key: cord-0805339-h297ukuu
authors: Olotu, Fisayo A.; Omolabi, Kehinde F.; Soliman, Mahmoud E.S.
title: Leaving no stone unturned: Allosteric targeting of SARS-CoV-2 Spike protein at putative druggable sites disrupts human angiotensin-converting enzyme interactions at the receptor binding domain.
date: 2020-10-16
journal: Inform Med Unlocked
DOI: 10.1016/j.imu.2020.100451
sha: ab97f34ab801c15c3f45b5dd709afc25470ceb91
doc_id: 805339
cord_uid: h297ukuu

The systematic entry of SARS-CoV-2 into host cells, as mediated by its Spike (S) protein, is highly essential for pathogenicity in humans. Hence, targeting the viral entry mechanisms remains a major strategy for COVID-19 treatment. Although recent efforts have focused on the direct inhibition of S-protein receptor-binding domain (RBD) interactions with human angiotensin-converting enzyme 2 (hACE2), allosteric targeting remains an unexplored possibility. Therefore, in this study, for the first time, we employed an integrative meta-analytical approach to investigate the allosteric inhibitory mechanisms of SARS-CoV-2 S-protein and its association with hACE2. Findings revealed two druggable sites (Sites 1 and 2) located at the N-terminal domain (NTD) and S2 regions of the protein. Two high-affinity binders; ZINC3939013 (Fosaprepitant – Site 1) and ZINC27990463 (Lomitapide – Site 2) were discovered via site-directed high-throughput screening against a library of ∼1500 FDA approved drugs. Interestingly, we observed that allosteric binding of both compounds perturbed the prefusion S-protein conformations, which in turn, resulted in unprecedented hACE2 displacement from the RBD. Estimated ΔG(binds) for both compounds were highly favorable due to high-affinity interactions at the target sites. In addition, Site 1 residues; R190, H207, K206 and K187, I101, R102, I119, F192, L226, V126 and W104 were identified for their crucial involvement in the binding and stability of ZINC3939013. Likewise, energy contributions of Q957, N953, Q954, L303, Y313, Q314, L858, V952, N953, and A956 corroborated their importance to ZINC27990463 binding at the predicted Site 2. We believe these findings would pave way for the structure-based discovery of allosteric SARS-CoV-2 S-protein inhibitors for COVID-19 treatment.

The novel coronavirus disease also referred to as COVID-19 is caused by the SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2), with incidences first reported in Wuhan China in December 2019. 1 This disease has, however, persisted till mid-2020, spreading across 212 countries with over 3,513,507 cases reported coupled with increasingly high casualties numbering over 245,544 globally. 2 SARS-CoV-2 belongs to a large group of coronaviruses which are known to cause respiratory infections and related complications. These RNA viruses are spherical, pleomorphic, positive-sensed, single-stranded and polyadenylated. 3 Of all known viruses, coronaviruses (CoVs) have the largest RNA genome 4 , with diverse pathogenic effects in animals and humans. This virus class is divided into four genera namely: alpha-CoV, beta-CoV, gamma-CoV and delta CoV [5] [6] [7] , with the beta-CoV class prominent for their disease-causing effects in humans (HCoVs). Seven HCoVs have been characterized to date [6] [7] [8] ; among which four (HCoV-HKU1, HCoV-OC43, HCoV-NL63 and HCoV-229E) cause very mild respiratory symptoms. 9, 10 On the other hand, MERS-CoV, SARS-CoV, and SARS-CoV-2 cause severe respiratory and gastrointestinal infections which, in most cases, can be fatal. 11 Although SARS-CoV-related infections were zoonotically transmitted into human populations, 12, 13 human to human transmissions has further contributed towards viral super-spread via respiratory aerosols. 14 The entry of SARS-CoV-2 coupled with its replication process in target human cells is achieved by the functionalities of a cohort of components, majorly non-structural and structural proteins, that make up the virus. Generally, about 16 non-structural proteins (NSPs) mediate diverse pro-pathogenic functions such as replication, processing and proof-reading of genomic frames, host immune evasion among many others, as previously reported. [15] [16] [17] More so, CoVs comprises of four major structural proteins that are integral to their pathogenesis. [18] [19] [20] These are the nucleocapsid (N), envelope (E), membrane (M) and spike (S) proteins. The N protein makes up the nucleocapsid and other viral genome-related processes 21 while the M protein is the most abundant of the four, playing major roles in maintaining viral structural integrity as well as coordinating other structural proteins. 22 E protein, on the other hand, is crucial to the maturation of the virus [23] [24] [25] [26] [27] while the trimeric S protein mediates viral entry into the host cell via the endosomal or non-endosomal route. 28 Two domains make up the S protein namely the N-terminal S1 domain and the C-terminal S2membrane-anchored domain. The S2 region is extensively conserved in CoVs while constituent S1 region residues are highly diverge across the CoV strains. 29 These domains have been further characterized into subdomains due to specific functionalities with respect to host receptor recognition and binding (S1), coupled with membrane fusion and entry (S2) (Figure 1 ). Similar to SARS-CoV architecture, some recent reports have sub-categorized the SARS-CoV-2 S1 ectodomain into the N-terminal domain (NTD), a conserved receptor-binding domain (RBD) which recognizes the human angiotensin-converting enzyme 2 (hACE2), 30 and subdomains 1 and 2 (SD1 and SD2). During infection, proteolytic cleavage or priming of the S protein is crucial for viral fusion and entry into host cells, a process mediated by host cell proteases such as the transmembrane serine protease 2 (TMPRSS2) and Cathepsin L, [31] [32] [33] at the S1/S2 (boundary between S1 and S2 subunits) and S2' (immediately upstream S2 fusion peptide -FP) cleavage sites. [34] [35] [36] The S protein primarily exists in a metastable prefusion complex prior to cleavage, after which notable conformational arrangements occur in order to fuse the viral membrane into J o u r n a l P r e -p r o o f the host cell. [37] [38] [39] In addition, the RBD adopts disparate conformational motions to engage the host cell receptor. 40, 41 conformations. [42] [43] [44] The up conformation corresponds to the hACE2 accessible state while the down state cannot engage the host cell receptor. 44 The S2 domain, on the other hand, consists of the functionally important fusion peptide (FP), which is critical for viral fusion and formation of the post-fusion complex; heptad repeats 1 and 2 (HR1 and HR2); transmembrane domain (TM) and cytoplasmic tail (CT). The HRs of the S-protein trimer interact to form a fusion core of sixhelical bundle which helps bring the membranes of the virus and host cell in close proximity for fusion and entry. 42 Therefore, the roles of SARS-CoV-2 S-protein present it as an important therapeutic target, which would enable the prevention of viral entry and fusion in host cells.

Numerous studies have been reported over the past months with regards to the possibility of blocking direct interactions between SARS-CoV-2 S-protein and hACE2. Most of these studies were aimed at targeting the S protein RBD domain with antibodies, peptide-based or small molecule compounds that binds with a much higher affinity to block S-protein-hACE2

interactions. [45] [46] [47] [48] [49] [50] Also, targeting host proteases such as TMPRSS2 was explored in a recent study, with consequential impediments on SARS-CoV-2 entry. 30 Identification of other functional (allosteric) sites on the prefusion S protein could present another dynamic and effective approach of preventing SARS-CoV-2 infectivity relative to its interaction with the host cell ACE2 and proteases. This alternative target approach for SARS-CoV-2 S protein is important because its RBD (similar to other CoVs) has been associated with a high mutational propensity which may in turn alter the affinity of small molecule inhibitors or peptide designed to bind therein. 51 Allosteric targeting was explored in a recent study wherein the CoV-conserved S2 HR1 region was identified as an important target site for the development of broad-spectrum inhibitors of human CoVs. The resulting peptide inhibitor (EK1) was evaluated in vivo and exhibited desirable safety and efficacy 52 . More so, the Protein Contact Network (PCN) paradigm was used to map functional allosteric loci on SARS-CoV S protein. 53 Relatively, this study was implemented to (i) identify potential druggable sites across the S1 and S2 domains of the SARS-CoV-2 S protein other than the RBD-hACE2 interface (ii) perform high-throughput (virtual) screening of ~1500 FDA approved drugs against the most druggable site(s) (iii) investigate the binding dynamics and interaction mechanisms of the compounds and their consequential effects on the S-protein RBD-ACE2 complex.

We believe this systematic study will be able to provide structural and molecular insights into possible allosteric sites on SARS-CoV-2 S protein suitable for selective targeting and structure- 

Computational methodologies

The three-dimensional structure of SARS-CoV-2 S-protein (prefusion) was retrieved from PDB with entry 6VSB. 44 This, as previously reported, represents the S-protein RBD conformation in

its up (open) state, which is most suitable for hACE2 binding. Also, to model binding interactions between the prefusion SARS-CoV-2 S-protein (S1/S2) and the hACE2, a crystalized structure with PDB entry 6M0J 54 was separately retrieved. This complex depicts binding between the RBD domain (truncated) of SARS-CoV-2 S-protein and the protease domain (PD) of hACE2. Co-crystallized molecules not relevant to this study were removed while missing residues (gaps) in the structures were filled using the MODELLER algorithm. 55 This preparation was performed on the UCSF Chimera Graphic User Interface (GUI). 56 Subsequently, using the structural superposition method, we were able to model a complex between prefusion S-protein (S1/S2) monomer (RBD -up conformation) and the hACE2 protein ( Figure 2 ).

J o u r n a l P r e -p r o o f 

Possible druggable sites other than the SARS-CoV-2 RBD interface were predicted using approaches previously reported. [57] [58] [59] [60] Herein, we employed multiple tools for site identification and validation, which include SiteMap 61 , Fpocket 62 , Discovery studio 2016 Client 63 and Prankweb. 64 SiteMap is an exhaustive tool which ranks protein pockets based on properties such as druggability, surface exposure, hydrophobicity and hydrophilicity among others [65] [66] [67] . These details were then used to characterize the predicted pockets after which other predictive algorithms were used complementarily for cross-validation. Two highly ranked sites were then selected for further analyses.

Furthering on the rationale of the study, we mapped out the two most druggable sites on the target protein and virtually screened against them a large chemical library of FDA approved drugs (~1500 compounds) derived from the ZINC repository (http://zinc.docking.org/substances/subsets/fda/). This screening was performed using highperformance computing-integrated Autodock vina 68 prior to which coordinates of the predicted sites were mapped using gridboxes. Corresponding binding scores were retrieved from the resulting .pdbqt files and were used to filter down to the topmost 20 compounds for each predicted Sites 1 and 2. Subsequently, two compounds with the highest binding scores (most negative) were selected for the two predicted sites yielding complexes that were subjected to further simulation studies. As explained in 2.1, the prefusion S-proteins (ligand-bound and unbound) were superimposed with the RBD-hACE2 complex (6M0J) after which the single J o u r n a l P r e -p r o o f truncated RBD was removed. By so doing, we obtained models of allosterically-bound and unbound pre-fusion S-protein-ACE2 complex. This, as aimed in this study, would provide structural and dynamical insights into the mechanistic effects of allosteric targeting on SARS-CoV-2 host entry machinery.

Although computationally expensive (1673 residues), we proceeded with long-timescale MD simulation runs for the systems on AMBER18 Graphical Processing Unit (GPU) using its embedded modules. 69 Protein parameters were defined using the FF14SB forcefield while ligand parameters were generated with the antechamber and parmchk modules. Likewise, the LEAP program was used to define coordinate and topology files for the ligand-bound and unbound protein complexes. This program, also, was used to neutralize (addition of counter-ions; Na + and Cl -) and solvate the systems in a TIP3P water box of size 10Å. Structural minimization was first carried out partially for 5000steps with a restraint potential of 500kcal mol -1 . Å 2 followed by another 100000 steps of full minimization with no restraints. A canonical (NVT) ensemble with a 5kcal mol -1 Å 2 harmonic restraints was used to heat the systems gradually from 0 -300k for 50ps, after which the systems were equilibrated for 10000ps at a constant 300k temperature without restraints in an NPT ensemble. Atmospheric pressure was maintained at 1bar with a Berendsen barostat 70 while each protein system was subjected to a production run of 350ns.

Studied systems include ZINC3939013-S-protein-hACE2 (allosteric Site 1), ZINC27990463-Sprotein-hACE2 (allosteric Site 2), and unbound S-protein-hACE2. Corresponding trajectories were saved at every 1ps time-frame until the end of the simulation followed by data plot analyses using Microcal Origin software. 71 Snapshots were also taken and analyzed to monitor structural events and ligand interaction dynamics across the trajectories on the UCSF Chimera user J o u r n a l P r e -p r o o f interface (GUI) and Discovery Studio Client. 63 

The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) method was used to evaluate binding affinities of the predicted allosteric S-protein binders at their target sites.

Binding energy profiles for both compounds, inclusive of their energy components, were estimated using 1000 snapshots from the terminal 30ns of MD trajectories where conformational stabilities were visible. This approach was important in order to minimize the effects of conformational disorder or entropy on ligand interactions. The equations below mathematically express binding energy calculations:

As shown, internal (∆E int ), electrostatic (∆E ele ) and van der Waals (∆E vdW ) energies sum up the gas-phase energy (∆G gas ) while the solvation free energy (∆G sol ) is defined by the polar solvation (∆G ele,sol ) and non-polar contribution to solvation (∆G np,sol ) terms. The MM/GBSA method was used to estimate the Generalized Born (GB) for ∆G ele,sol while the linear relationship between the surface tension proportionality constant (γ = 0.0072 mol -1 Å -2 ), solvent accessible surface area (SASA, Å 2 ), and β constant was used to solve ∆G np,sol . Furthermore, estimated ∆G bind was decomposed into individual residue energies, most especially those that constitute the predicted allosteric pockets where the ligands were bound. This method was essential to identify specific residues that contribute crucially to the stability and inhibitory activities of potential allosteric inhibitors.

J o u r n a l P r e -p r o o f

Based on the study rationale, we set out to identify possible sites for drugging the target protein Table 1 ). The architectures of these pockets are shown in Figure 3 . Furthermore, defining the druggability of a site on target proteins depends on the size (volume) and hydrophobicity (with minimal hydrophilicity) while, on the other hand, high hydrophilicity, reduced hydrophobicity, small pocket size and shallowness characterize "difficult-to-drug" and undruggable pockets 61, [65] [66] [67] . While large hydrophilicity could have repulsive effects on ligand mobility at the binding site, a small or shallow cavity would impede ligand access, fitness, optimal binding and stability.

J o u r n a l P r e -p r o o f From Table 2 , Sites 1 → 3 ranks above the 0.83 Halgren Dscore threshold making them suitable for therapeutic targeting. Relatively, Site 1 appears to be highly surface-exposed with a score of 0.933 while a large pocket size and volume for Site 2 could favor the use of large-molecule compounds. Taken together, high surface-exposure coupled with relatively large volumes, hydrophobicity and favorable donor/acceptor properties for Sites 1 and 2 could account for their suitability as targetable allosteric regions on the S-protein other than the RBD (Figure 3 ). These presumptions are also reflected by the estimated Dscore and SiteScore values. In addition, since these predicted sites are highly functional, particularly the overlapping FP, HR1 and CR, targeting them could 

High-throughput screening and identification of potential allosteric binders to the predicted Sites 1 and 2

High-throughput screening using a library of ~1500 FDA approved drug compounds (http://zinc.docking.org/substances/subsets/fda/) were performed against the two predicted allosteric sites. Results for the top 20 compounds with the highest binding scores are presented in Supplementary Table S1 and Supplementary Table S2 for Sites 1 and 2 respectively.

From the screening results, overall highest scores were estimated for ZINC3939013 (-10 J o u r n a l P r e -p r o o f kcal/mol) at Site 1 and ZINC27990463 (-9.3 kcal/mol) at Site 2.

As highlighted in our methods, MD simulations were performed for the prefusion S-protein-hACE2 complexes bound distinctly at two potential allosteric sites.

This approach was essential to investigate the likely effects of allosteric targeting on the entry/fusion mechanisms of SARS-CoV-2 via host hACE2. However, this conformation appeared distorted the allosterically-bound S-proteins and could account for displacement motions of the interacting hACE2 from the RBD interface. Therefore, the allosteric-mediated disruption of SARS-CoV-2 S-protein RBD and its interaction with hACE2, as reported herein, is a major finding that could indicate the viability of allosteric targeting in SARS-CoV-2 therapy.

Furthermore, we measured structural stabilities across the ligand-protein complexes relative to the unbound system using the RMSD metrics. As shown in Figure 6 , structural instability was highest in the unbound S-protein while its associated hACE2 was relatively stable compared to This could indicate the structural effects of allosteric targeting on the S-protein and its interaction with hACE2. Estimated mean RMSDs, as presented in Table 2 , corroborates conformational variations among the unbound and bound protein complexes.

To minimize the effects of structural disorderliness (entropy) in our calculations, we selected, from the MD trajectories, terminal time-frames (270-300ns) from which the systems appeared to relatively stabilize. These were defined as the finally equilibrated (FE) time-frames and were used for subsequent structural analyses ( Table 2 ). From the resulting FE-RMSD plots, unbound S-protein was highly unstable while its associated hACE2 exhibited low structural motion in line with the RMSD calculations, which could also imply that the binding of S-protein stabilized hACE2. In contrast, the allosterically-bound Sproteins (Sites 1 and 2) were notably stable while their corresponding hACE2 showed high structural instability that could correlate with their systemic motions at the S-protein RBD as earlier mentioned. 

Structural analyses of ligand orientations at the respective allosteric sites of SARS-CoV-2 Sprotein were performed using averaged structures from the MD trajectories ( Figure 9 ). Findings reveal that the allosteric binding of ZINC3939013 (Fosaprepitant) was stabilized at the NTD.

Fosaprepitant contains a terminal triphosphate group that orients towards residues such as N99, K187, N188, R190 and H207. Likewise, its trifluoromethyl group oriented towards D102 while constituent -O and -NH groups mediate interactions with Q173 and N121, among others. These altogether could facilitate high-affinity interactions accountable for its stability and allosteric inhibitory effects against the SARS-CoV-2 and associated hACE2.

J o u r n a l P r e -p r o o f 

Binding affinities of the compounds were determined using the MM/PBSA technique, which also allowed us to measure the energy contributions of interactive residues at the predicted allosteric sites. Energy calculations, as presented in Table 4 were performed using relatively stable time-frames (270-300ns) to minimize entropical effects that may interfere with ligand binding activities. In addition, we observed that electrostatic effects contributed most notably to the allosteric binding of ZINC3939013 at the NTD region while van der Waals contributions had the highest effect on the binding of ZINC27990463 at the predicted Site 2 pocket. Electrostatic contributions at Site 1 could be due to the high number of electropositive residues that constitute the pocket, as shown in Figure 10 , which may form high-affinity interactions with electronegative moieties of the compound. Calculations further revealed that ∆E vdW and ∆E ele were more favorable in the gas phase for ZINC3939013 while polar solvation energies were more favorable for ZINC27990463 J o u r n a l P r e -p r o o f at the S2 region of the S-protein. This could imply that while the former was buried in the deep hydrophobic pocket of the NTD, the latter was surface exposed due to its trans-domain binding activity as earlier reported. To understand the mechanistic binding of the compounds at both predicted sites, we decomposed the binding free energies into individual contributions of the interacting residues. These were juxtaposed with structural analysis that showed the type and (π-alkyl) interactions. More so, π-π stacked interaction between Y313 and a benzene ring (of the 4-tri-fluoromethyl-1,1'-biphenyl group) could be highly crucial for the stability of the compound.

Taken together, electrostatic energies favored the binding of ZINC3939013 at Site 1 while vdW energies favored ZINC27990463 binding at Site 2, which consequentially, were able to perturb the S-protein RBD and allosterically disrupt hACE2 interactions.

The systemic entry of SARS-CoV-2 into the human host cell is a crucial process that underlies its virulence and pathogenicity in humans and other animals it infects. This mechanism is mediated by its interaction with the host ACE2 (hACE2) via attachment and fusion. Potential intervention approaches in SARS-CoV-2 treatment include therapeutic strategies that could prevent SARS-CoV-2 S-protein binding to hACE2. In this study, we implemented an exhaustive approach to identify drug molecules that could potentially bind to SARS-CoV-2 S-protein at other sites other than the RBD. Pertinent to the allosteric targeting approach implemented herein J o u r n a l P r e -p r o o f was the identification of highly druggable sites inherent in the S-protein (S1/S2), which was carried out using multiple pocket prediction algorithms for identification and validation of possible allosteric sites. Predicted pockets were then characterized based on their attributes after which two highly probable pockets were selected. These were then screened distinctly against a library of ~1500 FDA approved drugs retrieved from the ZINC database. Amongst all, Thermophoresis (MST) can be employed for further validation. These implementations will provide additional insights into the targetability and suitability of these pockets for novel COVID-19 therapeutics. Findings from this study paves way for novelty in the structure-based design of high-affinity allosteric inhibitors or disruptors of SARS-CoV-2 association with host hACE2 thereby preventing viral entry.

Authors thank the college of health sciences, University of KwaZulu-Natal, South Africa for providing infrastructural support and we also acknowledge the Center for High Performance Computing (CHPC), Capetown, South Africa, for providing computational resources.

Authors declare no conflict of interest.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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