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The Integration of Pharmacophore-Based 3D-QSAR Modeling and Virtual Screening in Identification of Natural Product Inhibitors against SARS-CoV-2

Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC 50 <250 µM from the literature data. A 3D-QSAR model has been developed and validated to be utilized in virtual screening. Results: The best pharmacophore models have been utilized as 3D queries for virtual screening to gain promising inhibitors from a data set of thousands of natural compounds retrieved from PubChem. The hit compounds were subsequently used for molecular docking studies to investigate their affinity to the 3D structure of the SARS-CoV-2 receptors. The ADMET properties calculate for the hits with high binding affinity. Conclusion: The study outcomes can help understand the molecular characteristics and mechanisms of the binding of hit compounds to SARS-CoV-2 receptors and promising identification inhibitors that are likely to be evolved into drugs.


Introduction
The world was dismayed by the outbreak of a fatal infection at the beginning of 2020. The new severe viral cute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly spreads from birthplace in Wuhan, China, to countries and continents boundaries. 1 The spread of this virus is mainly by close contact with other people, direct contact with infectious materials, and the respiratory droplets of a person infected with the virus when they cough or sneeze. 2 The fatality rate of this new CoV is much less than SARS and MERS, and it is around %2 in China. Rapid and active transmission of the virus from human to human has led to its spread worldwide. [3][4][5] SARS-CoV-2belongs to the beta coronavirus family. 6 The SARS-CoV-2 showed the genetic similarity of about 79.6% compared to SARS-CoV and 50% sequence identity to MERS-CoV. Still, it is considered distinct from two of these high pathogenic viruses. 7 Angiotensin-converting enzyme 2 (ACE2) is the target receptor of 2019-nCoV. By entering the virus into the host cell, the positive genomic RNA connects directly to the host ribosome to translate two abundant, conterminal polyproteins. They are processed by proteolysis into components for packaging new virions. 8 The papain-like protease (PLpro) and the coronavirus main proteinase (3CLpro) plays an essential role in the proteolysis process. 9,10 when spike protein (S) binds the host cellsurface molecules, the replication of CoV starts inside the host cell. 11 The S protein contains two practical subunits that are in charge of linking to the host cell receptor (S1 subunit) and blending the viral and cellular membranes (S2 subunit). This protein is an ideal target for developing a vaccine and antiviral drugs due to its pivotal roles in receptor linkage and membrane fusion. Recent trial agents reported some potential therapy for SARS-CoV-2, such as remdesivir 12 and combined protease inhibitor lopinavir-ritonavir, 13 that are used to treat against SARS and MERS-CoV, but their efficacy is still unclear and needs further evaluation. Previously, native ligand peptidelike inhibitor PRD_002214 (N3) inhibitor was identified S95 | Pharmaceutical Sciences, 2021, 27(Suppl 1), S94-S108 and reported in the protein databank (PDB) with Mpro of the SARS-CoV-2 in the type of crystal structure complex (PDB ID, 6LU7). The native ligand (PRD_002214) was selected for ligand screening in the ZINC database using a pharmacophore-based online tool (ZINC Pharmer). Although many clinical trials are in progress, scientists have yet to find approved drugs or vaccines for SARS-CoV-2, and finding a cure can still be a great aid to the international community. 14 The development of novel drugs is a time-consuming process, and generally, several years of work are required for clinical approval. Hence, computational methods can be utilized for the design and engineering of drugs. [15][16][17] The short time requirements of computational methods are conducive for high throughput screening of available medications to identify potential drugs for new diseases and predict the adverse effects of novel drugs. [18][19][20] The pharmacophore-based ligands screening, 3D-QSAR modeling, and molecular docking were applied to discover new inhibitors acting against the SARS-CoV-2 essential receptors (main proteinase, spike ectodomain structure, and receptor-binding domain). In this regard, ligandbased pharmacophore models were built based on the structures with functional affinity inhibition displaying in vitro with IC 50 value <250 µM. The validation of the ligand-based pharmacophores was implemented by enrichment analysis; then, the validated models were used for the HTVS and docking analysis. Finally, the fifteen structures were proposed as lead compounds based upon the validated pharmacophore models, scored by molecular docking and 3D-QSAR predictions.

Computational details
The PHASE 3.4 module performed the generation of Pharmacophore features and 3D QSAR models in the Maestro 9.3 (Schrödinger, LLC). Virtual screening based on pharmacophore models and docking studies was executed utilizing the PHASE 3.4 module and Glide 5.8 module (Maestro 9.3). The prediction of ADME properties was carried out by QikProp 3.5 in Maestro 9.3

Preparation of protein structures
Three crystal structures of SARS-CoV-2 were obtained from the protein data bank (PDB). For the SARS-CoV-2 main protease (Mpro), the selected crystal structure was 6LU7, whereas 6VYB and 6VW1 were considered for spike ectodomain structure and receptor-binding domain, respectively. The preparation of all protein structures was performed by the protein preparation wizard (Maestro 9.3). The protein grid box was created around the active pocket for using the protein grid generation module of the Glide program. The native ligand peptide-like inhibitor PRD_002214 (N 3 ) was settled as the core of the grid box of the 6LU7; for the 6VYB and 6VW1, the sitemap module was used for a generation of the active site. We used the default setting for the van der Waals scaling as well as partial charges. The size of all structures binding pocket was settled to be proper for ligands with a dimension of 20 Å.

Chemical structure preparation
Conversion of 2D to 3D structures was performed by LigPrep 2.5 through the Schrödinger suite package. The hydrogens added to the structures, and salts have been removed; the possible stereoisomers generated using Epik (pH 7±2) and energetically minimized. The generation of 3D conformers was performed based on ≤10 per rotatable bond and ≤100 per ligand, and the RMSD cutoff value of 1.0 Å removed the redundant conformers. The OPLS3 force field performed the energy minimization of structures, and then to exclude high energy structures, filtration of conformers was done based upon a relevant energy window of 10.0 kcal/mol.

Creating pharmacophore-based models
Thirty-one structures with good affinity inhibition (antiviral drugs used for SARS and MERS) with IC 50 value and divers structures were collected from previously published data 11,[21][22][23][24] and prepared as said (The structures are reported in Table S1 in supplementary data). Generation of Pharmacophore hypothesis and statistical analyses carried out by PAHSE to form the 3D-QSAR model. After conformational development, the pharmacophore hypothesis was created on each ligand structure. The following formula shows the conversion of IC50 value into pIC50 to obtain a linear association in the QSAR equation as follow: pIC 50 = -log IC 50 (1) The IC 50 is the half-maximal response inhibition The structures and IC 50 value of these compounds showed in supporting information. We set an activity threshold range of pIC 50 for actives ≥ 5.5 and inactive < 5.0. The evaluation and identification of best pharmacophores obtained by the scoring of the generated pharmacophore hypothesis. That was done by the adjustment of the site points, vector alignment, and volume overlap. 25

Enrichment calculations
The enrichment calculation was used to investigate and validation of the quality of the pharmacophore hypothesis. EF(X %) is the number of actives gained after the random screening of X% of the decoy set. 26 The validation set was composed of 1089 molecules containing 1000 decoy set (retrieved from Schrödinger database), and 89 known inhibitors were used to calculate enrichment factors. The significance of pharmacophore models was ensured based on the Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), 27 Enrichment Factor (EF), Goodness of Hit (GH) and Robust Initial Enhancement (RIE). The EF and GH values will be determined by using equation 4 and 5. 28 Ht is the sum of compounds, and Ha is the sum number of actives in the hit list. D the sum number of molecules and A the total number of actives in the decoy set. These two parameters (EF manual and GH metrics) are utilized to calculate the entire dataset.

Pharmacophore-based 3D QSAR modeling
Atom-based partial least square regression (PLS) method was utilized for the generation of 3D QSAR models. The default parameters were used for PLS. For three top-scored pharmacophore hypothesis, QSAR models were created by training compounds aligned to the pharmacophores at least on three sites with four features and sections for pharmacophores, where five components showed best alignments. 25 A rectangular grid was set into cubes of 1 Å to involve occupied space by the training set active ligands.
Cubes were stared as filled in a hypothesis pharmacophore site within the relevant sphere's radius. A compound can be described by a string of zeros and ones based upon the cube's occupancy variation and the various types of sites that lodge in these cubes. It led to binary values as 3D descriptors. PLS generated 3D-QSAR models to the pool of binary-valued variables. 25 The validation of QSAR models was performed by predicting activities of test set compounds. 29

External validation and PLS analysis of QSAR models
For the generated 3D-QSAR models, we used the statistical method of partial least-squares analysis. With the aid of the "Leave One Out" (LOO) method, the predictive ability of the models and cross-validation analysis was evaluated. R 2 cv (cross-validated coefficient) was calculated as the following equation (4): Where Y predicted , Y observed , and Y mean are the predicted, observed, and mean values of the target property (pIC 50 ) of the training set, respectively. 30 The predictive correlation coefficient (r pred 2 ), based on molecules of the test set, was calculated as: 31 Y pred (test) and Y (test) show predicted as well as observed activity values respectively of the test set and Ȳ training shows the mean activity value of the training set compounds.
The following formula gained a high correlation coefficient R (or r 2 value): Y i and Ỹ i indicate the observed and predicted activities of the test set compounds, respectively, and Y̅ o and Y̅ p are the mean values of the observed and anticipated activities of the test set molecules. Utilizing regressions of Y i against Ỹ i , or Ỹ i against Y i through the origin, Y ro = kỸ and Ỹ ro = k′Y, respectively, 32 the slopes k and k′ were calculated as: R 0 2 and R′ 0 2 define the correlation coefficients for the regression lines through the origin (Y ro ) is described by R 0 2 and R′ 0 2 taht were calculated as follows: 33 The modified r 2 parameter that is utilized for the whole set compounds, r m 2 value is described by the following equation: 33 r 2 is the non-cross-validated correlation coefficient gainedfrom the PLS process, and R 0 2 is gained from eq8a. According to literature, 32,33 our 3D-QSAR models are valid and reliable if: r 2 >0.5, r 2 cv (Q 2 )>0.5, the value of R 0 2 and R′ 0 2 should be close to r 2 , [(r 2 − R 0 2 )/r 2 ]< 0.1, [(r 2 − R′ 0 2 )/ r 2 ] < 0.1, the values of k and k′ should be 0.85 ≤ k ≤ 1.15 or 0.85 ≤ k′ ≤1.15, and r m 2 > 0.5. Finally, the 3D-QSAR models build with the high prediction ability and the related pharmacophores to find the best models and new lead compounds.

Pharmacophore screening of 3D databases and 3D QSAR model
A 3D structure of two thousand natural compounds was energy minimized, and conformations were generated for making a PHASE library. Searching the natural compounds database with the best pharmacophores similarity was ( ) S97 | Pharmaceutical Sciences, 2021, 27(Suppl 1), S94-S108 carried out utilizing the PHASE module, and the tolerance distance set to 2.0 A° in the hypothesis. In pharmacophore screening, compounds must match 3 or 4 pharmacophoric sites on a hypothesis with four features and match four sites of the hypothesis with five or more sites. The hits derived from virtual screening ranked based on fitness scores. 25 This score shows the alignment of hits on the hypothesis and is based on on-site matching, RMSD, volume terms, and vector alignment. The fitness score range was 0 to 3, where the score of 3 indicates that our ligands have the maximum alignment with the hypothesis.

Molecular docking
A 3D database of hits with the best fitness scoring was used for docking study against different types of SARS-CoV-2 receptors. The docking study of these compounds against the active site of the protein structures was performed by Glide 5.7 (Glide, version 5.7, Schrödinger, LLC, New York, NY, 2011) in three steps: HTVS, standard precision (SP), and extra precision (XP). RMSD values calculation between the native and docked conformations were carried out utilizing core pattern comparison in Glide. The default settings were being used for both the grid generation and the docking.

ADME properties prediction
The QikProp (version 4.5, Schrodinger, LLC, and New York, NY) was utilized to study The ADME properties of the hits obtained from the virtual pharmacophore screening that showed high XP docking score. QikProp is being used for analyzing the related pharmaceutical futures, such as octanol/water log Ps, log S, predicted brain/blood partition coefficient (QPlogBB), human oral absorption, central nervous system activity, Caco-2, and Lipinski rule of five. 34

Protein preparation
Three crystal structures of SARS-CoV-2 with low resolution were downloaded from PDB. Protein preparation wizard prepared these proteins, and grid box generation was carried out by the Glide program. The active pocket of the SARS-CoV-2 main protease (PDB: 6lu7) was created around the ligand N 3 , and for indicating the active sit of spike ectodomain structure (PDB: 6vyb) and receptorbinding domain (PDB: 6vw1), site map generation was used. Finally, five site maps were generated for each receptor, and sites with the highest site score were used for grid generation. Table 1 indicates the grid generated for each protein.

Ligand-based pharmacophore models
We generated the ligand-based pharmacophores based on antiviral inhibitors acting against SARS and MERS by the PHASE module (Schrödinger). These compounds were divided into active ligand and inactive ligands. Finally, 14 pharmacophore hypothesis was generated, and then three pharmacophore hypothesis were chosen based upon their excellent survival activity, site scores, best active adjustment, and the number of matches ( Table 2). Hypothesis 1 (AADDR) indicated two hydrogen-bond acceptors, two hydrogen-bond donors, and one aromatic ring. Hypothesis 2 (AAADR) proved three hydrogen-bond acceptors, one hydrogen-bond donor and one aromatic ring, and hypothesis 3 (AAHR) showed two hydrogen bond acceptors, one hydrophobic and one aromatic ring as features (Figure 1). Table 3 shows the distances between the features of pharmacophore models. Validation of the pharmacophores for virtual screening was needed. Hence, the enrichment factor was carried out for validation of the generated pharmacophores. The results in Table 4 show that hypothesis 1 (AADR) with the best survival score also indicated the highest EF1%, BEDROC, and GH values compared to other hypothesis. This could be inferred that the prediction ability of hypothesis AADDR is much more than the other hypothesis. The validation of these three hypothesis was carried out for QSAR predictability and PLS analysis. Figure 2 indicates the active and inactive ligands alignment based upon the best pharmacophore model (AADDR). Figure 2A clearly shows that all the most active ligands match entirely over all the pharmacophoric features -AADDR, whereas in Figure 2B all the least active ligands match the pharmacophore less precisely. Pharmaceutical Sciences, 2021, 27(Suppl 1), S94-S108 | S98    Suppl 1), S94-S108

The generation of 3D-QSAR models and PLS analysis
The internal and external statistical validation is necessary for developing excellent 3D-QSAR models, which could show more reliable predictions. The more reliable estimation obtains from the models that can accomplish statistical validation parameter boundaries. Randomly, nineteen chosen for the training and twelve compounds in the test set were selected for a generation of the 3DQSAR model. Based on the LOO method, significant parameters were gained and validated using PLS analysis. Based on PLS analysis, hypothesis1 (AADDR) presented good external predictive ability compared to others. Hypothesis 1 showed an excellent R 2 value (0.9985) for the training set and a good predictive power Q 2 (0.5698) for the test sets, with an SD of 0.068 and F value of 3347.6. The r 2 pred predicted the unity of the model with a value of 0.5418 (Table 5). To attain functional predictive capacity in a QSAR model, R 2 (the accepted LOO-cross validated the value of training set) should show a value greater than 0.6, and Q 2 (the accepted LOO cross-validated value for test set) should be higher than 0.55. Also, a good model should show a low value of Standard deviation (SD) (< 0.3), a minimum root-mean-square error (RMSE), .and a high value of variance ratio (F). zQ 2 is commonly utilized for assessing the prediction ability of a QSAR model. Although a high value of Q 2 is imperative, Q 2 alone is not an adequate condition for a QSAR model to possess a high predictive ability. A high value of correlation coefficient R (or r 2 ) of the observed and predicted external test set activities is also needed for a reliable model. In this study, the model with the best predictive ability was identified by the correlation coefficient R 2 = 0.774. The R 0 2 and R′ 0 2 values were 0.988 and 0.986 respectively, which further utilized for calculating the association between r 2 , R 0 2 , and R′ 0 2 , [(r 2 − R 0 2 )/r 2 ] and [(r 2 − R′ 0 2 )/r 2 ], which gave a value of−0.751 and −0.753, respectively, within statistical limits (Table 6). Also, r m 2 -a parameter of modified r 2 regarded for the better external predictive potential for the whole set of compounds-was calculated and found to be 0.864. Hence, the prediction ability of the 3D-QSAR model in foretelling the active compounds was confirmed based upon the external numerical parameters. Figure 3 shows   Pharmaceutical Sciences, 2021, 27(Suppl 1), S94-S108 | S100

Contour map analyses
To generate contour maps, we used the last validated hypothesis AADDR gained from 3D-QSAR. With these maps aid, the substitutions place or atoms replacement can be identified to improve bioactivity. We can predict bioactivity by visualizing and realizing the Contour maps of the most active and least active ligands. It could lead to finding new scaffolds with valuable biological activity. The contour maps of the most and least active ligand are shown in Figure 4A and B indicate contour maps of the hydrogen-bond donor effect for the most productive and the least active compound with their most favorable zone, blue cubs, and unfavorable zone, red cubs. Hydrogenbond donor mapping displayed that promising zones are situated near the nitrogen atoms amid functional groups of the most active ligand; this suggests that hydrogen bond donor groups at this location could increase the activity. The presence of hydrogen donor and acceptor groups could establish polar contact with coronavirus receptors. Also, the located blue parts around the hydroxyl group of the least active compound indicate that the hydrogen donor group's presence with cyclic rings in the scaffold of hits might be required for bioactivity. Figure 4C and D show contour maps of the hydrophobic effect for the most and the least active compound with their favorable region, yellow cubs, and unfavorable regions, purple cubs. The favored yellow part around aromatic rings indicates that the presence of terminal hydrophobic rings could increase the activity of hit compounds, and the unfavorable purple cubs around ring hydrogen moieties suggest that increasing the length of a carbon chain could lead to Figure 4E and F show contour maps of the electron-withdrawing effect with the most favorable region and unfavorable regions, pale pink cubs, and green cubs, respectively. The favored areas were located nearby hydrogen-bond acceptors and acceptor features of most active ligand, which showed that hydrogen-bond acceptor moieties were very much needed for bioactivity, and these groups should be considered in lead adjustment. As shown in Figure 4F the unfavorable area enclosed the ring moiety that exposed the compound's inactivity in the least active compound.

Pharmacophore models-based molecular modeling
In this study, virtual screening of 2000 natural compounds was carried out based on 3D-QSAR and its relative pharmacophore models to help find the lead compounds. By applying a fitness score of more than 1, the HTVS data filtered and yielded 1174 compounds. The fitness score is a value of how well a compound matches the pharmacophore hypothesis. Therefore, the hit compounds with a great fitness score are likely very active. The obtained ligands were docked to the receptors utilizing the default grid, and finally, 926 compounds with the docking score above than−6 kcal/mol, fitness score > 1.2, and the number of H-bonds > 2 were selected as hits. The HTVS filtered ligands were subjected to Glide SP (standard precision) docking program, and 654 ligands were chosen based upon their docking score>-7 kcal/mol. Eventually, a docking analysis of hits was carried out using the Glide XP simulation. The best 15 ligand molecules with their docking score, RMSD value, fitness score, and predicted pIC 50 are listed in tables 7-9. In this study, we used the RMSD value to validate docking results. In fact, the docking accuracy is assessed by the lowe value of RMSD (RMSD<1.5 Å is wise to consider), and most hit ligands showed a RMSD value of less than 1 Å. Neoeriocitrin, with the docking score, -11 kcal/mol showed a more binding affinity than other compounds against spike ectodomain structure (PBD: 6vyb) ( Table 7). Docking results showed that this compound interacts by hydrogen bonding, pi-pi and pi-cation interactions with active pocket residues of 6VYB ( Figure 5). The analysis of the binding affinity of five top-ranked compounds against 6VYB shows that the significant binding includes amino acid residues Asp 428, Asp 228, Lys 222, Lys 41, and His519. Summarizing the between the five best ligands and the amino acid involved in the binding pocket of the crystal structure of the main protease (PBD: 6LU7) and five best ligands in the co-crystal structure, Gln127, Lys 5, Lys 102, Lys 137, Asp 289, and Glu290 were found to be the most frequently bound with the ligands (Table 8). Docking results of the five best compounds against the 3D structure of the receptor-binding domain (PBD: 6vw1) depict that Eriocitrin with docking score -10 has the best complements to the protein binding site (Table  9). Hydrogen bonds are the primary interaction between these compounds and active site residues of 6vw1, and amino acid residues Asn370, Thr 372, Trp436, and Val 367 play a significant role in docking interactions (Figure 7). In this study, we used native ligand peptide-like inhibitor PRD_002214 (N3) as a positive control. Against 3D structure of spike ectodomain (PBD: 6vyb), this ligand showed docking score -4.86 kcal/mol, and established   Table 8. Glide docking energy, RMSD, fitness score, and predicted activity of top five compounds against the SARS-CoV-2 main protease receptor.

3D QSAR Modeling and Virtual Screening of Natural Product Inhibitors against SARS-CoV-2
Pharmaceutical Sciences, 2021, 27(Suppl 1), S94-S108 | S104  Figure S1-S3). The comparison of N 3 against SARS-CoV-2 receptors and the interactions of hit ligands showed that our hit ligands interacted with the active pocket of SARS-CoV-2 receptors the same as N 3 did. The docking results of hit compounds were significantly correlated with the contour map analysis results and the number of hydrogen bond interactions between ligands and the amino acid residues. Among these hits, Glycyrrhizin, narirutin, and naringin indicated superior predicted activity based upon the generated 3D-QSAR model. Figure S4-S6 shows the binding mode of these hit compounds.

ADME predictions
The pharmaceutical properties of the15 lead compounds were evaluated to study drug resemblance and predict the drug's pharmacokinetics, involving ADME (absorption, distribution, metabolism, and excretion). We used the QikProp module of the Schrodinger software for assessing the drug-likeness of the 15 lead compounds by analyzing pharmacokinetic restrictions needed for ADME (Table 10). The factor partition coefficient (QPlogPo/w) is a crucial parameter to understand the compound's absorption and distribution, ranging from −2.770 to 2.221  Pharmaceutical Sciences, 2021, 27(Suppl 1), S94-S108 | S106 for the lead compounds. Most of the lead compounds indicated an excellent partition coefficient (QPlogPo/w). The factors like MW, H-bond donors, acceptors, blood-brain barrier, permeability (QPPCaco), predicted aqueous solubility (QplogS), and human oral absorption was also calculated for drug-like behavior of the lead compounds.

Conclusion
Three crystal structures of SARS-CoV-2 and thirty-one antiviral drugs with high inhibitory potential against SARS and MERS were utilized for pharmacophore analysis integrated with HTVS as well as docking studies to retrieve natural lead compounds. This study aimed to generate the pharmacophore hypothesis and 3D-QSAR models and utilize the models on virtual screening to investigate and identify new scaffolds, and predict the approximate hit molecules activity based on the best 3D-QSAR model. In this work, 3D pharmacophore models were developed from 31 antiviral compounds and the three best pharmacophore models were performed as 3D queries for virtual screening. Then, the hit ligands were submitted to molecular docking studies and RMSD calculations. Finally, 15 compounds were chosen as hit compounds based on the scoring function. These hit compounds were evaluated for ADME properties. From the overall analyses, it could be concluded that the best three pharmacophore models reflect the characteristics of SARS-CoV-2 inhibitors and these pharmacophore models could be utilized as a fast model to help in the identification and discovery of SARS-CoV-2 inhibitors. Also, both the 3D-QSAR Name MW a (<500) DonorHB (<5) Accept HB (<10) QPlogPo/w b (-2 to 6.5) a MW -molecular weight; b logPo/w -predicted octanol/water partition coefficient; c predicted apparent Caco-2 cell permeability in nm/s; d logBB -predicted brain/blood partition coefficient; e logS -predicted aqueous solubility; f PSA -polar surface area; g HOA -predicted qualitative human oral absorption: 1, 2, or 3 for low, medium, or high S107 | Pharmaceutical Sciences, 2021, 27(Suppl 1), S94-S108

Rule of Five
visualization and docking result of hit ligands against the target receptors presented considerable information about the relationship between structure and activity.