Evaluation of therapeutic potentials of selected phytochemicals against Nipah virus, a multi-dimensional in silico study

Abstract

The current study attempted to evaluate the potential of fifty-three (53) natural compounds as Nipah virus attachment glycoprotein (NiV G) inhibitors through in silico molecular docking study. Pharmacophore alignment of the four (4) selected compounds (Naringin, Mulberrofuran B, Rutin and Quercetin 3-galactoside) through Principal Component Analysis (PCA) revealed that common pharmacophores, namely four H bond acceptors, one H bond donor and two aromatic groups were responsible for the residual interaction with the target protein. Out of these four compounds, Naringin was found to have the highest inhibitory potential ( – 9.19 kcal mol⁻¹) against the target protein NiV G, when compared to the control drug, Ribavirin ( – 6.95 kcal mol⁻¹). The molecular dynamic simulation revealed that Naringin could make a stable complex with the target protein in the near-native physiological condition. Finally, MM-PBSA (Molecular Mechanics-Poisson–Boltzmann Solvent-Accessible Surface Area) analysis in agreement with our molecular docking result, showed that Naringin ( – 218.664 kJ mol⁻¹) could strongly bind with the target protein NiV G than the control drug Ribavirin ( – 83.812 kJ mol⁻¹).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03595-y.

Introduction

In the recent past, the world witnessed the COVID-19 pandemic caused by SARS-CoV-2 (Hu et al. 2021). Nipah virus (NiV), a deadly emergent infectious agent which has the potential to give rise to the next pandemic, causes severe respiratory illness and fatal encephalitis (Ahmad 2014; Thakur et al. 2022). Research is difficult to do on the live virus as it is a biosafety level 4 pathogen (BSL-4) (Tigabu et al. 2014). It belongs to the genus Henipavirus of the order Mononegavirales, family Paramyxoviridae, and subfamily Paramyxovirinae (Chua et al. 2000). The virus was named after Kampung Sungai Nipah village in Malaysia, first detected in 1998 (Chattu et al. 2018). The fruit-eating bats of Pteropus spp. are the primary reservoirs of the virus (Chua et al. 2002). This zoonotic agent transmits from infected animals to humans and, in turn, from one infected human to the other, mainly through respiratory droplets, body fluids, blood and urine (Gurley et al. 2007; de Wit and Munster 2015). So far, 639 human cases of Nipah virus infection have been reported from Malaysia, Bangladesh, the Philippines, Singapore and India (Devnath and Masud 2021). Despite being short-lived, the past outbreaks of the Nipah virus claimed many human lives and were associated with high mortality rates (40–75%) (Vanitha et al. 2019). In its 2018 research and development blueprint, the World Health Organisation (WHO) stressed the gravity of the threat to humanity and called for immediate attention regarding effective drug development (Mehand et al. 2018). Nipah is an enveloped virus with a negative-sense single-stranded RNA. The non-segmented viral genome codes for several structural and non-structural proteins and is about 18.2 kb in size. The analysis of the viral genome demonstrates an arrangement of six genes, namely nucleocapsid (N), phosphoprotein (P), matrix (M), fusion glycoprotein (F), attachment glycoprotein (G) and long polymerase (L). The P gene encodes P protein and three important accessory proteins, C, W and V (Martinez-Gil et al. 2017). Among all the proteins of Nipah, the attachment glycoprotein, NiV G plays a vital role in attachment with the host receptor, Ephrin B2 or B3. This protein primarily drives the spread of the infection by cell-to-cell fusion. Therefore, the interruption of viral entry into the cell can be brought about by blocking the active site of NiV G responsible for host receptor interaction. This makes NiV G an attractive target for identifying potential anti-Nipah drugs, as currently, there are no approved therapeutics for Nipah virus infection (Geisbert et al. 2021). The treatment of infected patients is limited to supportive care (Ang et al. 2018).

Nevertheless, antiviral medications such as Ribavirin and Acyclovir were used to treat Nipah infection during past outbreaks in Malaysia and Singapore. However, they were not fully effective in curing Nipah-infected individuals (Sharma et al. 2019). The vaccines being developed by scientists worldwide are yet to be time-tested to validate their effectiveness against the infection. Initial deployment of bioinformatics tools for the preliminary screening of drugs may reduce the cost and improve the turnover time significantly. Many drug candidates can be screened quickly, efficiently and cost-effectively using a bioinformatics approach like computer-aided drug design (CADD) (Gaieb et al. 2019). A few in silico studies showed the potential of small molecules against the target proteins of the Nipah virus. However, these studies were not backed by statistical analysis (Ropón-Palacios et al. 2020; Kalbhor et al. 2021; Glaab et al. 2021).

Phytochemicals have long been used to develop novel drugs and identify potential drug candidates against emerging infectious diseases considering their efficacy and safety compared with their synthetic counterparts. Anaemia, jaundice, and teratogenic effects are some side effects of Ribavirin (Chong et al. 2001). The side effects of Acyclovir are nausea, vomiting and headaches (Miserocchi et al. 2007). Plant-derived compounds are known for their usage as medicines traditionally (Nandagoapalan et al. 2016). Phytochemical classes such as flavonoids, terpenoids, phenols, xanthophylls, carotenoids, and essential oils are known for their immunomodulatory, antitumor, antimicrobial, and antioxidant properties. These compounds have successfully entered the modern world of drug development, one of the reasons being their prominent antiviral activities (Byler et al. 2016; Ben-Shabat et al. 2020; Pandey et al. 2021). We previously published a few research articles on the role of phytochemicals against viral targets using in silico strategy. Curcumin, for example, identified in our in-silico study, was later found to be effective against the SARS-CoV-2 variant in a clinical study (Pawar et al. 2021; Nag et al. 2021b, 2022a). This presents the scope for extending this approach to identify anti-Nipah compounds. Identification of phytocompounds as potential anti-Nipah drug candidates requires evaluation of the chemistry and stability of their binding interaction with Nipah proteins which can be facilitated through the integration of molecular docking study, molecular alignment of ligands (pharmacophore) and analysis of molecular dynamic simulation.

In this work, fifty-three (53) phytochemicals and the control drug, Ribavirin were tested for Nipah G protein inhibition potential through an in silico molecular docking study. Further, drug-like properties of the selected phytochemicals were also evaluated. We combined statistical and pharmacophore approaches to identify functional descriptors responsible for protein–ligand interaction. Finally, the stability of the ligand–protein complex was studied using the molecular dynamic simulation technique.

Materials and methodology

Preparation of the protein

The three-dimensional structure of the Nipah virus attachment glycoprotein (NiV G) in complex with the human cell receptor, Ephrin B2 (PDB id 2VSM, Chain A and B, X-Ray Diffraction, Resolution 1.8 Å) was downloaded from the RCSB PDB (https://www.rcsb.org/). This selection was based on the high resolution of the crystal structure (1.8 Å), determined by the X-ray diffraction methodology (Berman et al. 2002). Nipah virus attachment glycoprotein chain A (NiV G:A) was selected as the target protein for this study by using UCSF Chimera software (Pettersen et al. 2004). The heteroatoms and water molecules in the receptor protein were removed using Discovery Studio 2021 (Makhloufi et al. 2022). Maestro 13.3 and SwissPDB viewer were used for editing the protein structure, including removing non-polar hydrogens and adding polar hydrogens to the receptor protein and energy minimization at the physiological (pH 7.4) (Trott and Olson 2009). Additionally, the three-dimensional structure of the human cell receptor, Ephrin B2 (NiV G:B) was optimised for molecular docking using a similar methodology followed for Nipah virus attachment glycoprotein chain A.

Preparation of ligands

Fifty-three (53) phytochemicals, along with control drugs Ribavirin and Acyclovir were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and considered as ligands for this study (Kim et al. 2019). Ribavirin was commonly used as an antiviral agent in the literature. Tang et al. 2019 used Ribavirin as a control drug for inhibiting viral entry, in vivo. Further, in another study, it was found to inhibit Nipah virus glycoprotein in silico (Ghimire et al. 2022). Considering these evidences, Ribavirin was selected as the control drug for our work. Acyclovir was administered to workers in Singapore during Nipah virus outbreak in 1999. Hence, Acyclovir was also selected as a control drug in this study (Hauser et al. 2021). The three-dimensional structures of the phytochemicals were downloaded from PubChem in “SDF” format. The phytochemicals were structurally optimized and converted to “PDB” format using Avogadro software. This step was followed as a prerequisite for conducting molecular docking studies. For optimization of the ligands, a universal force field (UFF) algorithm was applied for the energy minimization of the ligands, followed by adding polar hydrogens to the ligands at pH, 7.4 (Hanwell et al. 2012; Nag et al. 2021b, 2023; Cho et al. 2022). Two-dimensional structures of the ligands are represented in Fig. 1.

Fig. 1.

Two dimensional structures of the fifty-three phytochemicals, along with the control drugs (Ribavirin and Acyclovir)

Active site prediction and molecular docking

Protein–protein docking

The protein–protein docking studies for Nipah virus attachment glycoprotein (NiV G) in complex with the human cell receptor, Ephrin B2 were performed using ClusPro 2.0 docking server (https://cluspro.bu.edu/login.php) (Kozakov et al. 2017). PIPER docking program used in ClusPro relies on the Fast Fourier Transform correlation approach. The interaction energy between two proteins is represented by PIPER using the following expression;

$$E=w_1{E}_{\text{rep}}+w_2{E}_{\text{attr}}+w_3{E}_{\text{elec}}+w_4{E}_{\text{DARS}}$$

where the attractive and repulsive contributions to the van der Waals interaction energy are denoted by E_rep and E_attr, electrostatic energy is denoted by E_elec, pairwise structure-based potential is represented by E_DARS and the weights of the corresponding residues are determined by the coefficients w₁, w₂, w₃, and w₄ (Bhattacharya et al. 2020). PDBSum web server (http://www.ebi.ac.uk/pdbsum) was used to visualise and analyse interacting amino acids and bonds of the protein–protein complex (Laskowski 2009).

Protein-phytochemical

Discovery Studio 2021 (BIOVIA, San Diego, USA) was used to predict the active sites in the receptor protein NiV G:A. The grid box encompassed GLN369A, GLU389A, TYR391A, and ILE398A residues (corresponds to GLN559, GLU579, TYR581, and ILE588 as per Kalbhor et al. 2021), which had a high binding affinity for Ephrin B2. The NiV G-Ephrin B2 complex was further used as a target protein for molecular docking studies with the phytochemicals and the control drugs, Ribavirin and Acyclovir. Ephrin B2 was also used as a target protein for molecular docking studies with the phytochemicals and the control drugs, Ribavirin and Acyclovir. The grid parameters for the respective receptor proteins are listed in Table 1. Protein-phytochemical molecular docking studies were performed by the DockThor web program (https://dockthor.lncc.br/v2/). DockThor uses Santos Dumont supercomputer (Santos et al. 2020) to conduct virtual screening experiments. It houses docking tools such as MMFF Ligand and PdbThorBox. The docking algorithm applies MMFF94S53 force field on the protein inputs (Nag et al. 2021a). The results of the protein–ligand docking studies (average of binding energies, Kcal mol⁻¹) were compared with that of the control drug complexes.

Table 1.

Grid parameters for protein–protein and protein–ligand docking studies

Docking type	Center			Size
	X	Y	Z	X	Y	Z
Protein (NiV G-Ephrin B2)-Ligand docking	5	66	– 3	20	20	20
Protein (NiV G:A)-Ligand docking	13.82	69.86	– 33.26
Protein (Ephrin B2)- Ligand docking	5	66	– 3

Evaluation of drug-like properties and toxicity analysis

The pharmacological and drug-likeness properties of the phytochemicals were evaluated by uploading their canonical smile formats to the SwissADME site (http://www.swissadme.ch/). Drug likeness parameters, namely topological polar surface area (TPSA), lipophilicity (iLOGP and WLOGP), estimated water solubility (ESOL Log S), five rules of Lipinski, gastrointestinal (GI) absorption and PGP substrate, were used for evaluation of drug-likeness of the input phytocompounds. The efflux of structurally unrelated compounds is mediated by the membrane-bound transporter P-Glycoprotein (PGP), which alters the bioavailability of the drugs. The physicochemical property guideline of Lipinski’s rule of five (RO5) should be followed by an ideal drug. As per RO5, a chemical compound with a certain biological activity for oral administration should have drug like properties like molecular weight less than 500 g mol⁻¹, log P (hydrophobicity) value less than 5, hydrogen bond donors less than or equal to 5 in numbers, and less than or equal to 10 hydrogen bond acceptor sites (Lipinski et al. 2001; Doak et al. 2014). SwissADME is an open-source web server that facilitates the drug discovery by predicting drug-likeness parameters. It also explores the physicochemical descriptors, and pharmacokinetic properties of the chemical entities (Daina et al. 2017). Toxicity can be attributed as one of the important reasons for the failure of the drug development pipeline. Hence, it is crucial to test the toxicity endpoints of compounds. The toxicological properties such as AMES toxicity, Rat Oral Acute Toxicity, Skin Sensitization and Respiratory Toxicity were evaluated using ADMETlab 2.0 web server (https://admetmesh.scbdd.com/) (Xiong et al. 2021).

Principal component analysis and protein–ligand interaction analysis

Ten phytochemicals out of fifty-three total phytochemicals, along with the control drug, were selected based on the highest docking scores and evaluation of drug-likeness properties, which were then grouped by the application of Principal Component Analysis (PCA) using Minitab 18 statistical software. PCA simplifies the interpretation of large data sets by ensuring minimum loss of data sets, thereby improving data interpretation. The principal components depict an individual dimension of variations from the measured datasets. Principal components (PCs) are uncorrelated variables where PC1 encloses the maximum variation followed by PC2, PC3 and so on (Jolliffe and Cadima 2016; Lever et al. 2017). In this study, binding energy (Kcal mol⁻¹), molecular weights, and drug likeness parameter values of the phytochemicals such as TPSA, iLOGP, WLOGP, ESOL Log S were used as inputs for the PCA. The outputs of PCA were observed as different clusters. The selection of one of the clusters for the molecular alignment study was done based on the presence of the phytochemical with the most optimum binding potentials to the active site of the target protein. The protein–ligand docking complexes associated with the phytochemicals of the selected cluster and the control drug were used for amino acid-ligand interaction analysis. The BIOVIA Discovery Studio Visualizer (Dassault Systems) software was used to visualise and analyse interacting amino acids and bonds of the protein-phytochemical complexes.

Phytochemical alignment and identification of pharmacophores

Based on the multivariate PCA modelling results, the cluster comprising phytochemicals with the optimum binding affinity and drug-like properties was subjected to molecular alignment using an open-source server, PharmaGist (https://bioinfo3d.cs.tau.ac.il/PharmaGist/). The alignment of phytochemicals facilitated the identification of common descriptors or functional groups. PharmaGist utilizes the DUD (directory of useful decoys) data set, which consists of 2950 active ligands for 40 different receptors and 36 decoy compounds for active ligands. The alignment scores of the input ligands from the chosen PCA cluster were generated based on their pivot and conformational results after pairwise alignments (Schneidman-Duhovny et al. 2008). The output was chosen based on the highest alignment score. The analysis of the common descriptors (pharmacophores) responsible for effective ligand–protein interaction was performed by using ZincPharmer (http://zincpharmer.csb.pitt.edu/). The structures were visualized using PyMol 2.5 software (Schrödinger). PyMol is known to generate high-quality three-dimensional images of biological macromolecules, including proteins and small molecules. It includes OpenGL Extension Wrangler Library (GLEW) and FreeGLUT and is capable of solving Poisson-Boltzmann equations. It uses python as its programming language (Seeliger and de Groot 2010).

Molecular dynamic (MD) simulation

The MD simulation of the NiV G:A and the phytochemical with the top binding affinity was performed using the GROMACS-2019.2 based bio-molecular package of Simlab, the University of Arkansas for Medical Sciences (UAMS), Little Rock, USA. The simulation utilised GROMOS96 43a1 force field. PRODRG software was used to generate the ligand topology file (Schüttelkopf and van Aalten 2004). A grid box was specified for the protein–ligand complex. An environment of SPC water and 0.15 M counter ions (Na⁺/Cl⁻) was specified for molecular dynamic simulation. The set-up parameters, NVT/NPT ensemble temperature 300 K and 1 bar atmospheric pressure were applied to the system. Parrinello-Rahmanbarostat and Parrinello-Danadio-Bussithermostat were used to maintain the pressure and temperature (Huang et al. 2017). Energy minimization by 5000 steepest descent integrators on the output model was performed. Based on the available literature (Tadayon and Garkani-Nejad 2019; Alamri et al. 2020; Keretsu et al. 2020; Basu et al. 2020; Kushwaha et al. 2021; Yadav et al. 2021; da Cruz Freire et al. 2022), the run time for the MD simulation was fixed at 50 ns.

The results of the MD simulation studies were expressed in terms of Root Mean Square Deviation (RMSD), Radius of Gyration (Rg), Root Mean Square Fluctuation (RMSF), and Ligand-H bonds. The measured distance between protein residues and ligands is determined by RMSD. The significance of the radius of gyration lies in the analysis of the compactness of the protein structure in the free and bound forms (Nag et al. 2023). RMSF represents the differences in flexibility among residues with respect to the average molecular dynamic simulation conformation (Rao et al. 2020). Additionally, a comparative analysis of structural changes between the ligand-bound and ligand-free proteins was performed by using PyMol measurement wizard.

Free energy analysis by MM-PBSA (molecular mechanics-poisson–boltzmann solvent-accessible surface area) calculation

The free energies of the top ligand-NiV G:A complex were estimated by Molecular Mechanics-Poisson–Boltzmann Solvent-Accessible surface area (MM-PBSA) method using g-mmbsa package (Kumari et al. 2014; Nag et al. 2022a, b) for the final 10 ns of the simulation time frame. The free energy included ΔG_van der Waals, ΔG_Electrostatic, ΔG_Polar, ΔG_Non-Polar, ΔG_Binding and residual contribution energy parameters.

The calculation of ΔG_Bind (kJ mol⁻¹) was represented by the following equation:

$$\mathrm{\Delta }\text{G}\_\text{Bind}=\text{G}\_\text{Comp}-\left(\text{G}\_\text{Prot}+\text{G}\_\text{Lig}\right)$$

The value of ΔG_Bind (kJ mol⁻¹) was derived by subtracting the summation of individual energy values of the protein (G_Prot) and ligand (G_Lig) from the energy of the protein–ligand complex (ΔG_Comp).

Results and discussion

Selection of phytochemicals

Fifty-three phytochemicals, and the control drug Ribavirin were selected for this study based on literature studies mentioned in Table 2.

Table 2.

List of compounds selected for the study and its corresponding literature evidences

Compound name	Rationale for selection
Ribavirin (Control drug)	Ribavirin was used as a control drug for the inhibition of viral entry, in vivo in BALB/c mice (Tang et al. 2019)
	Ribavirin was used to treat Nipah infection during past outbreak in Malaysia (Sharma et al. 2019)
	Ribavirin was found to inhibit Nipah virus glycoprotein in silico (Ghimire et al. 2022)
Naringin	Naringin was reported to have significant antiviral properties (Amin Huseen 2020)
	Naringin showed high binding affinity for the main protease of COVID-19 ( – 10.2 Kcal mol−1) (Liu et al. 2022)
Tribulusamide B	Tribulusamide B showed hepatoprotective activity in mice (Li et al. 1998)
Mulberrofuran B	The anti-oxidant properties (ABTS and DPPH) of Mulberrofuran B were reported in the literature (Martins et al. 2021)
Rutin	Rutin showed inhibitory activities against infectious agents like Zika virus in Escherichia coli BL21 (DE3) (Lima et al. 2021)
Asebotin	Asebotin showed anti- H5N1 properties in vitro (Ibrahim et al. 2013)
Kaempferol-3-glucoside	In an in-silico study, Kaempferol-3-glucoside showed inhibitory properties against RNA-dependent RNA polymerase of the Japanese encephalitis virus (Yadav et al. 2022)
Kuzubutenolide A	Antiviral activity not reported
Fisetin	Molecular docking and simulation studies showed that Fisetin could strongly bind with the RNA-dependent RNA polymerase (RdRp) enzyme of Dengue virus in silico (Fatriansyah et al. 2022)
Naringenin	Naringenin (NAR) prevented Zika virus infection in human A549 cells, in vitro (Cataneo et al. 2019)
Morin	Morin showed antiviral activity against proteins of SARS-CoV-2, SARS-CoV and MERS-CoV in silico (Gupta et al. 2022)
Verbascoside	Verbascoside inhibited the expressions of antigens of hepatitis B virus in vitro in HepG2.2.15 and HL-7702 cells (Mou et al. 2021)
Forsythoside D	Forsythoside D had hepatoprotective and anti-hepatitis properties (Chakravarthy 2021)
Kaempferol	Kaempferol showed antiviral properties by suppressing the protein kinase B activity of SARS-CoV in Xenopus oocyte. The phytochemical is also known for having antioxidant and anti-inflammatory effects (Khazdair et al. 2021)
Carpaine	Carpaine through an in silico study showed inhibitory potential against dengue serotype 3 RNA-dependent RNA polymerase (RdRp) (Radhakrishnan et al. 2017)
Dehydrocarpaine II	Dehydrocarpaine II inhibited dengue serotype 3 RNA-dependent RNA polymerase, in silico (RdRp) (Radhakrishnan et al. 2017)
Dehydrocarpaine I	Dehydrocarpaine II inhibited dengue serotype 3 RNA-dependent RNA polymerase, in silico (RdRp) (Radhakrishnan et al. 2017)
Moupinamide	Moupinamide strongly inhibited Spike protein and the Main Protease activities of SARS-CoV-2 in silico (Shukla et al. 2021)
Agrimol D	Agrimol D possessed antiviral, antimicrobial and antioxidant properties (Le et al. 2018)
Koaburaside	Koaburaside has antioxidant, anti-HIV and anti-inflammatory properties (Choi et al. 2013)
Prunasin	Prunasin showed inhibitory properties against neo-coronavirus targets, Mpro and RdRp proteins in silico (Chen et al. 2022)
Neosappanone A	Neosappanone A showed inhibitory properties against Xanthine Oxidase and is considered useful in the treatment of gout (Pournaghi et al. 2020)
Obovatol	Obovatol exhibited anti-hepatitis B virus activities (Li et al. 2013)
Thiamine	Thiamine administration effectively reduced the hepatitis B virus DNA to undetectable levels in infected individuals (Wallace 2001)
Stigmasterol	Stigmasterol exhibited antiviral activity against dengue virus in vitro using Vero culture, cell line C6/36 culture (Soekamto et al. 2019)
Quercetin 3-galactoside	Quercetin-3-galactoside (Isoquercetin) had inhibitory properties against the viral spike protein of SARS-CoV-2 in silico (Jesus et al. 2020)
Quercetin	Quercetin showed anti-dengue property using Vero cells in vitro and protected mice (in vivo) from lethal infection of meningoencephalitis virus (Colunga Biancatelli et al. 2020)
Isoarundinin II	Antiviral activity not reported
Myricetin	Myricetin showed antioxidant, anticarcinogenic properties. It also exhibited potential antiviral behaviour against human immune-deficiency virus (Ong and Khoo 1997)
Linolenic acid	Linolenic acid showed anti-hepatitis B properties in vitro using Hep G2 2.2.15 cell line (Chou et al. 2012)
Pseudocarpaine	Pseudocarpaine showed strong binding affinity for receptor binding domain (RBD)-spike protein of SARS-CoV-2 in silico (Adel et al. 2022)
Isorhamnetin	Isorhamnetin exhibited inhibitory properties against the main protease of SARS-CoV-2 in silico (Shahhamzehei et al. 2022)
Oleic acid	Oleic acid showed anti-inflammatory bactericidal and fungicidal property (Sales-Campos et al. 2013)
Riboflavin	Riboflavin demonstrated antiviral activity by significantly lowering Middle-East respiratory syndrome coronavirus titer in the presence of ultraviolet light (Keflie and Biesalski 2021)
Chlorogenic acid	Chlorogenic acid inhibited Influenza A (H5N1) virus neuraminidase activity (Luo et al. 2011)
Coniferol	Coniferol exhibited anti-hepatitis B virus activity (Aguilar-Guadarrama and Rios 2018)
Beta-carotene	Beta-carotene was reported to enhance the immunity against virus and tumor surveillance (Fatima et al. 2014)
Campesterol	Campesterol showed anti-HIV property. It exhibited inhibitory properties against SARS-CoV-2 in silico (Jamhour et al. 2022)
β-sitosterol	β-sitosterol showed inhibitory properties against SARS-CoV-2 in silico (Khan and Siddiqui 2020)
Myristic acid	Myristic acid and its derivatives exhibited inhibitory properties against human immunodeficiency virus and hepatitis B virus in vitro (Parang et al. 1997)
Pentadecylic acid	Pentadecylic acid showed antioxidant, anti-inflammatory and anticancer properties (Sharma et al. 2014)
Ferulic acid	Ferulic acid showed inhibitory properties against SARS-CoV-2 in silico (Bhowmik et al. 2020)
Sinapinic acid	Sinapinic acid showed anti SARS-CoV-2 activity in vitro using Vero-E6 and in silico (Orfali et al. 2021)
Caffeic acid	Caffeic acid showed anti-hepatitis C property (Erdemli et al. 2015)
p-coumaric acid	The p-coumaric acid prolonged survival rates and reduced virus titers in bronchoalveolar lavage (BAL) fluids in mice model and showed anti-influenza activities (Pei et al. 2016)
Vanillic acid	Vanillic acid showed neuroprotective properties. Treatment with vanillic acid for 14 consecutive days in animal model (Bilateral Common Carotid Artery Occlusion and Reperfusion) restored with the spatial memory (Sharma et al. 2020)
Salicylic acid	Salicylic acid inhibited the SARS-CoV-2 activity in vero, Huh-7, and A549-ACE2 cell lines and in precision-cut, patient-derived lung slices (Geiger et al. 2022)
Cinnamic acid	Cinnamic acid showed inhibitory properties against Zika virus infection in vitro in Vero cells, Huh7 cells and A549 cells It successfully blocked the RNA-dependent RNA Polymerase activity. Cinnamic acid reduced the mortality in Zika virus infected type I/II interferon receptor-deficient (ifnagr–/–) C57BL/6 (AG6) mice in vivo (Chen et al. 2021)
Albaspidin AA	Albaspidin AA exhibited inhibitory activities against neuraminidase of Influenza virus (Xie et al. 2023)
Quinic acid	Quinic acid exhibited anti-hepatitis B activity in vitro in Hep G2.2.15 cells and in vivo in DHBV-infected duckling model (Wang et al. 2009)
β-Cryptoxanthin	β-cryptoxanthin exhibited inhibitory properties against main protease (Mpro) of SARS-CoV-2 in silico (Karpiński et al. 2021)
Linalol	Linalol exhibited anti-hepatitis B virus properties in vitro using 2.2.15 cell line derived from hepatoblastoma Hep G2 cells (Chiang et al. 2005)
Malic acid	Malic acid showed inhibitory properties against 3C-like main proteinase (3CLpro) and RNA-dependent RNA Polymerase proteins of SARS-CoV-2 in silico (Qazi et al. 2021)
Lutein	Lutein exhibited anti—hepatitis B virus activity by inhibition of hepatitis B virus transcription in stable HBV-producing human hepatoblastoma Hep G2 2.2.15 cells (Pang et al. 2010)

Drug-likeness of selected compounds

The drug-like properties were effectively predicted by SwissADME for all the fifty-three phytochemicals, along with the control drugs, Ribavirin and Acyclovir (Table 3). The bioavailability of drug candidates was represented by Topological Polar Surface Area (TPSA). The suggested range is 20 to 140 Ų (Ertl et al. 2000). All the phytochemicals except for a few (Albaspidin AA, Beta-carotene, Chlorogenic acid, Riboflavin, Neosappanone A, Agrimol D, Forsythoside D, Verbascoside, Myricetin, Quercetin 3-galactoside, Kuzubutenolide A, Kaempferol-3-glucoside, Asebotin, Rutin, Mulberrofuran B) satisfied the criteria. Hou and Wang 2008, noted that in some cases, there might be no correlation between TPSA and other drug-like parameters. Water solubility also plays a vital role in determining drug-like properties. The solubility for drugs calculated by applying the ESOL model should be less than 6. The linear relationship between log S and five molecular parameters, namely, molecular weight, the number of rotatable bonds, the fraction of heavy aromatic atoms and Daylight's CLOGP was established by the ESOL model (Daina et al. 2017). All the phytochemicals were found to be within the recommended ESOL Log S value. Absorbance in the gastrointestinal (GI) tract is representative of the transcellular passive diffusion parameter, which is crucial to the cellular permeability of the drug candidates (Nag et al. 2022b). In this study, compounds like Malic acid, Linalol, Cinnamic acid, Salicylic acid, Vanillic acid, p-Coumaric acid, Caffeic acid, Sinapinic acid, Ferulic acid, Pentadecylic acid, Myristic acid, Coniferol, Oleic acid, Isorhamnetin, Pseudocarpaine, Linolenic acid, Thiamine, Obovatol, Prunasin, Moupinamide, Dehydrocarpaine I, Dehydrocarpaine II, Carpaine, Kaempferol, Morin, Isoarundinin II, Quercetin, Naringenin, Fisetin, Acyclovir and Tribulusamide B showed high GI absorption capacity. The calculation of iLOGP is dependent upon the Gibbs free energy of solvation. The value of Gibbs free energy of solvation is derived from the ratio of Generalized-born (GB) parameters and solvent-accessible surface area in water/n-octanol (SA) (GB/SA). The iLOGP values for all the phytochemicals in this work, with exceptions to a few compounds (Stigmasterol, Beta-carotene, β-Cryptoxanthin, Lutein), were found to meet the recommended value (less than 5) (Ibrahim et al. 2021). The extra cellular efflux of a wide range of structurally unrelated drugs is mediated by membrane-bound transporter PGP (P-glycoprotein). PGP-induced efflux of various substrates against a concentration gradient lead to the decrease in the intracellular concentration of the substrates, impacting their oral bioavailability (Constantinides and Wasan 2007). Phytochemicals Lutein, β-Cryptoxanthin, Quinic acid, Albaspidin AA, Beta-carotene, Naringin, Thiamine, Dehydrocarpaine I, Mulberrofuran B, Dehydrocarpaine II, Forsythoside D, Verbascoside, Naringenin, Kuzubutenolide A, Asebotin and Rutin were found to be PGP substrate (PGP +), and rest were PGP negative. Minimum Lipinski violations (0–3) were observed for all the phytochemicals. In general, literature indicated that neither drug-likeness of a compound should be derived based on one parameter nor that a compound is expected to pass all the drug-like tests (Ertl et al. 2000). Based on this understanding, all the phytochemicals passed one or more criteria, and substantially established themselves as the candidate drugs.

Table 3.

Evaluation of drug-likeness of the phytochemicals as determined by SwissADME

Molecule	Phytochemical name	TPSA	iLOGP	WLOGP	ESOL Log S	GI absorption	PgP substrate	Lipinski violations
Control drug	Ribavirin	143.72	0.13	– 3.34	– 0.21	Low	No	0
1	Acyclovir	119.05	0.48	– 1.48	– 0.41	High	No	0
2	Naringin	109.64	2.68	– 4.13	– 2.70	Low	Yes	0
3	Tribulusamide B	73.22	3.00	2.92	– 3.80	High	No	0
4	Mulberrofuran B	256.29	2.08	0.29	– 5.24	Low	Yes	3
5	Rutin	269.43	1.58	– 1.69	– 3.3	Low	Yes	3
6	Asebotin	166.14	2.49	0.10	– 2.94	Low	Yes	1
7	Kaempferol-3-glucoside	190.28	0.53	– 0.24	– 3.18	Low	No	2
8	Kuzubutenolide A	166.14	1.75	– 0.17	– 2.90	Low	Yes	1
9	Fisetin	111.13	1.50	2.28	– 3.35	High	No	0
10	Naringenin	86.99	1.75	2.19	– 3.49	High	Yes	0
11	Morin	131.36	1.47	1.99	– 3.16	High	No	0
12	Verbascoside	245.29	3.00	– 1.12	– 2.87	Low	Yes	3
13	Forsythoside D	145.53	3.14	0.32	– 3.36	Low	Yes	2
14	Kaempferol	111.13	1.7	2.28	– 3.31	High	No	0
15	Carpaine	76.66	4.71	4.80	– 6.77	High	No	0
16	Dehydrocarpaine II	77.32	4.62	5.77	– 6.35	High	Yes	0
17	Dehydrocarpaine I	76.99	4.56	5.29	– 6.56	High	Yes	0
18	Moupinamide	78.79	2.58	2.37	– 3.03	High	No	0
19	Agrimol D	211.28	2.55	5.71	– 7.86	Low	No	3
20	Koaburaside	138.07	1.44	– 1.41	– 1.20	Low	No	0
21	Prunasin	123.17	1.27	– 1.26	– 1.17	High	No	0
22	Neosappanone A	183.21	1.71	1.37	– 4.17	Low	No	3
23	Obovatol	49.69	3.18	4.35	– 4.69	High	No	0
24	Thiamine	104.15	– 1.60	0.62	– 2.32	High	Yes	0
25	Stigmasterol	20.23	5.01	7.80	– 7.46	Low	No	1
26	Quercetin 3-galactoside	210.51	2.11	– 0.54	– 3.04	Low	No	2
27	Quercetin	131.36	1.63	1.99	– 3.16	High	No	0
28	Isoarundinin II	69.92	2.54	4.19	– 5.26	High	No	0
29	Myricetin	151.59	1.08	1.69	– 3.01	Low	No	1
30	Linolenic acid	37.30	3.36	5.66	– 4.78	High	No	1
31	Pseudocarpaine	76.66	4.71	4.80	– 6.77	High	No	0
32	Isorhamnetin	120.36	2.35	2.29	– 3.36	High	No	0
33	Oleic acid	37.30	4.27	6.11	– 5.41	High	No	1
34	Riboflavin	161.56	0.97	– 1.68	– 1.31	Low	No	0
35	Chlorogenic acid	164.75	0.96	– 0.75	– 1.62	Low	No	1
36	Coniferol	49.69	2.16	1.30	– 2.23	High	No	0
37	Beta-carotene	0.00	7.79	12.61	– 11.04	Low	Yes	2
38	Campesterol	20.23	4.92	7.63	– 7.54	Low	No	1
39	β-sitosterol	20.23	4.79	8.02	– 7.90	Low	No	1
40	Myristic acid	37.30	3.32	4.77	– 4.31	High	No	0
41	Pentadecylic acid	37.30	3.66	5.16	– 4.66	High	No	0
42	Ferulic acid	66.76	1.62	1.39	– 2.11	High	No	0
43	Sinapinic acid	75.99	1.63	1.40	– 2.16	High	No	0
44	Caffeic acid	77.76	0.97	1.09	– 1.89	High	No	0
45	p-coumaric acid	57.53	0.95	1.38	– 2.02	High	No	0
46	Vanillic acid	66.76	1.40	1.10	– 2.02	High	No	0
47	Salicylic acid	57.53	1.13	1.09	– 2.50	High	No	0
48	Cinnamic acid	37.30	1.55	1.68	– 2.37	High	No	0
49	Albaspidin AA	149.20	0.51	3.02	– 2.95	Low	Yes	0
50	Quinic acid	118.22	– 0.12	– 2.32	0.53	Low	Yes	0
51	β-Cryptoxanthin	20.23	7.44	11.58	– 10.33	Low	Yes	2
52	Linalol	20.23	2.70	2.67	– 2.40	High	No	0
53	Malic acid	94.83	– 0.01	– 1.09	0.32	High	No	0
54	Lutein	40.46	7.15	10.40	– 9.64	Low	Yes	2

*TPSA: Total Polar Surface Area; ESOL: Estimated Solubility; GI: Gastrointestinal; PgP: P-glycoprotein

The toxicity analysis of the selected compounds is presented in Table 4. Four test parameters, namely Ames test, rat oral acute toxicity, skin sensitization and respiratory toxicity were studied to evaluate the toxicity of the compounds. While the Ames test for mutagenicity represents carcinogenicity of the compounds, skin sensitization revealed conditions like allergies and diseases like contact dermatitis. Other test parameters such as rat oral acute toxicity and respiratory toxicity, are related to the morbidity and mortality. Surveillance and treatment of such parameters, should be given importance to avoid drug withdrawal (Dong et al. 2018; Xiong et al. 2021). The results indicated that most of the compounds selected in this study were safe and could be used as candidate drugs.

Table 4.

Analysis of toxicity analysis of selected compounds as determined by ADMETlab 2.0 web server

The predicted output values of the toxicological parameters determined their probability of being toxic within a range of 0 to 1. An empirical decision taken based on the output categorized scores between 0 and 0.3 as excellent, 0.3–0.7 as medium and 0.7–1.0 as poor
Molecule	Phytochemical name	AMES	Rat oral acute toxicity	Skin sensitization	Respiratory toxicity
Control drug	Ribavirin	0.039	0.05	0.023	0.026
1	Acyclovir	0.866	0.279	0.483	0.949
2	Naringin	0.348	0.245	0.031	0.033
3	Tribulusamide B	0.235	0.114	0.905	0.049
4	Mulberrofuran B	0.01	0.032	0.792	0.763
5	Rutin	0.805	0.05	0.036	0.015
6	Asebotin	0.448	0.076	0.083	0.027
7	Kaempferol-3-glucoside	0.775	0.111	0.057	0.024
8	Kuzubutenolide A	0.719	0.452	0.026	0.033
9	Fisetin	0.73	0.178	0.919	0.074
10	Naringenin	0.342	0.694	0.925	0.34
11	Morin	0.616	0.053	0.87	0.057
12	Verbascoside	0.389	0.094	0.687	0.015
13	Forsythoside D	0.268	0.15	0.036	0.037
14	Kaempferol	0.672	0.156	0.856	0.09
15	Carpaine	0.012	0.045	0.877	0.941
16	Dehydrocarpaine II	0.025	0.056	0.964	0.918
17	Dehydrocarpaine I	0.015	0.027	0.944	0.933
18	Moupinamide	0.228	0.269	0.94	0.475
19	Agrimol D	0.043	0.012	0.966	0.022
20	Koaburaside	0.309	0.058	0.084	0.04
21	Prunasin	0.631	0.321	0.173	0.973
22	Neosappanone A	0.583	0.889	0.856	0.915
23	Obovatol	0.121	0.077	0.956	0.479
24	Thiamine	0.008	0.064	0.25	0.684
25	Stigmasterol	0.029	0.054	0.025	0.19
26	Quercetin 3-galactoside	0.809	0.073	0.163	0.025
27	Quercetin	0.657	0.065	0.919	0.072
28	Isoarundinin II	0.137	0.115	0.946	0.051
29	Myricetin	0.482	0.022	0.943	0.065
30	Linolenic acid	0.113	0.005	0.965	0.674
31	Pseudocarpaine	0.012	0.045	0.877	0.941
32	Isorhamnetin	0.596	0.074	0.774	0.121
33	Oleic acid	0.004	0.016	0.951	0.704
34	Riboflavin	0.046	0.025	0.01	0.216
35	Chlorogenic acid	0.026	0.028	0.156	0.031
36	Coniferol	0.141	0.386	0.949	0.502
37	Beta-carotene	0.128	0.132	0.988	0.313
38	Campesterol	0.032	0.023	0.176	0.502
39	β-sitosterol	0.026	0.018	0.133	0.536
40	Myristic acid	0.006	0.037	0.807	0.807
41	Pentadecylic acid	0.005	0.033	0.869	0.873
42	Ferulic acid	0.114	0.733	0.929	0.72
43	Sinapinic acid	0.016	0.321	0.949	0.428
44	Caffeic acid	0.183	0.833	0.942	0.422
45	p-coumaric acid	0.045	0.796	0.941	0.512
46	Vanillic acid	0.015	0.053	0.154	0.12
47	Salicylic acid	0.018	0.516	0.288	0.897
48	Cinnamic acid	0.07	0.14	0.951	0.624
49	Albaspidin AA	0.018	1.0	0.046	0.727
50	Quinic acid	0.031	0.011	0.031	0.019
51	β-Cryptoxanthin	0.146	0.133	0.986	0.487
52	Linalol	0.006	0.02	0.631	0.039
53	Malic acid	0.02	0.009	0.264	0.394
54	Lutein	0.359	0.193	0.971	0.357

Molecular docking

In the present study, docking results of all the fifty-three phytochemicals and the control drugs, Ribavirin and Acyclovir with the target protein (NiV G:A) were analysed (Table 5). However, the control drug, Acyclovir showed lower binding affinity (– 6.55 kcal mol⁻¹) towards NiV G:A compared to Ribavirin (– 6.95 kcal mol⁻¹). Literature showed the efficacy of Ribavirin against Nipah virus particles, in in vitro conditions (Wright et al. 2005; Aljofan et al. 2009). Acylovir, on the other hand, was administered to nine (9) Nipah-infected workers in Singapore; however, its role in curing the patients was unclear (Paton et al. 1999). Considering our current results and lack of evidence in the literature for anti-Nipah drugs, Ribavirin was used as the control drug in our study.

Table 5.

The results of molecular docking in terms of binding affinity (kcal mol⁻¹) between compounds and target protein as generated by the DockThor server

PubChem Id (CID)	Compounds	Phytochemical class	Binding affinity (kcal mol−1)
37542	Ribavirin (Control drug)	Triazole ribonucleosides and ribonucleotides	– 6.95
442428	Naringin	Flavonoids	– 9.19
10394345	Tribulusamide B	2-arylbenzofuran flavonoids	– 9.01
6440635	Mulberrofuran B	2-arylbenzofuran flavonoids	– 8.84
5280805	Rutin	Flavonoids	– 8.71
11190157	Asebotin	Flavonoids	– 8.56
5282102	Kaempferol-3-glucoside	Flavonoids	– 8.51
10671648	Kuzubutenolide A	Organooxygen compounds	– 8.32
5281614	Fisetin	Flavonoids	– 8.28
932	Naringenin	Flavonoids	– 8.25
5281643	Quercetin 3-galactoside	Flavonoids	– 8.23
5280343	Quercetin	Flavonoids	– 8.20
91542987	Isoarundinin II	Stilbenes	– 8.19
5281672	Myricetin	Flavonoids	– 8.08
5281670	Morin	Flavonoids	– 7.97
5281800	Verbascoside	Cinnamic acids and derivatives	– 7.91
24721571	Forsythoside D	Lignan glycosides	– 7.87
5280863	Kaempferol	Flavonoids	– 7.85
442630	Carpaine	Macrolides and analogues	– 7.82
131750992	Dehydrocarpaine II	Macrolides and analogues	– 7.80
131750991	Dehydrocarpaine I	Macrolides and analogues	– 7.75
5280537	Moupinamide	Cinnamic acids and derivatives	– 7.69
102402554	Agrimol D	Diarylheptanoids	– 7.68
5318820	Koaburaside	Organooxygen compounds	– 7.68
119033	Prunasin	Organooxygen compounds	– 7.67
101353537	Neosappanone A	Tetralins	– 7.58
100771	Obovatol	Benzene and substituted derivatives	– 7.51
1130	Thiamine	Diazines	– 7.39
5280794	Stigmasterol	Steroids and steroid derivatives	– 7.37
5280934	Linolenic acid	Fatty Acyls	– 7.35
12305270	Pseudocarpaine	Macrolides and analogues	– 7.33
5281654	Isorhamnetin	Flavonoids	– 7.27
445639	Oleic acid	Fatty Acyls	– 7.26
493570	Riboflavin	Pteridines and derivatives	– 7.21
1794427	Chlorogenic acid	Organooxygen compounds	– 7.03
1549095	Coniferol	Phenols	– 7.02
5280489	Beta-carotene	Prenol lipids	– 7.02
173183	Campesterol	Steroids and steroid derivatives	– 7.00
222284	β-sitosterol	Steroids and steroid derivatives	– 7.00
11005	Myristic acid	Fatty Acyls	– 6.65
13849	Pentadecylic acid	Fatty Acyls	– 6.64
445858	Ferulic acid	Cinnamic acids and derivatives	– 6.58
637775	Sinapinic acid	Cinnamic acids and derivatives	– 6.58
689043	Caffeic acid	Cinnamic acids and derivatives	– 6.50
637542	p-coumaric acid	Cinnamic acids and derivatives	– 6.48
8468	Vanillic acid	Benzene and substituted derivatives	– 6.47
338	Salicylic acid	Benzene and substituted derivatives	– 6.44
444539	Cinnamic acid	Cinnamic acids and derivatives	– 6.40
14378646	Albaspidin AA	Vinylogous acids	– 6.38
6508	Quinic acid	Organooxygen compounds	– 6.32
5281235	β-Cryptoxanthin	Prenol lipids	– 6.09
6549	Linalol	Prenol lipids	– 5.99
525	Malic acid	Hydroxy acids and derivatives	– 5.98
5281243	Lutein	Prenol lipids	– 3.08

*The highest binding affinity (– 9.19 kcal mol ⁻¹) associated with Naringin has been marked as bold

The evaluation of the top ten (10) docking scores showed that phytochemicals (Naringin, Tribulusamide B, Mulberrofuran B, Rutin, Asebotin, Kaempferol-3-glucoside, Kuzubutenolide A, Fisetin, Naringenin, Quercetin 3-galactoside) could strongly bind with the target NiV G:A (– 9.19, – 9.01, – 8.84, – 8.71, – 8.56, – 8.51, – 8.32, – 8.28, – 8.25, – 8.23 kcal mol⁻¹, respectively) when compared with the control drug, Ribavirin. The medicinal importance of these compounds is tabulated in Table 2. However, only two compounds namely, Naringin and Tribulusamide B showed a binding affinity of more than 9 kcal mol⁻¹, compared with other top-ranked phytochemicals. However, in the toxicity analysis of our study, Naringin was found to be safer than Tribulusamide B. Additionally, the affinity of phytochemicals towards the Ephrin B2-NiV G:A and human Ephrin B2 protein alone, were also evaluated (Tables S1 and S2). The result indicated that the top ligand Naringin, in particular, could selectively bind with NiV G:A (– 9.19 kcal mol⁻¹) and NiV G-Ephrin B2 complex (– 10.27 kcal mol⁻¹) over human Ephrin B2 protein alone (– 7.09 kcal mol⁻¹). However, Naringin showed higher affinity towards Ephrin B2, than the control drug Ribavirin (– 6.974 kcal mol⁻¹). Ephrin B2 is a B-class ephrin and potential henipaviral receptor, which facilitates viral entry and activates the virus-host fusion (Priyadarsinee et al. 2022). Naringin was reported to have significant anti-viral properties. In a recent study, Hussen et al. (2020), through molecular studies, showed that Naringin with binding affinity – 10.2 kcal mol⁻¹, could strongly inhibit the activity of SARS-CoV-2 main protease (Amin Huseen 2020). Our result showed that the phytochemical Naringin could effectively disrupt NiV G: A-Ephrin B2 complex. Due to its comparative selectivity towards viral protein than the human receptor, post-infection clearance shall be possible. The three-dimensional structures of Naringin-protein complexes are shown in Fig. 2.

Fig. 2.

Three-dimensional structures of ligand–protein complexes. a Nipah virus attachment glycoprotein (NiV G:A) in complex with Ephrin B2 (PDB id 2VSM) b NiV G:A-Ephrin B2-Naringin complex c Ephrin B2-Naringin complex d NiV G:A-Naringin complex

Principal component analysis

In this work, the first principal component (PC1) and the second component (PC2) explained approximately 49.70 and 24.20% of the variance, respectively. We observed four clusters in the PCA (Fig. 3). Cluster 4 consisted of Naringin, Quercetin 3-galactoside, Rutin and Mulberrofuran B, while cluster 1, comprised the control drug Ribavirin along with Kaempferol-3-glucoside and Kuzubutenolide A. Fisetin and Naringenin were observed in cluster 2 and Asebotin and Tribulusamide B were grouped into cluster 3. There were marginal differences observed concerning the selected parameters (binding affinities and drug-likeness parameters) for clusters 3 and 4. Clusters 3 and 4 grouped top-ranked ligands in binding affinities towards NiV G:A, which also had higher molecular weights, and similar ranges of values for iLOGP and ESOL Log S, respectively. Clusters 1 and 2 consisted of low-scoring ligands in binding affinities towards NiV G:A, which also had differences in molecular weights, WLOGP, iLOGP, and ESOL Log S. The TPSA values for phytochemicals of cluster 4 were the highest, followed by cluster 3, 1 and 2. The observed differences associated with selected parameters for cluster 1 and 2 were more when compared to the differences related to the parameters for clusters 3 and 4. The multidimensional data analysis tool of Principal Component Analysis (PCA) can be used to achieve insightful information on the protein–ligand binding affinities (Nag et al. 2022b). The molecular interactions of Mur enzymes of Mycobacterium tuberculosis by utilizing the advanced PCA tool were explored by Kumari et al. 2021. The uniqueness of PCA lies in the conversion of measured variables into principal components. The use of the PCA tool for categorization of ligands and thereby establishing a correlation between the ligands was successfully implemented in our previous works (Nag et al. 2021a, 2022b, 2023). Considering the robustness of the PCA tool and based on our results, the compounds in cluster 4 were selected for pharmacophore study.

Fig. 3.

Principal Component Analysis of selected top ten phytochemicals showing four clusters (1, 2, 3 and 4), x and y axis represents First and Second components respectively

Interaction analysis

PCA cluster 4, ligands-NiV G:A and Ephrin B2 docked complexes were selected for the amino acid-ligand interaction analysis (Fig. 4). The phytochemicals, Naringin, Rutin, Mulberrofuran B and Quercetin 3-galactoside interacted with the amino acid residues of the target protein through conventional hydrogen bonds, carbon-hydrogen bonds, alkyl bonds, pi-pi t shaped bonds and pi-sulfur bonds. Some residues GLN369A, GLU389A, TYR391A, and ILE398A (corresponds to GLN559, GLU579, TYR581, and ILE588 as per Kalbhor et al. 2021) involved with the interactions had a strong binding affinity towards Ephrin B2. The control drug, Ribavirin, interacted with NiV G:A through conventional hydrogen, pi-sulfur, pi-alkyl and pi-pi t shaped bonds. Among various amino acids, ALA342A and CYS50A of NiV G:A were found to be the common residues omnipresent in all interactions, including the control drug, Ribavirin. For Ephrin B2, two amino acid residues THR114B, and LYS60B were found to be common when compared with the control drug, Ribavirin. The details of the ligand–protein interactions are presented in Table 6. Further, the comparative interactions between the top ligand Naringin-NiV G:A-Ephrin B2 and NiV G:A-Ephrin B2 complexes revealed GLU36A and PRO122B as the common residues. Also, Naringin was found to be placed within the attachment site of NiV G:A and Ephrin B2 (PHE376A, LEU377A, LEU378A, LYS379A; GLY37A, TYR38A; LYS116B, GLN118B, GLU119B, PHE120B, SER121B; SER257A, LEU258A) (Fig. S1).

Fig. 4.

Two-dimensional representation of protein–ligand interactions (PCA cluster 4): a1 to e1 Interaction with Nipah virus attachment glycoprotein (NiV G:A, PDB id 2VSM, Chain A) [a1 Naringin, b1 Mulberrofuran B, c1 Quercetin 3-galactoside, d1 Rutin, e1 Ribavirin]; a2 to e2 Interaction with Ephrin B2 (PDB id 2VSM, Chain B) [a2 Naringin, b2 Mulberrofuran B, c2 Quercetin 3-galactoside, d2 Rutin, e2 Ribavirin]

Table 6.

Amino acid residues of target proteins interacting with the phytochemicals

Name (PubChem CID)	Nipah virus attachment glycoprotein (NiV G:A)				Ephrin B2
	Amino acid interactions	Hydrogen bonds/ carbon hydrogen bonds	Alkyl bonds	Other bonds	Amino acid interactions	Hydrogen bonds/ carbon hydrogen bonds	Alkyl bonds	Other bonds
Ribavirin (37542)	ALA342A, TYR391A, ILE27A, ILE390A, CYS26A, CYS50A	ALA342A, ILE27A, ILE390A, TYR391A	ILE398A	CYS26A, CYS50A, TYR391A	PHE120B, THR114B, LYS60B, LYS116B	PHE120B, THR114B, LYS60B, LYS116B	–	–LYS60B, LYS116B
Naringin (442428)	ALA342A, ASN367A, GLU389A, ASP395A, ILE398A, CYS50A, SER49	ALA342A, ASN367A, GLU389A, ASP395A, SER49	CYS50A	ILE398A	PRO122B, LYS60B, THR114B, LYS112B	PRO122B, LYS60B, THR114B, LYS112B	PRO122B	–
Mulberrofuran B (6440635)	ALA342A, CYS50A, CYS26A, ILE398A, GLU389A, TYR391A	GLU389A	ALA342A, CYS50A, CYS26A, ILE398A	TYR391A	THR114B, LYS60B, PHE113B, ILE111B, LYS112B	THR114B, LYS60B	PHE113B, ILE111B, LYS112B	–
Rutin (5280805)	VAL317A, PRO298A, ALA342A, TYR391A, SER49A, CYS50A, GLN369A	SER49A, TYR391A, GLN369A, ALA342A	CYS50A, ALA342A, PRO298A, VAL317A	CYS50A, TYR391A	THR114B, SER121B,LYS116B,PRO122B, PHE120B, LYS60B, LYS112B	THR114B, SER121B,LYS116B, PHE120B, LYS60B, LYS112B	PRO122B,	–
Quercetin 3-galactoside (5281643)	ASN367A, CYS50A, ALA342A, GLN369A, GLN340A	ASN367A, GLN369A, GLN340A, ALA342A	CYS50A, ALA342A	ALA342A	LYS60B, THR114B, LYS112B, PHE113B, PRO122B	LYS60B, THR114B, LYS112B, PRO122B	PRO122B	PHE113B

**The common residues for NiV G:A (ALA342A and CYS50A) are marked as bold. The common residues for Ephrin B2 (LYS60B and THR114B) are marked as bold

Phytochemical alignment and identification of pharmacophores

Based on the PCA results, as explained earlier, cluster 4 (Naringin, Rutin, Mulberrofuran B and Quercetin 3-galactoside) was selected for the pharmacophore (descriptors) dependent molecular alignment. Phytochemical alignment reveals molecular features responsible for the biological properties of the drug. The functional descriptors include hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), positive features, negative features, aromatic rings and hydrophobic features (Mohammed 2021). The interactions with the target proteins are carried out by the descriptors present in the ligands. In our recent study, a similar technique was used to align phytochemicals based on the descriptors in Camelia sinensis L. (Nag et al. 2022b).

Further, we successfully deployed molecular alignment elsewhere to evaluate the interaction of the phytochemicals with the residues of the target protein (Nag et al. 2021b, 2023; Nag and Banerjee 2021). All these studies combined PCA and molecular alignment. Recently, Glaab et al. 2021 performed pharmacophore modelling-based study which involved molecular alignment of ligands to virtually screen SARS-CoV-2 viral protease 3CL^pro inhibitors from ZINC database, SWEETLEAD library and MolPort library. In the current study, we identified common descriptors for all cluster 4 phytochemicals as four H bond acceptors, one H bond donor and two aromatic groups (Fig. 5a), with a high alignment score of 32.245, as provided by the PharmaGist webserver. While evaluating the interacting groups of the ligands with the target proteins, we observed that identified common descriptors of Naringin, Rutin, Mulberrofuran B and Quercetin 3-galactoside were the functional pharmacophores responsible for the protein–ligand interaction, as shown in Fig. 5b.

Fig. 5.

Molecular alignment of phytochemicals and pharmacophore interactions, a Identification of pharmacophores of four aligned phytochemicals (Naringin, Mulberrofuran B, Rutin and Quercetin 3 galactoside); b1-b2-b3-b4 Three-dimensional representation of protein–ligand pharmacophores interactions (ACC: Acceptor, DON: Donor, AR: Aromatic)

Molecular dynamic (MD) simulation

The molecular dynamic simulation tool is commonly used to validate the conformation and molecular stability of the docked complexes in physiological conditions (Dhiman and Purohit 2022). In the present study, MD simulation tool was used to examine the stability and conformational dynamics of the top ranked Naringin-NiV G:A complex. The stability of the complex was compared with that of the Ribavirin-NiV G:A in terms of results obtained collectively from all four parameters, namely RMSD, RMSF, gyration and ligand–protein H bonds. The binding profiles for the control drug Ribavirin and Naringin to the target protein, as demonstrated by the results, were similar. The application of the MD simulation technique is widespread in literature. Kumar et al. (2023) recently evaluated quinoline molecules mediated structure restoration and aggregate inhibition of V30M mutant transthyretin protein by applying a robust MD simulation technique. In another study, with the effective utilization of this technique, Singh and Purohit (2023) screened thirty-two 3-methyleneisoindolin-1-one molecules, and M24 was reported as the top cyclin-dependent kinases 4/6 (CDK4/6) inhibitor in comparison with the drug Palbociclib. The average RMSD values of the native complexes were 0.26, 0.24 and 0.23 nm for apo, Ribavirin-NiV G:A and Naringin-NiV G:A, respectively. Minimal fluctuations of RMSD (around ± 0.05 nm) for both the complexes observed were comparable to that of the apo (ligand free) protein (Fig. 6a). Literature indicated that RMSD value less than 2 Å represented the stability of protein structure, which in turn could be translated as less deviation of protein (Singh et al. 2022). Further, scientific evidence indicated that conformational changes resulting from protein–ligand interactions impacted the RMSD values, and thereby slight fluctuations in the RMSD profiles were expected during the simulation study (Alamri et al. 2020). Randhawa et al. (2022), in a five (5) ns simulation, established stability of the docked Nipah protein and selected phytochemical complexes. RMSD fluctuations during the simulation, were also observed in their study. Overall, our results in terms of RMSD for both the complexes and the apo protein, were in agreement with these literal evidences, indicating the stability of ligand–protein complexes in our study.

Fig. 6.

Molecular dynamic (MD) simulations of apo protein Nipah Virus Attachment Glycoprotein (PDB id 2VSM), 2VSM-Ribavirin complex, 2VSM-Naringin complex: a Root-Mean-Square Deviation (RMSD), b Radius of Gyration, c Root-Mean-Square Fluctuation (RMSF) and d Ligand–Protein H bonds

The Rg values ranged from 1.67 to 1.70 nm, demonstrating that the compactness of the protein was not impacted by ligand binding (Fig. 6b). We observed limited fluctuation of the ligand-bound (Naringin) protein gyration near 30–40 ns intervals. Minor fluctuations of the radius of gyration have been commonly observed in biological systems. In an in silico molecular dynamic simulation study of ATP-binding cassette super-family G member 2 enzyme and 2,4-Disubstituted pyridopyrimidine derivatives, lower fluctuations in the radius of gyration between 45 and 50 ns were reported (Tadayon and Garkani-Nejad 2019). Such fluctuations in RMSD and Rg parameters were reported elsewhere (Hasan et al. 2022). The root mean square fluctuations (RMSF) of individual amino acid residues was analysed to understand the residual mobility of ligand-bound and unbound forms (Kumar et al. 2023). The average RMSF values for apoprotein, Naringin-NiV G:A and Ribavirin-NiV G:A ranged from 0.1 to 0.5 nm (Fig. 6c). The structural stability of the complex is affected by the formation of stable hydrogen bonds between ligand and protein. For interaction with the NiV G:A, Naringin and Ribavirin showed a maximum of six and four hydrogen bonds, respectively. We observed at least one hydrogen was long-lived throughout the simulation of 50 ns for both the Naringin and Ribavirin complexes (Fig. 6d). The molecular docking simulation study results showed that Naringin could make an effective and stable interaction with the target protein NiV G:A at its active site and, thereby, could inhibit the Nipah Virus infection.

Free energy analysis by MM-PBSA calculation

In agreement with our molecular docking result, free energy analysis showed that Naringin ( – 218.664 kJ mol⁻¹) had a higher binding affinity to that of the control drug, Ribavirin ( – 83.812 kJ mol⁻¹). Low binding energy in MM-PBSA analysis represents a strong binding potential of the ligand with the target protein. The free energy can be estimated by using the advanced methodology of MM-PBSA. This method, associated with a high computational cost, generates significantly more accurate results than the conventional score-based molecular docking technique (Ren et al. 2015; Singh et al. 2022; Singh and Purohit 2023). Idris et al. (2021) performed MM-PBSA analysis and showed that two ZINC compounds ZINC64606047 and ZINC05296775 could strongly bind with Transmembrane serine protease 2. van der Waals with the binding energies  – 190.75 ± 16.39 and  – 140.16 ± 14.93 kJ mol⁻¹ respectively. Similarly, Elkarhat et al. (2022) reported high values like − 191.982 kJ mol⁻¹ for SARS-CoV-2 nsp12–streptolydigin and  – 153.583 kJ mol⁻¹ for nsp12-VXR complexes, respectively in, 30 ns MD simulation MM-PBSA analysis. Electrostatic, Non-Polar and Polar interaction parameters were associated with the dynamic stability of a protein–ligand complex. A low contribution in binding was shown by unfavourable polar solvation energy (He et al. 2014). In this study, van der Waals contributed more negative energy than its Electrostatic counterpart (Table 7). The residues, namely THR25, CYS26, THR28, SER51, GLN369, ILE390, THR393, ASN396, ILE398, and PRO400 had the major contributions in the binding of Naringin towards the NiV G protein Fig. 7. Important residues for Ribavirin- NiV G protein, were noted as CYS50, SER51, TYR391, THR393, ASN396 and ILE398 (Table 7 and Fig. 7). The hydrogen bonds along with the contribution energies of these residues are listed in Table 8.

Table 7.

MM-PBSA calculations of binding free energy for protein–ligand complexes

Types of binding energy	Binding energy Naringin-NiVG complex	Binding energy Ribavirin-NiVG complex
ΔG_Binding (kJ mol−1)	– 218.664	– 83.812
ΔG_Non Polar (kJ mol−1)	– 20.776	– 10.191
ΔG_Polar (kJ mol−1)	98.736	41.474
ΔG_Electrostatic (kJ mol−1)	– 25.587	– 9.041
ΔG_van der Waals (kJ mol−1)	– 271.038	– 106.053

Fig. 7.

Contribution energy plots of interacting amino acids of target proteins (Nipah virus attachment glycoprotein) to Naringin and the control drug, Ribavirin

Table 8.

Amino acid residues of target protein NiV:G and ligand complexes, with at least one hydrogen bond formed during MD simulation

Naringin		Ribavirin
Residues	Contribution energy (kJ mol−1)	Residues	Contribution energy (kJ mol−1)
THR-25	– 0.1337	CYS50	– 4.9453
CYS26	– 4.9108	SER51	– 2.5772
THR28	– 3.7219	TYR391	– 9.3564
SER51	– 6.8337	THR393	– 2.5875
GLN369	– 2.8437	ASN396	– 2.1573
ILE390	– 2.2079	ILE398	– 5.5287
THR393	– 2.8026
ASN396	– 3.7571
ILE398	– 11.497
PRO400	– 2.949

Structural changes in the native Nipah Virus attachment glycoprotein after binding of control drug and ligand

The distances between randomly flagged amino acids were measured to understand the structural impact on Nipah Virus Attachment Glycoprotein (apo) upon binding with the ligand, Naringin and the control drug, Ribavirin (Fig. 8). A steady decrease in the distances of amino acids in the ligand–protein complex, in comparison with the apo protein (ligand-free) indicated that the alteration of the size of the active site might lead to loss of the enzymatic activity of the target protein NiV G:A (Table 9).

Fig. 8.

Structural comparison by PyMol 2.0 software for a Nipah virus attachment glycoprotein chain A (NiV G:A) (Apo); b control drug, Ribavirin + NiV G:A and c Naringin + NiV G:A

Table 9.

Distance analysis between flagged amino acids of ligand free protein and selected complexes

Complex	Bond length (Å)
	CYS2-SER17	CYS2-PHE238	CYS2-ASN191	LEU124-ASN191	SER17-PHE238	SER17-PRO133	PRO133-PHE238
NiV G:A (Apo)	27.2	39.6	39.7	34.5	36.7	21.8	31.0
NiV G:A + Ribavirin	25.7	38.2	38.5	25.5	35.7	20.8	31.5
NiV G:A + Naringin	25.8	38.3	32.9	31.0	35.0	20.3	31.5

NiV G:A: Nipah Virus attachment glycoprotein (Chain A)

Conclusion

Naringin, among the fifty-three screened phytochemicals, showed the most potent inhibitory potential with the Nipah virus glycoprotein (NiV G) ( – 9.19 kcal mol⁻¹) when compared with the control drug, Ribavirin ( – 6.95 kcal mol⁻¹). Other three phytochemicals, Mulberrofuran B, Rutin and Quercetin 3-galactoside, were also found to have strong binding affinities for the target protein. The pharmacophores, four H bond acceptors, one H bond donor and two aromatic groups were found to be responsible for effective protein–ligand interaction. Finally, as revealed by MD simulation, Naringin could form a stable complex with the target protein, NiV G, in the near-native physiological environment.

Supplementary Information

Below is the link to the electronic supplementary material.

Data availability

All data supporting the findings of this study are available within the paper and its Supplementary Information. Additional data may be available on request.

Declarations

Conflict of interest

The authors declare there is no conflict of interest.