key: cord-1015416-855dbj30
authors: Li, Zhe; Li, Xin; Huang, Yi-You; Wu, Yaoxing; Liu, Runduo; Zhou, Lingli; Lin, Yuxi; Wu, Deyan; Zhang, Lei; Liu, Hao; Xu, Ximing; Yu, Kunqian; Zhang, Yuxia; Cui, Jun; Zhan, Chang-Guo; Wang, Xin; Luo, Hai-Bin
title: Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs
date: 2020-05-28
journal: bioRxiv
DOI: 10.1101/2020.03.23.004580
sha: 768a2929940a3ef8070aed99f7ed4e5a37857f81
doc_id: 1015416
cord_uid: 855dbj30

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and, thus, repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a new virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a new restraint energy distribution (RED) function designed to accelerate the FEP-ABFE calculations and make the practical FEP-ABFE-based virtual screening of the existing drug library possible for the first time. As a result, out of twenty-five drugs predicted, fifteen were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (Ki=0.04 μM) which has showed promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki=0.36 μM) and chloroquine (Ki=0.56 μM) were also found to potently inhibit SARS-CoV-2 Mpro for the first time. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts. Significance Statement Drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. It has been demonstrated that a new virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions can reach an unprecedently high hit rate, leading to successful identification of 16 potent inhibitors of SARS-CoV-2 main protease (Mpro) from computationally selected 25 drugs under a threshold of Ki = 4 μM. The outcomes of this study are valuable for not only drug repurposing to treat COVID-19, but also demonstrating the promising potential of the FEP-ABFE prediction-based virtual screening approach.

Drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. It has been demonstrated that a new virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions can reach an unprecedently high hit rate, leading to successful identification of 16 Then, M pro cleaves pp1a and pp1ab to release the functional proteins nsp4-nsp16 that are necessary for the viral replication. (4) In view of the essential functions of M pro in the viral life cycle and its high level of conservation, SARS-CoV-2 M pro is a naturally attractive target for treatment of COVID-19. Hence, there have been efforts to identify therapeutic candidates targeting M pro using various virtual screening methods based on pharmacophore, molecule docking, and molecular simulations. (5) As a result of the reported efforts, six drugs were found to inhibit SARS-CoV-2 4 M pro with IC50 ranging from 0.67 to 21.4 μM. 5 There have been also drug repurposing efforts associated with other potential targets of SARS-CoV-2. [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs through computational modeling and simulations, followed by experimental validation. However, the actual hit rate of a virtual screening using traditional computational methods has been rather low, with vast majority of computationally predicted drug candidates being false positives, because it is difficult to reliably predict protein-ligand binding free energies. Most recently, Gorgulla et al. (6) reported an interesting new virtual screening platform, called VirtualFlow, used to screen numerous compounds in order to identify inhibitors of Kelch-like ECH-associated protein 1 (KEAP1), but the hit rate was still not very high. Within 590 compounds predicted by the virtual screening, 69 were found to be KEAP1 binders (with a hit rate of ~11.7% for detectable binding affinity), and 10 of these compounds were confirmed to be displacers of nuclear factor erythroid-derived 2related factor 2 (NRF2) with a half-maximum inhibitory concentration (IC50) < 60 μM (with a hit rate of ~1.4% under the threshold of IC50 < 60 μM).(6) Obviously, the hit rate of a virtual screening is dependent on the reliability and accuracy of the receptor-ligand binding free energy predictions used in the virtual screening process. So, the key to the success of a virtual screening effort is use of a reliable computational approach to accurately predict binding free energies.

The free energy perturbation (FEP) simulation of intermolecular interactions (7, 8) is recognized a reliable method for binding free energy calculations with satisfactory accuracy, (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) but the traditional FEP method was limited to simulating some minor structural changes of ligands for the relative binding free energy (RBFE) calculations. (9, 19) The RBFE calculations can be used to guide lead optimization starting from a promising lead compound (or hit),(9, 19-22) but 5 not suitable for virtual screening of completely different molecular structures to identify new hits for drug repurposing. For the virtual screening to identify new hits or leads, it is necessary to predict absolute binding free energy (ABFE) for each ligand binding with the target without the requirement to use any reference ligand structure. The FEP-ABFE approach has the advantage of predicting binding affinities between ligands and their targets more accurately than conventional computational methods, such as pharmacophore, molecule docking, and molecular simulations. (23) However, the previously used FEP-ABFE calculations are extremely expensive and timeconsuming and, thus, not suitable for virtual screening purposes (that required to screen a large number of compounds). (24, 25) To make the FEP-ABFE approach practically feasible for our virtual screening and drug repurposing effort, here we report a new algorithm using a restraint energy distribution (RED) function to accelerate the FEP-ABFE prediction and its first application to a drug repurposing effort which targets SARS-CoV-2 M pro . Our FEP-ABFE prediction-based virtual screening (which predicted 25 drugs as potential inhibitors of SARS-CoV-2 M pro ) was followed by in vitro activity assays, confirming that 15 out of the 25 drugs can potently inhibit SARS-CoV-2 M pro with 0.04 to 3.3 M (with a remarkably high hit rate of 60% under a threshold of Ki = 4 M); nine drugs have Ki < 1 M (with a submicromolar hit rate of 36%). Particularly, among these drugs, the most potent inhibitor of SARS-CoV-2 M pro is dipyridamole (DIP, Ki = 0.04 M). Following the computational prediction and in vitro activity validation, DIP was tested for its antiviral activity against SARS-CoV-2 in vitro and in clinical studies for treatment of patients with COVID-19, and the preliminary clinical data are promising for its actual therapeutic effects. While the clinical data are reported separately elsewhere (26) to timely guide further clinical studies and possibly practical clinical application, we describe and discuss in this report the detailed computational and in vitro activity 6 results of DIP along with other promising drugs identified. The encouraging outcomes suggest that the FEP-ABFE prediction-based virtual screening is a truly promising approach to drug repurposing.

Prior to the virtual screening for drug repurposing, the accuracy of the accelerated FEP-ABFE prediction protocol was validated by using three different protein targets (BRD4, HIV-1 protease, and human factor Xa) and 28 ligands with diverse chemical scaffolds. According to the validation data, given in Supporting Information (SI) section S7, the accelerated FEP-ABFE algorithm can achieve a high accuracy for the ABFE predictions. So, in order to identify potent SARS-CoV-2 M pro inhibitors, we first carried out the FEP-ABFE based virtual screening of all existing drugs, followed by in vitro activity assays, as shown in Figure 1 . Specifically, after all the existing drugs were docked into the binding site of SARS-CoV-2 M pro , 100 molecules that had specific interactions with the six key amino acid residues, Cys145, His41, Ser144, His163, Gly143, and Gln166, were subjected to further FEP-ABFE calculations.

Among these 100 drugs, 49, 46, and 5 were electrically neutral, negatively charged, and positively charged, respectively. Since the FEP method is known to encounter systematic errors when the ligands are not electrically neutral, the drugs selected on the basis of the FEP-ABFE results were grouped by their formal charges to ensure that the error is cancelled within each group. In each group, the top 20% to 40% of the molecules were selected based on their ABFE values. As a result, 25 drugs were selected for subsequent in vitro experimental activity testing. According to the in vitro results, 15 out of these 25 drugs exhibited considerable potency of inhibiting SARS-CoV-2 M pro (Figures 2 and S8 ). DIP was found to be the most potent inhibitor, with Ki = 0.04 μM.

Following the computational prediction and in vitro activity confirmation, DIP was further tested for its antiviral activity against SARS-CoV-2, demonstrating that DIP dose-dependently suppressed the SARS-CoV-2 replication with EC50 = 0.1 M. The antiviral activity was consistent with the inhibitory activity against M pro . In addition, DIP was also tested clinically in treatment of patients with COVID-19, resulting in promising therapeutic data that are reported separately elsewhere (along with the raw antiviral activity data) (26) due to the urgent need of further clinical studies and possibly practical clinical application.

The FEP-ABFE results calculated for all the confirmed potent SARS-CoV-2 M pro inhibitors are given in Table 1 in comparison with the subsequently determined experimental activity data. 8 As seen in Table 1, a Ki values for hydroxychloroquine and chloroquine were determined using the Dixon plots using the data in Figure 3 Notably, candesartan cilexetil with Ki = 0.18 M against SARS-CoV-2 M pro is a prodrug for its labeled use (treatment of hypertension and congestive heart failure). Hence, we also computationally and experimentally examined its metabolite, candesartan (the active drug corresponding to the prodrug for the labeled use) which was not in the drug library screened. Interestingly, candesartan was also predicted and confirmed as a potent inhibitor of SARS-CoV-2 M pro , with a slightly lower inhibitory activity against SARS-CoV-2 M pro (Ki = 0.62 M). So, it is interesting to note that for potential treatment of patients with COVID-19, the prodrug candesartan cilexetil would serve as a more active molecular species against SARS-CoV-2 M pro compared to candesartan itself. Table 1 ; (31, 32) hydroxychloroquine, chloroquine, and indinavir were reported to be active in vitro against COVID-19, but their molecular targets were not reported; (27) (28) (29) montelukast sodium and maribavir was only predicted by calculations (29, 30) without experimental activity data reported. Disulfiram served as the positive control for the in vitro activity (its IC50 value is 5.72 M in the literature and 4.7 M in this work when the concentration of the same substrate used was as high as 20 µM).

Notably In summary, the virtual screening through accelerated FEP-ABFE predictions has demonstrated an excellent accuracy, with a remarkably high hit rate of 60% under a threshold of Ki = 4 M. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

The accelerated FEP-ABFE approach was based on the use of a new restraint energy distribution in SI section S1. The derivation of the RED function and extensive evaluations of the accelerated FEP-ABFE method are given in detail in SI section S2 to S7.

The pGEX4T1-M pro plasmid was constructed (AtaGenix, Wuhan) and transfected into the E. coli strain BL21 (CodonPlus, Stratagene). A GST-tagged protein was purified by GST-glutathione affinity chromatography and cleaved with thrombin. The purity of the recombinant protein was greater than Fluorescence was monitored once every 45 s. Initial reaction velocities were calculated by fitting the linear portion of the curves (within the first 5 min of the progress curves) to a straight line using the program SoftMax Pro and were converted to enzyme activity (substrate cleaved)/second.

* Tel: +1-859-323-3943. Fax: +1-859-257-7585 or +86-20-39943000. E-mail: zhan@uky.edu, luohb77@mail.sysu.edu.cn, wx8399@ouc.edu.cn, and cuij5@mail.sysu.edu.cn

# These authors contributed equally to this study.

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The authors declare no competing financial interest.

Additional computational details, computational data, and experimental (including Sections S1 to S7, Figures S1 to S10, and Tables S1 to S6).

There is no conflict of interests for all authors.