Pharmacophore Modelling-Based Drug Repurposing Approaches for SARS-CoV-2 Therapeutics

Exploring Binding Pockets in the Conformational States of the SARS-CoV-2 Spike Trimers for the Screening of Allosteric Inhibitors Using Molecular Simulations and Ensemble-Based Ligand Docking

Understanding mechanisms of allosteric regulation remains elusive for the SARS-CoV-2 spike protein, despite the increasing interest and effort in discovering allosteric inhibitors of the viral activity and interactions with the host receptor ACE2. The challenges of discovering allosteric modulators of the SARS-CoV-2 spike proteins are associated with the diversity of cryptic allosteric sites and complex molecular mechanisms that can be employed by allosteric ligands, including the alteration of the conformational equilibrium of spike protein and preferential stabilization of specific functional states. In the current study, we combine conformational dynamics analysis of distinct forms of the full-length spike protein trimers and machine-learning-based binding pocket detection with the ensemble-based ligand docking and binding free energy analysis to characterize the potential allosteric binding sites and determine structural and energetic determinants of allosteric inhibition for a series of experimentally validated allosteric molecules. The results demonstrate a good agreement between computational and experimental binding affinities, providing support to the predicted binding modes and suggesting key interactions formed by the allosteric ligands to elicit the experimentally observed inhibition. We establish structural and energetic determinants of allosteric binding for the experimentally known allosteric molecules, indicating a potential mechanism of allosteric modulation by targeting the hinges of the inter-protomer movements and blocking conformational changes between the closed and open spike trimer forms. The results of this study demonstrate that combining ensemble-based ligand docking with conformational states of spike protein and rigorous binding energy analysis enables robust characterization of the ligand binding modes, the identification of allosteric binding hotspots, and the prediction of binding affinities for validated allosteric modulators, which is consistent with the experimental data. This study suggested that the conformational adaptability of the protein allosteric sites and the diversity of ligand bound conformations are both in play to enable efficient targeting of allosteric binding sites and interfere with the conformational changes.

Toward Unified AI Drug Discovery with Multimodal Knowledge

Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.

CBPDdb: a curated database of compounds derived from Coumarin-Benzothiazole-Pyrazole

The present article describes the building of a small-molecule web server, CBPDdb, employing R-shiny. For the generation of the web server, three compounds were chosen, namely coumarin, benzothiazole and pyrazole, and their derivatives were curated from the literature. The two-dimensional (2D) structures were drawn using ChemDraw, and the .sdf file was created employing Discovery Studio Visualizer v2017. These compounds were read on the R-shiny app using ChemmineR, and the dataframe consisting of a total of 1146 compounds was generated and manipulated employing the dplyr package. The web server is provided with JSME 2D sketcher. The descriptors of the compounds are obtained using propOB with a filter. The users can download the filtered data in the .csv and .sdf formats, and the entire dataset of a compound can be downloaded in .sdf format. This web server facilitates the researchers to screen plausible inhibitors for different diseases. Additionally, the method used in building the web server can be adapted for developing other small-molecule databases (web servers) in RStudio. Database URL: https://srampogu.shinyapps.io/CBPDdb_Revised/.

Identifying inhibitors of NSP16-NSP10 of SARS-CoV-2 from large databases

The COVID-19 pandemic, which has already claimed millions of lives, continues to pose a serious threat to human health, requiring the development of new effective drugs. Non-structural proteins of SARS-CoV-2 play an important role in viral replication and infection. Among them, NSP16 (non-structured protein 16) and its cofactor NSP10 (non-structured protein 10) perform C2'-O methylation at the 5' end of the viral RNA, which promotes efficient virus replication. Therefore, the NSP16-NSP10 complex becomes an attractive target for drug development. Using a multi-step virtual screening protocol which includes Lipinski's rule, docking, steered molecular dynamics and umbrella sampling, we searched for potential inhibitors from the PubChem and anti-HIV databases. It has been shown that CID 135566620 compound from PubChem is the best candidate with an inhibition constant in the sub-μM range. The Van der Waals interaction was found to be more important than the electrostatic interaction in the binding affinity of this compound to NSP16-NSP10. Further in vitro and in vivo studies are needed to test the activity of the identified compound against COVID-19.Communicated by Ramaswamy H. Sarma.

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.

An overview on monkeypox virus: Pathogenesis, transmission, host interaction and therapeutics

Orthopoxvirus is one of the most notorious genus amongst the Poxviridae family. Monkeypox (MP) is a zoonotic disease that has been spreading throughout Africa. The spread is global, and incidence rates are increasing daily. The spread of the virus is rapid due to human-to-human and animals-to-human transmission. World Health Organization (WHO) has declared monkeypox virus (MPV) as a global health emergency. Since treatment options are limited, it is essential to know the modes of transmission and symptoms to stop disease spread. The information from host-virus interactions revealed significantly expressed genes that are important for the progression of the MP infection. In this review, we highlighted the MP virus structure, transmission modes, and available therapeutic options. Furthermore, this review provides insights for the scientific community to extend their research work in this field.

Explicit molecular dynamics simulation studies to discover novel natural compound analogues as Mycobacterium tuberculosis inhibitors

Tuberculosis (TB) in one of the dreadful diseases present globally. This is caused by Mycobacterium tuberculosis. Mycobacterium tuberculosis dethiobiotin synthetase (MtDTBS) is an essential enzyme in biotin biosynthesis and is an ideal target to design and develop novel inhibitors. In order to effectively combat this disease six natural compound (butein) analogues were subjected to molecular docking to determine their binding mode and the binding affinities. The resultant complex structures were subjected to 500 ns simulation run to estimate their binding stabilities using GROMACS. The molecular dynamics simulation studies provided essential evidence that the systems were stable during the progression of 500 ns simulation run. The root mean square deviation (RMSD) of all the systems was found to be below 0.3 nm stating that the systems are well converged. The radius of gyration (Rg) profiles indicated that the systems were highly compact without any major fluctuations. The principle component analysis (PCA) and Gibbs energy landscape studies have revealed that the comp3, comp5 and comp11 systems navigated marginally through the PC2. The intermolecular interactions have further demonstrated that all the compounds have displayed key residue interactions, firmly holding the ligands at the binding pocket. The residue Lys37 was found consistently to interact with all the ligands highlighting its potential role in inhibiting the MtDTBS. Our investigation further put forth two novel compounds (comp10 and comp11) as putative antituberculosis agents. Collectively, we propose six compounds has plausible inhibitors to curtail TB and further can act as scaffolds in designing new compounds.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

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.

Finding a prospective dual-target drug for the treatment of coronavirus disease by theoretical study

Spike protein of coronavirus is a key protein in binding and entrance of virus to the human cell via binding to the receptor-binding domain (RBD) domain of S1 subunit to peptidase domain region of ACE2 receptor. In this study, the possible effect of 24 antiviral drugs on the RBD domain of spike protein was investigated via docking and molecular dynamics simulation for finding a dual-target drug. At first, all drugs were docked to the RBD domain of spike protein, and then all complexes and free RBD domains were separately used for molecular dynamics simulation for 50 ns via amber18 software. The simulation results showed that 10 ligands from 28 ligands were separated from the RBD domain, and among 18 remained ligands, baloxavir marboxil, and danoprevir drugs, besides endonuclease activity and protease inhibitory, can bind to key residues of the RBD domain. Then these drugs have a dual target and should be more effective than current drugs, and experimental studies should be done on baloxavir marboxil and danoprevir as more potential drugs for coronavirus disease Communicated by Ramaswamy H. Sarma.

Development of novel fluorescence-based and label-free noncanonical G4-quadruplex-like DNA biosensor for facile, specific, and ultrasensitive detection of fipronil

Fipronil is a broad-spectrum insecticide widely used in agriculture and residential areas; its indiscriminate use leads to environmental pollution and poses health hazards. Early detection of fipronil is critical to prevent the deleterious effects. However, current insecticide analysis methods such as HPLC, LC/MS, and GC/MS are incompetent; they are costly, immobile, time-consuming, laborious, and need skilled technicians. Hence, a sensitive, specific, and cheap biosensor are essential to containing the contamination. Here, we designed two novel biosensors-the first design relied on fluorescent labeling/quenching, while the second sensor focused on label-free detection using Thioflavin T displacement. Altogether, we identified four candidate aptamers, predicted secondary structures, and performed 3D molecular modeling to predict the binding pocket of fipronil in FiPA6B aptamer. Furthermore, the aptameric sensors showed high sensitivity to fipronil of sub-ppb level LOD, attributed to stringent experimental design. The biosensors displayed high specificity against other phenylpyrazole insecticides and demonstrated robust sensitivity for fipronil in real samples like cabbage and cucumber. Notably, to the best of our knowledge, this is the first demonstration of noncanonical G4-quadruplex-like aptamer binding to fipronil, verified using CD spectroscopy. Such aptasensors possess considerable potential for real-time measurements of hazardous insecticides as point-of-care technology.

NF-κB Signaling and Inflammation-Drug Repurposing to Treat Inflammatory Disorders?

Simple Summary

Since its first description 35 years ago, the transcription factor NF-κB (nuclear factor κ-light-chain-enhancer of activated B cells) has been shown to be a key mediator of immune cell responses to inflammatory mediators, oxidative stress and genotoxic injury. Dysregulated NF-κB signalling drives inflammation in inflammatory disorders such as multiple sclerosis, rheumatoid arthritis or inflammatory bowel disease. Thus, re-establishing the appropriate regulation of NF-κB activity seems like a promising approach to treat inflammatory diseases. Current anti-inflammatory drugs have many, often serious, side effects. Thus, there is an unmet clinical need for safe and effective anti-inflammatory medicines that both decrease inflammatory mediator production and enhance endogenous anti-inflammatory and prorepair pathways. So far, traditional de novo drug discovery has fallen short of satisfying this need. Drug repurposing is a cost- and time-effective alternative to de novo drug development for the identification of novel applications and has already resulted in the identification of effective anti-inflammatories in the ongoing COVID-19 pandemic. In this paper we critically review NF-κB as a potential target for the development of anti-inflammatory drugs with an emphasis on drug repurposing as a strategy to identify new approaches to treat inflammatory diseases.

Abstract

NF-κB is a central mediator of inflammation, response to DNA damage and oxidative stress. As a result of its central role in so many important cellular processes, NF-κB dysregulation has been implicated in the pathology of important human diseases. NF-κB activation causes inappropriate inflammatory responses in diseases including rheumatoid arthritis (RA) and multiple sclerosis (MS). Thus, modulation of NF-κB signaling is being widely investigated as an approach to treat chronic inflammatory diseases, autoimmunity and cancer. The emergence of COVID-19 in late 2019, the subsequent pandemic and the huge clinical burden of patients with life-threatening SARS-CoV-2 pneumonia led to a massive scramble to repurpose existing medicines to treat lung inflammation in a wide range of healthcare systems. These efforts continue and have proven to be controversial. Drug repurposing strategies are a promising alternative to de novo drug development, as they minimize drug development timelines and reduce the risk of failure due to unexpected side effects. Different experimental approaches have been applied to identify existing medicines which inhibit NF-κB that could be repurposed as anti-inflammatory drugs.

Screening and Identification of Potential iNOS Inhibitors to Curtail Cervical Cancer Progression: an In Silico Drug Repurposing Approach

Cervical cancer is the second most common cause of cancer deaths in women worldwide and remains the main reason of mortality among women of reproductive age in developing countries. Nitric oxide is involved in several physiological functions inclusive of inflammatory and immune responses. However, the function of NO in tumor biology is debatable. The inducible NOS (iNOS/NOS2) isoform is the one responsible to maintain the levels of NO, and it exhibits pleotropic effects in various cancers with concentration-dependent pro- and anti-tumor effects. iNOS triggers angiogenesis and endothelial cell migration in tumors by regulating the levels of vascular endothelial growth factor (VEGF). In drug discovery, drug repurposing involves investigations of approved drug candidates to treat various other diseases. In this study, we used anti-cancer drugs and small molecules to target iNOS and identify a potential selective iNOS inhibitor. The structures of ligands were geometrically optimized and energy minimized using Hyperchem software. Molecular docking was performed using Molegro virtual docker, and ligands were selected based on MolDock score, Rerank score, and H-bonding energy. In the study shown, venetoclax compound demonstrated excellent binding affinity to iNOS protein. This compound exhibited the lowest MolDock score and Rerank score with better H-bonding energy to iNOS. The binding efficacy of venetoclax was analyzed by performing molecular docking and molecular dynamic simulations. Multiple parameters were used to analyze the simulation trajectory, like root mean square deviation (RMSD), radius of gyration (Rg), and hydrogen bond interactions. Based on the results, venetoclax emerges to be a promising potential iNOS inhibitor to curtail cervical cancer progression.

Novel Butein Derivatives Repress DDX3 Expression by Inhibiting PI3K/AKT Signaling Pathway in MCF-7 and MDA-MB-231 Cell Lines

Background

Breast cancer is one of the major causes of mortalities noticed in women globally. DDX3 has emerged as a potent target for several cancers, including breast cancer to which currently there are no reported or approved drugs.

Methods

To find effective cancer therapeutics, three compounds were computationally designed tweaking the structure of natural compound butein. These compounds were synthesized and evaluated for their anticancer property in MCF-7 and MDA-MB-231 cell lines targeting DDX3. The in silico molecular docking studies have shown that the compounds have occupied the binding site of the human DDX3 target. Furthermore, to investigate the cell viability effect of 3a, 3b, and 3c on MCF-7 and MDA-MB-231 cell lines, the cell lines were treated with different concentrations of compounds for 24 and 48 h and measured using MTT assay.

Results

The cell viability results showed that the have induced dose dependent suppression of DDX3 expression. Additionally, 3b and 3c have reduced the expression of DDX3 in MCF-7 and MDA-MD-231 cell lines. 3b or 3c treated cell lines increased apoptotic protein expression. Both the compounds have induced the apoptotic cell death by elevated levels of cleaved PARP and cleaved caspase 3 and repression of the anti-apoptosis protein BCL-xL. Additionally, they have demonstrated the G2/M phase cell cycle arrest in both the cell lines. Additionally, 3c decreased PI3K and AKT levels.

Conclusions

Our results shed light on the anticancer ability of the designed compounds. These compounds can be employed as chemical spaces to design new prospective drug candidates. Additionally, our computational method can be adapted to design new chemical scaffolds as plausible inhibitors.

Unravelling the therapeutic potential of marine drugs as SARS-CoV-2 inhibitors: An insight from essential dynamics and free energy landscape

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic. The virus that causes the disease, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), predominantly infects the respiratory tract, which may lead to pneumonia and death in severe cases. Many marine compounds have been found to have immense medicinal value and have gained approval from the Food and Drug Administration (FDA), and some are being tested in clinical trials. In the current investigation, we redirected a number of marine compounds toward SARS-CoV-2 by targeting the main protease (M^pro, PDB ID: 6Y2F), subjecting them to several advanced computational techniques using co-crystallised ligand as the reference compound. The results of the binding affinity studies showed that two compounds, eribulin mesylate (eri) and soblidotin (sob), displayed higher docking scores than did the reference compound. When these compounds were assessed using molecular dynamics simulation, it was evident that they demonstrated stable binding at the binding pocket of the target protein. The systems demonstrated stable root mean square deviation and radius of gyration values, while occupying the binding pocket during the simulation run. Furthermore, the essential dynamics and free energy landscape exploration revealed that the protein had navigated through a minimal energy basin and demonstrated favourable conformation while binding to the proposed inhibitors. Collectively, our findings suggest that two marine compounds, namely eri and sob, show potential as SARS-CoV-2 main protease inhibitors.