Ligand-binding Residues Research Articles

Searching for new mTOR kinase inhibitors: Analysis of binding sites and validation of docking protocols.

The mammalian target of rapamycin (mTOR) is an important biological target for development of novel anticancer drugs and potential antiageing agents. Therefore, many scientific groups search for mTOR kinase inhibitors. Herein, we present structure-based approach which could be helpful in the studies on new bioactive compounds. Method validation was preceded by structural analysis of ATP catalytic cleft and FRB domain. In silico studies allowed us to point crucial amino acid residues for ligand binding and develop optimal docking protocols. The presented methodology could be applied for design and development of potential mTOR kinase inhibitors.

Discovery of Small Molecules from Echinacea angustifolia Targeting RNA-Dependent RNA Polymerase of Japanese Encephalitis Virus.

The Japanese encephalitis virus (JEV), a mosquito-borne flavivirus that causes viral encephalitis leading to neural damage, is a major threat in most Asian countries. The RNA-dependent RNA polymerase (RdRp) present in the viral genome is the key component for genome replication, making it an attractive target for antiviral drug development. In this study, the natural products from Echinacea angustifolia were retrieved for structure-based virtual screening against JEV–RdRp. The top six compounds (Echinacoside, Echinacin, Rutin, Cynaroside, Quercetagetin 7-glucoside, and Kaempferol-3-glucoside) were obtained based on the highest negative docking score, ADMET (absorption, distribution, metabolism, excretion, and toxicity), and molecular interaction. The computational analysis of these selected compounds against the co-crystallized ligands, i.e., ATP and GTP, were performed. Further, 100 ns molecular dynamic simulation and post-free binding energy calculation of all the selected compounds complexed with JEV–RdRP were performed to check the stability of the complexes. The obtained results showed considerable stability and intermolecular interaction with native ligand-binding site residues of JEV–RdRp. Hence, selected natural compounds are admissible inhibitors of JEV–RdRp protein and can be considered for future antiviral drug development studies.

Recognizing protein-metal ion ligands binding residues by random forest algorithm with adding orthogonal properties

Accurately identifying protein-metal ion ligand binding residues is the key to study protein functions. Because the number of binding residues and non-binding residues is significantly imbalanced, false positives is hard to be eliminated from the binding residues prediction result. Therefore, identification of protein-metal ion ligand binding residues remains challenging. In this paper, the binding site of 7 metal ions (Ca2+, Mg2+, Zn2+, Fe3+, Mn2+, Cu2+ and Co2+) were used as the objects of the study. Besides generally adopted parameters: amino acids and predicted secondary structure information, we creatively introduced ten orthogonal properties as a parameter. These orthogonal properties are clustering of 188 physical and chemical characteristics that can be used to describe three-dimension structural information. With the optimized parameters, we used the Random Forest algorithm to predict ion ligand binding residues. The proposed method obtained good prediction results with the MCC values of Mg2+, Ca2+ and Zn2+ reaching 0.255, 0.254, 0.540, respectively. Comparing to the IonSeq method, the method developed in this paper has advantages on the binding residues prediction of some ions.

3DLigandSite: structure-based prediction of protein-ligand binding sites.

3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualization of the protein and the predicted binding sites.

Engineering ligand-specific biosensors for aromatic amino acids and neurochemicals.

Microbial biosensors have diverse applications in metabolic engineering and medicine. Specific and accurate quantification of chemical concentrations allows for adaptive regulation of enzymatic pathways and temporally precise expression of diagnostic reporters. Although biosensors should differentiate structurally similar ligands with distinct biological functions, such specific sensors are rarely found in nature and challenging to create. Using E.coli Nissle 1917, a generally regarded as safe microbe, we characterized two biosensor systems that promiscuously recognize aromatic amino acids or neurochemicals. To improve the sensors' selectivity and sensitivity, we applied rational protein engineering by identifying and mutagenizing amino acid residues and successfully demonstrated the ligand-specific biosensors for phenylalanine, tyrosine, phenylethylamine, and tyramine. Additionally, our approach revealed insights into the uncharacterized structure of the FeaR regulator, including critical residues in ligand binding. These results lay the groundwork for developing kinetically adaptive microbes for diverse applications. A record of this paper's transparent peer review process is included in the supplemental information.

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors.

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

Mechanistic insight into the impact of a bivalent ligand on the structure and dynamics of a GPCR oligomer

Development of effective bivalent ligands has become the focus of intensive research toward modulation of G protein-coupled receptor (GPCR) oligomers, particularly in the field of GPCR pharmacology. Experimental studies have shown that they increased binding affinity and signaling potency compared to their monovalent counterparts, yet underlying molecular mechanism remains elusive. To address this, we performed accelerated molecular dynamics simulations on bivalent-ligand bound Adenosine 2A receptor (A2AR) dimer in the context of a modeled tetramer, which consists of A2AR and dopamine 2 receptor (D2R) homodimers and their cognate G proteins. Our results demonstrate that bivalent ligand impacted interactions between pharmacophore groups and ligand binding residues, thus modulating allosteric communication network and water channel formed within the receptor. Moreover, it also strengthens contacts between receptor and G protein, by modulating the volume of ligand binding pocket and intracellular domain of the receptor. Importantly, we showed that impact evoked by the bivalent ligand on A2AR dimer was also transmitted to apo D2R, which is part of the neighboring D2R dimer. To the best of our knowledge, this is the first study that provides a mechanistic insight into the impact of a bivalent ligand on dynamics of a GPCR oligomer. Consequently, this will pave the way for development of effective ligands for modulation of GPCR oligomers and hence treatment of crucial diseases such as Parkinson’s disease and cancer.

Nanoparticles and photochemistry for native-like transmembrane protein footprinting

Mass spectrometry-based footprinting can probe higher order structure of soluble proteins in their native states and serve as a complement to high-resolution approaches. Traditional footprinting approaches, however, are hampered for integral membrane proteins because their transmembrane regions are not accessible to solvent, and they contain hydrophobic residues that are generally unreactive with most chemical reagents. To address this limitation, we bond photocatalytic titanium dioxide (TiO2) nanoparticles to a lipid bilayer. Upon laser irradiation, the nanoparticles produce local concentrations of radicals that penetrate the lipid layer, which is made permeable by a simultaneous laser-initiated Paternò–Büchi reaction. This approach achieves footprinting for integral membrane proteins in liposomes, helps locate both ligand-binding residues in a transporter and ligand-induced conformational changes, and reveals structural aspects of proteins at the flexible unbound state. Overall, this approach proves effective in intramembrane footprinting and forges a connection between material science and biology.

Probing Allosteric Regulation Mechanism of W7.35 on Agonist-Induced Activity for μOR by Mutation Simulation.

The residue located at 15 positions before the most conserved residue in TM7 (7.35 of Ballesteros-Weinstein number) plays important roles in ligand binding and the receptor activity for class A GPCRs. Nevertheless, its regulation mechanism has not been clearly clarified in experiments, and some controversies also exist for its impact on μ-opioid receptors (μOR) bound by agonists. Thus, we chose the μ-opioid receptor (μOR) of class A GPCRs as a representative and utilized a microsecond accelerated molecular dynamics simulation (aMD) coupled with a protein structure network (PSN) to explore the effect of W3187.35 on its functional activity induced by the agonist endomorphin2 mainly by a comparison of the wild system and its W7.35A mutant. When endomorphin2 binds to the wild-type μOR, TM6 in μOR moves outward to form an open intracellular conformation that is beneficial to accommodating the β-arrestin transducer, rather than the G-protein transducer due to the clash with the α5 helix of G-protein, thus acting as a β-arrestin biased agonist. However, the W318A mutation induces the intracellular part of μOR to form a closed state, which disfavors coupling with either G-protein or β-arrestin. The allosteric pathway analysis further reveals that the binding of endomorphin2 to the wild-type μOR transmits more activation signals to the β-arrestin binding site while the W318A mutation induces more structural signals to transmit to common binding residues of the G protein and β-arrestin. More interestingly, the residue at the 7.35 position regulates the shortest allosteric pathway in indirect ways by influencing the interactions between other ligand-binding residues and endomorphin2. W2936.48 and F2896.44 are important for regulating the different activities of μOR induced either by the agonist or by the mutation. Y3367.53, F3438.50, and D3408.47 play crucial roles in activating the β-arrestin biased signal induced by the agonist endomorphin2, while L1583.43 and V2866.41 devote important contributions to the change in the activity of endomorphin2 from the β-arrestin biased agonist to the antagonist upon the W318A mutation.

In Silico Docking Studies of Vascular Endothelial Growth Factor-A (VEGFA): Possible Implications in Chronic Myeloid Leukemia (CML) Therapy

Background: Dysregulated angiogenesis resulting in neovascularization is a critical event in the expansion and progression of Chronic Myeloid Leukemia (CML), hematopoietic cancer. Vascular Endothelial Growth Factor- A (VEGFA), an important angiogenesis mediator, has been a target for treating cancer. Although several anti-VEGFA drugs are available, they are associated with adverse side effects, promoting the need to identify better drugs that may be less toxic. Objective: Our aim was to investigate whether Tyrosine Kinase Inhibitors (TKIs) could be repurposed for use as VEGFA inhibitors via in silico docking software. We also investigated the potential of phytochemicals as VEGFA inhibitors. Methods: We performed molecular docking using Schrödinger Maestro software suite 2014-3 to determine the most potent phytochemical and TKI VEGFA antagonists. Results: Among the TKIs investigated, Bosutinib had the best binding affinity and may be the most potent TKI against VEGFA. The order of binding affinities for the top ten docked ligands was: Ginsenoside Rg3> Bosutinib> Vitamin D> Paclitaxel> Dasatinib> Saponins> Ponatinib> Squalamine> Imatinib> Nilotinib. We found that Ginsenoside Rg3 had the highest binding affinity (MMGBSA score= -99.4 kcal/mol, glide Gscore = -9.16 kcal/mol) to VEGFA. Conclusion: Our study has shown for the first time the binding poses of these TKIs and phytochemicals to VEGFA, using computational methods. We propose that the use of the top scoring ligands, in isolation or a combination of phytochemicals plus TKI, could serve as potent angiogenesis inhibitors via their binding to and inhibiting VEGFA expression to prevent CML progression. We have also profiled the ligand binding residues, which may be explored in designing pharmacophores.

In silico study to predict potential precursors of human dipeptidyl peptidase-IV inhibitors from hazelnut

Chinese hazelnut was chosen to become a probable precursor of biological active peptides via computer simulations in this article. There were a large number of bioactive peptides in Chinese hazelnut sequences according to analytical results from the BIOPEP database. The most prominent of these was the inhibitory peptide for dipeptidyl peptidase-IV (DPP-IV; EC 3.4.14.5), which can be used to treat type 2 diabetes, so the theoretical method to obtain DPP-IV inhibitory peptides by hydrolysis with a single or combination of enzymes was studied. Cytotoxicity analysis performed by ToxinPred showed that all of the DPP-IV inhibitory peptides generated from protein hydrolysis were not cytotoxic. Structural interaction fingerprint analysis revealed that Asp663 and Phe357 may be important residues for ligand binding. In order to further understand the inhibitory mechanism of peptide, VR with lowest half maximum inhibitory concentration (IC50) and IPI (inhibitors have been reported) were selected as ligand of DPP-IV to perform steered molecular dynamics simulations and PMF calculations. The results showed that P1 is the preferred (un)binding tunnel for the inhibitors obtained. Our findings help in the development of new DPP-IV inhibitors which were derived from common food. Communicated by Ramaswamy H. Sarma

Critical APJ receptor residues in extracellular domains that influence effector selectivity.

Human APJ receptor/apelin receptor (APJR), activated by apelin peptide isoforms, regulates a wide range of physiological processes. The role of extracellular loop (ECL) domain residues of APJR in ligand binding and receptor activation has not been established yet. Based on multiple sequence alignment of APJ receptor from various organisms, we identified conserved residues in the extracellular domains. Alanine substitutions of specific residues were characterized to evaluate their ligand binding efficiency and Gq -, Gi -, and β-arrestin-mediated signaling. Mutation-dependent variation in ligand binding and signaling was observed. W197 A in ECL2 and L276 L277 W279 -AAA in ECL3 were deficient in Gi and β-arrestin signaling pathways with relatively preserved Gq -mediated signaling. T169 T170 -AA, Y182 A, and T190 A mutants in ECL2 showed impaired β-arrestin-dependent cell signaling while maintaining G protein- mediated signaling. Structural comparison with angiotensin II type I receptor revealed the importance of ECL2 and ECL3 residues in APJR ligand binding and signaling. Our results unequivocally confirm the specific role of these ECL residues in ligand binding and in orchestrating receptor conformations that are involved in preferential/biased signaling functions.

Probing the Diacylglycerol Binding Site of Presynaptic Munc13-1.

Munc13-1 is a presynaptic active zone protein that acts as a master regulator of synaptic vesicle priming and neurotransmitter release in the brain. It has been implicated in the pathophysiology of several neurodegenerative diseases. Diacylglycerol and phorbol ester activate Munc13-1 by binding to its C1 domain. The objective of this study is to identify the structural determinants of ligand binding activity of the Munc13-1 C1 domain. Molecular docking suggested that residues Trp-588, Ile-590, and Arg-592 of Munc13-1 are involved in ligand interactions. To elucidate the role of these three residues in ligand binding, we generated W588A, I590A, and R592A mutants in full-length Munc13-1, expressed them as GFP-tagged proteins in HT22 cells, and measured their ligand-induced membrane translocation by confocal microscopy and immunoblotting. The extent of 1,2-dioctanoyl-sn-glycerol (DOG)- and phorbol ester-induced membrane translocation decreased in the following order: wild type > I590A > W588A > R592A and wild type > W588A > I590A > R592A, respectively. To understand the effect of the mutations on ligand binding, we also measured the DOG binding affinity of the isolated wild-type C1 domain and its mutants in membrane-mimicking micelles using nuclear magnetic resonance methods. The DOG binding affinity decreased in the following order: wild type > I590A > R592A. No binding was detected for W588A with DOG in micelles. This study shows that Trp-588, Ile-590, and Arg-592 are essential determinants for the activity of Munc13-1 and the effects of the three residues on the activity are ligand-dependent. This study bears significance for the development of selective modulators of Munc13-1.

Identifying the impact of structurally and functionally high-risk nonsynonymous SNPs on human patched protein using in-silico approach

BackgroundHuman PTCH1 is a negative receptor of Hedgehog (HH) signaling required to sustain cancer stem-like cells (CSCs) in a tumor microenvironment to drive tumor growth. If mutations are present in the PTCH1 gene results in the autocrine signaling and switches pathway on even in the absence of HH ligand. Further functional reduction in native PTCH1 protein may result in the impaired function of the protein. ObjectiveTo identify the functionally deleterious SNPs in human PTCH1 protein. MethodsVarious computational tools like SIFT, PolyPhen2, PROVEAN, SNAP2, SNPs&GO, PhD-SNP, I-Mutant, iPTREE-STAB and MUpro were used to predict most deleterious SNPs of PTCH1. ConSurf and NCBI conserved domain search tool was used to find conserved domain. Post translational modification sites were predicted using ModPred. SPARKS-X was used to generate 3D structure of the PTCH1 protein. ResultsIn human PTCH1 gene, a total of 60 nonsynonymous SNPs (nsSNPs) were identified to be deleterious or non-tolerable by SIFT. Further, out of 60 nsSNPs, P298L, V907G, R1024H, R134Q, W235R, D259A, and R570Q were predicted to be present in the conserved domain of PTCH1 protein and potentially damaging by all the prediction tools. Amongst them, P298, V907 and R570 were predicted as a site for post-translational modifications (PTM) while R1024 to be involved in a ligand-binding residue. ConclusionCurrent study demonstrated that most deleterious nsSNPs found is arginine to histidine at position 1024 responsible for disease occurrence which might be helpful to eliminate CSCs in a tumor bulk.

Bioinformatics analysis and biochemical characterisation of ABC transporter-associated periplasmic substrate-binding proteins ModA and MetQ from Helicobacter pylori strain SS1

The human gastric pathogen Helicobacter pylori relies on the uptake of host-provided nutrients for its proliferation and pathogenicity. ABC transporters that mediate import of small molecules into the cytoplasm of H. pylori employ their cognate periplasmic substrate-binding proteins (SBPs) for ligand capture in the periplasm. The genome of the mouse-adapted strain SS1 of H. pylori encodes eight ABC transporter-associated SBPs, but little is known about their specificity or structure. In this study, we demonstrated that the SBP annotated as ModA binds molybdate (MoO42−, KD = 3.8 nM) and tungstate (WO42−, KD = 7.8 nM). In addition, we showed that MetQ binds D-methionine (KD = 9.5 μM), but not L-methionine, which suggests the existence of as yet unknown pathway for L-methionine uptake. Homology modelling has led to identification of the ligand-binding residues.

Structural and Functional Investigation of the Periplasmic Arsenate-Binding Protein ArrX from Chrysiogenes arsenatis.

The anaerobic bacterium Chrysiogenes arsenatis respires using the oxyanion arsenate (AsO43-) as the terminal electron acceptor, where it is reduced to arsenite (AsO33-) while concomitantly oxidizing various organic (e.g., acetate) electron donors. This respiratory activity is catalyzed in the periplasm of the bacterium by the enzyme arsenate reductase (Arr), with expression of the enzyme controlled by a sensor histidine kinase (ArrS) and a periplasmic-binding protein (PBP), ArrX. Here, we report for the first time, the molecular structure of ArrX in the absence and presence of bound ligand arsenate. Comparison of the ligand-bound structure of ArrX with other PBPs shows a high level of conservation of critical residues for ligand binding by these proteins; however, this suite of PBPs shows different structural alterations upon ligand binding. For ArrX and its homologue AioX (from Rhizobium sp. str. NT-26), which specifically binds arsenite, the structures of the substrate-binding sites in the vicinity of a conserved and critical cysteine residue contribute to the discrimination of binding for these chemically similar ligands.

Role of Ligand-Binding and Non-Active Site Residues in the Dynamics, Conformational Integrity, and Catalysis of the Disordered Enzyme Monomeric Chorismate Mutase

The dominant view in enzymology is that an increase in structural disorderedness tends to decrease catalytic efficiency. Surprisingly, engineered monomeric chorismate mutase (mCM), an enzyme with properties of molten globule (intrinsically disordered), efficiently catalyses the conversion of chorismate to prephenate. Therefore, here, we investigate the underlying structural plasticity mechanisms that facilitate this catalysis. We generated rationally designed mutations of mCM and used a battery of spectroscopic tests including Forster resonance energy transfer (FRET) and time-resolved fluorescence anisotropy measurements to probe what influences catalysis. We found that the integrity of the secondary structure, and ligand-induced global compaction (disorder-to-order transition) play a crucial role in catalysis. Intriguingly, we find that even minor perturbation in regions distant from the active site can significantly impact the enzyme efficiency by changing the conformation, segmental dynamics, ligand-induced global compaction, and stability.

GRaSP: a graph-based residue neighborhood strategy topredict binding sites.

The discovery of protein-ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein-ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost. We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level. Results show that GRaSP made compatible or superior predictions when compared with methods described in the literature. GRaSP outperformed six other residue-centric methods, including the one considered as state-of-the-art. Also, our method achieved better results than the method from CAMEO independent assessment. GRaSP ranked second when compared with five state-of-the-art pocket-centric methods, which we consider a significant result, as it was not devised to predict pockets. Finally, our method proved scalable as it took 10-20 s on average to predict the binding site for a protein complex whereas the state-of-the-art residue-centric method takes 2-5 h on average. The source code and datasets are available at https://github.com/charles-abreu/GRaSP. Supplementary data are available at Bioinformatics online.

Identification of naphthyridine and quinoline derivatives as potential Nsp16-Nsp10 inhibitors: a pharmacoinformatics study

This research is a recent effort to explore some new heterocyclic compounds as novel and potential nonstructural protein-16-nonstructural protein-10 (Nsp16-Nsp10) inhibitors for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inhibition. The SARS-CoV-2 is causative agent of coronavirus disease 2019 (COVID-19) pandemic. A set of 58 molecules belongs to the naphthyridine and quinoline derivatives have been recently synthesized and considered for structure-based virtual screening against Nsp16-Nsp10. Molecular docking was virtually performed to screen for anti-SARS-CoV-2 activity against Nsp16-Nsp10. Fourteen out of fifty-eight compounds were exhibited binding affinity higher than co-crystal bound ligand s-adenosylmethionine (SAM) toward Nsp16-Nsp10. Further, the in silico pharmacokinetics assessment was carried out and it was found that two molecules possess the acceptable pharmacokinetic profile, hence considered promising Nsp16-Nsp10 inhibitors. The binding interaction analysis was revealed some crucial binding interactions between the final selected two molecules and ligand-binding amino acid residues of Nsp16-Nsp10 protein. In order to explore the characteristics of the protein–ligand complex and how selected small molecules retained inside the receptor cavity in dynamic states, all-atoms conventional molecular dynamics (MD) simulation was performed. Several factors were obtained from the MD simulation trajectory evidently suggested the potentiality of the molecules and stability of the protein–ligand complex. Finally, the binding affinity of both molecules and SAM was explored through the MM-GBSA approach which explained that both molecules possess strong affection towards the Nsp16-Nsp10. Hence, from the pharmacoinformatics assessment, it can be concluded that both heterocyclic compounds might be crucial for SARS-CoV-2 inhibition, subjected to experimental validation. Communicated by Ramaswamy H. Sarma

Molecular docking and pharmacokinetic screening of eucalyptol (1,8 cineole) from eucalyptus essential oil against SARS-CoV-2

SARS-CoV-2 (COVID-19), member of corona virus family, is a positive single stranded RNA virus. Due to lack of drugs it is spreading its tentacles across the world. Being associated with cough, fever, and respiratory distress, this disease caused more than 15% mortality worldwide. Mpro/3CLpro has recently been regarded as a suitable target for drug design due to its vital role in virus replication. The current study focused on the inhibitory activity of eucalyptol (1,8 cineole), an essential oil component from eucalyptus oil, against Mpro/3CLprofrom SARS-CoV-2. Till date there is no work is undertaken on in-silico analysis of this compound against Mpro/3CLproof SARS-CoV-2. Molecular docking studies were conducted by using 1-click dock tool and Patchdock analysis. In-silico absorption, distribution, metabolism, excretion and toxicity (ADMET) profile were also studied. The calculated parameters such as docking score indicated effective binding of eucalyptol to COVID-19 Mpro protein. Active site prediction revealed the involvement of active site residues in ligand binding. Interactions results indicated that, Mpro/3CLpro/eucalyptol complexes forms hydrophobic interactions. ADMET studies provided guidelines and mechanistic scope for identification of potent anti-COVID 19 drug. Therefore, eucalyptol may represent potential herbal treatment to act as COVID-19 Mpro/3CLproinhibitor, a finding which must be validated in vivo.