# Computational Design and Evaluation of Peptides to Target SARS-CoV-2 Spike-ACE2 Interaction

Saja Almabhouh, Erika Cecon, Florence Basubas, Ruben Fernandez-Molina, Maciej Tomasz, Jana Selent, Ralf Jockers, Amal Rahmeh, Baldo Oliva, Narcis Fernandez-Fuentes

► To cite this version:

Saja Almabhouh, Erika Cecon, Florence Basubas, Ruben Fernandez-Molina, Maciej Tomasz, et al. Computational Design and Evaluation of Peptides to Target SARS-CoV-2 Spike-ACE2 Interaction. Molecules, 2025, 30 (8), pp.1750. 10.3390/molecules30081750. [hal-05406454](https://hal-05406454)

HAL Id: hal-05406454

https://cnrs.hal.science/hal-05406454v1

Submitted on 9 Dec 2025

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# Computational Design and Evaluation of Peptides to Target SARS-CoV-2 Spike — ACE2 Interaction

Saja Almabhouh (1), Erika Cecon(2), Florence Basubas (3), Ruben Fernandez-Molina (1), Tomasz Maciej Stepniewski(4), Jana Selent (4), Ralf Jockers(2), Amal Rahmeh (3), Baldo Oliva (1) and Narcis Fernandez-Fuentes (5)*

1)Structural Bioinformatics Laboratory (GRIB-IMIM). Department of Medicine and Life Sciences. Universitat Pompeu Fabra, Barcelona, Spain 6
7
2)Université Paris Cité, Institute Cochin, INSERM, CNRS, F-75014 PARIS, France 8
3) Synthetic Biology. Department of Medicine and Life Sciences. Universitat Pompeu Fabra, Barcelona, Spain 9
4)GPCR Drug Discovery. Hospital del Mar Research Institute, Barcelona, Spain 10
5)Aberystwyth University, Aberystwyth, United Kingdom 11
* Correspondence: Corresponding author: naf4@aber.ac.uk 12

**Abstract:** The receptor binding domain (RBD) of SARS-CoV-2 spike protein is responsible for the recognition of the Angiotensin Converting Enzyme 2 receptor (ACE2) in human cells, and thus plays a critical role in the viral infection. It is not surprising therefore the therapeutic value of the targeting of this interaction as proved by a sizable body of research in antibodies, small proteins, aptamers and peptides. Here, we join these efforts by presenting a novel peptide that impinges the interaction between RBD and ACE2. Starting from a very large pool of structurally designed peptides extracted from our database PepI-Covid19, a diverse set of peptides were studied using molecular dynamic simulations. Ten of the most promising were chemically synthesized and validated both *in vitro* and in a cell-based assay. Our results indicated one of the peptides (PEP10) which exhibited the highest disruption of the RBD/ACE2 complex, leading to effective blockage of the binding of the two molecules and consequently inhibiting SARS-CoV-2 Spike mediated cell entry of viruses pseudotyped with the Spike of D614G, Delta and Omicron Variants. PEP10 can potentially serve as a scaffold that can be further optimized for improved affinity and efficacy.

**Keywords:** Peptide design; SARS-CoV-2; Spike protein; ACE2 receptor; Molecular Dynamics; Time-Resolved FRET assay; Pseudotyped viral particles

## 1. Introduction

The COVID-19 pandemic has profoundly impacted global health, highlighting the urgent need for effective therapeutics against SARS-CoV-2, the virus responsible for the disease. The spike protein of SARS-CoV-2, specifically its receptor binding domain (RBD), plays a crucial role in mediating viral entry into host cells by interacting with the Angiotensin-Converting Enzyme 2 ACE2 receptor¹. Given the critical nature of this interaction, targeting the RBD-ACE2 interface represents a promising strategy for inhibiting viral infection and mitigating the spread of the virus.

A great deal of research has been devoted to target the interaction of RBD-ACE2 for therapeutic intervention that can effectively disrupt this interface. As shown in these extensive recent reviews, these include the development of antibodies ², nanobodies ³, and mini proteins and peptides⁴. A fantastic progress has then been made in the development
of novel potential therapies, however, challenges persist given the rapid evolution of SARS-CoV-2 5,6. In particular mutations in the RBD, widely documented 7-9, can compromise the effectiveness of existing therapeutics, and thus there is still a need for novel agents. 43 44 45 46

Structure-based peptide design has emerged as a potent approach for developing inhibitors targeting protein-protein interactions¹⁰. The accumulation of structural data on SARS-CoV-2, with over 4500 entries in the Protein Data Bank (PDB) ¹¹, provides a rich resource for identifying potential peptide candidates. This wealth of information led to the creation of our PepI-Covid19 database ¹², which catalog designed peptides based on their predicted binding affinities and structural characteristics. 47 48 49 50 51 52

In this study, we explore the use and evaluation of peptides selected from the PepI-Covid19 database to target the RBD of SARS-CoV-2, assessing their binding efficiency through molecular dynamics simulations and their inhibitory effects using *in vitro* and a cell-based assay. As mentioned earlier, the use of peptides is a valid approach to complement existing therapeutic avenues and this work seeks to contribute valuable insights into the development of therapeutic strategies against SARS-CoV-2. We leverage the use of targeted peptide design to exploit the large repertoire of structural information available as a complementary avenue to development of novel agents against COVID-19. 53 54 55 56 57 58 59 60

Currently, PepI-Covid19 database contains over 1M designed peptides derived from six different RBD protein complexes. Through a set of stringent filters, a selection of highly structurally diverse designed peptides was selected. Molecular dynamics simulations were used to assess the binding kinetics of peptides and to select the ten most promising candidates. These candidates were synthesized and assessed as inhibitors of RBD-ACE2 interaction both *in vitro* and in a cell-based assay. Our results show that one of the candidates, PEP10, was able to interfere with the interaction between both receptors. Most importantly, PEP10 was able to inhibit the cell entry of viruses pseudotyped with the spike receptor of D614, Delta and Omicron variants. 61 62 63 64 65 66 67 68 69

## 2. Results and Discussion

### 2.1. *Selection of Peptides from PepI-Covid19 Database*

The peptides used in this study were selected from the PepI-Covid19 database. Currently the PepI-Covid19 database contains a large number of designed peptides derived from the structure of RBD bound to the native receptor, i.e. ACE2, but also bound to nanobody and monoclonal antibodies. Each peptide classified in PepI-Covid19 database is characterized by a predicted binding energy as per the Rosetta energy score ¹³, including a reweighted score specific for protein-peptides interactions as described ¹⁴. Moreover, it includes several features describing the interface: (i) the surface area of the interface between protein-peptide (Ang²); (ii) the number of hydrogen bonds, including unsatisfied bonds at the interfaces (e.g., donor/acceptor groups not forming hydrogen bonds) and a packing and peptide scores as described ¹⁵. These features were used to make an initial selection of 20 peptides (Table 1). 70 71 72 73 74 75 76 77 78 79 80 81 82

The main driver on this selection was the maximization of the conformational diversity of the peptides as well as coverage of the native interface between RBD and ACE2, i.e. peptides were targeting different regions of the interface. As shown in Table 1 the secondary structure of the designed peptides was also quite diverse, of which half of them showing a mainly extended conformation (PEP1 to PEP10) while the rest are predominantly having a high content of helical conformation (PEP11 to PEP20). As shown, the dominating element on the interaction between RBD and ACE2 is an alpha-helix, named H1 helix ¹⁶,¹⁷, and thus it was very important to include peptides displaying such conformation. The coordinates of models can be found in the supplementary material. Figure 1 illustrates 83 84 85 86 87 88 89 90 91
the interface between ACE2 (blue) and the RBD (orange) with PEP10 (green) modeled to interfere with their interaction.

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<caption>**Table 1.** List of peptides used in this study.</caption>

94

<table><thead><tr><th>ID</th><th>Sequence</th><th>Secondary Structure<sup>1</sup></th><th>Size<sup>2</sup></th></tr></thead><tbody><tr><td>PEP1*</td><td>QDGRDDETKHED</td><td>CCHHHHHHTTCC</td><td>12</td></tr><tr><td>PEP2*</td><td>QASSLDSAHWRDLYGEYY</td><td>CCGGGGGSCHHHHSCSCC</td><td>18</td></tr><tr><td>PEP3</td><td>TLNRGLDESSREHHRE</td><td>CCCSSCCHHHHHHHHC</td><td>16</td></tr><tr><td>PEP4</td><td>DEDKERHEKEDYDNQK</td><td>CHHHHHHHHTTSTTTC</td><td>16</td></tr><tr><td>PEP5*</td><td>TRDKYRFGESEYED</td><td>CTTTTCTTSHHHHC</td><td>14</td></tr><tr><td>PEP6</td><td>DKADGANTGGGGTK</td><td>CCCCCSSCCCCCCC</td><td>14</td></tr><tr><td>PEP7</td><td>DKYWHQWEDERHSGGQ</td><td>CHHHHHHHHTTSSTTC</td><td>16</td></tr><tr><td>PEP8</td><td>GKGHTSTGTTQ</td><td>CCSCCCCCCCC</td><td>11</td></tr><tr><td>PEP9</td><td>GGGQSSTGRGKD</td><td>CCCCCEECTTCC</td><td>12</td></tr><tr><td>PEP10*</td><td>EWHGAHIKVTQLF</td><td>CCSSCCCCCCCCC</td><td>13</td></tr><tr><td>PEP11*</td><td>QDDTQEDKDRHLKDEIYK</td><td>CHHHHHHHHHHHHHHHHC</td><td>18</td></tr><tr><td>PEP12*</td><td>QRFSEERYRAWVSHEND</td><td>CCCCHHHHHHHHHHTC</td><td>17</td></tr><tr><td>PEP13*</td><td>QLGDLHRDRKGEENNRQ</td><td>CCCCCCHHHHHHHHHHC</td><td>17</td></tr><tr><td>PEP14</td><td>QEQTERDKRQHEKDSDWYQ</td><td>CHHHHHHHHHHHHHHHSSCC</td><td>19</td></tr><tr><td>PEP15</td><td>TDEDKKYH</td><td>CHHHHHHC</td><td>8</td></tr><tr><td>PEP16*</td><td>TDAGKDGWADHIYHRQY</td><td>CGGGGGGHHHHHHHHHC</td><td>17</td></tr><tr><td>PEP17*</td><td>TSDDFAEEHWKAHAGAYKL</td><td>CHHHHHHHHHHHHTHHHHHC</td><td>19</td></tr><tr><td>PEP18</td><td>NEDKNRHGEASYGNQYG</td><td>CHHHHHHHHHTCCCCC</td><td>17</td></tr><tr><td>PEP19*</td><td>DAERRREEEKGRDQ</td><td>CHHHHHHHHHTTCC</td><td>14</td></tr><tr><td>PEP20</td><td>AAEEQRKRDEWWRKGTS</td><td>CHHHHHHHHHHHHTTCC</td><td>17</td></tr></tbody></table>

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<footnote n="1">¹ C: Coil; H: Helical; E: Extended Beta; G: 3¹⁰ Helix; T: Turn; S: bend. ² Number of residues. * Selected peptides for TR-FRET assay.</footnote>
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Interface between ACE2 (blue) and the RBD (orange) with PEP10 (green) docked in a pose that would interfere with their interaction. Each molecule is represented with its solvent-accessible surface, highlighting the spatial arrangement and potential interaction regions.</figcaption></figure>

## 2.2. Assessment of Peptide/RBD Interactions Using MD

Molecular dynamics (MD) simulations were performed to evaluate the binding stability and conformational dynamics of the selected peptides when interacting with the SARS-CoV-2 RBD. This approach has been widely used to predict the dynamic behaviour of peptide inhibitors, with previous studies demonstrating that stable interactions observed in MD simulations often correlate with experimental efficacy¹⁸,¹⁹,²⁰. Each peptide was simulated in triplicate, and the binding interactions were assessed across the simulation frames.

The simulations showed that peptides PEP16 and PEP17 remained attached to the interface of RBD, forming stable interactions with RBD in all simulation replicas. PEP10 (Figure 1A-D), PEP11, PEP12, PEP13 and PEP19 showed a consistent binding also to the interface but not in all the replicas. Indeed, the simulations of the peptides indicated a variable binding behavior resulting in either the dissociation of the peptides or the transitioning to alternative sites along the simulation. In contrast, the simulation of PEP6 (Figure 1E-H), PEP3 and PEP8 showed a detachment of the peptides earlier in the simulation and in all the replicas. Based on the MD analyses, nine peptides that demonstrated binding in at least one replicate, were chosen for subsequent experimental validation. These were PEP 2, 5, 10, 11, 12, 13, 16, 17, 19 (Table 1). Additionally, PEP 1 was included as a negative control, as it did not exhibit binding. The figures depicting the different replicates for all peptides are provided in the supplementary material (Figure 1 in supplementary material.)
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Interactions of PEP10 and PEP6 with the RBD region observed in (MD) Simulations. The figure summarizes the interaction dynamics between each peptide and RBD of SARS-CoV-2 based on MD simulation data. The top row corresponds to the persistent replicates of PEP10, while the bottom row represents one of the non-persistent three replicates of PEP6. **A-C, E-G**, distances (Å) between the α carbons of the peptide and α carbon of RBD, focusing only on residues with distances less than 10 Å. **A, E**, Initial distances (Å) between α carbons of the peptide and RBD residues at the start of the simulation. **B, F**, Average distances (Å) over the simulation trajectory. **C, G**, Final distances (Å) at the end of the simulation. The color gradient represents the distance between α carbons, with warmer colors (red to yellow) representing closer interactions (distances <10 Å). **D, H**, Time evolution of the number of contacts between the Cα of the peptide and the RBD along the simulation timeline, with a threshold distance of 7 Å used to define a contact.</figcaption></figure>
<table>
  <tbody>
    <tr>
      <td>2.3. Experimental validation by TR-FRET</td>
      <td>135</td>
    </tr>
    <tr>
      <td>To experimentally validate the binding efficiency of the selected peptides, we employed the Time-Resolved Förster Resonance Energy Transfer (TR-FRET) assay. The assay relies on energy transfer between the fluorescent-labelled ACE2 and d2-labeled RBD tracer, which occurs only if the two proteins are in close proximity (10 nm). This assay enabled us to quantitatively assess the inhibitory effects of various peptides on the interaction between the RBD domain and ACE2 <sup>21</sup>. This method has been previously demonstrated to effectively monitor SARS-CoV-2 spike-ACE2 interactions in living cells, as well as to characterize compounds impacting this interaction<sup>22,23,24</sup>. Among the peptides tested, PEP10 exhibited a significant binding affinity to the Spike RBD, confirming its potential as an effective inhibitor of the RBD-ACE2 interaction.</td>
      <td>136<br>137<br>138<br>139<br>140<br>141<br>142<br>143<br>144<br>145</td>
    </tr>
    <tr>
      <td>2.3.1. Experimental Validation of Peptides Using TR-FRET</td>
      <td>146</td>
    </tr>
    <tr>
      <td>To validate the inhibitory potential of the selected peptides, HEK293 cells expressing the SNAP-ACE2 receptor were treated with a series of peptides at two concentrations (10 µM and 100 µM), and the binding of RBD-d2 to ACE2 was measured by TR-FRET (Figure 3). Each experimental condition was performed in triplicate, with consistent results across all replicates, confirming the reproducibility of the inhibitory effects of the selected peptides.</td>
      <td>147<br>148<br>149<br>150<br>151<br>152</td>
    </tr>
    <tr>
      <td>As shown in Figure 3, most peptides displayed minimal to no inhibitory effect, with TR-FRET ratios comparable to the vehicle control. However, PEP10 demonstrated significant disruption of the RBD-ACE2 interaction at both concentrations, with a marked reduction in the TR-FRET signal, particularly at 100 µM, where inhibition reached p &lt; 0.001 compared to the control group. While PEP10 significantly reduced the TR-FRET signal, its inhibitory effect was observed alongside the positive control LCB1v3<sup>25</sup>, a mini-protein known for its potent ability to block the RBD-ACE2 interaction and included as a reference.</td>
      <td>153<br>154<br>155<br>156<br>157<br>158<br>159<br>160</td>
    </tr>
    <tr>
      <td>The results reinforce the findings from the peptide selection process, demonstrating the effectiveness of PEP10 and suggesting further exploration for its potential role in disrupting the SARS-CoV-2 infection pathway.</td>
      <td>161<br>162<br>163</td>
    </tr>
  </tbody>
</table>
<figure><img src="image_4.png" /><figcaption>**Figure 3.** The inhibitory effect of peptides, at different concentrations, on Spike (RBD) binding to ACE2 by TR-FRET. This figure assesses the inhibitory effect of different peptides at two concentrations in blocking the RBD-ACE2 interaction. PEP10 at 100 µM stands out as a strong inhibitor, significantly reducing the binding, making it a promising candidate for further studies.</figcaption></figure>

## 2.4. *Effect of PEP10 on SARS-CoV-2 Spike-Mediated Virus Infection*

To examine the effect of PEP10 on Spike function in virus entry into cell and spread, we used replicative recombinant vesicular stomatitis virus pseudotypes where the VSV glycoprotein G was deleted and replaced by SARS-CoV-2 Spike in a VSV genome expressing GFP (rVSV-GFP-ΔG-Spike). The pseudotypes included the Spike variants from SARS-CoV-2 D614G, Delta and Omicron B.A1. Vero cells naturally expressing ACE-2 and stably overexpressing TMPRSS2 (Vero-TMPRSS2) were infected with rVSV-GFP-ΔG-Spike in vehicle control or in the presence of increasing concentrations of PEP10 (0-100 µM) and virus infection was monitored and quantified for GFP expression by high content imaging (Figure 4). PEP10 inhibited infection by viruses pseudotyped with D614G, Delta and Omicron B.A1 spike with an IC50 of 46, 47, and 140 µM, respectively.

<figure><img src="image_5.png" /></figure>

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**Figure 4.** Effect of PEP10 on infectivity of SARS-CoV-2 Spike pseudotyped viruses. A) Representative images of infection of Vero-TMPRSS2 cells with rVSV-GFP-ΔG-Spike variants (D614G, Delta, Omicron BA.1) -/+100 uM PEP10. B) Inhibitory dose response curve of PEP10 on infection. Virus infection was expressed as the ratio of the sum of intensity of GFP/area of phase transition. Data was normalized to no peptide control and expressed as mean ± S.E.M. of 3 independent experiments. IC50 was estimated using a non-linear fit of log inhibitor vs. normalized response (three parameters). 181 182 183 184 185 186 187

Our peptide, PEP10, exhibited IC₅₀ values ranging from 46 to 140 µM against SARS-CoV-2 variants. While certain peptide inhibitors, such as the EK1-C16 lipopeptide (IC₅₀ = 0.11-0.43 µM)²⁶ [1] and D-peptides (~5-6 µM)²⁷ [2], show greater potency, these inhibitors often require extensive modifications, such as lipid conjugation or unnatural peptide backbones, to enhance their efficacy. In contrast, PEP10 was designed using a natural peptide scaffold, making it a promising candidate for further optimization. Future modifications, such as residue substitutions or modifications, could improve its affinity while maintaining its favourable stability and specificity. 188 189 190 191 192 193 194 195

## 2.5. *Structural Analyses of PEP10* 196

As shown in the *in vitro* and in the cell-based assay validations, PEP10 was able to target the interaction between the RBD of the spike protein for SARS-COV-2 and ACE2. The structural model shows that the peptide binds across the interaction interface (Figure 5), suggesting that the binding of the peptide will cause a steric hindrance preventing the recognition of ACE2. To further investigate binding stability and identify key interacting residues, snapshots from the final frame of the MD simulation were analyzed. 197 198 199 200 201 202

Figure 5 illustrates these structural interactions, highlighting the peptide's role in disrupting the RBD-ACE2 binding. We observed some residues in the RBD with a significant interaction. These were Tyr489, Phe490, Leu492, Gln493, and Glu484, which consistently interacted with residues on PEP10 (His6, Gly4, Ala5, and Ile7). These interactions suggest a stable binding interface, particularly involving some hydrogen bonding (between His6 and Gln493) and hydrophobic/aromatic contacts (between Ile7, Leu12, Phe13 and Phe490). Additional interactions were observed with RBD residues Asn487, Tyr473, and Gly447, highlighting slight shifts in binding orientation during the simulation. Notably, residues Leu12 and Phe13 on PEP10 also engaged in hydrophobic/aromatic interactions in the final frame, suggesting additional stabilizing contacts at later stages of binding. 203 204 205 206 207 208 209 210 211 212
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Final frame of the MD simulation showing the interaction between Peptide 10 (in green) and the receptor-binding domain RBD( in pink). A preserved hydrogen bond (red dashed line) between the Gln493 residue on the RBD and the His 6 on PEP10 is highlighted with a red dashed line. Hydrophobic/aromatic interactions between nonpolar side chains of the RBD (e.g., Tyr489, Phe490, and Leu492) and PEP10 (e.g. Ala5, and Ile7) are also illustrated, suggesting stabilizing forces in the peptide-receptor complex.</figcaption></figure>

## 3. Materials and Methods

### 3.1. *Selection of Peptides from PepI-Covid19 Database*

The selection of peptides followed a multistep approach as previously described 28, with the exception of filtering out peptides with helical conformation. In the initial step, peptides were ranked based on their Rosetta energy scores, which estimate stability and binding potential. Following this, peptides were prioritized for their ability to mimic critical residues at the RBD-ACE2 interface, specifically targeting hot-spot residues identified by PCRPi 29, as well as interface packing efficiency. Additional factors such as hydrogen bonding capacity and sequence diversity were also considered, culminating in a final selection of 20 peptides, as shown in Table 1.

### 3.2. *Molecular Dynamics Simulations*

#### 3.2.1. *System Preparation and Equilibration*

MD simulations were employed to analyze the structural stability and interaction dynamics between the selected peptides and the RBD domain. The system was prepared using Charmm-GUI input generator 30,31,32 with default parameters, which generated the necessary input files for equilibration in NAMD2 33.
For system preparation, the structural models of peptides bound to RBD were used as the starting model. After that, the protein-peptide complex was then solvated in a 10nm cubic box filled with TIP3P water molecules, and the system was neutralized by adding 0.15M of KCl ions. Parameters for the system were derived from the Charmm36M forcefield ³⁴.

During the equilibration step, the system temperature and pressure were maintained at 300K and 1 atm, respectively. Temperature was coupled using the algorithm of Nose-Hoover thermostat with a temperature coupling value of 20.05. Pressure was coupled with the Parrinello-Rahman barostat algorithm using a τp = 5 and isotropic conditions. For electrostatic interactions, the Particle Mesh Ewald (PME) method³⁵ was used to evaluate long-range electrostatic interactions, while LINCS constraints³⁶ were applied to all bonds.

### 3.2.2. Production Runs

Production runs were performed using the AceMD engine³⁷, with each system simulated for 1 µs in triplicate under an NVT ensemble. The temperature was maintained at 310 K using the Langevin thermostat, while hydrogen bonds were constrained via the RATTLE algorithm.

Non-bonded interactions were truncated at 9 Å with a smooth switching function applied at 7.5 Å. A timestep of 4 fs was used, enabled by a hydrogen mass repartitioning scheme implemented within AceMD.

## 3.3. Time-Resolved Fluorescence Energy Transfer Analyses

### 3.3.1. Peptides

Peptides were purchased from the Proteomics and Protein chemistry unit at the University Pompeu Fabra. The synthesis was done in C-terminal carboxamide form by Fmoc solid-phase peptide synthesis on a H-Rink Amide-ChemMatrix resin of 0.50 mmol/g substitution (PCAS BioMatrix, Quebec, Canada) at a 0.05 mmol scale in a Prelude automated synthesizer (Protein Technologies, Tucson, AZ). Side chains of trifunctional residues were protected with TFA-labile t-butyl (Asp, Glu, Ser, Thr), trityl (Asn, Gln, His), Boc (Lys) and 2,2,4,6,7-pentamethyldihydrobenzofuran-5-sulfonyl (Arg) groups. Couplings were systematically performed with a 5-fold excess of Fmoc-amino acid in the presence of N,N,N',N'-tetramethyluronium hexafluorophosphate (5 eq) and N,N-diisopropylethylamine (10 eq) with DMF as solvent. Fmoc removal was done with piperidine/DMF (20:80 v/v) followed by DMF washes. Cleavage and deprotection of the peptide resins was done with TFA-water-triisopropylsilane (95:2.5:2.5, v/v/v, 90 min, r.t.). Peptides were isolated by precipitation with cold diethyl ether and centrifugation, then solubilized in water and lyophilized.

The synthetic crude products were analyzed by analytical RP-HPLC and LC-MS and purified by preparative RP-HPLC. Analytical RP-HPLC was performed on a LC-20AD instrument (Shimadzu, Kyoto, Japan) fitted with a Luna C18 column (4.6 mm × 50 mm, 3 µm; Phenomenex, Torrance, CA, USA) using linear gradients of solvent B (0.036% TFA in ACN) into A (0.045% TFA in H₂O) over 15 min, at 1 mL/min flow rate and with UV detection at 220 nm. Preparative RP-HPLC was performed on a LC-8 instrument (Shimadzu) fitted with a Luna C18 column (21.2 mm × 250 mm, 10 µm; Phenomenex), using linear gradients of solvent D (0.1% TFA in ACN) into C (0.1% TFA in H₂O) over 30 min, with a flow rate of 25 mL/min. MS analysis was performed on a LC-MS 2010EV instrument (Shimadzu) fitted with an XBridge C18 column (4.6 mm × 150 mm, 3.5 µm, Waters, Cerdanyola del Vallès, Spain), eluting with linear gradients of F (0.08% formic acid (FA) in ACN) into E (0.1% FA in H₂O) over 15 min at 1 mL/min flow rate. Fractions of >95% HPLC purity and with the expected mass by LC-MS were pooled and lyophilized.
### 3.3.2. TR-FRET 285

In this TR-FRET binding assay, HEK293 cells were first transfected to express the SNAP-tagged ACE2 receptor. After 48 hours, the cells were labeled with the SNAP Lumi4-Tb fluorophore substrate for one hour at 4°C. Once labeled, the cells were washed, collected, and distributed into a 384-well plate at a density of 10 µL per well. Competitors, including peptides, LCB1v3 (used as a positive control and benchmark for inhibition), and controls, were then added to each well in 2 µL volumes at the specified final concentrations. Following this, 2 µL of the RBD-d2 labeled Spike protein was added to each well, with a final concentration of 5 nM. After a 2-hour incubation period at room temperature, the TR-FRET signal was read using an Envision plate reader. The signal was then read again after 4 hours of incubation. All treatments were performed in triplicates, and the data were expressed as the TR-FRET ratio of donor to acceptor fluorescence. To pool the results from three independent experiments (100 µM) or four independent experiments (10 µM), the data from each experiment was normalized, with the control group (vehicle-treated) representing 100%. Statistical analysis was performed using one-way ANOVA, with **p** < 0.001 indicating significant inhibition of binding by the tested peptides. PEP10 demonstrated significant inhibition.

### 3.4. Infection by SARS-CoV-2 Spike Pseudotyped Viruses 302

Replicative recombinant vesicular stomatitis virus pseudotypes (rVSV-GFP-ΔG-Spike) for Spike variants from SARS-CoV-2 D614G, Delta and Omicron B.A1 were a gift from Dr. Sean Whelan (University of St. Louis, MO). rVSV-GFP-ΔG-Spike viruses were preincubated with various concentrations of PEP10 (0-100 uM) at 37°C for 1 hr. The virus/peptide mixture was added to Vero-TMPRSS2 cells growing in 96 well plates at a MOI=0.01. After 1 hr incubation, the mixture was removed and replaced by media containing the corresponding concentration of peptide. GFP expression in cells was measured at 24 hrs using High Content Imaging (Operetta, PerkinElmer) and the images were analyzed using the Harmony software (PerkinElmer). Virus infection was expressed as the ratio of the sum of intensity of GFP/ area of phase transition.

## 4. Conclusions 313

In this study, we investigated the ability of computationally designed and selected peptides to inhibit the binding interaction between the SARS-CoV-2 spike protein's receptor-binding domain (RBD) and the ACE2 receptor—a key initial step in the viral entry process. By utilizing molecular dynamics (MD) simulations alongside TR-FRET binding and cell-based (Vero-TMPRSS2/pseudotyped viruses) assays, we identified PEP10 as a potent candidate. PEP10 demonstrated stable binding interactions with critical residues on the RBD, highlighting its significant potential to disrupt the RBD-ACE2 interaction.

The MD simulations provided detailed insights into the conformational stability and binding dynamics of PEP10, underscoring consistent interactions with key residues on the RBD over time. These computational findings were further supported by experimental validation, which revealed that PEP10 exhibits substantial inhibitory activity at micromolar concentrations which demonstrated that PEP10 exhibits substantial inhibitory activity. This efficacy at low concentrations strengthens PEP10's potential as a lead candidate for antiviral development, with possible applications that are both potent and resource efficient.

Notably, our study employed a SARS-CoV-2 spike pseudotyped virus infection assay, using rVSV-GFP-ΔG-Spike pseudoviruses for variants including D614G, Delta, and Omicron BA.1. This setup allowed us to evaluate PEP10's efficacy in blocking SARS-CoV-2 spike mediated cell entry and spread. PEP10 demonstrated a robust efficacy in reducing
viral entry, highlighting its therapeutic potential. This promising *in vitro* performance suggests strong potential for PEP10 in future *in vivo* applications, reinforcing its viability as a candidate for antiviral development. 333 334

Moving forward, further studies should focus on enhancing PEP10's stability, exploring structural modifications to improve its inhibitory potency, and assessing its efficacy in *in vivo* models. These efforts could pave the way for PEP10 to serve as a foundation for future antiviral therapeutics targeting COVID-19. Our findings demonstrate the significant potential of computationally designed peptides as a molecular tool for generating anti-viral therapeutics blocking SARS-CoV-2 entry pathways. 336 337 338 339 340 341

**Acknowledgments:** We thank Dr. Sean Whelan (University of St. Louis, MO) for generously providing the replicative recombinant vesicular stomatitis virus (rVSV-GFP-ΔG-Spike) pseudotypes for SARS-CoV-2 Spike variants D614G, Delta, and Omicron B.A1, which were instrumental to this study. This work was also supported by Agence Nationale de la Recherche ((ANR-RA-COVID-19 (ANR-20-COV4-0001 to RJ), Institut National de la Santé et de la Recherche Médicale (INSERM) and Centre National de la Recherche Scientifique (CNRS), and La Marato (202130). This work was supported as well by grant PID2020-113203RB-I00 and "Unidad de Excelencia María de Maeztu" (ref: CEX2018-000792-M), funded by the MCIN and the AEI (DOI: 10.13039/5011000111033), the SGR from the Generalitat de Catalunya (ref: 4413015318- J.SELENT/SGR-22), Biotechnology and Biological Science Research Council (BBS/E/IB/230001D), and Welsh Government under the Ser Cymru III Tackling COVID program (WG#009); S. A. acknowledges support of Erasmus+ for studies Programme. 342 343 344 345 346 347 348 349 350 351 352

**Supplementary Materials:** 353

- **File: models_coordinates.tar.** A tar-compressed file containing the atomic coordinates of the PEP1 to PEP20 RBD-peptide complexes. 354 355

- **File: Supplementary_Fig1.pdf.** A PDF file containing the supplementary figures 1. 356

**References** 357

1. Yan, R.; Zhang, Y.; Li, Y.; Xia, L.; Guo, Y.; Zhou, Q. Structural Basis for the Recognition of SARS-CoV-2 by Full-Length Human ACE2. *Science* **2020**, *367* (6485), 1444–1448. https://doi.org/10.1126/science.abb2762. 358 359

2. Liu, M.; Gan, H.; Liang, Z.; Liu, L.; Liu, Q.; Mai, Y.; Chen, H.; Lei, B.; Yu, S.; Chen, H.; Zheng, P.; Sun, B. Review of Therapeutic Mechanisms and Applications Based on SARS-CoV-2 Neutralizing Antibodies. *Front. Microbiol.* **2023**, *14*. https://doi.org/10.3389/fmicb.2023.1122868. 360 361 362

3. Yang, Y.; Li, F.; Du, L. Therapeutic Nanobodies against SARS-CoV-2 and Other Pathogenic Human Coronaviruses. *J. Nanobiotechnology* **2024**, *22* (1), 304. https://doi.org/10.1186/s12951-024-02573-7. 363 364

4. Pomplun, S.; Jbara, M.; Quartararo, A. J.; Zhang, G.; Brown, J. S.; Lee, Y.-C.; Ye, X.; Hanna, S.; Pentelute, B. L. De Novo Discovery of High-Affinity Peptide Binders for the SARS-CoV-2 Spike Protein. *ACS Cent. Sci.* **2021**, *7* (1), 156–163. https://doi.org/10.1021/acscentsci.0c01309. 365 366 367

5. Markov, P.V., Ghafari, M., Beer, M. et al. The evolution of SARS-CoV-2. Nat Rev Microbiol **21**, 361–379 (2023). https://doi.org/10.1038/s41579-023-00878-2 368 369

6. Duarte, C.M., Ketcheson, D.I., Eguíluz, V.M. et al. Rapid evolution of SARS-CoV-2 challenges human defenses. *Sci Rep* **12**, 6457 (2022). https://doi.org/10.1038/s41598-022-10097-z 370 371

7. Garcia-Beltran, W. F.; Lam, E. C.; St. Denis, K.; Nitido, A. D.; Garcia, Z. H.; Hauser, B. M.; Feldman, J.; Pavlovic, M. N.; Gregory, D. J.; Poznansky, M. C.; Sigal, A.; Schmidt, A. G.; Iafrate, A. J.; Naranbhai, V.; Balazs, A. B. Multiple SARS-CoV-2 Variants Escape Neutralization by Vaccine-Induced Humoral Immunity. *Cell* **2021**, *184* (9), 2372-2383.e9. https://doi.org/10.1016/j.cell.2021.03.013. 372 373 374 375

8. Hoffmann, M.; Krüger, N.; Schulz, S.; Cossmann, A.; Rocha, C.; Kempf, A.; Nehlmeier, I.; Graichen, L.; Moldenhauer, A.-S.; Winkler, M. S.; Lier, M.; Dopfer-Jablonka, A.; Jäck, H.-M.; Behrens, G. M. N.; Pöhlmann, S. The Omicron Variant Is Highly Resistant against Antibody-Mediated Neutralization: Implications for Control of the COVID-19 Pandemic. *Cell* **2022**, *185* (3), 447-456.e11. https://doi.org/10.1016/j.cell.2021.12.032. 376 377 378 379
9. Zhao, F.; Zai, X.; Zhang, Z.; Xu, J.; Chen, W. Challenges and Developments in Universal Vaccine Design against SARS-CoV-2 Variants. *NPJ Vaccines* **2022**, *7* (1), 167. https://doi.org/10.1038/s41541-022-00597-4. 380 381

10. Wójcik, P.; Berlicki, Ł. Peptide-Based Inhibitors of Protein-Protein Interactions. *Bioorg. Med. Chem. Lett.* **2016**, *26* (3), 707–713. https://doi.org/10.1016/j.bmcl.2015.12.084. 382 383

11. Bank, R. P. D. *RCSB PDB: Homepage*. https://www.rcsb.org/ (accessed 2025-02-21). 384

12. *Covid-19 Peptide Repository*. http://bioinsilico.org/pepicovid19/ (accessed 2025-01-19). 385

13. Leaver-Fay, A.; Tyka, M.; Lewis, S. M.; Lange, O. F.; Thompson, J.; Jacak, R.; Kaufman, K. W.; Renfrew, P. D.; Smith, C. A.; Sheffler, W.; Davis, I. W.; Cooper, S.; Treuille, A.; Mandell, D. J.; Richter, F.; Ban, Y.-E. A.; Fleishman, S. J.; Corn, J. E.; Kim, D. E.; Lyskov, S.; Berrondo, M.; Mentzer, S.; Popović, Z.; Havranek, J. J.; Karanicolas, J.; Das, R.; Meiler, J.; Kortemme, T.; Gray, J. J.; Kuhlman, B.; Baker, D.; Bradley, P. Rosetta3. In *Methods in Enzymology*; Johnson, M. L., Brand, L., Eds.; Computer Methods, Part C; Academic Press, 2011; Vol. 487, pp 545–574. https://doi.org/10.1016/B978-0-12-381270-4.00019-6. 386 387 388 389 390

14. Raveh, B.; London, N.; Schueler-Furman, O. Sub-Angstrom Modeling of Complexes between Flexible Peptides and Globular Proteins. *Proteins Struct. Funct. Bioinforma.* **2010**, *78* (9), 2029–2040. https://doi.org/10.1002/prot.22716. 391 392

15. Sheffler, W.; Baker, D. RosettaHoles2: A Volumetric Packing Measure for Protein Structure Refinement and Validation. *Protein Sci.* **2010**, *19* (10), 1991–1995. https://doi.org/10.1002/pro.458. 393 394

16. Lan, J.; Ge, J.; Yu, J.; Shan, S.; Zhou, H.; Fan, S.; Zhang, Q.; Shi, X.; Wang, Q.; Zhang, L.; Wang, X. Structure of the SARS-CoV-2 Spike Receptor-Binding Domain Bound to the ACE2 Receptor. *Nature* **2020**, *581* (7807), 215–220. https://doi.org/10.1038/s41586-020-2180-5. 395 396 397

17. Yang, J.; Petitjean, S. J. L.; Koehler, M.; Zhang, Q.; Dumitru, A. C.; Chen, W.; Derclaye, S.; Vincent, S. P.; Soumillion, P.; Alsteens, D. Molecular Interaction and Inhibition of SARS-CoV-2 Binding to the ACE2 Receptor. *Nat. Commun.* **2020**, *11* (1), 4541. https://doi.org/10.1038/s41467-020-18319-6. 398 399 400

18. Sarma S, Herrera SM, Xiao X, Hudalla GA, Hall CK. Computational Design and Experimental Validation of ACE2-Derived Peptides as SARS-CoV-2 Receptor Binding Domain Inhibitors. *J Phys Chem B.* 2022 Oct 20;126(41):8129-8139. doi: 10.1021/acs.jpcb.2c03918. 401 402

19. Luo H, Ma Y, Bi J, Li Z, Wang Y, Su Z, Gerstweiler L, Ren Y, Zhang S. Experimental and molecular dynamics simulation studies on the physical properties of three HBc-VLP derivatives as nanoparticle protein vaccine candidates. *Vaccine*. 2024 Sep 17;42(22):125992. doi: 10.1016/j.vaccine.2024.05.040. 404 405 406

20. Chieochansin T, Sanachai K, Darai N, Chiraphaphaiboon W, Choomee K, Yenchitsomanus PT, Thuwajit C, Rungrotmongkol T. In silico advancements in Peptide-MHC interaction: A molecular dynamics study of predicted glypican-3 peptides and HLA-A*11:01. *Heliyon*. 2024 Aug 22;10(17):e36654. doi: 10.1016/j.heliyon.2024.e36654. 407 408 409

21. Cecon, E.; Burridge, M.; Cao, L.; Carter, L.; Ravichandran, R.; Dam, J.; Jockers, R. SARS-COV-2 Spike Binding to ACE2 in Living Cells Monitored by TR-FRET. *Cell Chem. Biol.* **2022**, *29* (1), 74-83.e4. https://doi.org/10.1016/j.chembiol.2021.06.008. 410 411

22. Chauhan J, Cecon E, Labani N, Gbahou F, Real F, Bomsel M, Dubey KD, Das R, Dam J, Jockers R, Sen S. Development of indolealkylamine derivatives as potential multi-target agents for COVID-19 treatment. *Eur J Med Chem.* 2023 Mar 5;249:115152. doi: 10.1016/j.ejmech.2023.115152. Epub 2023 Jan 27. PMID: 36724633; PMCID: PMC9882955. 412 413 414

23. Cecon E, Fernandois D, Renault N, Coelho CFF, Wenzel J, Bedart C, Izabelle C, Gallet S, Le Poder S, Klonjkowski B, Schwaninger M, Prevot V, Dam J, Jockers R. Melatonin drugs inhibit SARS-CoV-2 entry into the brain and virus-induced damage of cerebral small vessels. *Cell Mol Life Sci.* 2022 Jun 13;79(7):361. doi: 10.1007/s00018-022-04390-3. PMID: 35697820; PMCID: PMC9191404. 415 416 417

24. Cecon E, Dam J, Jockers R. Detection of SARS-CoV-2 spike protein binding to ACE2 in living cells by TR-FRET. *STAR Protoc.* 2022 Mar 18;3(1):101024. doi: 10.1016/j.xpro.2021.101024. Epub 2021 Nov 22. PMID: 34841271; PMCID: PMC8606263. 418 419

25. Cao, L.; Goreshnik, I.; Coventry, B.; Case, J. B.; Miller, L.; Kozodoy, L.; Chen, R. E.; Carter, L.; Walls, A. C.; Park, Y.-J.; Strauch, E.-M.; Stewart, L.; Diamond, M. S.; Veesler, D.; Baker, D. De Novo Design of Picomolar SARS-CoV-2 Miniprotein Inhibitors. *Science* **2020**, *370* (6515), 426–431. https://doi.org/10.1126/science.abd9909. 420 421 422

26. Lan Q, Chan JF, Xu W, Wang L, Jiao F, Zhang G, Pu J, Zhou J, Xia S, Lu L, Yuen KY, Jiang S, Wang Q. A Palmitic Acid-Conjugated Peptide-Based pan-CoV Fusion Inhibitor Potently Inhibits Infection of SARS-CoV-2 Omicron and Other Variants of Concern. *Viruses*. 2022 Mar 6;14(3):549. doi: 10.3390/v14030549. 423 424 425

27. Valiente PA, Wen H, Nim S, Lee J, Kim HJ, Kim J, Perez-Riba A, Paudel YP, Hwang I, Kim KD, Kim S, Kim PM. Computational Design of Potent D-Peptide Inhibitors of SARS-CoV-2. *J Med Chem.* 2021 Oct 28;64(20):14955-14967. doi: 10.1021/acs.jmed-chem.1c00655. 426 427 428
<table>
  <tbody>
    <tr>
      <td>28.</td>
      <td>Badia-Villanueva, M.; Defaus, S.; Foj, R.; Andreu, D.; Oliva, B.; Sierra, A.; Fernandez-Fuentes, N. Evaluation of Computationally Designed Peptides against TWEAK, a Cytokine of the Tumour Necrosis Factor Ligand Family. Int. J. Mol. Sci. 2021, 22 (3), 1066. https://doi.org/10.3390/ijms22031066.</td>
      <td>429</td>
    </tr>
    <tr>
      <td>29.</td>
      <td>Segura, J.; Fernandez-Fuentes, N. PCRPi - DB: A Database of Computationally Annotated Hot Spots in Protein Interfaces. Nucleic Acids Res. 2011, 39 (suppl_1), D755–D760. https://doi.org/10.1093/nar/gkq1068.</td>
      <td>430</td>
    </tr>
    <tr>
      <td>30.</td>
      <td>Jo, S.; Kim, T.; Iyer, V. G.; Im, W. CHARMM-GUI: A Web-Based Graphical User Interface for CHARMM. J. Comput. Chem. 2008, 29 (11), 1859–1865. https://doi.org/10.1002/jcc.20945.</td>
      <td>431</td>
    </tr>
    <tr>
      <td>31.</td>
      <td>Brooks, B. R.; Brooks, C. L.; Mackerell, A. D.; Nilsson, L.; Petrella, R. J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A. R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M.; Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R. W.; Post, C. B.; Pu, J. Z.; Schaefer, M.; Tidor, B.; Venable, R. M.; Woodcock, H. L.; Wu, X.; Yang, W.; York, D. M.; Karplus, M. CHARMM: The Biomolecular Simulation Program. J. Comput. Chem. 2009, 30 (10), 1545–1614. https://doi.org/10.1002/jcc.21287.</td>
      <td>432</td>
    </tr>
    <tr>
      <td>32.</td>
      <td>Lee, J.; Cheng, X.; Swails, J. M.; Yeom, M. S.; Eastman, P. K.; Lemkul, J. A.; Wei, S.; Buckner, J.; Jeong, J. C.; Qi, Y.; Jo, S.; Pande, V. S.; Case, D. A.; Brooks, C. L.; MacKerell, A. D.; Klauda, J. B.; Im, W. CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J. Chem. Theory Comput. 2016, 12 (1), 405–413. https://doi.org/10.1021/acs.jctc.5b00935.</td>
      <td>433</td>
    </tr>
    <tr>
      <td>33.</td>
      <td>Phillips, J. C.; Hardy, D. J.; Maia, J. D. C.; Stone, J. E.; Ribeiro, J. V.; Bernardi, R. C.; Buch, R.; Fiorin, G.; Hénin, J.; Jiang, W.; McGreevy, R.; Melo, M. C. R.; Radak, B. K.; Skeel, R. D.; Singharoy, A.; Wang, Y.; Roux, B.; Aksimentiev, A.; Luthey-Schulten, Z.; Kalé, L. V.; Schulten, K.; Chipot, C.; Tajkhorshid, E. Scalable Molecular Dynamics on CPU and GPU Architectures with NAMD. J. Chem. Phys. 2020, 153 (4), 044130. https://doi.org/10.1063/5.0014475.</td>
      <td>434</td>
    </tr>
    <tr>
      <td>34.</td>
      <td>Huang, J.; Rauscher, S.; Nawrocki, G.; Ran, T.; Feig, M.; de Groot, B. L.; Grubmüller, H.; MacKerell, A. D. CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins. Nat. Methods 2017, 14 (1), 71–73. https://doi.org/10.1038/nmeth.4067.</td>
      <td>435</td>
    </tr>
    <tr>
      <td>35.</td>
      <td>Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N·log(N) method for Ewald sums in large systems. J. Chem. Phys. 15 June 1993; 98 (12): 10089–10092. https://doi.org/10.1063/1.464397</td>
      <td>436</td>
    </tr>
    <tr>
      <td>36.</td>
      <td>Hess, B., Bekker, H., Berendsen, H.J.C. and Fraaije, J.G.E.M. (1997), LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem., 18: 1463–1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12&lt;1463::AID-JCC4&gt;3.0.CO;2-H</td>
      <td>437</td>
    </tr>
    <tr>
      <td>37.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>438</td>
    </tr>
    <tr>
      <td>38.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>439</td>
    </tr>
    <tr>
      <td>39.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>440</td>
    </tr>
    <tr>
      <td>40.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>441</td>
    </tr>
    <tr>
      <td>41.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>442</td>
    </tr>
    <tr>
      <td>42.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>443</td>
    </tr>
    <tr>
      <td>43.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>444</td>
    </tr>
    <tr>
      <td>44.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>445</td>
    </tr>
    <tr>
      <td>45.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>446</td>
    </tr>
    <tr>
      <td>46.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>447</td>
    </tr>
    <tr>
      <td>47.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>448</td>
    </tr>
    <tr>
      <td>48.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>449</td>
    </tr>
    <tr>
      <td>49.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>450</td>
    </tr>
    <tr>
      <td>50.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>451</td>
    </tr>
    <tr>
      <td>51.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>452</td>
    </tr>
    <tr>
      <td>52.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>453</td>
    </tr>
    <tr>
      <td>53.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>454</td>
    </tr>
    <tr>
      <td>54.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>455</td>
    </tr>
    <tr>
      <td>55.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>456</td>
    </tr>
    <tr>
      <td>56.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>457</td>
    </tr>
    <tr>
      <td>57.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>458</td>
    </tr>
    <tr>
      <td>58.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>459</td>
    </tr>
    <tr>
      <td>59.</td>
      <td>Harvey, M. J.; Giupponi, G.; Fabritiis, G. D. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. J. Chem. Theory Comput. 2009, 5 (6), 1632–1639. https://doi.org/10.1021/ct9000685.</td>
      <td>460</td>
    </tr>
  </tbody>
</table>