key: cord-0827544-5i2abiht authors: Mohebbi, Alireza; Askari, Fatemeh Sana; Sammak, Ali Salehnia; Ebrahimi, Mohsen; Najafimemar, Zahra title: Druggability of cavity pockets within SARS-CoV-2 spike glycoprotein and pharmacophore-based drug discovery date: 2021-06-01 journal: Future virology DOI: 10.2217/fvl-2020-0394 sha: 372ac7b5e334784c2b5c208110bb6582999397bb doc_id: 827544 cord_uid: 5i2abiht Aim: Virus spike glycoprotein of SARS-CoV-2 is a good target for drug discovery. Objective: To examine the potential for druggability of spike protein for pharmacophore-based drug discovery and to investigate the binding affinity of natural products with SARS-CoV-2 spike protein. Methods: Druggable cavities were searched though CavityPlus. A pharmacophore was built and used for hit identification. Autodock Vina was used to evaluate the hits' affinities. 10 chemical derivatives were also made from the chemical backbone to optimize the lead compound. Results: 10 druggable cavities were found within the glycoprotein spike. Only one cavity with the highest score at the binding site was selected for pharmacophore extraction. Hit identification resulted in the identification of 410 hits. Discussion: This study provides a druggable region within viral glycoprotein and a candidate compound to block viral entry. Step 1 Step 1 Step 1 Step 1 Step Step 1 3D structure prediction Step 1 Pharmacophore modeling Step 1 Preparation of SARS-CoV-2 spike glycoprotein Prediction of 3D protein structures from amino acid sequence SARS-CoV-2 glycoprotein (accession number: YP 009724390.1) was performed by using the I-TASSER server (http://zhang.bioinf ormatics.ku.edu/I-TASS ER) [36] . In comparison with homology-based tools, I-TASSER server generates full-length 3D protein structure that enables one to make amino acid changes. The best model with highest confidence score was used for the study. The cavity with highest drugability score was used for further studies. Finding druggable cavity pockets of SARS-CoV-2 spike glycoprotein Predicted 3D structure of SARS-CoV-2 Spike glycoprotein used for analysis of its binding cavities by using CavityPlus (www.pkumdl.cn/cavityplus) server [37] . CAVITY module was used to detect potential binding sites on the surface of the given protein structure. The druggable cavity with the highest drug score was used for pharmacophore modeling with CavPharmer module. The resulting pharmacophore feature.mol2 file was manually modified to increase chance of hit identification. Briefly, excluded volume center features were removed from the file. Additionally, positive/negative electrostatic centers were reduced to one. Further features including h-bond donor/acceptor and hydrophobic centers both were reduced to minimum three. Two screening servers ZINCPharmer [38] (206,433,075 conformers of 21,777,093 compounds) and Pharmit [39] (1,599,077,712 conformers of more than 300 million compounds) were used for pharmacophore-matched hit identification. Briefly, pharmacophore features file was uploaded in each server and it was screened for shape-matched hits. Hits were checked for duplication by using OpenBabel [40] software command line. Affinity of identified hits was further investigated by using molecular docking. For this purpose, the open-source tool Autodock Vina [41] was used in the setting of PaDEL-ADV (www.yapcwsof t.com/dd/padeladv/). Vina is a fast and accurate tool for ligand-receptor docking and with PaDEL-ADV it allows high-throughput screenings of several ligands in one run. Because of large structure of SARS-CoV-2 Spike glycoprotein, docking was performed on the predicted druggable sites only. In this regard, the monomeric viral glycoprotein was treated as receptor in the MGLTools 1.5.6 software (Molecular Graphics Laboratory, The Scripps Research Institute). Accordingly, a grid-box was defined in 3D dimension to encompass the entire cavity. The gridbox center coordinates were 119.371, 145.957, and 141.39 along x, y, and z axis, respectively. The number of grid points and the spacing were kept to default values. Compound(s) with higher affinity to SARS-CoV-2 spike glycoprotein were further optimized by ChemT software [42] . For this purpose, a functional group (R 1 ) was chosen based on interaction analysis of the compound(s) and receptor. R 1 group was substituted with the software pre-existing ten functional groups for building templatebased chemical libraries (Supplementary material, Library.sdf ). Autodock Vina was used to further screen the library for evaluation of their affinities to the receptor. Chemicals descriptor's values were kept as default. A total of 42 cavities were found within the virus spike glycoprotein and five of them were druggable ( Figure 1 ). The druggable cavity No.10 with highest drug score (10041) was selected for pharmacophore modeling. The residues within the cavity No.10 are provided in Supplementary Table 2 . Pharmacophore of druggable cavity No. 10 As it is shown in the Figure 2 , a pharmacophore was modeled with several features. For increasing the chance of hit identification, features of pharmacophore were reduced in a stepwise manner until at least 100 hits were identified. within the SARS-CoV-2 Spike glycoprotein, one lead (compound 38) with the highest affinity to the virus protein was discovered (-10.0 Kcal.mol 1 ). The amino acids involved in interaction with the lead compound were G381, E516, L517, L518, H519, R567, D571 and T572. Of these residues, E516, L517 and D571 were at close contact ( Figure 3 ). According to the identified residues within the viral glycoprotein, Compound 38 was used as a backbone for building chemical compound libraries (Table 1 ). In this regard, nitrogen number four (N4) in Compound 38 10 SMILES was substituted with ten functional groups (R 1 ) to investigate the impact of N4 on Compound 38 affinity to S protein ( Figure 4) . As a result, N4 substitutions with CCN and H functional groups increased the affinity (-10.5 and -10.1 Kcal.mol -1 , respectively) of the compound to the receptor. Drug-likeness and ADME properties of the ten molecules was predicted by SwissADME [43] and is provided in the Supplementary materials (Supplementary Table 1 ). Accordingly, no significant toxicity was predicted. Pandemic SARS-CoV-2 infection requires quick diagnosis and vaccine interventions. Potential drug targets for SARS-CoV-2 include spike glycoprotein, envelope protein, membrane protein, nucleocapsid protein (N), proteases, Nsp1, Nsp3 (Nsp3b, Nsp3c, Nsp3e), Nsp7 Nsp8 complex, Nsp9-Nsp10 and Nsp14-Nsp16, ORF7a, helicase and RdRp [13, 14] . Several studies already worked on drug discovery of viral targets like 3CLpro, PLpro and RdRp [44, 45] , Mpro [45] [46] [47] [48] [49] [50] [51] [52] [53] , Nsp3 [54] , EndoU [55, 56] and spike glycoprotein [57] [58] [59] . The spike glycoprotein plays an important role in virus entry [60] . S1 domain is as a major antigen on the surface of the virus that causes the initial interaction between the SARS-CoV-2 spike RBD and ACE2 receptor [17] . In addition, S2 domain causes viral-cell membranes fusion and so the virus enters the host body. Therefore, this protein can be considered as a potent drug target [20] [21] [22] . In the present study, SARS-CoV-2 spike glycoprotein was screened for druggable cavities. It was found that one major druggable cavity adjacent to the RBD domain of S glycoprotein can be used for pharmacophore-based drug discovery. Virtual library of natural products was used for the virtual screening. Most of the investigated molecules are FDA-approved chemical drugs, hoping to highlight high affinity molecules in the context of drug repurposing [61] . Although some reports are promising, the chance of discovering novel drug candidates is not high due to the low number of chemicals within the drug-bank library [62] . Therefore, we used virtual screening of a library containing a large number of natural products to improve the odds of finding targetspecific hits. Molecular docking was further used for screening and finding compounds with higher affinities for the SARS-CoV-2 spike protein. One chemical compound (compound 38) was identified as a potent inhibitor for blocking SARS-CoV-2. The results of this in silico study suggest that compound 38 might be able to interfere with RBD attachment to human ACE2 receptor. Data were further validated by molecular docking of compound 38 to energy minimized crystallographic structure of S glycoprotein (PDB ID: 6VXX) [63] . The affinity of compound 38 to the S protein was -9.3 Kcal.mol -1 and its binding site was the same as predicted in this study (data are not shown). Two derivative compounds (H and CCN) showed promising interaction energies when functional group N4 of compound 38 was substituted with the functional groups. This indicates minor impact of this residue on compound 38 affinity to SARS-coV-2 S glycoprotein. In vitro research is being undertaken to determine the effectiveness of compound 38 on the viral propagation. This study presents a comprehensive search for significant druggable cavities within the SARS-CoV-2. In addition, millions of natural products have been screened. A chemical candidate was highlighted to block viral entry by interacting with the binding domain of viral spike glycoprotein. The findings of the study presented may be used in future studies on COVID-19 therapy. • SARS-CoV-2 spike glycoprotein is a main target for blocking the virus attachment process. • SARS-CoV-2 spike glycoprotein was searched for druggable cavities. • The best druggable cavity was chosen for pharmacophore-based drug discovery of a library with more than 1 billion compounds. • 410 hits were identified with good matching identity within druggable pharmacophore. • Docking screenings showed one compound (Compound 38) with the highest affinity to the cavity. 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organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.