA interference (siRNA) and liquid chromatography–mass spectrometry (LC–MS/MS). To predict the functions and signaling pathways of CIAPIN1 in CCA cells, the identified proteins were analyzed using bioinformatics tools. Then, to validate the biological functions of CIAPIN1 in the CCA cell line, transwell migration/invasion assays were used. CIAPIN1 was overexpressed in CCA tissues compared with adjacent noncancerous tissues. Its overexpression was correlated with lymph node metastasis. Bioinformatic analyses predicted that CIAPIN1 is connected to the TGF-β/SMADs signaling pathway via nitric oxide synthase 1 (NOS1) and is involved in the metastasis of CCA cells. In fact, cell migration and invasion activities of the KKU-100 CCA cell line were significantly suppressed by CIAPIN1 gene silencing. Our results unravel its novel function and potential signaling pathway in metastasis of CCA cells. CIAPIN1 can be a poor prognostic factor and can be a promising target molecule for CCA chemotherapy.

Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs

SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the "gene-for-gene and "network for network" concepts as platforms for future directions toward designing efficient anti-viral therapies.

Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.

Identification of bio-active food compounds as potential SARS-CoV-2 PLpro inhibitors-modulators via negative image-based screening and computational simulations

Despite significant studies on the COVID-19 pandemic, scientists around the world are still battling to find a definitive therapy against the ongoing severe global health crisis. In this study, advanced computational approaches have been employed to identify bioactive food constituents as potential SARS-CoV-2 PLpro inhibitors-modulators. As a validated antiviral drug target, PLpro has gained tremendous attention for therapeutics developments. Therefore, targeting the multifunctional SARS-CoV-2 PLpro protein, ∼1039 bioactive dietary compounds have been screened extensively through novel techniques like negative image-based (NIB) screening and molecular docking approaches. In particular, the three different models of NIB screening have been generated and used to re-score the dietary compounds based on the negative image which is created by reversing the shape and electrostatics features of PLpro protein's ligand-binding cavity. Further, 100 ns molecular dynamics simulation has been performed and MM-GBSA based binding free energies have been estimated for the final proposed four dietary compounds (PC000550, PC000361, PC000558, and PC000573) as potential inhibitors/modulators of SARS-CoV-2 PLpro protein. Employed computational study outcome also has been compared with respect to the earlier experimentally investigated compound GRL0617 against SARS-CoV-2 PLpro protein, which suggests much greater interaction potential in terms of binding affinity and other energetic contributions for the proposed dietary compounds. Hence, the present study suggests that proposed dietary compounds can be suitable chemical entities for modulating the activity of PLpro protein or can be further utilized for optimizing or screening of novel chemical surrogates, however also needs experimental evaluation for entry in clinical studies for better assessment.

Structural Elucidation of Rift Valley Fever Virus L Protein towards the Discovery of Its Potential Inhibitors

Rift valley fever virus (RVFV) is the causative agent of a viral zoonosis that causes a significant clinical burden in domestic and wild ruminants. Major outbreaks of the virus occur in livestock, and contaminated animal products or arthropod vectors can transmit the virus to humans. The viral RNA-dependent RNA polymerase (RdRp; L protein) of the RVFV is responsible for viral replication and is thus an appealing drug target because no effective and specific vaccine against this virus is available. The current study reported the structural elucidation of the RVFV-L protein by in-depth homology modeling since no crystal structure is available yet. The inhibitory binding modes of known potent L protein inhibitors were analyzed. Based on the results, further molecular docking-based virtual screening of Selleckchem Nucleoside Analogue Library (156 compounds) was performed to find potential new inhibitors against the RVFV L protein. ADME (Absorption, Distribution, Metabolism, and Excretion) and toxicity analysis of these compounds was also performed. Besides, the binding mechanism and stability of identified compounds were confirmed by a 50 ns molecular dynamic (MD) simulation followed by MM/PBSA binding free energy calculations. Homology modeling determined a stable multi-domain structure of L protein. An analysis of known L protein inhibitors, including Monensin, Mycophenolic acid, and Ribavirin, provide insights into the binding mechanism and reveals key residues of the L protein binding pocket. The screening results revealed that the top three compounds, A-317491, Khasianine, and VER155008, exhibited a high affinity at the L protein binding pocket. ADME analysis revealed good pharmacodynamics and pharmacokinetic profiles of these compounds. Furthermore, MD simulation and binding free energy analysis endorsed the binding stability of potential compounds with L protein. In a nutshell, the present study determined potential compounds that may aid in the rational design of novel inhibitors of the RVFV L protein as anti-RVFV drugs.

Pyrazolone-type compounds: synthesis and in silico assessment of antiviral potential against key viral proteins of SARS-CoV-2

Coronavirus outbreak is still a major public health concern. The high mutation ability of SARS-CoV-2 periodically delivers more transmissible and dangerous variants. Hence, the necessity for an efficient and inexpensive antiviral agent is urgent. In this work, pyrazolone-type compounds were synthesised, characterised using spectroscopic methods and theoretical tools, and evaluated in silico against proteins of SARS-CoV-2 responsible for host cell entry and reproduction processes, i.e., spike protein (S), Mpro, and PLpro. Five of twenty compounds are newly synthesised. In addition, the crystal structure of a pyrazolone derivative bearing a vanillin moiety is determined. The obtained in silico results indicate a more favourable binding affinity of pyrazolone analogues towards Mpro, and PLpro in comparison to drugs lopinavir, remdesivir, chloroquine, and favipiravir, while in the case of S protein only lopinavir exerted higher binding affinity. Also, the investigations were performed on ACE2 and the spike RBD-ACE2 complex. The obtained results for these proteins suggest that selected compounds could express antiviral properties by blocking the binding to the host cell and viral spreading, also. Moreover, several derivatives expressed multitarget antiviral action, blocking both binding and reproduction processes. Additionally, in silico ADME/T calculations predicted favourable features of the synthesised compounds, i.e., drug-likeness, oral bioavailability, as well as good pharmacokinetic parameters related to absorption, metabolism, and toxicity. The obtained results imply the great potential of synthesised pyrazolones as multitarget agents against SARS-CoV-2 and represent a valuable background for further in vitro investigations.

Identification of novel TMPRSS2 inhibitors against SARS-CoV-2 infection: a structure-based virtual screening and molecular dynamics study

The scientific insights gained from the severe acute respiratory syndrome (SARS) and the middle east respiratory syndrome (MERS) outbreaks are helping scientists to fast-track the antiviral drug discovery process against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronaviruses, as well as influenza viruses, depend on host type 2 transmembrane serine protease, TMPRSS2, for entry and propagation in the human cell. Recent studies show that SARS-CoV-2 also uses TMPRSS2 for its cell entry. In the present study, a structure-based virtual screening of 52,337, protease ligands downloaded from the Zinc database was carried out against the homology model of TMPRSS2 protein followed by the molecular dynamics-based simulation to identify potential TMPRSS2 hits. The virtual screening has identified 13 hits with a docking score range of -10.447 to -9.863 and glide energy range of -60.737 to -40.479 kcal/mol. The binding mode analysis shows that the hit molecules form H-bond (Asp180, Gly184 & Gly209), Pi-Pi stacking (His41), and salt bridge (Asp180) type of contacts with the active site residues of TMPRSS2. In the MD simulation of ZINC000013444414, ZINC000137976768, and ZINC000143375720 hits show that these molecules form a stable complex with TMPRSS2. The complex equilibrates well with a minimal RMSD and RMSF fluctuation. All three structures, as predicted in Glide XP docking, show a prominent interaction with the Asp180, Gly184, Gly209, and His41. Further, MD simulation also identifies a notable H-bond interaction with Ser181 for all three hits. Among these hits, ZINC000143375720 shows the most stable binding interaction with TMPRSS2. The present study is successful in identifying TMPRSS2 ligands from zinc data base for a possible application in the treatment of COVID-19.

Potential Inhibitors Targeting Papain-Like Protease of SARS-CoV-2: Two Birds With One Stone

Severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), the pathogen of the Coronavirus disease-19 (COVID-19), is still devastating the world causing significant chaos to the international community and posing a significant threat to global health. Since the first outbreak in late 2019, several lines of intervention have been developed to prevent the spread of this virus. Nowadays, some vaccines have been approved and extensively administered. However, the fact that SARS-CoV-2 rapidly mutates makes the efficacy and safety of this approach constantly under debate. Therefore, antivirals are still needed to combat the infection of SARS-CoV-2. Papain-like protease (PLpro) of SARS-CoV-2 supports viral reproduction and suppresses the innate immune response of the host, which makes PLpro an attractive pharmaceutical target. Inhibition of PLpro could not only prevent viral replication but also restore the antiviral immunity of the host, resulting in the speedy recovery of the patient. In this review, we describe structural and functional features on PLpro of SARS-CoV-2 and the latest development in searching for PLpro inhibitors. Currently available inhibitors targeting PLpro as well as their structural basis are also summarized.

A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis

In the last century, the emergence of in silico tools has improved the quality of healthcare studies by providing high quality predictions. In the case of COVID-19, these tools have been advantageous for bioinformatics analysis of SARS-CoV-2 structures, studying potential drugs and introducing drug targets, investigating the efficacy of potential natural product components at suppressing COVID-19 infection, designing peptide-mimetic and optimizing their structure to provide a better clinical outcome, and repurposing of the previously known therapeutics. These methods have also helped medical biotechnologists to design various vaccines; such as multi-epitope vaccines using reverse vaccinology and immunoinformatics methods, among which some of them have showed promising results through in vitro, in vivo and clinical trial studies. Moreover, emergence of artificial intelligence and machine learning algorithms have helped to classify the previously known data and use them to provide precise predictions and make plan for future of the pandemic condition. At this contemporary review, by collecting related information from the collected literature on valuable data sources; such as PubMed, Scopus, and Web of Science, we tried to provide a brief outlook regarding the importance of in silico tools in managing different aspects of COVID-19 pandemic infection and how these methods have been helpful to biomedical researchers.