key: cord-1026528-3u1odzon
authors: Feng, Siqin; Luan, Xiaodong; Wang, Yifei; Wang, Hui; Zhang, Zhiyu; Wang, Yiyang; Tian, Zhuang; Liu, Meixi; Xiao, Ying; Zhao, Yong; Zhou, Ruilin; Zhang, Shuyang
title: Eltrombopag is a potential target for drug intervention in SARS-CoV-2 spike protein
date: 2020-06-12
journal: Infect Genet Evol
DOI: 10.1016/j.meegid.2020.104419
sha: f1e1d584359b8896d2b82a9d7aabb1e2703c2be2
doc_id: 1026528
cord_uid: 3u1odzon

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a current global threat for which there is an urgent need to search for an effective therapy. The transmembrane spike (S) glycoprotein of SARS-CoV-2 directly binds to the host angiotensin-converting enzyme 2 (ACE2) and mediates viral entrance, which is therefore considered as a promising drug target. Considering that new drug development is a time-consuming process, drug repositioning may facilitate rapid drug discovery dealing with sudden infectious diseases. Here, we compared the differences between the virtual structural proteins of SARS-CoV-2 and SARS-CoV, and selected a pocket mainly localizing in the fusion cores of S2 domain for drug screening. A virtual drug design algorithm screened the Food and Drug Administration-approved drug library of 1234 compounds, and 13 top scored compounds were obtained through manual screening. Through in vitro molecular interaction experiments, eltrombopag was further verified to possess a high binding affinity to S protein plus human ACE2 and could potentially affect the stability of the ACE2-S protein complex. Hence, it is worth further exploring eltrombopag as a potential drug for the treatment of SARS-CoV-2 infection.

virulence may be attributable to the differences in the spike (S) gene. SARS-CoV-2 genome sequencing revealed that the S protein receptor-binding domain directly binds the host angiotensin-converting enzyme 2 (ACE2), thereby acting as a receptor for viral entry (Wan, Shang et al. 2020) . Computational modeling of the S protein structure has also confirmed this result (Xu, Chen et al. 2020) , indicating the possibility for drug screening using computer simulation.

The ongoing pandemic is regarded as a "Public Health Emergency of International Concern." However, there are still no specific antiviral drugs and vaccines available.

S proteins comprise a large class of glycoproteins with N-terminal (S1) and C-terminal (S2) domains, which have distinct functions. It is generally believed that the S1 hypervariable region is closely related to coronavirus tropism, while S2 is necessary for mediating membrane fusion. The S1 fragment has pronounced variability, while the S2 fragment also changes in some variants. Therefore, a complete understanding of the pathogenicity of the coronavirus requires detailed insights into the processes of receptor recognition and membrane fusion (Gui, Song et al. 2017) . Because of the indispensability of S protein in viral entrance to host cells, it is a hot target for drug screening to prevent the entrance of SARS-CoV-2 into the host cells.

In this study, we describe a structure-based virtual screening model and subsequent high throughput screening methodology, based on the S protein structure J o u r n a l P r e -p r o o f Journal Pre-proof of SARS-CoV-2 ( Figure S1 ). The data will provide insights into the development of potentially efficacious drugs. More importantly, the virtual screening model also offers a reasonable, economic, and rapid method to screen drugs and find out-of-guide application of approved drugs in other diseases and in future possible epidemic or pandemic situations.

The virtualized S protein, reported by Zhang et al., and cryo-EM S protein structure reported by McLellan et al., were used for the virtualization of the S protein, assessment, characteristic analysis, and drug screening (Wrapp, Wang et al. 2020 , Zhang, Zheng et al. 2020 . The SARS-CoV-2 S protein sequence was downloaded from the NCBI database (https://www.ncbi.nlm.nih.gov/nuccore/NC_045512). SARS-CoV-2 S protein structure was generated using information of the SARS-CoV and SARS-CoV-2 spike glycoprotein structure, downloaded from the PDB database (https://www.rcsb.org/, PDB ID: 5X58, 5WRG, 6U7H, 6CRV, 6LZG). The SARS-CoV-2 spike protein binding pocket was generated using PyMOL software J o u r n a l P r e -p r o o f Journal Pre-proof num_modes = 9). The top 100 drugs were selected for further visual inspection. Of these, 13 with highest docking scores were used for more assays.

TM-align is an algorithm for the comparison of sequence independent protein structure, which generates optimized residue-to-residue alignment based on structural alignment, using heuristic dynamic programming iterations. The TM-score is generated to scale the structural similarity of proteins, with values in the range of 0-1.

A perfect match between two structures is indicated by a value of 1, values below 0.2 indicates two randomly chosen unrelated proteins, and values higher than 0.5 generally assume the same fold in SCOP/CATH. The similarity between spike protein model (QHD43416.pdb), reported by the Zhang lab (Zhang, Zheng et al. 2020 ) and the reported SARS-CoV-2 spike protein structure (6LZG.pdb) was analyzed by TM-align (Version 20190822). For comparison, chain 1 (A316309, 1273 residues) and chain 2 (B316309, 959 residues) of QHD4316 were used.

Equilibrium dissociation constant (K D ) values were measured by surface plasmon resonance (SPR) method using, BIACore™ -S200 at 25°C and a fixed Protein-tag CM5 chip (GE health care) at a certain resonance unit (RU). The details of each chip (human ACE2, SARS-CoV-2 S1+S2 ECD, SARS-CoV2 S2 ECD) are shown in Table   S1 . Briefly, the methylated dextran biosensor chips were activated by hydrochloric acid N-ethyl-N′-(3-dimethylaminopropyl) carbodiimide and N-hydroxy-succinimide, J o u r n a l P r e -p r o o f Journal Pre-proof according to the supplier's instructions. All proteins were acquired from Sino Biological Inc. Human ACE2 or SARS-CoV-2 spike protein (S1+S2，S2) was diluted with 10 mM sodium acetate pH (4.5 or 5.5) to 50 μg/mL, and then injected at a flow rate of 5 μL/min, until approximately 12,000 or 17,000 RU of the coupled protein were obtained. After injection of human ACE2 or SARS-CoV-2 S protein, 1 M ethanolamine was injected to block unreacted groups. For kinetic measurements, eltrombopag (97.7 nM to 1562.5 nM, or 97.7 nM to 6250 nM) was serially diluted twice at 25°C, at a flow rate of approximately 30 μL/min in PBS (containing 0.05% Tween 20 and 3% dimethylsulfoxide). A simple one-to-one Langmuir combination model (BIAcore S200 Evaluation Software version 1.1) was used to calculate the association rate (k on ) and dissociation rate (k off ) by simultaneously fitting the association and dissociation sensorgrams. K D was calculated as the ratio of k off /k on .

S-RBD (S protein receptor binding domain) and ACE2 protein complex was prepared by mixing S-RBD and ACE2 ( Figure S9 -A) in a 1:1 ratio and incubating at 4℃ for 3 h. Then, the sample was concentrated to 0.2 mL for thermo shift assay (TSA) ( Figure S9 -B). To test the Tm (melting temperature) of the RBD-ACE2 complex, 5 μL metal-ion working solution was added to 96-well RE-PCR plate (AXYGEN, USA), and then 10 μL RBD-ACE2 complex was added to each well and the mixture incubated at 4℃ for 1 h. Subsequently, 5 μL dye SYPRO orange working solution was added to each well. The plate was run in an ABI7500 fast real-time PCR system with J o u r n a l P r e -p r o o f Journal Pre-proof a ramp rate of 1℃ per minute from 25℃ and ending at 99℃. Next, different compounds were assessed to determine their effect on RBD-ACE2 protein stability, using the same procedure.

We compared the amino acid sequences of SARS-CoV-2 structural proteins, including the S protein, membrane protein (M), envelope protein (E), and nucleocapsid protein (N) with that of SARS-CoV. Although high similarity was observed, alterations in part of the amino acid sequences were also evident ( Figure   S2 ).

To screen effective drugs for COVID-19 therapy, it is important to elucidate the structures of proteins involved in the pathogenesis of the disease. By simulating the binding affinity between candidate drugs and protein, the drugs showing high binding affinity can be screened and further evaluated for efficacy. However, during "Public Health Emergency" such as a pandemic, structure-based drug screening of drugs is not effective in addressing the urgency for the search of effective drugs. By virtualizing the protein structure, it is possible to obtain a reliable structure that can be used to perform rapid and economic drug screening. Considering that the invasion of SARS-CoV-2 into host cells is mediated by S protein, a virtualized S protein was used for our drug screening. J o u r n a l P r e -p r o o f As recently reported by Zhang et al., the virtualized SARS-CoV-2 S protein tertiary structure was successfully established (Zhang, Zheng et al. 2020) . Since the structure of SARS-CoV-2 S protein was also reported recently (Wrapp, Wang et al. 2020) , we compared the virtualized S protein from Zhang lab (QHD43416.pdb) (Zhang, Zheng et al. 2020) with the reported S protein (6LZG.pdb) via TM-align by two different chains and obtained TM scores of 0.646 and 0.849, respectively; suggesting a high structural similarity between the two proteins ( Figure S3 ). The characteristic analysis and drug screening were completed by the S protein from with only a small portion of β-sheets, and this kind of structure is not stable enough to allow the binding of small molecular drugs. After calculation, the pocket with enough stability for future protein-drug interaction was selected for drug screening ( Figure   1 -H).

The selected pocket comprised multiple parts of S protein, including: S1: Val 42-Ser50, Glu 281-Val289, Lys300-Ile312, Gly601-Ser605; S2: Pro812-Glu868, Ser937-Val976, and Asn1192-Ala1222. Most of the binding sites were identified in the S2 domain. Notably, the two binding sites Ser937-Val976 and Asn1192-Ala1222 showed high overlaps with the HR1 and HR2 fusion core of S2 domain, respectively (Xia, Liu et al. 2020) , thus suggesting that the selected pocket possessed a high probability to affect the S2 domain-mediated fusion of SARS-CoV-2.

3.3 In silico screening identified 13 FDA-approved drugs as potential binders J o u r n a l P r e -p r o o f

High-throughput virtual screening is widely used in the process of drug discovery.

Since ACE2 works as the receptor of S protein, a series of drugs or small-molecule peptides could be designed to prevent the virus from fusing further with the membrane of the target host cell. Total 1,234 small molecules in the FDA-approved drug library were docked into the selected binding pocket of S2 domain. The top 100 computationally scored molecules were identified. Finally, 13 potentially therapeutic compounds were selected through manual screening of relevant mechanisms of action, drug toxicity, and binding power (range of -9.3 to -12.3 kcal/mol).

The in silico studies showed that all the selected 13 FDA drugs had relatively good interaction with the S2 subunits and were ranked according to their binding affinities ( Table 1 ). The ligand-amino acid interactions are summarized in Table 2 Table 1 , 2) displayed a docking score of -9.3 kcal/mol with viral proteins. Azilsartan medoxomil formed hydrogen bonds with THR827, GLN949, and TRP1217. It had carbon-hydrogen interactions with ASN824 and TYR1209, and had hydrophobic interactions with LEU945, ILE1210, and PRO1213. Azilsartan medoxomil is an angiotensin receptor blocker (ARB) used for the treatment of hypertension. Azilsartan medoxomil is a prodrug and is further hydrolyzed in the intestine to the active component azilsartan (Hjermitslev, Grimm et al. 2017) . Azilsartan blocks the AT1 receptor without affecting the AT2 receptor, thus mediating vasodilatation, reducing aldosterone release, and reducing sympathetic stimulus of blood vessels and the kidney to lower blood pressure (Hjermitslev, Grimm et al. 2017) .

Similarly, the other 9 drugs also showed high affinity with the designed S2 pocket.

The information for the drugs is briefly summarized in Table 1 and Table 2 , and the interaction model was shown in Figure S7 .

3.3 Verification of the protein-drug interaction via surface plasmon resonance and thermo shift assay Immediately after the simulation, we used SPR to verify ten potential compounds.

Considering that the pocket used for screening comprised mainly the S2 domain and partially the S1 domain, the chips were prepared via different S protein domains (S1+S2, S2) of SARS-CoV-2. Based on the binding force and non-specific binding, we found that eltrombopag may be a potentially useful drug. With SPR, we showed J o u r n a l P r e -p r o o f that at pH 5.5, eltrombopag can not only bind efficiently to S2 domain as expected (K D =2.172 × 10 -6 M), but also bind to S1+S2 domain (K D =2.007 × 10 -6 M) ( Figure   3 -A, B, Table 3 ), without much difference. Meanwhile, the R max in both assays are lower than 20RU (resonance units), indicating the specificity of the interactions tested.

This result confirmed that the S2 domain works as the major drug-binding site, and the existence of S1 domain does not disturb the interaction between eltrombopag and the pocket, thus suggesting the potential of eltrombopag binding to the intact SARS-CoV-2 S protein.

In contrast, glycyrrhizic acid, the herb reported to possess wide-spectrum anti-viral activity which showed high binding affinity in the virtual screening, did not provide good results in the SPR assay. The calculated K D is 0.0024M, with the R max of 623.7RU, showing a very weak and unspecific binding (Figure 3 -C) and is therefore marked as ND (non-detectable) in Table 3 , which suggests a poor interaction between glycyrrhizic acid and S2 domain. In addition, the SPR assay results of other drugs also showed high K D and R max value, suggesting similar poor interactions ( Figure S8 ).

Taking into account that ACE2 functions as an indispensable receptor of viral S protein, an assay was performed to further test the binding affinity between drugs and Hence, the single curve of ZnCl 2 was unreliable. The addition of compound eltrombopag had a slight effect on the RBD-ACE2 protein complex, which reduced the Tm from 51.88℃ to 49.9℃ ( Figure 5-C, D) . These results confirmed that eltrombopag lowered the stability of S-RBD-ACE2 protein complex.

Under the premise that the structure is not completely resolved, homology modeling and de novo modeling are considered to be reliable and rapid methods for drug screening. We combined sequence analysis with homology modeling to quickly obtain the SARS-CoV-2 S protein. The sequence analysis and homology modeling results showed that the S protein of SARS-CoV and SARS-CoV-2 share high similarity. Importantly, consistent with previous report, 4 of the 5 key amino acids of SARS-CoV-2 RBD changed when compared to SARS-CoV; yet the structure was completely preserved and showed similar characteristics with SARS-CoV (Wan, J o u r n a l P r e -p r o o f Journal Pre-proof Shang et al. 2020) . Moreover, the modeling S protein structure also matched well to the recently published SARS-CoV-2 S protein tertiary structure, according to the TM score, thus validating the accuracy of our homology modeling (Wrapp, Wang et al. 2020 ).The structure of this protein and the structures of other related structural proteins were ascertained through virtual drug design, rapid screening, and manual screening of FDA-approved drugs.

Both SARS-CoV and SARS-CoV-2 recognize human ACE2 via the RBD of S protein, which makes the domain a hot target for drug screening. Nevertheless, our virtual structure indicated that the RBD of SARS-CoV-2 is mainly composed of loops and is not stable enough for drug screening. As a replacement, we selected a stable pocket whose binding sites mainly localize in the S2 domain, more specifically, the HR1 and HR2 fusion core. Therefore, it could be inferred that drugs screened through this pocket may have a high probability to affect the S2 domain-mediated membrane fusion.

Based on the pocket selected, the 13 molecules identified in this study may be potential therapeutic drugs. These include a series of drugs that are used to treat viral infection, tumors, diabetes, hypertension, and other diseases.

We also discovered a Chinese herbal medicine glycyrrhizic acid that had a combination with S protein. Glycyrrhizic acid has been reported to be efficacious for the treatment of SARS infections (Hoever, Baltina et al. 2005 ).

Notably, multiple ACEI/ARB drugs were screened in this study, showing their potential interaction with the pocket of SARS-CoV-2 protein. However, these drugs should be used with caution in COVID-19 patients. Much attention has been paid to ACEI/ARB drugs for their regulation of the level of ACE2, the specific receptor of SARS-CoV-2. However, the exact role of ACEI/ARB drugs in SARS-CoV-2 infection is not clear yet. ACEI/ARB drugs can indirectly upregulate ACE2 by blocking ARS, which is believed to be a risk factor. However, ACE2 possesses anti-inflammatory effect and many other protective functions, which means that upregulated ACE2 can protect humans from damages caused by SARS-CoV-2.

Furthermore, it has been reported, that for patients suffering from both hypertension diseases and COVID-19, ACEI may protect them from SARS-CoV-2 infection (Meng, Xiao et al. 2020) . In summary, risky or protective, there is still much work to be done to figure out the relationship between ACEI/ARB drugs and COVID-19.

Eventually, 10 compounds were selected to undergo SPR test. The SPR results confirmed that eltrombopag possesses good binding affinity to both SARS-CoV-2 S protein and human ACE2, indicating its potential to affect both sides of viral entrance.

Considering the domain participating in the formation of the pocket, eltrombopag could affect the S2 domain-mediated membrane fusion, thus preventing the viral entrance. This requires further verification in the future.

However, how eltrombopag functions through ACE2 remains to be explored.

TSA verified that eltrombopag could lower RBD-ACE2 complex stability. This result J o u r n a l P r e -p r o o f Journal Pre-proof showed that eltrombopag acts by binding to the S-RBD-ACE2 complex and lowering the protein complex stability. Nevertheless, it is still hard to draw a conclusion, since eltrombopag is mainly used for increasing the platelet count but not for blood pressure regulation. Although increased blood pressure presents as an adverse event in some patients, the mechanism is not clear yet (Ptushkin, Vinogradova et al. 2018) . It is worth exploring how eltrombopag functions after binding to ACE2, which can help us further understand the potential anti-SARS-CoV-2 mechanism of eltrombopag and its adverse effect of increasing the blood pressure.

Although the results from this virtual drug screening provided many drug options, there are few shortcomings in the study. Cell and animal experimental studies still need to be carried out for these drug candidates. Additionally, the accuracy of homology modeling is limited, compared to the structure analyzed by Cryo-EM, since the precision is affected by both parameters and experience of operators. For example, discrepancy between virtual screening and SPR test is observed, which further confirms that the screened drug requires more verification experiments to illustrate its characteristics. We used SPR assay in this research to show the binding affinity, and together with the virtual screening, we showed the enormous potential of eltrombopag as an anti SARS-CoV-2 treatment. The discrepancy between virtual screening and SPR test, such as in the case of glycyrrhizic acid, could have been caused by unspecific binding, which suggests the need for further verification experiments in the future.

This study utilized homology modeling to analyze the SARS-CoV-2 S protein structure; besides, virtual drug screening was also performed. Moreover, the study describes the high possibility of eltrombopag as a candidate for anti SARS-CoV-2 therapy. These findings will help guide future efforts to screen for other SARS-CoV-2 structural proteins and potential therapeutic compounds. In addition, this method to screen drugs based on virtualized protein structure is also a reasonable, economic, and fast method to screen drugs and reveal out-of-guide application of approved drugs, which can be used against many other diseases and in future possible epidemic or pandemic situations. 

The authors thank Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd and Beijing Novogene Co. Ltd. We would like to thank Editage (www.editage.cn) for English language editing. J o u r n a l P r e -p r o o f Table 2 

The amino acids interacting with each drug and the interaction types are summarized. The amino acid sequence of SARS-CoV-2 was downloaded from NCBI and analyzed.

The structure of S, M, E, N protein was acquired by ab initio modeling or homology modeling (S protein), and the S protein docking pocket for drug screening was analyzed by comparing with SARS-CoV. By scoring the 1234 compounds from the FDA drug database compound library via the drug screening pocket, the top 100 compounds were selected. Among these, 13 compounds were further selected through visual inspection, which underwent surface plasmon resonance test and Thermo Shift Assay to verify the efficacy.

Amino acid sequences of SARS-CoV-2 or SARS-CoV S protein (A), M protein (B), E protein, (C) and N protein (N) were downloaded and compared. The structure of spike model (QHD43416.pdb) was compared to the reported S protein (6LZG.pdb) via TM-align. Two different chains, chain 1 (A316309) and chain 2 (B316309), were used, respectively. 

Supplementary Table S1. Information about the protein chips used in SPR test.  Compare the differences between the structural proteins of SARS-CoV-2 and SARS-CoV.  Screen approved drugs via virtual drug design algorithm based on S2 domain pocket.

 Surface plasmon resonance and Thermo Shift Assay are established for validation.  Eltrombopag showed a high binding affinity to spike protein and ACE2.  Provide a novel and rapid way to screen potent drugs during the pandemic. 

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Siqin Feng: conceptualization, methodology, software, investigation, formal analysis, data-curation, writing-original draft preparation Xiaodong Luan: conceptualization, methodology, software, formal analysis, writing-original draft preparation Yifei Wang: conceptualization, formal analysis, visualization, writing-original draft preparation Hui Wang: resources, funding acquisition Shuyang Zhang: conceptualization, supervision