Innate sensing pathways: Defining new innate immune and inflammatory cell death pathways has shaped translational applications.

The past 20 years of research has elucidated new innate immune sensing and cell death pathways with disease relevance. Future molecular characterization of these pathways and their crosstalk and functional redundancies will aid in development of therapeutic strategies.

Cluster Headache Genomewide Association Study and Meta-Analysis Identifies Eight Loci and Implicates Smoking as Causal Risk Factor.

OBJECTIVE: The objective of this study was to aggregate data for the first genomewide association study meta-analysis of cluster headache, to identify genetic risk variants, and gain biological insights. METHODS: A total of 4,777 cases (3,348 men and 1,429 women) with clinically diagnosed cluster headache were recruited from 10 European and 1 East Asian cohorts. We first performed an inverse-variance genomewide association meta-analysis of 4,043 cases and 21,729 controls of European ancestry. In a secondary trans-ancestry meta-analysis, we included 734 cases and 9,846 controls of East Asian ancestry. Candidate causal genes were prioritized by 5 complementary methods: expression quantitative trait loci, transcriptome-wide association, fine-mapping of causal gene sets, genetically driven DNA methylation, and effects on protein structure. Gene set and tissue enrichment analyses, genetic correlation, genetic risk score analysis, and Mendelian randomization were part of the downstream analyses. RESULTS: The estimated single nucleotide polymorphism (SNP)-based heritability of cluster headache was 14.5%. We identified 9 independent signals in 7 genomewide significant loci in the primary meta-analysis, and one additional locus in the trans-ethnic meta-analysis. Five of the loci were previously known. The 20 genes prioritized as potentially causal for cluster headache showed enrichment to artery and brain tissue. Cluster headache was genetically correlated with cigarette smoking, risk-taking behavior, attention deficit hyperactivity disorder (ADHD), depression, and musculoskeletal pain. Mendelian randomization analysis indicated a causal effect of cigarette smoking intensity on cluster headache. Three of the identified loci were shared with migraine. INTERPRETATION: This first genomewide association study meta-analysis gives clues to the biological basis of cluster headache and indicates that smoking is a causal risk factor. ANN NEUROL 2023;94:713-726.

Unexpected frequency of the pathogenic AR CAG repeat expansion in the general population.

CAG repeat expansions in exon 1 of the AR gene on the X chromosome cause spinal and bulbar muscular atrophy, a male-specific progressive neuromuscular disorder associated with a variety of extra-neurological symptoms. The disease has a reported male prevalence of approximately 1:30 000 or less, but the AR repeat expansion frequency is unknown. We established a pipeline, which combines the use of the ExpansionHunter tool and visual validation, to detect AR CAG expansion on whole-genome sequencing data, benchmarked it to fragment PCR sizing, and applied it to 74 277 unrelated individuals from four large cohorts. Our pipeline showed sensitivity of 100% [95% confidence interval (CI) 90.8-100%], specificity of 99% (95% CI 94.2-99.7%), and a positive predictive value of 97.4% (95% CI 84.4-99.6%). We found the mutation frequency to be 1:3182 (95% CI 1:2309-1:4386, n = 117 734) X chromosomes-10 times more frequent than the reported disease prevalence. Modelling using the novel mutation frequency led to estimate disease prevalence of 1:6887 males, more than four times more frequent than the reported disease prevalence. This discrepancy is possibly due to underdiagnosis of this neuromuscular condition, reduced penetrance, and/or pleomorphic clinical manifestations.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints.

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

Phytochemical Analysis, In Vitro Biological Activities, and Computer-Aided Analysis of Potentilla nepalensis Hook Compounds as Potential Melanoma Inhibitors Based on Molecular Docking, MD Simulations, and ADMET.

Potentilla nepalensis Hook is a perennial Himalayan medicinal herb of the Rosaceae family. The present study aimed to evaluate biological activities such as the antioxidant, antibacterial, and anticancer activities of roots and shoots of P. nepalensis and its synergistic antibacterial activity with antibacterial drugs. Folin-Ciocalteau and aluminium chloride methods were used for the calculation of total phenolic (TPC) and flavonoid content (TFC). A DPPH radical scavenging assay and broth dilution method were used for the determination of the antioxidant and antibacterial activity of the root and shoot extracts of P. nepalensis. Cytotoxic activity was determined using a colorimetric MTT assay. Further, phytochemical characterization of the root and shoot extracts was performed using the Gas chromatography-mass spectrophotometry (GC-MS) method. The TPC and TFC were found to be higher in the methanolic root extract of P. nepalensis. The methanolic shoot extract of P. nepalensis showed good antioxidant activity, while then-hexane root extract of P. nepalensis showed strong cytotoxic activity against tested SK-MEL-28 cells. Subsequently, in silico molecular docking studies of the identified bioactive compounds predicted potential anticancer properties. This study can lead to the production of new herbal medicines for various diseases employing P. nepalensis, leading to the creation of new medications.

Mice with endogenous TDP-43 mutations exhibit gain of splicing function and characteristics of amyotrophic lateral sclerosis.

TDP-43 (encoded by the gene TARDBP) is an RNA binding protein central to the pathogenesis of amyotrophic lateral sclerosis (ALS). However, how TARDBP mutations trigger pathogenesis remains unknown. Here, we use novel mouse mutants carrying point mutations in endogenous Tardbp to dissect TDP-43 function at physiological levels both in vitro and in vivo Interestingly, we find that mutations within the C-terminal domain of TDP-43 lead to a gain of splicing function. Using two different strains, we are able to separate TDP-43 loss- and gain-of-function effects. TDP-43 gain-of-function effects in these mice reveal a novel category of splicing events controlled by TDP-43, referred to as "skiptic" exons, in which skipping of constitutive exons causes changes in gene expression. In vivo, this gain-of-function mutation in endogenous Tardbp causes an adult-onset neuromuscular phenotype accompanied by motor neuron loss and neurodegenerative changes. Furthermore, we have validated the splicing gain-of-function and skiptic exons in ALS patient-derived cells. Our findings provide a novel pathogenic mechanism and highlight how TDP-43 gain of function and loss of function affect RNA processing differently, suggesting they may act at different disease stages.

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.

Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.

Genome-Wide Association Study Identifies Risk Loci for Cluster Headache.

OBJECTIVE: This study was undertaken to identify susceptibility loci for cluster headache and obtain insights into relevant disease pathways. METHODS: We carried out a genome-wide association study, where 852 UK and 591 Swedish cluster headache cases were compared with 5,614 and 1,134 controls, respectively. Following quality control and imputation, single variant association testing was conducted using a logistic mixed model for each cohort. The 2 cohorts were subsequently combined in a merged analysis. Downstream analyses, such as gene-set enrichment, functional variant annotation, prediction and pathway analyses, were performed. RESULTS: Initial independent analysis identified 2 replicable cluster headache susceptibility loci on chromosome 2. A merged analysis identified an additional locus on chromosome 1 and confirmed a locus significant in the UK analysis on chromosome 6, which overlaps with a previously known migraine locus. The lead single nucleotide polymorphisms were rs113658130 (p = 1.92 × 10-17 , odds ratio [OR] = 1.51, 95% confidence interval [CI] = 1.37-1.66) and rs4519530 (p = 6.98 × 10-17 , OR = 1.47, 95% CI = 1.34-1.61) on chromosome 2, rs12121134 on chromosome 1 (p = 1.66 × 10-8 , OR = 1.36, 95% CI = 1.22-1.52), and rs11153082 (p = 1.85 × 10-8 , OR = 1.30, 95% CI = 1.19-1.42) on chromosome 6. Downstream analyses implicated immunological processes in the pathogenesis of cluster headache. INTERPRETATION: We identified and replicated several genome-wide significant associations supporting a genetic predisposition in cluster headache in a genome-wide association study involving 1,443 cases. Replication in larger independent cohorts combined with comprehensive phenotyping, in relation to, for example, treatment response and cluster headache subtypes, could provide unprecedented insights into genotype-phenotype correlations and the pathophysiological pathways underlying cluster headache. ANN NEUROL 2021;90:193-202.

Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking, molecular dynamics simulation and binding free energy calculation.

The pandemic outbreak of the Corona viral infection has become a critical global health issue. Biophysical and structural evidence shows that spike protein possesses a high binding affinity towards host angiotensin-converting enzyme 2 and viral hemagglutinin-acetylesterase (HE) glycoprotein receptor. We selected HE as a target in this study to identify potential inhibitors using a combination of various computational approaches such as molecular docking, ADMET analysis, dynamics simulations and binding free energy calculations. Virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin as potential HE inhibitors with better binding energy. Furthermore, molecular dynamics simulations for 100 ns time scale revealed that most of the key HE contacts were retained throughout the simulations trajectories. Binding free energy calculations using MM/PBSA approach ranked the top-five potential NPACT compounds which can act as effective HE inhibitors.

Energetic contributions of amino acid residues and its cross-talk to delineate ligand-binding mechanism.

Receptor-based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein-ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the Energetic CONTributions of Amino acid residues and its possible Cross-Talk (ECONTACT) to study ligand binding using per-residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross-talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein-ligand interaction energies deduced from multiple structures are decomposed into per-residue energy terms, which are employed as variables to principal component analysis and generated cross-terms. Out of 16 cross-talks derived from eight datasets of protein-ligand systems, the ECONTACT approach is able to associate 10 potential cross-talks with site-directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross-talks in protein structures. The top two cross-talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross-talks in any protein-ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact/.

A multiparametric organ toxicity predictor for drug discovery.

The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.

Cardiotonic steroids as potential Na+/K+-ATPase inhibitors - a computational study.

Cardiotonic steroids (CTS) are steroidal drugs, processed from the seeds and dried leaves of the genus Digitalis as well as from the skin and parotid gland of amphibians. The most commonly known CTS are ouabain, digoxin, digoxigenin and bufalin. CTS can be used for safer medication of congestive heart failure and other related conditions due to promising pharmacological and medicinal properties. Ouabain isolated from plants is widely utilized in in vitro studies to specifically block the sodium potassium (Na+/K+-ATPase) pump. For checking, whether ouabain derivatives are robust inhibitors of Na+/K+-ATPase pump, molecular docking simulation was performed between ouabain and its derivatives using YASARA software. The docking energy falls within the range of 8.470 kcal/mol to 7.234 kcal/mol, in which digoxigenin was found to be the potential ligand with the best docking energy of 8.470 kcal/mol. Furthermore, pharmacophore modeling was applied to decipher the electronic features of CTS. Molecular dynamics simulation was also employed to determine the conformational properties of Na+/K+-ATPase-ouabain and Na+/K+-ATPase-digoxigenin complexes with the plausible structural integrity through conformational ensembles for 100 ns which promoted digoxigenin as the most promising CTS for treating conditions of congestive heart failure patients.

Exploration of Mycobacterium tuberculosis structural proteome: An in-silico approach.

Pharmacophore approaches are of central contour in drug discovery. However, the dependence of ligand-based pharmacophore model on appropriate training set molecules and typical use of apo-protein or single protein-ligand complex for the construction of structure-based pharmacophore models might skip some vital information. Therefore, multiple-complex based approach was employed for the construction of pharmacophore models of the Mycobacterium structural proteome. Moreover, the strategy of clustering of common pharmacophore hypotheses was made to gain an insight about the pharmacophore-similarity across the protein classes and share of features among the inhibitors. Rationale behind the present work was to present the scenario of virtual screening and guiding principle for designing efficient inhibitor by taking into account the available pharmacophoric space.

Humanized mutant FUS drives progressive motor neuron degeneration without aggregation in 'FUSDelta14' knockin mice.

Mutations in FUS are causative for amyotrophic lateral sclerosis with a dominant mode of inheritance. In trying to model FUS-amyotrophic lateral sclerosis (ALS) in mouse it is clear that FUS is dosage-sensitive and effects arise from overexpression per se in transgenic strains. Novel models are required that maintain physiological levels of FUS expression and that recapitulate the human disease-with progressive loss of motor neurons in heterozygous animals. Here, we describe a new humanized FUS-ALS mouse with a frameshift mutation, which fulfils both criteria: the FUS Delta14 mouse. Heterozygous animals express mutant humanized FUS protein at physiological levels and have adult onset progressive motor neuron loss and denervation of neuromuscular junctions. Additionally, we generated a novel antibody to the unique human frameshift peptide epitope, allowing specific identification of mutant FUS only. Using our new FUSDelta14 ALS mouse-antibody system we show that neurodegeneration occurs in the absence of FUS protein aggregation. FUS mislocalization increases as disease progresses, and mutant FUS accumulates at the rough endoplasmic reticulum. Further, transcriptomic analyses show progressive changes in ribosomal protein levels and mitochondrial function as early disease stages are initiated. Thus, our new physiological mouse model has provided novel insight into the early pathogenesis of FUS-ALS.

Qualitative and quantitative pharmacophore-similarity assessment of anthranilamide-based factor Xa inhibitors: applications on similar molecules with identical biological endpoints.

It is a conventional practice to exclude molecules with identical biological endpoints to avoid bias in the resulting hypothesis model. Despite the diverse chemical functionalities, the receptor interactions of such molecules are often unexplored. The present study motivates the selection of these molecules diversified by single atom or functional group compared to internal molecules as external set and helps in the understanding of corresponding effects toward receptor interactions and biological endpoints. Applied on anthranilamide-series of factor Xa analogs, the inhibitory activities were correlated (r(2) = 0.99) and validated (q(2) = 0.68) with distance-based pharmacophore descriptors using support vector machine. The selected external set molecules exhibited better prediction accuracy by securing activities less than one residual threshold. The effect on inhibitory activity was assessed by the examination of pharmacophore-similarity and its interactions with key residues of Human factor Xa enzyme using molecular docking approach. Furthermore, qualitative pharmacophore models were developed on the subset of molecular dataset divided as most actives, moderately actives and least actives, to recognize crucial activity governing pharmacophore features. The outcome of this study will bring new insights about the requirements of pharmacophore features and prioritizes its selection in the design and optimization of potent Xa inhibitors.

Compound prioritization from inverse docking experiment using receptor-centric and ligand-centric methods: a case study on Plasmodium falciparum Fab enzymes.

Prioritization of compounds using inverse docking approach is limited owing to potential drawbacks in its scoring functions. Classically, molecules ranked by best or lowest binding energies and clustering methods have been considered as probable hits. Mining probable hits from an inverse docking approach is very complicated given the closely related protein targets and the chemically similar ligand data set. To overcome this problem, we present here a computational approach using receptor-centric and ligand-centric methods to infer the reliability of the inverse docking approach and to recognize probable hits. This knowledge-driven approach takes advantage of experimentally identified inhibitors against a particular protein target of interest to delineate shape and molecular field properties and use a multilayer perceptron model to predict the biological activity of the test molecules. The approach was validated using flavone derivatives possessing inhibitory activities against principal antimalarial molecular targets of fatty acid biosynthetic pathway, FabG, FabI and FabZ, respectively. We propose that probable hits can be retrieved by comparing the rank list of docking, quantitative-structure activity relationship and multilayer perceptron models.

Design, synthesis, biological evaluation, and molecular modeling of coumarin-piperazine derivatives as acetylcholinesterase inhibitors.

Acetylcholinesterase (AChE) is an important drug target for the treatment of Alzheimer's disease. A novel series of coumarin-piperazine derivatives were synthesized and their potency to inhibit human AChE enzyme (hAChE) was studied. All the final compounds were characterized by infrared, (1)H NMR, (13)C NMR, and elemental analysis. Docking experiments of the designed coumarin-piperazine derivatives were carried out in order to compare the theoretical and experimental binding affinities toward hAChE, to delineate the inhibitory mechanism. Subsequently, a structure-activity relationship (SAR) study using the molecular field method showed that the hydrophobic field and positive charge center conferred by the coumarin and piperazine moieties demonstrated an inhibitory mechanism. Among the compounds tested, 3f, 3j, and 3m were found to be the most potent inhibitors of hAChE.

Targeting leukemic stem cell subpopulation in AML using phytochemicals: An in-silico and in-vitro approach.

It has been indicated that leukemic stem cells (LSCs), a subset of leukaemia cells, are responsible for therapy resistance and relapse in acute myeloid leukaemia (AML). Therefore, the current study aimed to discover an LSC biomarker in AML patients and identify a natural compound that may target the same. By performing the different gene expression analyses, we identified 12 up-regulated and 192 down-regulated genes in LSCs of AML compared to normal bone marrow-derived HSCs. Further STRING interaction, GO enrichment and KEGG pathway analysis were carried out to top hub genes. Wilms' tumour-1 (WT1) transcription factor was pointed out as the top hub gene and a potential biomarker for LSCs in AML. For the targeted inhibition of WT1, we performed screening and stimulation of potential natural compounds. The results revealed Gallic acid (GA) and Chlorogenic acid (CA) as promising WT1 inhibitors. In-vitro validation of cytotoxic effects of both GA and CA on THP-1 and HL-60 cell lines suggested that both these compounds inhibited cell proliferation. Still, GA has a more cytotoxic effect compared to CA. Next, we performed cell cycle analysis and apoptosis analysis and found that both compounds arrested cells in G0/G1 phase and induced apoptosis in both cell lines. Surprisingly, a significant decrease in colony formation and cell migration was also observed. However, GA gave more promising results in all cellular assays than CA. Furthermore, we studied the mRNA expression of WT1 and BCL2, which are transcriptionally activated by it. We found that GA significantly downregulated both these genes compared to CA. Our results suggested that GA is a potential inhibitor of WT1 and might be an excellent anti-LSCs natural drug for AML patients.

Repurposing of anticancer phytochemicals for identifying potential fusion inhibitor for SARS-CoV-2 using molecular docking and molecular dynamics (MD) simulations.

The viral particle, SARS-CoV-2 is responsible for causing the epidemic of Coronavirus disease 2019 (COVID-19). To combat this situation, numerous strategies are being thought for either creating its antidote, vaccine, or agents that can prevent its infection. For enabling research on these strategies, several target proteins are identified where, Spike (S) protein is of great potential. S-protein interacts with human angiotensin-converting-enzyme-2 (ACE2) for entering the cell. S-protein is a large protein and a portion of it designated as a receptor-binding domain (RBD) is the key region that interacts with ACE2, following to which the viral membrane fuses with the alveolar membrane to enter the human cell. The hypothesis is to identify molecules from the pool of anticancer phytochemicals as a lead possessing the ability to interact and mask the amino acids of RBD, making them unavailable to form associations with ACE2. Such a molecule is termed as 'fusion inhibitor'. We hypothesized to identify fusion inhibitors from the NPACT library of anticancer phytochemicals. For this, all the molecules from the NPACT were screened using molecular docking, the five top hits (Theaflavin, Ginkgetin, Ursolic acid, Silymarin and Spirosolane) were analyzed for essential Pharmacophore features and their ADMET profiles were studied following to which the best two hits were further analyzed for their interaction with RBD using Molecular Dynamics (MD) simulation. Binding free energy calculations were performed using MM/GBSA, proving these phytochemicals containing anticancer properties to serve as fusion inhibitors.Communicated by Ramaswamy H. Sarma.