Computational Design of Peptides to Block Binding of the SARS-CoV-2 Spike Protein to Human ACE2

Xiaoqiang Huang; Robin Pearce; Yang Zhang

. CC-BY-NC-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03. 28.013607 doi: bioRxiv preprint In previous studies, we found that evolutionary information can facilitate the design of proteins, improving their ability to fold into desired structures 28, 38 . To examine whether the evolutionary profile is important for peptide design here, we also performed four sets of designs with different weight settings for the evolution energy; for each design set, 1000 independent design simulation trajectories were carried out and the unique sequences out of the 1000 lowest energy designs were analyzed (Table 2 ). In general, giving a higher weight to the evolutionary energy facilitated the convergence of the design simulations, as indicated by the fewer unique designed sequences. It also helped identify sequences that were closer to the wild-type peptide as demonstrated by the higher sequence identities and the lower average evolutionary energy, which were both much more similar to those of the wild-type than the designs created using the physical score alone. We also found that incorporation of the profile energy moderately increased the ability of the designed sequences to maintain the original secondary structure. However, despite these improvements, giving a higher value to the profile weight clearly hindered the identification of binders that exhibited better binding energy than the wild-type. We performed sequence logo analyses of the four sets of designs obtained from the evolutionbased method and the results are illustrated in Figure 4 . Overall, the evolutionary profile did not have a dramatic effect on most interface residues (e.g. Q24, K31, H34, E35, E37, D38, Y41, Q42 and K353), because the dominating residue types identified in the EvoEF2-based designs were also top ranked ( Figure 3A and Figure 4 ). However, some interface residues were indeed influenced. For instance, T27 could be substituted for either lysine or isoleucine without evolution ( Figure 3A ), but it was only mutated to lysine when the evolutionary weight was ≥0.75 ( Figure 4C -D). Additionally, without evolutionary profiles, F28 preferred glutamine over all other residues ( Figure 3A ), but it was conserved as phenylalanine when the evolutionary weight was ≥0.5 ( Figure 4B -D). The naturally occurring residues, glutamic acid and arginine never appeared at positions 335 and 337, respectively, without evolutionary profile-guided design ( Figure 3A) ; however, both of them were ranked second when a weight of 1.0 was given to the profiles. The residues that were most affected by evolution were those nonpolar residues that were not at the interface (e.g. A25, L29, F32, A36, L39, L351 and F356); without the evolutionary profile, polar or charged residue types were preferred at these positions ( Figure 3A) , while nonpolar residues were more frequently chosen for most of them when the weight of the profile energy was high ( Figure 4B-D) . As discussed above, most of these residues were buried in the original hACE2 structure, but they were solvent exposed in the peptide, and therefore it might not be necessary to maintain the hydrophobic nature at these positions. The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.28.013607 doi: bioRxiv preprint The copyright holder for this preprint