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Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies

Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening,... Background: Porcine reproductive and respiratory syndrome respiratory sickness in weaned and growing pigs, as well as sow reproductive failure, and its infection is regarded as one of the most serious swine illnesses worldwide. Given the current lack of an effective treatment, in this study, we identified natural compounds capable of inhibiting non-structural protein 4 (Nsp4) of the virus, which is involved in their replication and pathogenesis. Results: We screened natural compounds (n = 97,999) obtained from the ZINC database against Nsp4 and selected the top 10 compounds for analysing protein–ligand interactions and physicochemical properties. The five compounds demonstrating strong binding affinity were then subjected to molecular dynamics simulations (100 ns) and binding free energy calculations. Based on analysis, we identified four possible lead compounds that represent potentially effective drug-like inhibitors. Conclusions: These methods identified that these natural compounds are capable of inhibiting Nsp4 and possibly effective as antiviral therapeutics against PRRSV. Keywords: PRRSV, Swine, Nsp4, Molecular dynamics, Protein–ligand interaction Introduction the virus [5]. Antivirals might be useful in controlling Porcine reproductive and respiratory syndrome virus and managing PRRSV, and recent studies have reported (PRRSV) infection is an economically important disease the ability of herbal extracts to inhibit PRRSV infection in swine and accountable for significant losses to the [6–8]. Many compounds derived from natural sources pork industry worldwide [1]. PRRSV is an enveloped, such as plants have shown inhibitory activity against vi- single-stranded RNA virus of the genus Arterivirus [1–4] ruses and a wide variety of pathogens [9, 10]. Several that causes respiratory disease and is responsible for se- natural compounds have demonstrated antiviral activity vere reproductive failure in pigs [4]. Generally, the dis- (for instance, immense potential for inhibiting viral rep- ease is further complicated by secondary infection and lication) and drug-like activity [10]. The identification of results in a high mortality rate [1]. The available treat- potent inhibitors of PRRSV from natural sources is chal- ments, including vaccines, poorly control the disease, lenging, as it requires compound extraction and evalu- owing to the high genetic and antigenic heterogeneity of ation of antiviral activity, both of which require funds and specific experimental equipment. Given the diffi- culty in screening large numbers of natural compounds, * Correspondence: junmokim@cau.ac.kr chemo-informatics approaches are useful for identifying Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea © The Author(s). 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 2 of 11 lead compounds from databases by targeting essential docked/screened compounds using drug discovery pro- viral proteins [11, 12]. grams [24]. AutoDock vina is a molecular docking and vir- Open reading frame (ORF)1a and ORF1b comprise ~ 80% tual screening program that determines the preferred relative of the PRRSV genome and respectively encode pp1a and orientation of a ligand during docking or interaction with a pp1b polyproteins that are cleaved by viral proteases into molecular target and provides a stable protein–ligand com- non-structural proteins [13]. Papain-like proteases (PL1pro plex structure that exhibits a minimum binding energy [20]. and PL2pro) and a 3C-like serine protease [3CLSP; non- Here, we used AutoDock vina to screen the retrieved natural structural protein 4 (Nsp4)] are the viral proteases involved compounds against PRRSV Nsp4 and generated protein–lig- in polyprotein cleavage and required for Arterivirus replica- and complexes of the top 10 screened compounds using tion. Additionally, the reported involvement of Nsp4 in inter- PyMol (https://pymol.org/2/). Two-dimensional (2D) models feron inhibition is linked with PRRSV pathogenesis, of the complexes were visualised using Discovery Studio suggesting it as a promising molecular target for novel thera- Visualizer (https://discover.3ds.com/discovery-studio- peutics [14–16]. visualizer-download) to determine the amino acid residues The goal of this study was to identify natural com- involved in the interactions [25]. pounds capable of serving as novel inhibitors of PRRSV replication via their targeting of Nsp4. Specifically, we Drug-likeness analysis aimed to identify natural compounds through structure- A total of seven principal descriptors were included to evalu- based virtual screening, analyse their physicochemical ate the drug-likeness of the top 10 screened natural com- properties, perform molecular dynamics (MD) simula- pounds. These included molecular weight (MW), logP value, tions, and determine the binding affinities of the potential status as a hydrogen-bond donor (HBD), and acceptor inhibitors using molecular mechanics Poisson–Boltzmann (HBA), polar surface area (2D; PSA), polarizability (P), and surface area (MM-PBSA) methods. van der Waals surface area (VWSA). MW, logP, HBD, and HBA were obtained from the ZINC database [17], whereas Materials and methods PSA, P, and VWSA were calculated using the MarvinSketch Retrieval and preparation of ligand structures software (https://chemaxon.com/products/marvin). We retrieved 97,999 compounds from a subset of the ZINC database housing natural compounds [17]. All natural com- MD simulation pounds were downloaded in the structure-data file format, The MD simulations were performed using GROMACS and these files were subsequently converted to AutoDock (v.2018.1; http://www.gromacs.org/) for stability predic- PDBQT [Protein Data Bank (PDB), partial charge (Q), and tions of the Nsp4–ligand complexes [26, 27]. Six systems atom type (T)] files using OpenBabel software (https:// were generated and subjected to 100 ns MD simula- openbabel.org/wiki/Main_Page). These files were then used tions—one system estimated the stability of Nsp4 and for interaction studies with target protein through structure- the other five estimated the stability of the Nsp4–ligand based virtual screening and molecular docking using Auto- complexes. The dynamic nature of the target protein Dock vina [18–20]. and the docked-ligand complex was predicted in the presence of a solvent. All six systems were solvated in a Retrieval and preparation of the target protein structure box using a simple point-charge model. The topology of The crystal structure of PRRSV Nsp4 (PDB ID: 5Y4L) was the ligands was created using ProDRG [28] and that for retrieved from RCSB-PDB and visualised and analysed using Nsp4 was generated using the GROMOS9653a6 force UCSF Chimera-1.15 software [21, 22]. An AutoDock tool field [29]. The systems were neutralised by adding 1 Na was used for the addition of partial atomic charges (Kollman ion. To eliminate steric hindrance, the steepest energy charge), hydrogen atoms, generation of gridbox, and prepar- minimization was used for all systems in order to obtain ation of the Nsp4 structure. The grid box was generated with the maximal force below 1000 kJ/mol/nm. Long-range centre (X = − 4.034, Y = 6.285, Z = 17.57) and size (X = 46, electrostatic interactions were determined using the par- Y=44, Z=42)coordinatesthatweredefined in aconfigur- ticle mesh Ewald (PME) method [30]. For computation ation file (exhaustiveness and energy ranges: 8 and 4, respect- of Lennard–Jones and Coulomb interactions, we used a ively). The prepared structure was saved in the PDBQT file radius cut-off of 1.0 nm; the LINCS algorithm was used format formoleculardocking [23]. to constrain H-bond lengths [31]. All simulations ap- plied a consistent time step of 2 fs. Short-range non- Structure-based virtual screening bonded interactions were predicted using a 10-Å cut-off Virtual screening is a computational method widely used distance, whereas long-range electrostatics were pre- for the identification of lead molecules by docking large dicted using the PME method with 1.6-Å Fourier grid numbers of compounds with a molecular target of inter- spacing. Shake algorithms were used to fix all bonds, in- est to allow evaluation of the binding free energy of the cluding H-bonds [32]. After energy minimisation, the Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 3 of 11 systems were equilibrated, followed by position-restraint Analysis and visualisation of the screened compounds simulations under NVT and NPT conditions to maintain The active site residues of Nsp4 include His39, Asp64, the volume, temperature, and pressure. Finally, all systems and Ser118, as well as His133 and Ser136 that are report- were subjected to a 100 ns MD simulation; coordinates edly essential for protein activity. The compound showing were saved at 2 fs intervals. Root-mean-square deviation the optimal binding free energy (− 10.0 kcal/mol; (RMSD), root-mean-square fluctuation (RMSF), radius of ZINC38167083) demonstrated ligand interactions such as gyration (Rg), solvent-accessible surface area (SASA), H- His39-mediated van der Waals interactions and Asp64- bonds, and Gibbs free energy landscapes were calculated, mediated Pi-anion interaction. Fig. 1 shows protein–ligand and principal component analysis (PCA) was performed H-bond interactions involving the active site residues to predict correlated motions generated during protein– His39, Ser118, and Ser136, besides, Asp64 and His133 is ligand interactions;‘gmx rms’, ‘gmx rmsf’, ‘gmx gyrate’, interacted with van der Waals and pi-anion interaction ‘gmx sasa’, ‘gmx hbond’, ‘gmx sham’,and ‘gmx covar’ with ZINC08877407. The interacting amino acid residues (GROMACSv.2018.1; http://www.gromacs.org/), respect- of top 10 compounds are shown in Table 1. ively, were used for these purposes. The resulting files were analysed and visualised using xmgrace (https:// Assessment of drug likeness through physicochemical plasma-gate.weizmann.ac.il/Grace/). property analysis Physicochemical property analysis is one of the fundamental Binding-energy calculation tasks in any drug discovery program. The top 10 screened MM-PBSA is a widely used and well-accepted method compounds were then subjected to analysis of their physico- for calculating the binding free energy of protein–ligand chemical properties according to 7 principal descriptors complexes [33]. Here, we used the g_mmpbsa tool (MW, logP value, status of HBDs and HBAs, 2D PSA, P, and (https://rashmikumari.github.io/g_mmpbsa/) to calculate VWSA). According to a previous study, a good drug should the binding energy by integrating high-throughput MD have an MW < 500 Da, an HBD < 5, and an HBA < 10. The simulation data [34]. The binding energy calculations MW, logP, HBD, and HBA of the selected compounds met can be described in the following equation: the Lipinski’s rule. Additionally, PSA, P, and VWSA results displayed drug-like behaviour. (Table 2). ΔG ¼ ΔG þ ΔG þ ΔG -TΔS bind mm ps nps MD simulation analysis Here, van der Waals and electrostatic interaction were The structure of Nsp4 and top five screened compound- calculated in molecular mechanics energy (ΔG ). ΔG complex with Nsp4 was employed for 100 ns MD simu- mm ps and ΔG are the polar and non-polar solvation ener- lation study for predicting the dynamic changes during nps gies, and TΔS is refer to the entropic contribution where protein-ligand interaction and their nature of stability. temperature and entropy are denoted by T and S, re- The present study included various parameters i.e. spectively. The average binding energies of the top five RMSD, RMSF, Rg, SASA, H-bond, PCA, Gibbs free en- protein–ligand complexes and amino acid residues con- ergy landscape, and binding free energy calculation. tributing to the binding activity were calculated by using ‘python’ scripts included in the g_mmpbsa tool. Structural deviation analysis through RMSD The RMSD value describes the dynamic behaviour Results among native structures to a new pose. After a 70 ns of Identification of lead compounds through structure-based simulation to obtain a stable trajectory, the RMSD values virtual screening were 0.35, 0.25, 0.29, 0.23, 0.38, and 0.39 nm for Nsp4, Structure-based virtual screening enables the prediction Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4- of optimal interactions between ligands and a macro- ZINC08792350, Nsp4-ZINC01510656, and Nsp4- molecular target for complex formation. The ligands are ZINC08877407, respectively. These data suggest that subsequently sorted according to their binding free en- Nsp4-ZINC08792350 and Nsp4-ZINC38167083 are ergy for the target. This requires the three-dimensional highly stable complexes relative to the others. Because structure of the target, with the compounds obtained each Nsp4–compound complex demonstrated stability from a database and categorised according to their affin- after the 70 ns simulation, we performed further evalua- ity. In the present study, we downloaded a subset of nat- tions on each for last 30 ns trajectory (Fig. 2A). ural compounds (n = 97,999) from the ZINC database for virtual screening against PRRSV Nsp4. We subse- Flexibility analysis through RMSF quently identified the top 10 compounds sorted accord- Evaluation of the RMSF values used to assess structural ing to their minimum binding free energy (range:− 10.0 rigidity revealed values of 0.08, 0.11, 0.12, 0.10, 0.11, and to − 9.2 kcal/mol) for further analyses (Table 1). 0.11 nm for Nsp4, Nsp4-ZINC38167083, Nsp4- Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 4 of 11 Table 1 Binding free energies of the top 10 screened compounds along with the amino acid residues involved in interactions. The amino acid residues shown in bold are involved in hydrogen-bonding interactions S.No. Compound Binding free energy Amino acid residues involved in interactions via different types of bonding (ZINC ID) (Kcal/ mol) 1. ZINC38167083 −10.0 Ser18, Ala38, His39, Leu41, Thr42, Gly43, Asn44, Val61, Gly63, Asp64, Thr134, Ile143, Thr145, Phe151 2. ZINC16919178 −9.9 Phe3, Thr5, Ser9, Leu10, Asn11, Phe26, Pro78, Tyr92, Leu94, Val99, Pro101, Ile123, Gly127 3. ZINC08792350 −9.5 Phe3, Thr5, Ser9, Leu10, Asn11, Val76, Pro78, Arg90, Val99, Tyr92, Pro101, Phe166, Asp192, Ile123, Leu196 4. ZINC01510656 −9.4 Thr5, Ser9, Leu10, Asn11, Phe26, Val76, Pro78, Tyr92, Leu94, Val99, IIe123, Gly127 5. ZINC08877407 −9.3 His39, Gly63, Asp64, Ala114, Cys115, Gly116, ASP117, Ser118, His133, Thr134, Gly135, Ser136, Lys138, Ile143, Thr145, Phe151 6. ZINC32124273 −9.3 Phe3, Thr5, Ser9, Asn11, Phe26, Pro78, Lys79, Ala80, Tyr92, Leu94, Arg90, Val99, Pro101, Ile123, Thr124, Glu125, Ala126, Gly127 7. ZINC00852708 −9.2 Thr5, Ser9, Leu10, Asn11, Phe26, Val76, Pro78, Tyr92, Leu94, Val99, IIe123, Gly127 8. ZINC01225926 −9.2 Thr5, Ser9, Leu10, Asn11, Phe26, Val76, Pro78, Leu94,Tyr92,Val99, Ile123, Gly127 9. ZINC02116980 −9.2 Gly63, Asp64, Ala114, Cys115, Gly116, Asp117,Ser118, His133, Gly135, Thr134, Ser136, Lys138, ILE143, Thr145, Phe151 10. ZINC08790125 −9.2 Ser18, Ala38, Leu41, Gly43, Asn44, His39, Val61, Gly63, Asp64, Thr134, IIe143, Thr145, Phe151 Fig. 1 2D representation of the binding interactions of top five screened natural compounds with Nsp4 depicted key amino acid residues contributed in protein-ligand interactions. A ZINC38167083, (B) ZINC16919178, (C) ZINC08792350, (D) ZINC01510656, and (E) ZINC08877407 Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 5 of 11 Table 2 Physicochemical properties of the top 10 screened compounds S.No. Compound MW (g/mol) LogP HBD HBA PSA (2D) (Å) P VWSA (3D) (Å) 1. ZINC38167083 446.422 3.317 4 4 116.40 45.91 494.66 2. ZINC16919178 448.518 5.584 0 4 68.28 49.15 579.49 3. ZINC08792350 488.547 4.32 0 6 67.67 57.27 660.92 4. ZINC01510656 379.459 4.7 0 2 37.38 43.18 524.87 5. ZINC08877407 453.535 3.995 0 6 93.14 47.17 667.66 6. ZINC32124273 464.525 4.578 0 7 82.60 50.46 614.48 7. ZINC00852708 365.432 4.392 0 2 37.38 41.41 492.81 8. ZINC01225926 379.459 4.566 0 2 37.38 43.26 527.17 9. ZINC02116980 477.488 4.828 0 6 97.05 50.01 641.83 10. ZINC08790125 460.537 4.478 3 2 80.99 53.60 620.08 ZINC16919178, Nsp4-ZINC08792350, Nsp4- Radius of gyration (Rg) analysis ZINC01510656, and Nsp4-ZINC08877407, respectively Assessment of complex compactness according to Rg (Fig. 2B). Higher RMSF values were due to ligand bind- calculation revealed values of 1.50, 1.27, 1.43, 1.46, 1.39, ing, causing alterations in protein geometry. Minimal and 1.44 nm for Nsp4, Nsp4-ZINC38167083, Nsp4- fluctuations were observed in Nsp4-ZINC08792350 and ZINC16919178, Nsp4-ZINC08792350, Nsp4- Nsp4-ZINC38167083 complex compared with that in ZINC01510656, and Nsp4-ZINC08877407, respectively other complexes. (Fig. 2C). The results indicate that the Nsp4- Fig. 2 Stability analysis (A) RMSD values for the Nsp4–compound complexes. Flexibility analysis (B) RMSF values for the Nsp4–compound complexes over the final 30 ns of the simulations. Compactness (C) Rg, and Solvent accessible surface area analysis (D) SASA values for the final 30 ns of the simulations. Black, red, green, blue, orange, and violet colours represent Nsp4, Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4- ZINC08792350, Nsp4-ZINC01510656, and Nsp4-ZINC08877407, respectively. E Changes in the number of hydrogen bonds in each respective complex according to data from the final 30 ns of the simulations. Red, green, blue, orange, and violet colour represent Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4-ZINC08792350, Nsp4-ZINC01510656, and Nsp4-ZINC08877407 respectively Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 6 of 11 ZINC38167083 complex showed a more compact struc- 71.05% of the motions for Nsp4, Nsp4-ZINC38167083, ture than the other complexes. Nsp4-ZINC16919178, Nsp4-ZINC08792350, Nsp4- ZINC01510656, and Nsp4-ZINC08877407 respectively Solvent accessible surface area (SASA) analysis (Fig. 3A), suggesting increased movement after the bind- To identify changes in the solvent-accessible regions of the ing of each ligand. Moreover, Nsp4-ZINC38167083, complexes, we determined SASA values over the course of Nsp4-ZINC16919178, Nsp4-ZINC08792350, and Nsp4- the final 30 ns of the simulation. Our study revealed values ZINC01510656 showed less overall motion relative to of 95.88, 98.33, 98.98, 97.13, 96.97, and 100.92 nm for Nsp4, Nsp4-ZINC08877407. Additionally, generation of a 2D Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4- plot for assessing protein dynamics after ligand binding ZINC08792350, Nsp4-ZINC01510656, and Nsp4- suggested the overall stability (lowcorrelated motions) of ZINC08877407 (Fig. 2D), revealing relatively minimal Nsp4, Nsp4-ZINC38167083, and Nsp4- changes after binding by each of the compounds. ZINC08792350(Fig. 3B), indicating these compounds as possible leads for further evaluation as inhibitors. Interaction analysis through hydrogen bonding Hydrogen bonding is the most important bond for stabil- Gibbs free energy landscape izing protein–ligand interactions. The average number of We then calculated the Gibbs free energy landscape hydrogen bonds for the complexes Nsp4-ZINC38167083, using the first two principal components (PC1 and PC2) Nsp4-ZINC16919178, Nsp4-ZINC08792350, Nsp4- in order to visualize the results. Fig. 4 shows the colour- ZINC01510656, and Nsp4-ZINC08877407 over the final coded plots generated for Nsp4 along with each com- 30 ns of the simulations was 0–1 and that for Nsp4- plex. The lowest free energy values (≤9.08 kJ/mol) were ZINC38167083 and Nsp4-ZINC16919178 was 0–2and observed for Nsp4-ZINC38167083, suggesting that this 0–3, respectively (Fig. 2E). Hence, these compounds inter- complex demonstrated overall thermodynamic stability. acted with Nsp4 and provided a stable complex during The other complexes (Nsp4-ZINC16919178, Nsp4- protein–ligand interactions. ZINC08792350, Nsp4-ZINC01510656, and Nsp4- ZINC08877407) had values of to 11.4 kJ/mol, implying Principal component analysis (PCA) that these complexes have numerous high-energy In PCA, the sum of the eigenvalues suggests the overall minima. flexibility of a structure under different conditions. Therefore, the first 5 of 50 eigenvectors used to calculate Binding free energy eigenvalues from the final 30 ns of the simulation were We then evaluate the binding free energy associated with used to determine the percentage change in structural each ligand through MM-PBSA using the final 10 ns of movement. The results revealed that these five eigenvec- the simulation, for calculation of van der Waals and tors accounted for 42.85, 63.97, 63.27, 59.14, 64.83, and electrostatic interactions, Polar solvation, and SASA. Fig. 3 Principal component analysis (A) Eigenvalues derived from the final 30 ns of each simulation and used for PCA depicted Eigenvalues vs. first fifty eigenvector. B First two eigenvectors depicted Nsp4 motion in space for all the systems. Black, red, green, blue, orange, and violet colours represent Nsp4, Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4-ZINC08792350, Nsp4-ZINC01510656, and Nsp4-ZINC08877407 respectively Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 7 of 11 Fig. 4 The color-coded illustration of the Gibbs free energy landscape plotted using PC1 and PC2. The lower energy systems are represented by the deeper blue color on the contour map. A Nsp4, (B) Nsp4-ZINC38167083, (C) Nsp4-ZINC16919178, (D) Nsp4-ZINC08792350, (E) Nsp4- ZINC01510656, and (F) Nsp4-ZINC08877407 The calculated binding free energy for Nsp4- ZINC01510656, which are the catalytic residues in the ZINC38167083, Nsp4-ZINC16919178, Nsp4- active site. Fewer contacts were observed in relation to ZINC08792350, Nsp4-ZINC01510656, and Nsp4- ZINC08877407 binding, suggesting that ZINC38167083, ZINC08877407 was − 124.54, − 128.44, − 159.33, − ZINC16919178, ZINC08792350, and ZINC01510656 − 1 122.50, and − 78.19 kJ mol respectively (Table 3). represent potential Nsp4 inhibitors. The investigation of residual binding energy is a key method for identifying residues important to ligand Discussion binding. Fig. 5 shows that amino acid residues at posi- PRRSV is a recalcitrant and intricate disease in a pig tions 5 to 142 contributed significantly to binding of when working as a cofactor in a porcine respiratory dis- ZINC38167083, ZINC16919178, ZINC08792350, and ease complex (PRDC) or primary infectious agent. It was − 1 Table 3 Average binding free energies of Nsp4 complexes in kJ mol Compounds van der Waals interactions Electrostatic interactions Polar solvation SASA Binding energy ZINC38167083 −161.742 ± 16.571 −36.716 ± 14.192 89.570 ± 30.038 −15.652 ± 1.890 −124.540 ± 17.142 ZINC16919178 −202.964 ± 19.700 −14.995 ± 12.321 107.288 ± 24.499 −17.775 ± 1.752 −128.446 ± 13.116 ZINC08792350 − 210.397 ± 12.126 −5.732 ± 4.973 76.102 ± 13.673 −19.303 ± 1.248 −159.330 ± 14.200 ZINC01510656 −145.554 ± 11.730 −3.108 ± 4.634 40.772 ± 10.125 −14.615 ± 1.279 −122.505 ± 12.199 ZINC08877407 −101.513 ± 9.428 −7.238 ± 5.481 40.488 ± 23.110 −9.935 ± 2.168 −78.199 ± 21.645 Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 8 of 11 Fig. 5 Plot depicting the amino acid residues of Nsp4 contributing to the binding with natural compounds. Red, green, blue, orange, and violet colours represent ZINC38167083, ZINC16919178, ZINC08792350, ZINC01510656, and ZINC08877407, respectively identified as the most frequent virus linked to PRDC bonds. Ala38 and Phe151 participated in interaction [35–39]. Furthermore, PRRSV has been shown to impair through amide-pi and pi-pi t-shaped bonding. Addition- the host immune system, which can lead to more serious ally, Asp64 contributed to interaction through the pi- secondary infections, and chronic disorders [35]. The in- anion bond. ZINC16919178 bonded with Nsp4 at pos- volvement of Nsp4 in PRRSV replication and pathogen- ition Asn11 by one conventional hydrogen bond. In esis is decoded and recommended as one of the key addition, amino acid residues Phe3, Thr5, Ser9, Leu10, molecular targets for drug development [14]. Therefore, Phe26, Ile123, and Gly127 formed van der Waals inter- identification of Nsp4 inhibitors is needed to prevent actions; Pro78, Tyr92, Leu94, Val99, and Pro101 formed and manage the disease. Natural compounds have made alkyl and pi-alkyl bonds. ZINC08792350 interacted with immense contributions in the identification of lead mol- Nsp4 Thr5, Ser9, Asn11, Val76, Pro78, Ile123, Phe166, ecule(s) with antiviral potential. It is believed that the and Asp192 through van der Waals interactions; Phe3 disease can be controlled successfully by developing and Tyr92 formed pi-pi t-shaped bonding. In addition, small molecules that can inhibit Nsp4 activity linked the amino acid residues Leu10, Pro101, Leu196 contrib- with pathogenesis [14]. In the present study, computa- uted to interaction through pi-alkyl bonding; Arg90 and tional approaches are utilized for the identification of Val99 formed pi-anion and pi-sigma bonds with possible lead compounds via molecular docking of nat- ZINC08792350, respectively. ZINC01510656 bonded ural compounds database through structure-based vir- with Nsp4 at Thr5, Asn11, Val76, Phe26, Leu94, and tual screening followed by downstream analysis. Gly127 through van der Waals interactions; amino acid Structure based-virtual screening is a powerful computa- residues Leu10, Pro78, and IIe123 formed pi-alkyl bonds, tional approach that is used to investigate important lead and Ser9, Tyr92, Val99 contributed in interaction by molecule(s) from a big set of a compound database carbon-hydrogen bonding, pi-pi t-shaped, and pi-sigma based on the lowest binding energy required for stabiliz- interactions, respectively. ZINC08877407 formed con- ing the protein-ligand complex [40]. ventional hydrogen bonds with Nsp4 at position His39, From the structure-based virtual screening, we have Ser118, ASP117, and Ser136; amino acid residues Gly63, selected the top ten natural compounds that show inter- Asp64, Ala114, Cys115, Gly116, Thr134, Lys138, and action with key residues. Further, protein-ligand analysis Thr145 contributed to protein-ligand interaction of the top 5 compounds demonstrated that the through van der Waals. Additionally, Gly135 formed a ZINC38167083 interacted with Nsp4 and formed one carbon-hydrogen bond, and His133, Ile143, and Phe151 conventional hydrogen bond at position Gly63. Besides, interacted through pi-anion, pi-alkyl, and pi-sigma bond- amino acid residues Ser18, His39, Leu41, Thr42, Gly43, ing, respectively. Medicinal chemists have traditionally Asn44, Thr134, and Thr145 were involved in van der been interested in noncovalent interactions that are indi- Waals interactions, and Val61 and Ile143 formed alkyl cative of attraction, directed intermolecular forces in Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 9 of 11 their search for the “glue” that keeps ligand and their of protein-ligand complexes [51]. MM-PBSA analysis molecular target together. In recent years, with the rapid demonstrated that the compounds ZINC38167083, increase in the number of solved biomolecular structures ZINC16919178, ZINC08792350, and ZINC01510656 can and the performance enhancement of computational act as a potential lead for inhibition of Nsp4 [52, 53]. methods, it is now possible to provide a more thorough Whereas, ZINC08877407 was not recommended as a understanding of protein-ligand interaction [41]. There- lead because their binding energy was found to be higher fore, based on the results, it was concluded that the as compared to other compounds. screened compounds can inhibit the virulence activity of In past years, the identification of lead compounds for Nsp4 [14]. Besides, the results of physicochemical prop- drug development take much time and cost as well as erties prediction suggest that the screened compounds required good infrastructure experimental facilities [11, demonstrated good drug-like behavior and could be con- 54]. Due to advances in structural biology, computer sci- sidered for further analysis [42, 43]. Therefore, 100 ns ence, and bioinformatics, it becomes easy to find out pu- MD simulation analysis was conducted for Nsp4 and top tative molecule(s) by a screening of a big database that 5 natural compounds i.e. ZINC38167083, has a strong affinity with the target for experimental ZINC16919178, ZINC08792350, ZINC01510656, and evaluation [24, 55]. It saves the cost and time of the sci- ZINC08877407, respectively with Nsp4 to evaluate the entific community. Most of the medicines available in dynamic behavior of protein and protein-ligand com- the market are from a natural source or it is a derivative plexes. It is recognized as a powerful approach for pre- of naturally occurring molecules [11, 24]. Natural com- dicting the conformational stability of macromolecules pounds have immense potential to inhibit virus and before and after ligand binding, besides the simulated pathogenic proteins and act as antiviral drugs [56–58]. data can be utilized for calculation of real binding energy The results presented in this work are, therefore, in- of small molecules concerning time along with a contri- formative for understanding the antiviral potential of bution of binding amino acid residues present in the suggested compounds as therapeutics for PRRSV. It macromolecular target [44]. Several structural parame- might be also useful for the prevention of pigs and other ters were calculated, including RMSD, RMSF, Rg, SASA, animals from different viral diseases [59, 60]. H-bonding, PCA, and gibbs free energy [45–48]. The RMSD value indicated that all of the complexes were Conclusions stable and creating an equilibrated trajectory for further PRRSV infection is a main concern for the global swine investigation. As a result, we determined RMSF, Rg, industry, and there is a need to identify novel and effect- SASA, PCA, and Gibbs free energy to determine the na- ive therapeutic agents. Given the importance of Nsp4 in ture of each system subjected for MD simulation. Drug PRRSV replication and pathogenesis, we employed com- selectivity, metabolization, and stability all require H- putational and MD approaches to screen and identify bonds. To better understand the H-bond and its contri- natural compounds as novel inhibitors of Nsp4 activity. butions to the overall stability of each system, an H- The results identified four possible lead compounds that bond analysis of natural compounds-Nsp4 complexes represent potentially effective drug-like inhibitors for ap- were calculated. The hydrogen bonding study indicates plication as antiviral therapeutics. Further studies are that all of the Nsp4-complexes are stable and made warranted to confirm these findings through experimen- bonding with essential catalytic residues [49]. The over- tal and clinical evaluations in order to promote future all analysis revealed that each complex was stabilizing management of PRRSV infection. after 70 ns indicating better interaction with Nsp4 in Abbreviations terms of stability that is required for its inhibition. Fur- 2D: Two-dimensional; 3CLSP: 3C-like serine protease; HBA: Hydrogen-bond ther, MM-PBSA binding free energy and residual bind- acceptor; HBD: Hydrogen-bond donor; MD: Molecular dynamics; MM- ing energy were calculated to assess the binding PBSA: Molecular mechanics Poisson–Boltzmann surface area; MW: Molecular weight; Nsp4: Non-structural protein 4; ORF: Open reading frame; PL1/ affinities of natural compounds with Nsp4. For deter- 2pro: Papain-like protease; PME: Particle mesh Ewald; PRRSV: Porcine mining the binding free energy of protein–ligand com- reproductive and respiratory syndrome virus; PSA: Polar surface area; plexes by using MD simulation trajectory, it is a VWSA: van der Waals surface area frequently used and well-accepted method [33, 50]. The Acknowledgements strength of the binding contacts between the ligand and The authors thank Chung-Ang University, Anseong-si, the Republic of Korea the target protein is measured by ligand binding affinity, for providing High-Performance Computing (HPC) and other necessary which is directly linked to ligand potency. In the field of facilities. drug discovery, its evaluation is crucial. Furthermore, in Authors’ contributions favorable reactions, the free energy is negative. So, low- JMK designed the experiments and supervised the research; RKP performed ering the binding energy improves interactions, and low experiments, analyse results and wrote the manuscript. YJS helped in binding energy corresponds to the high binding affinity analysis. All authors read and approved the final manuscript. Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 10 of 11 Funding 14. Tian X, Lu G, Gao F, Peng H, Feng Y, Ma G, et al. Structure and cleavage This research was supported by the Basic Science Research Program through specificity of the chymotrypsin-like serine protease (3CLSP/nsp4) of porcine the National Research Foundation of Korea (NRF) funded by the Ministry of reproductive and respiratory syndrome virus (PRRSV). J Mol Biol. 2009;392(4): Education (NRF-2018R1A6A1A03025159). 977–93. https://doi.org/10.1016/j.jmb.2009.07.062. 15. Shi Y, Lei Y, Ye G, Sun L, Fang L, Xiao S, et al. Identification of two antiviral inhibitors targeting 3C-like serine/3C-like protease of porcine reproductive Availability of data and materials and respiratory syndrome virus and porcine epidemic diarrhea virus. Vet All data generated or analysed during this study are included in the Microbiol. 2018;213:114–22. https://doi.org/10.1016/j.vetmic.2017.11.031. manuscript. 16. An TQ, Li JN, Su CM, Yoo D. Molecular and cellular mechanisms for PRRSV pathogenesis and host response to infection. Virus Res. 2020;286:197980. 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Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies

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Abstract

Background: Porcine reproductive and respiratory syndrome respiratory sickness in weaned and growing pigs, as well as sow reproductive failure, and its infection is regarded as one of the most serious swine illnesses worldwide. Given the current lack of an effective treatment, in this study, we identified natural compounds capable of inhibiting non-structural protein 4 (Nsp4) of the virus, which is involved in their replication and pathogenesis. Results: We screened natural compounds (n = 97,999) obtained from the ZINC database against Nsp4 and selected the top 10 compounds for analysing protein–ligand interactions and physicochemical properties. The five compounds demonstrating strong binding affinity were then subjected to molecular dynamics simulations (100 ns) and binding free energy calculations. Based on analysis, we identified four possible lead compounds that represent potentially effective drug-like inhibitors. Conclusions: These methods identified that these natural compounds are capable of inhibiting Nsp4 and possibly effective as antiviral therapeutics against PRRSV. Keywords: PRRSV, Swine, Nsp4, Molecular dynamics, Protein–ligand interaction Introduction the virus [5]. Antivirals might be useful in controlling Porcine reproductive and respiratory syndrome virus and managing PRRSV, and recent studies have reported (PRRSV) infection is an economically important disease the ability of herbal extracts to inhibit PRRSV infection in swine and accountable for significant losses to the [6–8]. Many compounds derived from natural sources pork industry worldwide [1]. PRRSV is an enveloped, such as plants have shown inhibitory activity against vi- single-stranded RNA virus of the genus Arterivirus [1–4] ruses and a wide variety of pathogens [9, 10]. Several that causes respiratory disease and is responsible for se- natural compounds have demonstrated antiviral activity vere reproductive failure in pigs [4]. Generally, the dis- (for instance, immense potential for inhibiting viral rep- ease is further complicated by secondary infection and lication) and drug-like activity [10]. The identification of results in a high mortality rate [1]. The available treat- potent inhibitors of PRRSV from natural sources is chal- ments, including vaccines, poorly control the disease, lenging, as it requires compound extraction and evalu- owing to the high genetic and antigenic heterogeneity of ation of antiviral activity, both of which require funds and specific experimental equipment. Given the diffi- culty in screening large numbers of natural compounds, * Correspondence: junmokim@cau.ac.kr chemo-informatics approaches are useful for identifying Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea © The Author(s). 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 2 of 11 lead compounds from databases by targeting essential docked/screened compounds using drug discovery pro- viral proteins [11, 12]. grams [24]. AutoDock vina is a molecular docking and vir- Open reading frame (ORF)1a and ORF1b comprise ~ 80% tual screening program that determines the preferred relative of the PRRSV genome and respectively encode pp1a and orientation of a ligand during docking or interaction with a pp1b polyproteins that are cleaved by viral proteases into molecular target and provides a stable protein–ligand com- non-structural proteins [13]. Papain-like proteases (PL1pro plex structure that exhibits a minimum binding energy [20]. and PL2pro) and a 3C-like serine protease [3CLSP; non- Here, we used AutoDock vina to screen the retrieved natural structural protein 4 (Nsp4)] are the viral proteases involved compounds against PRRSV Nsp4 and generated protein–lig- in polyprotein cleavage and required for Arterivirus replica- and complexes of the top 10 screened compounds using tion. Additionally, the reported involvement of Nsp4 in inter- PyMol (https://pymol.org/2/). Two-dimensional (2D) models feron inhibition is linked with PRRSV pathogenesis, of the complexes were visualised using Discovery Studio suggesting it as a promising molecular target for novel thera- Visualizer (https://discover.3ds.com/discovery-studio- peutics [14–16]. visualizer-download) to determine the amino acid residues The goal of this study was to identify natural com- involved in the interactions [25]. pounds capable of serving as novel inhibitors of PRRSV replication via their targeting of Nsp4. Specifically, we Drug-likeness analysis aimed to identify natural compounds through structure- A total of seven principal descriptors were included to evalu- based virtual screening, analyse their physicochemical ate the drug-likeness of the top 10 screened natural com- properties, perform molecular dynamics (MD) simula- pounds. These included molecular weight (MW), logP value, tions, and determine the binding affinities of the potential status as a hydrogen-bond donor (HBD), and acceptor inhibitors using molecular mechanics Poisson–Boltzmann (HBA), polar surface area (2D; PSA), polarizability (P), and surface area (MM-PBSA) methods. van der Waals surface area (VWSA). MW, logP, HBD, and HBA were obtained from the ZINC database [17], whereas Materials and methods PSA, P, and VWSA were calculated using the MarvinSketch Retrieval and preparation of ligand structures software (https://chemaxon.com/products/marvin). We retrieved 97,999 compounds from a subset of the ZINC database housing natural compounds [17]. All natural com- MD simulation pounds were downloaded in the structure-data file format, The MD simulations were performed using GROMACS and these files were subsequently converted to AutoDock (v.2018.1; http://www.gromacs.org/) for stability predic- PDBQT [Protein Data Bank (PDB), partial charge (Q), and tions of the Nsp4–ligand complexes [26, 27]. Six systems atom type (T)] files using OpenBabel software (https:// were generated and subjected to 100 ns MD simula- openbabel.org/wiki/Main_Page). These files were then used tions—one system estimated the stability of Nsp4 and for interaction studies with target protein through structure- the other five estimated the stability of the Nsp4–ligand based virtual screening and molecular docking using Auto- complexes. The dynamic nature of the target protein Dock vina [18–20]. and the docked-ligand complex was predicted in the presence of a solvent. All six systems were solvated in a Retrieval and preparation of the target protein structure box using a simple point-charge model. The topology of The crystal structure of PRRSV Nsp4 (PDB ID: 5Y4L) was the ligands was created using ProDRG [28] and that for retrieved from RCSB-PDB and visualised and analysed using Nsp4 was generated using the GROMOS9653a6 force UCSF Chimera-1.15 software [21, 22]. An AutoDock tool field [29]. The systems were neutralised by adding 1 Na was used for the addition of partial atomic charges (Kollman ion. To eliminate steric hindrance, the steepest energy charge), hydrogen atoms, generation of gridbox, and prepar- minimization was used for all systems in order to obtain ation of the Nsp4 structure. The grid box was generated with the maximal force below 1000 kJ/mol/nm. Long-range centre (X = − 4.034, Y = 6.285, Z = 17.57) and size (X = 46, electrostatic interactions were determined using the par- Y=44, Z=42)coordinatesthatweredefined in aconfigur- ticle mesh Ewald (PME) method [30]. For computation ation file (exhaustiveness and energy ranges: 8 and 4, respect- of Lennard–Jones and Coulomb interactions, we used a ively). The prepared structure was saved in the PDBQT file radius cut-off of 1.0 nm; the LINCS algorithm was used format formoleculardocking [23]. to constrain H-bond lengths [31]. All simulations ap- plied a consistent time step of 2 fs. Short-range non- Structure-based virtual screening bonded interactions were predicted using a 10-Å cut-off Virtual screening is a computational method widely used distance, whereas long-range electrostatics were pre- for the identification of lead molecules by docking large dicted using the PME method with 1.6-Å Fourier grid numbers of compounds with a molecular target of inter- spacing. Shake algorithms were used to fix all bonds, in- est to allow evaluation of the binding free energy of the cluding H-bonds [32]. After energy minimisation, the Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 3 of 11 systems were equilibrated, followed by position-restraint Analysis and visualisation of the screened compounds simulations under NVT and NPT conditions to maintain The active site residues of Nsp4 include His39, Asp64, the volume, temperature, and pressure. Finally, all systems and Ser118, as well as His133 and Ser136 that are report- were subjected to a 100 ns MD simulation; coordinates edly essential for protein activity. The compound showing were saved at 2 fs intervals. Root-mean-square deviation the optimal binding free energy (− 10.0 kcal/mol; (RMSD), root-mean-square fluctuation (RMSF), radius of ZINC38167083) demonstrated ligand interactions such as gyration (Rg), solvent-accessible surface area (SASA), H- His39-mediated van der Waals interactions and Asp64- bonds, and Gibbs free energy landscapes were calculated, mediated Pi-anion interaction. Fig. 1 shows protein–ligand and principal component analysis (PCA) was performed H-bond interactions involving the active site residues to predict correlated motions generated during protein– His39, Ser118, and Ser136, besides, Asp64 and His133 is ligand interactions;‘gmx rms’, ‘gmx rmsf’, ‘gmx gyrate’, interacted with van der Waals and pi-anion interaction ‘gmx sasa’, ‘gmx hbond’, ‘gmx sham’,and ‘gmx covar’ with ZINC08877407. The interacting amino acid residues (GROMACSv.2018.1; http://www.gromacs.org/), respect- of top 10 compounds are shown in Table 1. ively, were used for these purposes. The resulting files were analysed and visualised using xmgrace (https:// Assessment of drug likeness through physicochemical plasma-gate.weizmann.ac.il/Grace/). property analysis Physicochemical property analysis is one of the fundamental Binding-energy calculation tasks in any drug discovery program. The top 10 screened MM-PBSA is a widely used and well-accepted method compounds were then subjected to analysis of their physico- for calculating the binding free energy of protein–ligand chemical properties according to 7 principal descriptors complexes [33]. Here, we used the g_mmpbsa tool (MW, logP value, status of HBDs and HBAs, 2D PSA, P, and (https://rashmikumari.github.io/g_mmpbsa/) to calculate VWSA). According to a previous study, a good drug should the binding energy by integrating high-throughput MD have an MW < 500 Da, an HBD < 5, and an HBA < 10. The simulation data [34]. The binding energy calculations MW, logP, HBD, and HBA of the selected compounds met can be described in the following equation: the Lipinski’s rule. Additionally, PSA, P, and VWSA results displayed drug-like behaviour. (Table 2). ΔG ¼ ΔG þ ΔG þ ΔG -TΔS bind mm ps nps MD simulation analysis Here, van der Waals and electrostatic interaction were The structure of Nsp4 and top five screened compound- calculated in molecular mechanics energy (ΔG ). ΔG complex with Nsp4 was employed for 100 ns MD simu- mm ps and ΔG are the polar and non-polar solvation ener- lation study for predicting the dynamic changes during nps gies, and TΔS is refer to the entropic contribution where protein-ligand interaction and their nature of stability. temperature and entropy are denoted by T and S, re- The present study included various parameters i.e. spectively. The average binding energies of the top five RMSD, RMSF, Rg, SASA, H-bond, PCA, Gibbs free en- protein–ligand complexes and amino acid residues con- ergy landscape, and binding free energy calculation. tributing to the binding activity were calculated by using ‘python’ scripts included in the g_mmpbsa tool. Structural deviation analysis through RMSD The RMSD value describes the dynamic behaviour Results among native structures to a new pose. After a 70 ns of Identification of lead compounds through structure-based simulation to obtain a stable trajectory, the RMSD values virtual screening were 0.35, 0.25, 0.29, 0.23, 0.38, and 0.39 nm for Nsp4, Structure-based virtual screening enables the prediction Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4- of optimal interactions between ligands and a macro- ZINC08792350, Nsp4-ZINC01510656, and Nsp4- molecular target for complex formation. The ligands are ZINC08877407, respectively. These data suggest that subsequently sorted according to their binding free en- Nsp4-ZINC08792350 and Nsp4-ZINC38167083 are ergy for the target. This requires the three-dimensional highly stable complexes relative to the others. Because structure of the target, with the compounds obtained each Nsp4–compound complex demonstrated stability from a database and categorised according to their affin- after the 70 ns simulation, we performed further evalua- ity. In the present study, we downloaded a subset of nat- tions on each for last 30 ns trajectory (Fig. 2A). ural compounds (n = 97,999) from the ZINC database for virtual screening against PRRSV Nsp4. We subse- Flexibility analysis through RMSF quently identified the top 10 compounds sorted accord- Evaluation of the RMSF values used to assess structural ing to their minimum binding free energy (range:− 10.0 rigidity revealed values of 0.08, 0.11, 0.12, 0.10, 0.11, and to − 9.2 kcal/mol) for further analyses (Table 1). 0.11 nm for Nsp4, Nsp4-ZINC38167083, Nsp4- Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 4 of 11 Table 1 Binding free energies of the top 10 screened compounds along with the amino acid residues involved in interactions. The amino acid residues shown in bold are involved in hydrogen-bonding interactions S.No. Compound Binding free energy Amino acid residues involved in interactions via different types of bonding (ZINC ID) (Kcal/ mol) 1. ZINC38167083 −10.0 Ser18, Ala38, His39, Leu41, Thr42, Gly43, Asn44, Val61, Gly63, Asp64, Thr134, Ile143, Thr145, Phe151 2. ZINC16919178 −9.9 Phe3, Thr5, Ser9, Leu10, Asn11, Phe26, Pro78, Tyr92, Leu94, Val99, Pro101, Ile123, Gly127 3. ZINC08792350 −9.5 Phe3, Thr5, Ser9, Leu10, Asn11, Val76, Pro78, Arg90, Val99, Tyr92, Pro101, Phe166, Asp192, Ile123, Leu196 4. ZINC01510656 −9.4 Thr5, Ser9, Leu10, Asn11, Phe26, Val76, Pro78, Tyr92, Leu94, Val99, IIe123, Gly127 5. ZINC08877407 −9.3 His39, Gly63, Asp64, Ala114, Cys115, Gly116, ASP117, Ser118, His133, Thr134, Gly135, Ser136, Lys138, Ile143, Thr145, Phe151 6. ZINC32124273 −9.3 Phe3, Thr5, Ser9, Asn11, Phe26, Pro78, Lys79, Ala80, Tyr92, Leu94, Arg90, Val99, Pro101, Ile123, Thr124, Glu125, Ala126, Gly127 7. ZINC00852708 −9.2 Thr5, Ser9, Leu10, Asn11, Phe26, Val76, Pro78, Tyr92, Leu94, Val99, IIe123, Gly127 8. ZINC01225926 −9.2 Thr5, Ser9, Leu10, Asn11, Phe26, Val76, Pro78, Leu94,Tyr92,Val99, Ile123, Gly127 9. ZINC02116980 −9.2 Gly63, Asp64, Ala114, Cys115, Gly116, Asp117,Ser118, His133, Gly135, Thr134, Ser136, Lys138, ILE143, Thr145, Phe151 10. ZINC08790125 −9.2 Ser18, Ala38, Leu41, Gly43, Asn44, His39, Val61, Gly63, Asp64, Thr134, IIe143, Thr145, Phe151 Fig. 1 2D representation of the binding interactions of top five screened natural compounds with Nsp4 depicted key amino acid residues contributed in protein-ligand interactions. A ZINC38167083, (B) ZINC16919178, (C) ZINC08792350, (D) ZINC01510656, and (E) ZINC08877407 Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 5 of 11 Table 2 Physicochemical properties of the top 10 screened compounds S.No. Compound MW (g/mol) LogP HBD HBA PSA (2D) (Å) P VWSA (3D) (Å) 1. ZINC38167083 446.422 3.317 4 4 116.40 45.91 494.66 2. ZINC16919178 448.518 5.584 0 4 68.28 49.15 579.49 3. ZINC08792350 488.547 4.32 0 6 67.67 57.27 660.92 4. ZINC01510656 379.459 4.7 0 2 37.38 43.18 524.87 5. ZINC08877407 453.535 3.995 0 6 93.14 47.17 667.66 6. ZINC32124273 464.525 4.578 0 7 82.60 50.46 614.48 7. ZINC00852708 365.432 4.392 0 2 37.38 41.41 492.81 8. ZINC01225926 379.459 4.566 0 2 37.38 43.26 527.17 9. ZINC02116980 477.488 4.828 0 6 97.05 50.01 641.83 10. ZINC08790125 460.537 4.478 3 2 80.99 53.60 620.08 ZINC16919178, Nsp4-ZINC08792350, Nsp4- Radius of gyration (Rg) analysis ZINC01510656, and Nsp4-ZINC08877407, respectively Assessment of complex compactness according to Rg (Fig. 2B). Higher RMSF values were due to ligand bind- calculation revealed values of 1.50, 1.27, 1.43, 1.46, 1.39, ing, causing alterations in protein geometry. Minimal and 1.44 nm for Nsp4, Nsp4-ZINC38167083, Nsp4- fluctuations were observed in Nsp4-ZINC08792350 and ZINC16919178, Nsp4-ZINC08792350, Nsp4- Nsp4-ZINC38167083 complex compared with that in ZINC01510656, and Nsp4-ZINC08877407, respectively other complexes. (Fig. 2C). The results indicate that the Nsp4- Fig. 2 Stability analysis (A) RMSD values for the Nsp4–compound complexes. Flexibility analysis (B) RMSF values for the Nsp4–compound complexes over the final 30 ns of the simulations. Compactness (C) Rg, and Solvent accessible surface area analysis (D) SASA values for the final 30 ns of the simulations. Black, red, green, blue, orange, and violet colours represent Nsp4, Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4- ZINC08792350, Nsp4-ZINC01510656, and Nsp4-ZINC08877407, respectively. E Changes in the number of hydrogen bonds in each respective complex according to data from the final 30 ns of the simulations. Red, green, blue, orange, and violet colour represent Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4-ZINC08792350, Nsp4-ZINC01510656, and Nsp4-ZINC08877407 respectively Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 6 of 11 ZINC38167083 complex showed a more compact struc- 71.05% of the motions for Nsp4, Nsp4-ZINC38167083, ture than the other complexes. Nsp4-ZINC16919178, Nsp4-ZINC08792350, Nsp4- ZINC01510656, and Nsp4-ZINC08877407 respectively Solvent accessible surface area (SASA) analysis (Fig. 3A), suggesting increased movement after the bind- To identify changes in the solvent-accessible regions of the ing of each ligand. Moreover, Nsp4-ZINC38167083, complexes, we determined SASA values over the course of Nsp4-ZINC16919178, Nsp4-ZINC08792350, and Nsp4- the final 30 ns of the simulation. Our study revealed values ZINC01510656 showed less overall motion relative to of 95.88, 98.33, 98.98, 97.13, 96.97, and 100.92 nm for Nsp4, Nsp4-ZINC08877407. Additionally, generation of a 2D Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4- plot for assessing protein dynamics after ligand binding ZINC08792350, Nsp4-ZINC01510656, and Nsp4- suggested the overall stability (lowcorrelated motions) of ZINC08877407 (Fig. 2D), revealing relatively minimal Nsp4, Nsp4-ZINC38167083, and Nsp4- changes after binding by each of the compounds. ZINC08792350(Fig. 3B), indicating these compounds as possible leads for further evaluation as inhibitors. Interaction analysis through hydrogen bonding Hydrogen bonding is the most important bond for stabil- Gibbs free energy landscape izing protein–ligand interactions. The average number of We then calculated the Gibbs free energy landscape hydrogen bonds for the complexes Nsp4-ZINC38167083, using the first two principal components (PC1 and PC2) Nsp4-ZINC16919178, Nsp4-ZINC08792350, Nsp4- in order to visualize the results. Fig. 4 shows the colour- ZINC01510656, and Nsp4-ZINC08877407 over the final coded plots generated for Nsp4 along with each com- 30 ns of the simulations was 0–1 and that for Nsp4- plex. The lowest free energy values (≤9.08 kJ/mol) were ZINC38167083 and Nsp4-ZINC16919178 was 0–2and observed for Nsp4-ZINC38167083, suggesting that this 0–3, respectively (Fig. 2E). Hence, these compounds inter- complex demonstrated overall thermodynamic stability. acted with Nsp4 and provided a stable complex during The other complexes (Nsp4-ZINC16919178, Nsp4- protein–ligand interactions. ZINC08792350, Nsp4-ZINC01510656, and Nsp4- ZINC08877407) had values of to 11.4 kJ/mol, implying Principal component analysis (PCA) that these complexes have numerous high-energy In PCA, the sum of the eigenvalues suggests the overall minima. flexibility of a structure under different conditions. Therefore, the first 5 of 50 eigenvectors used to calculate Binding free energy eigenvalues from the final 30 ns of the simulation were We then evaluate the binding free energy associated with used to determine the percentage change in structural each ligand through MM-PBSA using the final 10 ns of movement. The results revealed that these five eigenvec- the simulation, for calculation of van der Waals and tors accounted for 42.85, 63.97, 63.27, 59.14, 64.83, and electrostatic interactions, Polar solvation, and SASA. Fig. 3 Principal component analysis (A) Eigenvalues derived from the final 30 ns of each simulation and used for PCA depicted Eigenvalues vs. first fifty eigenvector. B First two eigenvectors depicted Nsp4 motion in space for all the systems. Black, red, green, blue, orange, and violet colours represent Nsp4, Nsp4-ZINC38167083, Nsp4-ZINC16919178, Nsp4-ZINC08792350, Nsp4-ZINC01510656, and Nsp4-ZINC08877407 respectively Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 7 of 11 Fig. 4 The color-coded illustration of the Gibbs free energy landscape plotted using PC1 and PC2. The lower energy systems are represented by the deeper blue color on the contour map. A Nsp4, (B) Nsp4-ZINC38167083, (C) Nsp4-ZINC16919178, (D) Nsp4-ZINC08792350, (E) Nsp4- ZINC01510656, and (F) Nsp4-ZINC08877407 The calculated binding free energy for Nsp4- ZINC01510656, which are the catalytic residues in the ZINC38167083, Nsp4-ZINC16919178, Nsp4- active site. Fewer contacts were observed in relation to ZINC08792350, Nsp4-ZINC01510656, and Nsp4- ZINC08877407 binding, suggesting that ZINC38167083, ZINC08877407 was − 124.54, − 128.44, − 159.33, − ZINC16919178, ZINC08792350, and ZINC01510656 − 1 122.50, and − 78.19 kJ mol respectively (Table 3). represent potential Nsp4 inhibitors. The investigation of residual binding energy is a key method for identifying residues important to ligand Discussion binding. Fig. 5 shows that amino acid residues at posi- PRRSV is a recalcitrant and intricate disease in a pig tions 5 to 142 contributed significantly to binding of when working as a cofactor in a porcine respiratory dis- ZINC38167083, ZINC16919178, ZINC08792350, and ease complex (PRDC) or primary infectious agent. It was − 1 Table 3 Average binding free energies of Nsp4 complexes in kJ mol Compounds van der Waals interactions Electrostatic interactions Polar solvation SASA Binding energy ZINC38167083 −161.742 ± 16.571 −36.716 ± 14.192 89.570 ± 30.038 −15.652 ± 1.890 −124.540 ± 17.142 ZINC16919178 −202.964 ± 19.700 −14.995 ± 12.321 107.288 ± 24.499 −17.775 ± 1.752 −128.446 ± 13.116 ZINC08792350 − 210.397 ± 12.126 −5.732 ± 4.973 76.102 ± 13.673 −19.303 ± 1.248 −159.330 ± 14.200 ZINC01510656 −145.554 ± 11.730 −3.108 ± 4.634 40.772 ± 10.125 −14.615 ± 1.279 −122.505 ± 12.199 ZINC08877407 −101.513 ± 9.428 −7.238 ± 5.481 40.488 ± 23.110 −9.935 ± 2.168 −78.199 ± 21.645 Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 8 of 11 Fig. 5 Plot depicting the amino acid residues of Nsp4 contributing to the binding with natural compounds. Red, green, blue, orange, and violet colours represent ZINC38167083, ZINC16919178, ZINC08792350, ZINC01510656, and ZINC08877407, respectively identified as the most frequent virus linked to PRDC bonds. Ala38 and Phe151 participated in interaction [35–39]. Furthermore, PRRSV has been shown to impair through amide-pi and pi-pi t-shaped bonding. Addition- the host immune system, which can lead to more serious ally, Asp64 contributed to interaction through the pi- secondary infections, and chronic disorders [35]. The in- anion bond. ZINC16919178 bonded with Nsp4 at pos- volvement of Nsp4 in PRRSV replication and pathogen- ition Asn11 by one conventional hydrogen bond. In esis is decoded and recommended as one of the key addition, amino acid residues Phe3, Thr5, Ser9, Leu10, molecular targets for drug development [14]. Therefore, Phe26, Ile123, and Gly127 formed van der Waals inter- identification of Nsp4 inhibitors is needed to prevent actions; Pro78, Tyr92, Leu94, Val99, and Pro101 formed and manage the disease. Natural compounds have made alkyl and pi-alkyl bonds. ZINC08792350 interacted with immense contributions in the identification of lead mol- Nsp4 Thr5, Ser9, Asn11, Val76, Pro78, Ile123, Phe166, ecule(s) with antiviral potential. It is believed that the and Asp192 through van der Waals interactions; Phe3 disease can be controlled successfully by developing and Tyr92 formed pi-pi t-shaped bonding. In addition, small molecules that can inhibit Nsp4 activity linked the amino acid residues Leu10, Pro101, Leu196 contrib- with pathogenesis [14]. In the present study, computa- uted to interaction through pi-alkyl bonding; Arg90 and tional approaches are utilized for the identification of Val99 formed pi-anion and pi-sigma bonds with possible lead compounds via molecular docking of nat- ZINC08792350, respectively. ZINC01510656 bonded ural compounds database through structure-based vir- with Nsp4 at Thr5, Asn11, Val76, Phe26, Leu94, and tual screening followed by downstream analysis. Gly127 through van der Waals interactions; amino acid Structure based-virtual screening is a powerful computa- residues Leu10, Pro78, and IIe123 formed pi-alkyl bonds, tional approach that is used to investigate important lead and Ser9, Tyr92, Val99 contributed in interaction by molecule(s) from a big set of a compound database carbon-hydrogen bonding, pi-pi t-shaped, and pi-sigma based on the lowest binding energy required for stabiliz- interactions, respectively. ZINC08877407 formed con- ing the protein-ligand complex [40]. ventional hydrogen bonds with Nsp4 at position His39, From the structure-based virtual screening, we have Ser118, ASP117, and Ser136; amino acid residues Gly63, selected the top ten natural compounds that show inter- Asp64, Ala114, Cys115, Gly116, Thr134, Lys138, and action with key residues. Further, protein-ligand analysis Thr145 contributed to protein-ligand interaction of the top 5 compounds demonstrated that the through van der Waals. Additionally, Gly135 formed a ZINC38167083 interacted with Nsp4 and formed one carbon-hydrogen bond, and His133, Ile143, and Phe151 conventional hydrogen bond at position Gly63. Besides, interacted through pi-anion, pi-alkyl, and pi-sigma bond- amino acid residues Ser18, His39, Leu41, Thr42, Gly43, ing, respectively. Medicinal chemists have traditionally Asn44, Thr134, and Thr145 were involved in van der been interested in noncovalent interactions that are indi- Waals interactions, and Val61 and Ile143 formed alkyl cative of attraction, directed intermolecular forces in Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 9 of 11 their search for the “glue” that keeps ligand and their of protein-ligand complexes [51]. MM-PBSA analysis molecular target together. In recent years, with the rapid demonstrated that the compounds ZINC38167083, increase in the number of solved biomolecular structures ZINC16919178, ZINC08792350, and ZINC01510656 can and the performance enhancement of computational act as a potential lead for inhibition of Nsp4 [52, 53]. methods, it is now possible to provide a more thorough Whereas, ZINC08877407 was not recommended as a understanding of protein-ligand interaction [41]. There- lead because their binding energy was found to be higher fore, based on the results, it was concluded that the as compared to other compounds. screened compounds can inhibit the virulence activity of In past years, the identification of lead compounds for Nsp4 [14]. Besides, the results of physicochemical prop- drug development take much time and cost as well as erties prediction suggest that the screened compounds required good infrastructure experimental facilities [11, demonstrated good drug-like behavior and could be con- 54]. Due to advances in structural biology, computer sci- sidered for further analysis [42, 43]. Therefore, 100 ns ence, and bioinformatics, it becomes easy to find out pu- MD simulation analysis was conducted for Nsp4 and top tative molecule(s) by a screening of a big database that 5 natural compounds i.e. ZINC38167083, has a strong affinity with the target for experimental ZINC16919178, ZINC08792350, ZINC01510656, and evaluation [24, 55]. It saves the cost and time of the sci- ZINC08877407, respectively with Nsp4 to evaluate the entific community. Most of the medicines available in dynamic behavior of protein and protein-ligand com- the market are from a natural source or it is a derivative plexes. It is recognized as a powerful approach for pre- of naturally occurring molecules [11, 24]. Natural com- dicting the conformational stability of macromolecules pounds have immense potential to inhibit virus and before and after ligand binding, besides the simulated pathogenic proteins and act as antiviral drugs [56–58]. data can be utilized for calculation of real binding energy The results presented in this work are, therefore, in- of small molecules concerning time along with a contri- formative for understanding the antiviral potential of bution of binding amino acid residues present in the suggested compounds as therapeutics for PRRSV. It macromolecular target [44]. Several structural parame- might be also useful for the prevention of pigs and other ters were calculated, including RMSD, RMSF, Rg, SASA, animals from different viral diseases [59, 60]. H-bonding, PCA, and gibbs free energy [45–48]. The RMSD value indicated that all of the complexes were Conclusions stable and creating an equilibrated trajectory for further PRRSV infection is a main concern for the global swine investigation. As a result, we determined RMSF, Rg, industry, and there is a need to identify novel and effect- SASA, PCA, and Gibbs free energy to determine the na- ive therapeutic agents. Given the importance of Nsp4 in ture of each system subjected for MD simulation. Drug PRRSV replication and pathogenesis, we employed com- selectivity, metabolization, and stability all require H- putational and MD approaches to screen and identify bonds. To better understand the H-bond and its contri- natural compounds as novel inhibitors of Nsp4 activity. butions to the overall stability of each system, an H- The results identified four possible lead compounds that bond analysis of natural compounds-Nsp4 complexes represent potentially effective drug-like inhibitors for ap- were calculated. The hydrogen bonding study indicates plication as antiviral therapeutics. Further studies are that all of the Nsp4-complexes are stable and made warranted to confirm these findings through experimen- bonding with essential catalytic residues [49]. The over- tal and clinical evaluations in order to promote future all analysis revealed that each complex was stabilizing management of PRRSV infection. after 70 ns indicating better interaction with Nsp4 in Abbreviations terms of stability that is required for its inhibition. Fur- 2D: Two-dimensional; 3CLSP: 3C-like serine protease; HBA: Hydrogen-bond ther, MM-PBSA binding free energy and residual bind- acceptor; HBD: Hydrogen-bond donor; MD: Molecular dynamics; MM- ing energy were calculated to assess the binding PBSA: Molecular mechanics Poisson–Boltzmann surface area; MW: Molecular weight; Nsp4: Non-structural protein 4; ORF: Open reading frame; PL1/ affinities of natural compounds with Nsp4. For deter- 2pro: Papain-like protease; PME: Particle mesh Ewald; PRRSV: Porcine mining the binding free energy of protein–ligand com- reproductive and respiratory syndrome virus; PSA: Polar surface area; plexes by using MD simulation trajectory, it is a VWSA: van der Waals surface area frequently used and well-accepted method [33, 50]. The Acknowledgements strength of the binding contacts between the ligand and The authors thank Chung-Ang University, Anseong-si, the Republic of Korea the target protein is measured by ligand binding affinity, for providing High-Performance Computing (HPC) and other necessary which is directly linked to ligand potency. In the field of facilities. drug discovery, its evaluation is crucial. Furthermore, in Authors’ contributions favorable reactions, the free energy is negative. So, low- JMK designed the experiments and supervised the research; RKP performed ering the binding energy improves interactions, and low experiments, analyse results and wrote the manuscript. YJS helped in binding energy corresponds to the high binding affinity analysis. All authors read and approved the final manuscript. Pathak et al. Journal of Biological Engineering (2022) 16:4 Page 10 of 11 Funding 14. Tian X, Lu G, Gao F, Peng H, Feng Y, Ma G, et al. Structure and cleavage This research was supported by the Basic Science Research Program through specificity of the chymotrypsin-like serine protease (3CLSP/nsp4) of porcine the National Research Foundation of Korea (NRF) funded by the Ministry of reproductive and respiratory syndrome virus (PRRSV). J Mol Biol. 2009;392(4): Education (NRF-2018R1A6A1A03025159). 977–93. https://doi.org/10.1016/j.jmb.2009.07.062. 15. Shi Y, Lei Y, Ye G, Sun L, Fang L, Xiao S, et al. Identification of two antiviral inhibitors targeting 3C-like serine/3C-like protease of porcine reproductive Availability of data and materials and respiratory syndrome virus and porcine epidemic diarrhea virus. Vet All data generated or analysed during this study are included in the Microbiol. 2018;213:114–22. https://doi.org/10.1016/j.vetmic.2017.11.031. manuscript. 16. An TQ, Li JN, Su CM, Yoo D. Molecular and cellular mechanisms for PRRSV pathogenesis and host response to infection. Virus Res. 2020;286:197980. 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Journal

Journal of Biological EngineeringSpringer Journals

Published: Feb 22, 2022

Keywords: PRRSV; Swine; Nsp4; Molecular dynamics; Protein–ligand interaction

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