key: cord-0816606-1bvw9f96 authors: Alexander, MR; Schoeder, CT; Brown, JA; Smart, CD; Moth, C; Wikswo, JP; Capra, JA; Meiler, J; Chen, W; Madhur, MS title: Which animals are at risk? Predicting species susceptibility to Covid-19 date: 2020-07-10 journal: bioRxiv DOI: 10.1101/2020.07.09.194563 sha: ea8f7acd4ba4068f7efdbe2e3b12588143f162a9 doc_id: 816606 cord_uid: 1bvw9f96 In only a few months, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic, leaving physicians, scientists, and public health officials racing to understand, treat, and contain this zoonotic disease. SARS-CoV-2 has made the leap from animals to humans, but little is known about variations in species susceptibility that could identify potential reservoir species, animal models, and the risk to pets, wildlife, and livestock. While there is evidence that certain species, such as cats, are susceptible, the vast majority of animal species, including those in close contact with humans, have unknown susceptibility. Hence, methods to predict their infection risk are urgently needed. SARS-CoV-2 spike protein binding to angiotensin converting enzyme 2 (ACE2) is critical for viral cell entry and infection. Here we identified key ACE2 residues that distinguish susceptible from resistant species using in-depth sequence and structural analyses of ACE2 and its binding to SARS-CoV-2. Our findings have important implications for identification of ACE2 and SARS-CoV-2 residues for therapeutic targeting and identification of animal species with increased susceptibility for infection on which to focus research and protection measures for environmental and public health. In only a few months, the novel coronavirus severe acute respiratory syndrome coronavirus 2 42 (SARS-CoV-2) has caused a global pandemic, leaving physicians, scientists, and public health 43 officials racing to understand, treat, and contain this zoonotic disease. SARS-CoV-2 has made 44 the leap from animals to humans, but little is known about variations in species susceptibility 45 that could identify potential reservoir species, animal models, and the risk to pets, wildlife, and 46 livestock. While there is evidence that certain species, such as cats, are susceptible, the vast 47 majority of animal species, including those in close contact with humans, have unknown 48 susceptibility. Hence, methods to predict their infection risk are urgently needed. SARS-CoV-2 49 spike protein binding to angiotensin converting enzyme 2 (ACE2) is critical for viral cell entry 50 and infection. Here we identified key ACE2 residues that distinguish susceptible from resistant 51 species using in-depth sequence and structural analyses of ACE2 and its binding to SARS-52 CoV-2. Our findings have important implications for identification of ACE2 and SARS-CoV-2 53 residues for therapeutic targeting and identification of animal species with increased 54 susceptibility for infection on which to focus research and protection measures for 55 environmental and public health. 56 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for the 58 global pandemic of coronavirus disease-2019 (Covid-19) that is impacting millions of lives and 59 the global economy. Covid-19 is a zoonotic infection capable of crossing the species barrier. 60 SARS-CoV-2 is thought to have originated in bats and subsequently transmitted to humans, 61 perhaps through a secondary host. 1,2 Emerging experimental and observational evidence 62 demonstrates differences in species susceptibility to infection. For example, humans, house 63 cats, tigers, and lions are all susceptible to infection by SARS-CoV-2. 3-6 Golden Syrian hamsters 64 and rhesus monkeys are also capable of being experimentally infected by SARS-CoV-2 and 65 developing Covid-19 pathologies. 7,8 In contrast, observational and experimental studies with 66 direct intranasal inoculation have demonstrated that chickens, ducks, and mice are not 67 susceptible to SARS-CoV-2 infection. 5, [9] [10] [11] Interestingly however, susceptibility is not 68 dichotomous. Although ferrets are also susceptible to infection, intranasal inoculation failed to 69 result in spread of infection to the lower respiratory tract, significantly limiting symptom 70 development. 5 In addition, although dogs failed to exhibit infection of the respiratory tract and 71 appear asymptomatic, a minority of experimentally or environmentally exposed dogs exhibited 72 evidence of infection by SARS-CoV-2 PCR or SARS-CoV-2 seroconversion with production of 73 SARS-CoV-2-specific antibodies. 5,12 While pigs have not demonstrated evidence of infection 74 after intranasal inoculation, overexpression of swine ACE2 in cultured cells supports some 75 degree of viral entry. 5, 9, 13 Hence, ferrets, dogs, and pigs are classified as having intermediate 76 susceptibility to infection. Despite these findings, the number of animal species tested for 77 susceptibility to infection in experimental or observational studies is very limited. Thus, methods 78 of determining risk of species with unknown susceptibility are urgently needed to reduce risk of 79 propagating transmission, protect food supplies, identify potential intermediate hosts, and 80 discover animal models for research. Identifying the key residues mediating susceptibility to 81 infection can also guide rational drug design. 82 SARS-CoV-2 is a member of the coronavirus family of single-stranded RNA viruses. 9 The spike 83 protein on the surface of the SARS-CoV-2 virus mediates interaction with its receptor, 84 angiotensin converting enzyme 2 (ACE2), to promote membrane fusion and virus entry into the 85 cell. The receptor binding domain (RBD) of the spike protein contains a receptor binding motif 86 (RBM) that binds to the peptidase domain of ACE2. 14 Following spike protein cleavage, fusion of 87 the viral and host cell membranes occurs to enable viral entry into the cell. 15 Interaction of the 88 SARS-CoV-2 spike protein RBD and ACE2 is thus critical for viral cell entry and infection. 9 The 89 importance of this interaction in infection is further supported by evidence that exogenous 90 soluble ACE2 limits infection in human organoids, 10 and that overexpression of human ACE2 is 91 necessary to enable viral cell entry in HeLa cells in vitro and SARS-CoV-2 infection in mouse 92 models in vivo. 9,16 93 ACE2 is present in almost all vertebrates, however sequence differences exist that may hold 94 clues to differences in SARS-CoV-2 susceptibility, as has been observed for SARS-CoV. 17,18 95 Understanding such differences could provide insight into key structural interactions between 96 ACE2 and SARS-CoV-2 RBD important for infection, and permit development of a susceptibility 97 score for estimating the infection risk of various species. In this manuscript we integrate 98 experimentally validated differences in susceptibility to SARS-CoV-2 infection with ACE2 99 sequence comparisons and in-depth structural analyses to determine how differences in ACE2 100 across species influence interaction with SARS-CoV-2 RBD. We identified multiple key residues 101 mediating structural interactions between ACE2 and SARS-CoV-2 RBD and use these residues 102 to generate a susceptibility score to predict animals with elevated risk of infection. We also 103 demonstrate that SARS-CoV-2 is nearly optimal for binding ACE2 of humans compared to other 104 animals, which may underlie the highly contagious nature of this virus amongst humans. Our 105 findings have important implications for identification of ACE2 and SARS-CoV-2 residues for 106 therapeutic targeting and identification of animal species with increased susceptibility for 107 infection on which to focus research and protection efforts. 108 109 RESULTS 110 Given experimental evidence for susceptibility of humans, house cats, tigers, lions, rhesus 112 macaques, and Golden Syrian hamsters to SARS-CoV-2 infection, and experimental evidence 113 for non-susceptibility of mice, ducks, and chickens, 3-5,7,9-11,19 we performed protein sequence 114 alignment of ACE2 from these organisms using MAFFT (Extended Data Figure 1 ). 20 We also 115 included species with intermediate susceptibility, including dogs, pigs, and ferrets, 5,9,12 as well 116 as species with unknown susceptibility, including camels, horses, Malayan pangolin, and sheep. 117 The degree of similarity of ACE2 protein sequences largely fell along expected phylogenetic 118 relationships among species (Extended Data Figure 2 ). Susceptibility to SARS-CoV-2 119 infection, however, did not match either phylogenetic relationships or ACE2 sequence 120 similarities across species. For example, mouse (Mus musculus) is not susceptible to infection. 121 However, mouse ACE2 sequence is more similar to a susceptible species, Golden Syrian 122 hamster (Mesocricetus auratus), than non-susceptible species such as duck (Aythya fuligula) or 123 chicken (Gallus gallus). 9,16 In addition, mice are phylogenetically more similar to susceptible 124 species such as humans (Homo sapiens) and rhesus macaques (Macaca mulatta) than non-125 susceptible species such as ducks and chicken. 9,16 These findings suggest that neither 126 phylogenetic relationships nor overall ACE2 protein sequence similarity across species is able 127 to predict susceptibility to SARS-CoV-2 infection. 128 An alternative approach is to use the experimentally validated differences in infection 131 susceptibility across species to focus on ACE2 amino acids that most differ between susceptible 132 and non-susceptible species. We thus calculated a weighted score of how well the aligned 133 amino acids stratify susceptible versus non-susceptible species, incorporating amino acid 134 similarity. This score, termed GroupSim, permits quantitative determination of which amino 135 acids in the alignment best stratify susceptible from non-susceptible species. 21 This analysis 136 demonstrated that multiple amino acid positions in the ACE2 alignment, including Leu79, His34, 137 Tyr83, and Gln24, are highly similar in susceptible species and quite different in non-susceptible 138 species (Extended Data Table 1 and Supplemental Table 1 ). When mapping these scores 139 onto the structure of the SARS- CoV-2 RBD and ACE2 complex, multiple residues with high 140 GroupSim scores were present at or near the binding interface including His34, Asp30, Thr92, 141 Gln24, Lys31, and Leu79 (Figure 1) . We then extended this analysis by focusing on key 142 residues previously demonstrated from prior structural analysis to be important for ACE2 and 143 SARS-CoV-2 RBD interactions (Table 1) . 7,22-24 Interestingly, this revealed that key amino acids 144 for the ACE2 and SARS-CoV-2 spike protein interaction were enriched among the top scoring 145 GroupSim positions (7 of 35; p<0.0001; Fisher's exact test). Such key residues based on 146 structural analysis being over-represented in amino acid positions that best discriminated 147 susceptible from non-susceptible species suggests that structural interactions between ACE2 148 and SARS-CoV-2 spike protein importantly determine differences in species susceptibility to 149 infection. In addition, these data suggest that certain ACE2 amino acid residues may be 150 particularly important for determining susceptibility, including Leu79, His34, Tyr83, Gln24, 151 Lys31, Asp30, and Glu329. 152 We used homology modeling to identify structural determinants of binding the ACE2 protein 155 from species with known differences in susceptibility to SARS-CoV-2 infection. The models 156 were based on previously reported crystal structures of the human ACE2 in complex with 157 SARS-CoV-2 (PDB: 6LZG and 6M0J). 14 We modeled ACE2 in the presence of the SARS-CoV-2 158 RBD to allow backbone adjustment to the binder and refined by redocking of the RBD domain to 159 optimize sidechains. Models were selected by overall calculated protein stability of the SARS-160 CoV-2 RBD complex, predicted binding energy between ACE2 and SARS-CoV-2 RBD, and 161 similarity (as Cα-root mean square deviation [Cα-RMSD], Extended Data Figure 3 and 162 Extended Data Figure 4 ). Based on these models, multiple approaches where undertaken to 163 investigate the structural interactions between SARS-CoV-2-RBD and ACE2. 164 We evaluated the overall calculated protein stability and predicted binding energy for SARS-165 CoV-2-RBD and ACE2 complexes for each species. We considered the 100 best models for 166 each species and evaluated evidence for difference in binding energy or stability between 167 susceptible and non-susceptible species. The average mean predicted binding energy and 168 calculated protein stability differs across species (Figure 2) . Consistent with the lack of 169 susceptibility of chickens (Gallus gallus), chicken ACE2 in complex with SARS-CoV-2-RBD was 170 the lowest scoring, or most energetically unfavorable model. The complex with duck ACE2 171 (Aythya fuligula) shows similarly unfavorable scores, indicating that ACE2 sequence differences 172 leading to a lower structural binding ability in these two avian species may explain their lack of 173 susceptibility to SARS-CoV-2 infection. However, the complex of SARS-CoV-2-RBD and ACE2 174 of the non-susceptible mouse (Mus musculus) exhibits lower binding energy and higher protein 175 stability than several species that are susceptible, including the lion (Panthera leo), tiger 176 (Panthera tigris), and cat (Felis catus). Thus, differences in SARS-CoV-2 and ACE2 complex 177 stability have some discriminative power but are not the sole factor in differences in 178 susceptibility across species. 179 As a complementary approach to determine whether particular residues may discriminate 182 susceptible from non-susceptible species, we performed energetic modeling of residue-residue 183 interactions in the interface of SARS-CoV-2 and ACE2 using Rosetta. Although the overall 184 interaction pattern across residues is similar between susceptible, non-susceptible, and 185 intermediate susceptibility species, there are significant differences in the magnitude of residue-186 residue interactions (Figure 3) (Figure 4 ). This analysis also identifies residue 83 of ACE2 as having 196 differential energetic interactions across species. Residue 83 is a tyrosine in susceptible species 197 and a phenylalanine in non-susceptible species (Table 1) . Compared to susceptible species, 198 this position exhibits significantly decreased binding energy with residues Asn487 and Tyr489 in 199 SARS-CoV-2 RBD in non-susceptible species (Figure 3) . Although ACE2 residue 83 also 200 interacts with SARS-CoV-2 RBD phenylalanine 486, this interaction is unlikely to be significantly 201 affected by differences between tyrosine and phenylalanine. However, the hydroxyl group of 202 tyrosine at position 83 forms a hydrogen bond with the backbone oxygen of asparagine 487 that 203 is negatively impacted by substitution to phenylalanine in non-susceptible species ( Figure 5A) . In addition to this residue-residue structural analysis, both ACE2 positions 30 and 83 were 205 identified through the GroupSim analysis described above to be top residues discriminating 206 susceptible from non-susceptible species based on sequence alignment (Extended Data Table 207 1). These results suggest that these amino acid positions of ACE2 may be important mediators 208 of the structural interaction of ACE2 and SARS-CoV-2 RBD and determinants of differences to 209 susceptibility to infection across species. 210 It is an evolutionary advantage for SARS-CoV-2 to maintain its ability to infect multiple species. 212 Thus, we hypothesized that the sequence of SARS-CoV-2 RBD is not optimized for a single 213 species but is capable of binding ACE2 of multiple species. Multistate design is a computational 214 approach to test this hypothesis. It allows us to determine the sequence of SARS-CoV-2 RBD 215 that is optimal for binding ACE2 of multiple species. We used Restraint Convergence (RECON) 216 multistate design to test this hypothesis. This method determines how many mutations one 217 protein requires to acquire affinity for multiple targets at once. 25,26 218 We adapted this strategy to evaluate the ability of the SARS-CoV-2-RBD to bind non-human 219 ACE2 variants starting from the constraint of the known binding to human ACE2. We 220 hypothesized that engineering a SARS-CoV-2 RBD with binding affinity for ACE2 from non-221 susceptible species would require more changes to binding interface residues than for 222 susceptible species. To test this hypothesis, we redesigned the SARS-CoV-2 RBD interface 223 sequence using RECON in the presence of the known binder, human ACE2, and ACE2 from 224 other species in turn ( Figure 6A) . 225 As an initial positive control, the SARS-CoV-2 RBD was redesigned against human ACE2 only. 226 By mutating multiple SARS-CoV-2 RBD residues to improve binding affinity, we tested at each 227 designable position the frequency of native sequence recovery, which measures the fraction of 228 models in which the native SARS-CoV-2 RBD amino acid is retained. This resulted in very few 229 proposed amino acid changes of SARS-CoV-2 RBD to optimally bind human ACE2, indicating 230 that the SARS-CoV-2 RBD sequence overall represents a solution close to optimal ( Figure 6B ). 231 The exception is valine 503, for which more polar amino acids were deemed optimal. This 232 valine, however, is near a glycosylation site at asparagine 322 in ACE2 at the SARS-CoV-2 and 233 ACE2 interface (Extended Data Figure 5 ). Since glycans are not incorporated into the RECON 234 multistate design technique, this valine 503 may have a higher affinity binding partner when 235 considering the presence of ACE2 glycosylation sites.. 236 Designing SARS-CoV-2 RBD in the presence of ACE2 from additional species revealed that 237 ACE2 from a number of species have lower sequence recovery (including non-susceptible 238 species such as duck and chicken, but also hamster, macaque, cat, lion and dog). When 239 evaluating residue-specific interactions based on the native sequence recovery from RECON 240 multistate design, tyrosine 505 shows no sequence recovery in avian species as compared to 241 the human ACE2 control. This tyrosine interacts very prominently with lysine 353 in ACE2, 242 however this residue is highly conserved across all species examined ( Table 1) . Tyrosine 505 243 also interacts less strongly with glycine 354, which is occupied by an asparagine in the avian 244 species (chicken and duck) ( Table 1 and Figure 5B ). This secondary interaction might explain 245 the differences in native sequence recovery. However, another experimentally verified non-246 susceptible species, the mouse (Mus musculus), has a high degree of sequence recovery, 247 similar to human ACE2. This suggests that other factors beyond residue-residue interactions of 248 ACE2 and SARS-CoV-2 RBD at the interface may determine susceptibility to infection, at least 249 in the mouse, and that differences in RECON multistate design explain only partially differences 250 in species susceptibility to SARS-CoV-2 infection. 251 As a final additional approach to structurally evaluate differences in species susceptibility, we 253 investigated the predicted glycosylation profiles of various species in comparison to human 254 ACE2. Protein glycosylation is increasingly recognized as a critical contributor to receptor-ligand 255 interactions; 27 however, given the challenges in identifying glycans in protein crystal structures, 256 glycosylation has received considerably less attention than SARS-CoV-2 RBD and ACE2 257 protein-protein interactions. Naturally occurring glycans as posttranslational modifications are 258 not fully visible in crystal structures. Normally only the first N-actylglucosamine is visible or no 259 sugar moiety can be observed, or glycosylation sites are mutated prior to crystallization. In the 260 crystal structures of the human ACE2 used here, a sugar moiety bound to an asparagine at a 261 surface exposed NXT/S sequon was seen three times in proximity to the binding interface on 262 the ACE2. To understand whether the ACE2 of other species have similar glycosylation 263 patterns, glycosylation was predicted using NetNGlyc 1.0, a neural network for predicting N-264 glycosylation sites, and compared to the glycosylation patterns of human ACE2. 28 species. This suggests a potential mechanism by which mice may be non-susceptible despite 276 having similar binding energy and SARS-CoV-2 native sequence recovery to susceptible 277 species. 278 Taken together, results of these studies reveal a set of key ACE2 residues important for 280 interaction with SARS-CoV-2 RBD and for which differences help discriminate susceptible from 281 non-susceptible species. These differences include ACE2 amino acid positions 30 and 83, 282 which exhibit differential residue-residue binding energy, position 354, which exhibits low native 283 sequence recovery in interaction with SARS-CoV-2, and positions 90 and 322, which exhibit 284 differences in glycosylation. Using these key residues in aggregate, we developed a SARS-285 CoV-2 susceptibility score based on similarity to the human ACE2 sequence using the 286 BLOSUM62 similarity matrix (Table 3) . 29 sheep (Ovis aries). 296 To permit wider use of this susceptibility score for evaluation of additional species with unknown 297 susceptibility, including those species that in the future may be of particular concern, we 298 developed an implementation of the susceptibility score algorithm in R for public use. This 299 implementation takes as input human ACE2 aligned with ACE2 of another species of interest 300 and provides a susceptibility score using differences in ACE2 positions 30, 83, 90, 322, and 301 354. R code for implementation of this algorithm as a graphical user interface is available in 302 Supplemental Methods. 303 Here we tested the hypothesis that differences in ACE2 proteins across various species alter 305 structural interactions with SARS-CoV-2 RBD, leading to differences in species susceptibility to 306 SARS-CoV-2 infection. Our results, combining prior knowledge of experimentally validated 307 differences in species susceptibility with multiple methods of determining effects on ACE2 308 structure and interaction with SARS-CoV-2 RBD, reveal five key residues that in aggregate help 309 discriminate susceptibility across species. These include ACE2 positions 30, 83, and 354, which 310 exhibit alterations in binding energy, and positions 90 and 322, which exhibit alterations in 311 glycosylation that likely contribute to differences in interactions at the interface. Taken together, 312 our results provide insight into the molecular determinants of species susceptibility to SARS-313 CoV-2 infection and have important implications for identification of key residues for therapeutic 314 targeting and determining susceptibility of additional species to infection. 315 Our study has several unique features that permit rigorous evaluation of differences in species 316 susceptibility to infection. Prior studies have similarly performed ACE2 sequence alignments 317 across species and modeled structural effects of the amino acid changes on the SARS-CoV-2 318 and ACE2 interface. 7,30-35 However, our study integrates experimentally validated susceptibility 319 to SARS-CoV-2 with in-depth structural analyses to determine critical ACE2 residues for 320 infection. In addition, we performed multiple structural analyses, including residue-residue 321 interactions, RECON multistate design, and glycosylation analysis, to rigorously determine the 322 structural basis for species differences in ACE2 interaction with SARS- humans, and the importance of these animals as domestic companions and laborers worldwide 361 make determination of their susceptibility an urgent need. The use of the susceptibility score 362 developed here can also be applied to additional species of interest to help direct resources for 363 focused research and protection efforts in the future. 364 ACE2 residues identified in this paper that provide a structural basis to differences in species 365 susceptibility to infection reveal important insights into the SARS-CoV-2 RBD and ACE2 366 structural interaction and potential for therapeutic targeting. By incorporating differences in 367 species susceptibility into the structural analysis, our findings enhance the potential to identify 368 particularly important residues mediating the ACE2 and SARS-CoV-2 RBD interaction. Indeed, 369 although GroupSim scores were not used in the structural analysis, three of the five key 370 identified residues (30, 83, and 90) from the structural modeling are in the top scoring ACE2 371 positions by GroupSim score. This suggests that the amino acids at these positions in ACE2 372 differ significantly between susceptible and non-susceptible species, consistent with an 373 important contribution of these residues to differences in susceptibility. Amino acid positions 30 374 and 83 of ACE2 in particular exhibited large differences in residue-residue interaction binding 375 energies between susceptible and non-susceptible species. Asp30 on ACE2 interacts with 376 residues Lys417, Phe456, and Tyr473 of SARS-CoV-2 RBD, and ACE2 Tyr83 interacts with 377 Asn487 and Tyr489 of SARS-CoV-2 RBD. These amino acids mark sites of SARS-CoV-2 378 interaction with ACE2 that may be important for development of antibody-based therapies or 379 small molecule inhibitors. 380 Applying a multistate design algorithm to probe the SARS-CoV-2-RBD interactions for their 381 ability to cross-bind to ACE2 of multiple species yielded several novel observations. First, this 382 technique identified ACE2 position 354 as an important site for differentiating binding and non-383 binding ACE2 of different species to SARS-CoV-2 RBD. Second, this approach demonstrated 384 that the SARS-CoV-2 RBD sequence is nearly optimal for binding to human ACE2 compared to 385 other species. This is a remarkable finding, and likely underlies the high transmissibility of this 386 virus amongst humans. This finding is also consistent with recent results that compared SARS-387 CoV and SARS-CoV-2 and determined that a number of differences in the SARS-CoV-2 RBD 388 have made it a much more potent binder to human ACE2 through the introduction of numerous 389 hydrogen bonding and hydrophobic networks. 39 390 Although ACE2 and SARS-CoV-2 RBD interactions are critical to SARS-CoV-2 infection, 9,10,16 391 differences in other factors across species may also contribute to differences in susceptibility. This includes differences in ACE2 expression levels 40 and differences in the protein sequence of 393 TMPRSS2, a protein that contributes to viral and host cell membrane fusion through cleavage of 394 spike protein. 15, 41 With further experimental and observational data on infectability of currently 395 unknown species, the susceptibility score we have developed can also help determine species 396 for which differences in ACE2 protein may not inadequately predict differences in susceptibility. For these species future studies could compare differences in expression levels of ACE2 and/or 398 differences in TMPRSS2 structure. These structural comparisons of TMPRSS2, however, will 399 require elucidation of the protein crystal structure, which is not yet available. 400 We combined in-depth structural analyses with knowledge of varying species susceptibility to 403 SARS-CoV-2 infection to determine key structural determinants of infection susceptibility. First, 404 we identified multiple key residues mediating structural interactions between ACE2 and SARS-405 CoV-2 RBD. Differences in these residues were used to generate a susceptibility score that When two true binders are redesigned they should require few sequence changes, thus 624 resulting in a higher native sequence recovery. In contrast, if the native sequence recovery for 625 the interface residues is lower, then many sequence changes are required, indicating that one 626 of the ACE2 proteins is a non-binder. (B) Residue-specific native sequence recovery as 627 determined from RECON multistate design against the SARS-CoV-2-RBD complex with human 628 ACE2. Only residues of the SARS-CoV-2-RBD, which are in the protein-protein interface and 629 show changes are depicted. Homology modeling of ACE2-SARS-CoV2 co-crystal structures using RosettaCM 660 ACE2 of human and non-human species was modeled based on two co-crystal structures of 661 SARS-CoV-2-RBD with the human ACE2 (PDB-IDs 6LZG and 6M0J). 14 One co-crystal structure 662 (PDB-ID 6VW1) was excluded due to its lower resolution as compared to the aforementioned 663 structures. The target sequences were threaded over the ACE2-SARS-CoV-2-RBD co-crystal 664 structure, which was first relaxed with backbone constraints using RosettaRelax. 47 A total of 665 1000 homology models were constructed using RosettaCM, and subsequently relaxed with 666 backbone constraints. 47,48 Of these, 25 models were selected based on the total energy as a 667 measure of protein stability, predicted binding energy, and Cα-root mean square deviation (Cα-668 RMSD) to the best scoring model (Extended Data Figure 3) . The SARS-CoV-2-RBD-ACE2 669 complex was optimized using a rigid-body docking with limited degrees for rotational and 670 torsional sampling. 49,50 A final ensemble of 100 models was selected based on the total energy 671 as measure of protein stability, predicted binding energy and Cα-RMSD to the best scoring 672 model (Extended Data Figure 4) . The pairwise binding interaction between SARS-CoV-2 and 673 ACE2 was evaluated by retrieving the decomposed Rosetta scores for each residue. The complex. 25,26,51 As a control, this was also performed solely using the human SARS-CoV-2-682 RBD-ACE2 complex. A total of 5000 models were sampled and trajectories with final models 683 that scored lower than -2400 REU were evaluated. The native sequence recovery was 684 calculated for each pairwise experiment and also for the control run for the SARS-CoV-2-RBD 685 complex with the human ACE2 (Extended Data Figure 6) . 686 All protocols were executed using Rosetta-3.12 (www.rosettacommons.org). Evaluation was 687 performed using the numpy, pandas, matplotlib and seaborn libraries in Python 3.7, PyMOL 688 2.7 52-54 and GraphPad Prism version 8.3.0 for Windows (GraphPad Software, San Diego, 689 California). Example commands and RosettaScripts protocols can be found in the 690 Supplementary Methods. 691 The NetNGlyc 1.0 server (http://www.cbs.dtu.dk/services/NetNGlyc/) was used to predict 693 glycosylation sites. 28 Based from the observation that asparagine in positions 53, 90, and 322 694 carried glycosylation in the crystal structures PDB: 6LZG and 6M0J, and scored with high 695 confidence from NetNGlyc 1.0, these were selected as reliably glycosylated. Position 103 was 696 included, as it was strongly predicted to be glycosylated by NetNGlyc 1.0, although no 697 glycosylation was observed in the crystal structures. Furthermore, it was evaluated whether the 698 NxT/S sequons were surface accessible and in proximity to the ACE2-SARs-CoV-2-RBD 699 binding interface. 700 Using identified ACE2 key amino acid positions 30, 83, 90, 322, and 354 in the alignment of 702 ACE2 across species, a global susceptibility score was calculated as the sum of the Blosum62 703 scoring matrix substitutions for the amino acid at each position compared to the human ACE2 704 sequence. 29 This was calculated for each species, with higher scores suggesting greater 705 susceptibility. An R implementation of this susceptibility score algorithm was also developed in 706 RStudio. The software takes as input alignment of human ACE2 protein sequence with ACE2 of 707 another species of interest and provides a susceptibility score as output. Susceptibility scores of 708 species examined in this manuscript are also graphically demonstrated as reference. Code for 709 implementing this algorithm in R as a graphical user interface is available in Supplemental 710 Methods. 711 Extended Data against the best performing model and plotted versus predicted binding energy (dG_separated) 808 after redocking of the SARS-CoV-2-RBD for all SARS-CoV-2-RBD-ACE2 co-complexes. This 809 measure describes the similarity of the models compared to their predicted binding energy. 810 Models from the lowest left corner represent the highest quality models and where chosen for 811 further analysis. The models for Mus musculus were recalculated to the second-best model 812 (magenta), as they did not converge on the best model. species <-c("Felis catus", "Panthera tigris altaica", "Panthera leo", 953 "Mesocriceteus auratus", "Macaca mulatta", "Mus musculus", "Aythya fuligula", 954 "Gallus gallus", "Mustela putoriusfuro", "Sus scrofa", "Canis lupus familiaris", 955 "Rhinolophus sinicus", "Equus caballus", "Bos taurus", "Manis javanica", 956 "Capra hircus", "Ovis aries", "Camelus dromedarius", "Camelus bactrianus") 957 species.scores <-c (27, 27, 27, 23, 31, 11, 8, 8, 14, 21, 22, 31, 27, 19, 13, 19, 19, 27, 27) 958 df.species.score <-data.frame("Species" = species, "Score" = species.scores) 959 df.species.score <-df.species.score[order(df.species.score$Species),] 960 # We also add shading to represent cutoffs for susceptible and non-susceptible species 961 output$plot <-renderPlot(ggplot(df.species.score, aes(y=Score,x='')) + 962 geom_jitter(aes(color=Species, shape=Species), size = 2.5, 963 width=0.5) + 964 965 scale_shape_manual(values=c (15,16,17,18,15,16,17,18,15,16,17,18,15,16,17,18,15,16,17 Contingency testing was performed with Fisher's exact test as a two-sided comparison and 713 alpha equal to 0.05 using GraphPad Prism version 8 shinyApp(ui = ui, server = server) Homology modeling of ACE2 based on the ACE2-SARS-CoV-2-RBD co-crystal structure 984 using RosettaCM 985 986 Structure and input preparation 987 For all modeling purposes Rosetta-3.12 was used Preparation of input structures using RosettaRelax linuxgccrelease -995 in:file:fasta felis_RBD_02.fasta -in:file:alignment 996 human_felis_align_for_thread_with_RBD_02.txt -database Construction of the initial ACE2-SARS-CoV-2-RBD complex with RosettaCM 1000 Command used for executing RosettaCM 1001 1017 1018 1030 Fragments 3mers="1u19_3.frags" 9mers="1u19_9.frags"/> 1031