pith. sign in

arxiv: 2402.05416 · v2 · submitted 2024-02-08 · ⚛️ physics.bio-ph · cond-mat.other

From atomic to global connectivity in the structure of the SARS-CoV2-Human ACE2 receptor complex

Varsha Subramanyan , Arinnia Anto , Moitrayee Bhattacharyya , Smitha Vishveshwara , Saraswathi Vishveshwara This is my paper

Pith reviewed 2026-05-24 04:21 UTC · model grok-4.3

classification ⚛️ physics.bio-ph cond-mat.other
keywords SARS-CoV2ACE2network topologypercolation analysismolecular dynamicsprotein cliquesinterface connectivityvariants
0
0 comments X

The pith

SARS-CoV2 forms a more robust connection to the ACE2 receptor than SARS-CoV1 through stronger interface networks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper uses a side-chain network method on molecular dynamics simulations to compare the SARS-CoV2 and SARS-CoV1 complexes with human ACE2. It finds that the SARS-CoV2 interface maintains its connected cluster even when only strong bonds are considered, while the SARS-CoV1 interface breaks apart. At the atomic level, SARS-CoV2 shows richer clique structures among residues. The analysis identifies key residues and notes that many mutations in variants occur at this interface.

Core claim

SARS-CoV2 forms a more dominant, robust connection with the ACE2-receptor as compared to the less virulent SARS-CoV1. This is reflected by percolation analysis where the interface cluster persists when restricted to stronger and stronger bonds, and by richer atomic-level clique structure.

What carries the argument

Protein side chain-based network method that averages clusters and cliques over MD simulation snapshots to map from atomic non-covalent interactions to global amino acid connectivity.

If this is right

  • The interface cluster in SARS-CoV2 persists under stronger bond restrictions unlike in SARS-CoV1.
  • Key functional residues in SARS-CoV2 are pinpointed for their role in higher connectivity.
  • Many mutations in variants of concern occur at the ACE2 interface.
  • The method provides an objective way to map spatial connectivity across scales.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the connectivity difference holds, it could guide design of receptor-like peptides to block escape variants.
  • Similar network analysis might apply to other virus-receptor pairs to predict virulence.
  • Variations in MD snapshots highlight conformational landscapes that could be targeted.

Load-bearing premise

The side-chain contact definition and chosen MD snapshots from simulations are free of artifacts and their average represents the dominant binding mode.

What would settle it

A recalculation of the percolation threshold showing that the SARS-CoV2 interface cluster breaks at the same bond strength as the SARS-CoV1 cluster, or fewer cliques in SARS-CoV2.

Figures

Figures reproduced from arXiv: 2402.05416 by Arinnia Anto, Moitrayee Bhattacharyya, Saraswathi Vishveshwara, Smitha Vishveshwara, Varsha Subramanyan.

Figure 1
Figure 1. Figure 1: Mesoscopic view of global connectivity maps of spike protein – ACE2 receptor complexes reveal stronger percolation behavior in SARS-CoV2 compared to SARS-CoV1 : (a-c) Percolation analyses of the largest cluster, particularly those at the interface (highlighted as I in red) have revealed a tighter interface. We see retention of interface clusters at the higher stringency criteria of Imin 3-4% and higher dyn… view at source ↗
Figure 2
Figure 2. Figure 2: (a,b) Atomistic details of the stronger interface in 6M17 versus 2AJF. This is a zoomed-in view of all edge interaction strengths in the spike-ACE2 complex structure network across the conformational ensemble obtained from the MD simulations. We plot the edge interaction strengths for all pair of residues averaged over all the MD trajectory snapshots against the standard deviation in the interaction streng… view at source ↗
Figure 3
Figure 3. Figure 3: The interface cliques obtained for the averaged network at Imin 2.75, 50% dynamic stability are presented here for the (a) SARS-CoV2 complex and (b) SARS-CoV1 complex. The residues/nodes corresponding to the ACE2 receptor are denoted as blue circles while those corresponding to the virus spike protein are denoted as magenta squares. Node labelling: First letter represents the Chain number in the PDB for ea… view at source ↗
Figure 4
Figure 4. Figure 4: The largest cluster obtained for the averaged network at Imin 2.75, 50% dynamic stability are presented here for the (a) SARS-CoV1 complex and (b) SARS-CoV2 complex. The entire complex is represented by network metrics of edge, clique, community and cluster. The residues connected by green edges represent the interface nodes, and highlight the nature of their extended connection in the SARS-CoV2 complex as… view at source ↗
Figure 5
Figure 5. Figure 5: The Pymol representation of the interface clusters of SARS-CoV1 complex at various Imin at 50% dynamical stability are presented here. The nodes corresponding to ACE2 receptor residues are represented in blue, while virus residues are represented in pink. The breaking up of the largest interface cluster for increasing values of Imin is indicative of the percolation-like behaviour highlighted in Fig.1. The … view at source ↗
Figure 6
Figure 6. Figure 6: The Pymol representation of the interface clusters of SARS-CoV2 complex at various Imin at 50% dynamical stability are presented here. The nodes corresponding to ACE2 receptor residues are represented in blue, while virus residues are represented in pink. The breaking up of the largest interface cluster for increasing values of Imin is indicative of the percolation-like behaviour highlighted in Fig.1. The … view at source ↗
Figure 7
Figure 7. Figure 7: The interface cliques/communities of the SARS-CoV2 complex in three selected snapshots (Imin =3.5) are displayed here for the (a) Reference structure, (b) High RMSD and (c) End RMSD respectively. The mutated residues in variants are represented with a yellow outline on the nodes. The cliques obtained here as well as the nodes involved are similar to those obtained for the average network in Fig.3 but are s… view at source ↗
Figure 8
Figure 8. Figure 8: The interface clusters of the SARS-CoV2 complex in three selected snapshots (Imin =3.5) are displayed here for the (a) Reference structure, (b) High RMSD and (c) End RMSD respectively. The residues connected by green edges represent the interface nodes [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The interface cliques in the three selected snapshots (Imin =3.5) are represented over the PYMOL structure corresponding to the SARS-CoV2 complex, denoting their positions along the interface. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Mutations in designed ACE2-Receptor are present in regions outlined by orange colour, which are represented on the interface cluster of the reference structure (Figure 8a) [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The nodes in the ACE2-Receptor in the basic framework of the interface cluster, shown in [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Post Omicron evolutionary path of SARS-CoV2 lineages leading to complex-recombinant strains. This figure is reproduced as is from Reference (29) under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). 17 [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The mutation sites for enhanced affinity in designing decoy ACE2 receptors are indicated in this figure. Possible residue substitutions are listed in the table below. The loop held by the disulphide bridge in SARS-CoV2 (Cys480-Cys488) is also highlighted as a pink ribbon. This figure is loosely adapted from Reference (35) [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The above figure offers a brief explanation for the concept of dynamic stability, which is a measure of statistical significance of a certain edge interaction over the entire period of simulation. We elucidate this idea using an example here. Consider a network whose edge interactions are being modified during the course of a certain amount of simulation time. Let the networks A-E in (a) be a collection o… view at source ↗
read the original abstract

We investigate connectivity properties of the SARS-CoV2 spike protein-human ACE2-receptor complex employing a protein side chain-based network method that allows us to span a range from atomic to global protein scales. We analyze network topology in terms of clusters and cliques obtained from averaging over snapshots of MD simulations (from D.E. Shaw Research). We demonstrate that SARS-CoV2 forms a more dominant, robust connection with the ACE2-receptor as compared to the less virulent SARS-CoV1. Globally, this stronger connectivity is reflected by our percolation analysis where the interface cluster for the SARS-CoV2-ACE2 complex persists when restricted to stronger and stronger bonds, as compared to the SARSCoV1- ACE2 complex. At the atomic level, interface clique structure reflects a stronger connectivity in the former complex. We pinpoint key functional residues in SARSCoV2 that play important roles in establishing this higher connectivity. Thus, our studies provide an objective method to map spatial connectivity of atomic level non-covalent interactions to global connectivity between any two amino acids in the complex. We also analyze specific snapshots of the MD simulations to highlight prominent variations in network topology that explore diverse conformational landscapes. Finally, we demonstrate that a majority of mutations that occur in the SARSCoV2 spike protein in variants of concern/interest (including the currently circulating JN.1) have been observed at the interface with the ACE2 receptor. Our analyses highlight the importance of interface interactions and provide a rationale for designing receptor-like peptides and proteins to combat immunity-escaping variants.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript applies a side-chain contact network analysis to MD simulation snapshots of the SARS-CoV2–ACE2 and SARS-CoV1–ACE2 complexes. It reports that the SARS-CoV2 interface exhibits stronger global connectivity, demonstrated by a percolation threshold that survives stricter bond-strength cutoffs, together with richer atomic-scale clique structure; key functional residues are identified and the locations of mutations in variants of concern are mapped onto the interface.

Significance. If the percolation and clique results are robust to the contact definition and snapshot ensemble, the work supplies a concrete, multi-scale mapping from atomic non-covalent interactions to global interface connectivity that could inform both mechanistic understanding of virulence differences and the design of receptor-mimetic inhibitors. The approach is parameter-light once the contact criterion is fixed and directly uses publicly available trajectories, which are positive features.

major comments (3)
  1. [Methods] Methods (percolation analysis paragraph): the claim that the SARS-CoV2 interface cluster “persists when restricted to stronger and stronger bonds” is load-bearing for the central robustness conclusion, yet no numerical bond-strength cutoff values, occupancy thresholds, or energy criteria are stated, nor is any sensitivity test to modest changes in these criteria reported.
  2. [Methods] Methods (snapshot selection): the percolation and clique results rest on averaging over an unspecified subset of D.E. Shaw trajectories; no justification is given for the choice of frames, no error bars or bootstrap estimates on the percolation threshold are supplied, and no comparison against the corresponding crystal structures is presented to rule out force-field or sampling artifacts.
  3. [Results] Results (clique enumeration): the atomic-level claim of “richer clique structure” requires an explicit description of how cliques are enumerated (e.g., Bron–Kerbosch parameters, minimum size, weighting) and whether the reported difference survives variation in those parameters; this information is absent.
minor comments (2)
  1. [Abstract] Abstract: inconsistent hyphenation (“SARSCoV1” vs. “SARS-CoV1”) and missing space before “ACE2” in one instance should be standardized.
  2. [Figures] Figure captions: the percolation plots should explicitly label the bond-strength axis with the numerical cutoff values used in each panel.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments that highlight areas where methodological details can be strengthened for clarity and reproducibility. We address each point below and will incorporate the requested information in a revised manuscript.

read point-by-point responses

  1. Referee: [Methods] Methods (percolation analysis paragraph): the claim that the SARS-CoV2 interface cluster “persists when restricted to stronger and stronger bonds” is load-bearing for the central robustness conclusion, yet no numerical bond-strength cutoff values, occupancy thresholds, or energy criteria are stated, nor is any sensitivity test to modest changes in these criteria reported.

    Authors: We agree that explicit numerical values and sensitivity tests are essential. In the revised manuscript we will state the precise bond-strength cutoffs, occupancy thresholds, and any energy criteria used to define contacts in the percolation analysis. We will also add a sensitivity analysis demonstrating how the percolation threshold for the SARS-CoV2 interface responds to modest changes in these parameters, thereby supporting the robustness claim with quantitative evidence. revision: yes

  2. Referee: [Methods] Methods (snapshot selection): the percolation and clique results rest on averaging over an unspecified subset of D.E. Shaw trajectories; no justification is given for the choice of frames, no error bars or bootstrap estimates on the percolation threshold are supplied, and no comparison against the corresponding crystal structures is presented to rule out force-field or sampling artifacts.

    Authors: We acknowledge the need for greater transparency on trajectory handling. The revised Methods section will specify the exact frames selected from the D.E. Shaw trajectories, the rationale for that selection (e.g., equilibration and convergence checks), and will report error bars or bootstrap estimates on the percolation threshold. We will also include a direct comparison of key network metrics between the MD snapshots and the corresponding crystal structures to address potential force-field or sampling concerns. revision: yes

  3. Referee: [Results] Results (clique enumeration): the atomic-level claim of “richer clique structure” requires an explicit description of how cliques are enumerated (e.g., Bron–Kerbosch parameters, minimum size, weighting) and whether the reported difference survives variation in those parameters; this information is absent.

    Authors: We agree that the clique-analysis procedure must be fully specified. In revision we will describe the enumeration algorithm (including Bron–Kerbosch parameters if used), the minimum clique size, any weighting scheme, and will demonstrate that the observed difference in clique richness between the SARS-CoV2–ACE2 and SARS-CoV1–ACE2 interfaces remains robust under reasonable variations of these parameters. These details will be added to the Results section. revision: yes

Circularity Check

0 steps flagged

No significant circularity; standard network analysis on external MD data

full rationale

The paper constructs side-chain contact networks from external D.E. Shaw MD trajectories, then applies standard graph operations (clustering, clique detection, percolation under bond-strength cutoffs) to compare SARS-CoV2 vs SARS-CoV1 interfaces. No equations, fitted parameters, or self-definitional reductions appear; the central robustness claim is a direct output of these operations on the input contact graphs rather than a quantity forced by construction or by a self-citation chain. The method itself is cited as prior work but functions as an independent, externally applicable tool whose results on this dataset are falsifiable against other contact definitions or trajectories.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities are stated. The work implicitly assumes that (1) MD trajectories adequately sample the relevant ensemble and (2) a side-chain contact graph faithfully encodes functional connectivity.

pith-pipeline@v0.9.0 · 5843 in / 1145 out tokens · 17281 ms · 2026-05-24T04:21:47.771403+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

42 extracted references · 42 canonical work pages

  1. [1]

    This feature is consistent with the largest cluster transition profiles as function ofImin at 50% dynamic stability as seen in Figure 1(a). The stability of the largest interface cluster at higher interaction strengths in SARS-CoV2 complex as compared to SARS-CoV1 complex indicates that information regarding higher binding affinity can be extracted from t...

    work page 2019

  2. [2]

    Science 375, 760–764 (2022)

    D Mannar, et al., Sars-cov-2 omicron variant: Antibody evasion and cryo-em structure of spike protein–ace2 complex. Science 375, 760–764 (2022)

    work page 2022

  3. [3]

    PH Guzzi, et al., Computational analysis of the sequence-structure relation in sars-cov-2 spike protein using protein contact networks. Sci. Reports 13, 2837 (2023)

    work page 2023

  4. [4]

    Animal Dis

    A Zabiegala, Y Kim, KO Chang, Roles of host proteases in the entry of sars-cov-2. Animal Dis. 3, 12 (2023)

    work page 2023

  5. [5]

    DA Jamison, et al., A comprehensive sars-cov-2 and covid-19 review, part 1: Intracellular overdrive for sars-cov-2 infection. Eur. J. Hum. Genet . 30, 889–898 (2022)

    work page 2022

  6. [6]

    SA Narayanan, et al., A comprehensive sars-cov-2 and covid-19 review, part 2: host extracel- lular to systemic effects of sars-cov-2 infection. Eur. J. Hum. Genet . 32, 10–20 (2024)

    work page 2024

  7. [7]

    N Maroli, Riding the wave: Unveiling the conformational waves from rbd of sars-cov-2 spike protein to ace2. The J. Phys. Chem. B 127, 8525–8536 (2023)

    work page 2023

  8. [8]

    BT Kaynak, et al., Sampling of protein conformational space using hybrid simulations: A critical assessment of recent methods. Front. Mol. Biosci. 9 (2022)

    work page 2022

  9. [9]

    B Wingert, J Krieger, H Li, I Bahar, Adaptability and specificity: how do proteins balance opposing needs to achieve function? Curr Opin Struct Biol 67, 25–32 (2020)

    work page 2020

  10. [10]

    D Scaramozzino, RL Jernigan, Special issue on computational approaches for protein dynam- ics and function. Appl. Sci. 13 (2023)

    work page 2023

  11. [11]

    B Jawad, P Adhikari, R Podgornik, WY Ching, Key interacting residues between rbd of sars- cov-2 and ace2 receptor: Combination of molecular dynamics simulation and density functional calculation. J. Chem. Inf. Model . 61, 4425–4441 (2021)

    work page 2021

  12. [12]

    Protein Sci

    AS Pillai, GK Hochberg, JW Thornton, Simple mechanisms for the evolution of protein complexity. Protein Sci. 31, e4449 (2022)

    work page 2022

  13. [13]

    A Ali, R Vijayan, Dynamics of the ace2–sars-cov-2/sars-cov spike protein interface reveal unique mechanisms. Sci. Reports 10, 14214 (2020)

    work page 2020

  14. [14]

    C Bai, A Warshel, Critical differences between the binding features of the spike proteins of sars-cov-2 and sars-cov. The J. Phys. Chem. B 124, 5907–5912 (2020)

    work page 2020

  15. [15]

    Science 369, 1586– 1592 (2020)

    Y Cai, et al., Distinct conformational states of sars-cov-2 spike protein. Science 369, 1586– 1592 (2020)

    work page 2020

  16. [16]

    C Xu, et al., Conformational dynamics of sars-cov-2 trimeric spike glycoprotein in complex with receptor ace2 revealed by cryo-em. Sci. Adv. 7, eabe5575 (2021)

    work page 2021

  17. [17]

    M Noval Rivas, RA Porritt, MH Cheng, I Bahar, M Arditi, Multisystem inflammatory syndrome in children and long covid: The sars-cov-2 viral superantigen hypothesis. Front. Immunol. 13 (2022)

    work page 2022

  18. [18]

    G Verkhivker, S Agajanian, R Kassab, K Krishnan, Computer simulations and network-based profiling of binding and allosteric interactions of sars-cov-2 spike variant complexes and the host receptor: Dissecting the mechanistic effects of the delta and omicron mutations. Int. J. Mol. Sci. 23 (2022). 7

    work page 2022

  19. [19]

    R Nussinov, M Zhang, Y Liu, H Jang, Alphafold, artificial intelligence (ai), and allostery. The J. Phys. Chem. B 126, 6372–6383 (2022)

    work page 2022

  20. [20]

    JPA Ioannidis, F Zonta, M Levitt, Variability in excess deaths across countries with different vulnerability during 2020–2023. Proc. Natl. Acad. Sci . 120, e2309557120 (2023)

    work page 2020

  21. [21]

    DES Research, Molecular dynamics simulations related to sars-cov-2 (2020) D. E. Shaw Research Technical Data, https://www.deshawresearch.com/downloads/download_trajectory_ sarscov2.cgi/

    work page 2020

  22. [22]

    A Halder, et al., Surveying the side-chain network approach to protein structure and dynamics: The sars-cov-2 spike protein as an illustrative case. Front. Mol. Biosci. 7 (2020)

    work page 2020

  23. [23]

    V Gadiyaram, S Vishveshwara, S Vishveshwara, From quantum chemistry to networks in biology: A graph spectral approach to protein structure analyses. J. Chem. Inf. Model . 59, 1715–1727 (2019) PMID: 30912941

    work page 2019

  24. [24]

    Curr Protein Pept Sci 17, 4–25 (2016)

    M Bhattacharyya, S Ghosh, S Vishveshwara, Protein structure and function: Looking through the network of Side-Chain interactions. Curr Protein Pept Sci 17, 4–25 (2016)

    work page 2016

  25. [25]

    Protein Sci

    M Bhattacharyya, CR Bhat, S Vishveshwara, An automated approach to network features of protein structure ensembles. Protein Sci. 22, 1399–1416 (2013)

    work page 2013

  26. [26]

    N Kannan, S Vishveshwara, Identification of side-chain clusters in protein structures by a graph spectral method11edited by j. m. thornton. J. Mol. Biol . 292, 441–464 (1999)

    work page 1999

  27. [27]

    K Guruprasad, Mutations in human sars-cov-2 spike proteins, potential drug binding and epitope sites for covid-19 therapeutics development. Curr. Res. Struct. Biol . 4, 41–50 (2022)

    work page 2022

  28. [28]

    Cytokine & Growth Factor Rev

    B Cosar, et al., Sars-cov-2 mutations and their viral variants. Cytokine & Growth Factor Rev. 63, 10–22 (2022)

    work page 2022

  29. [29]

    C Roemer, et al., Sars-cov-2 evolution, post-omicron (2022) https://virological.org/t/ sars-cov-2-evolution-post-omicron/911

    work page 2022

  30. [30]

    Nature 614, 521–529 (2023)

    Y Cao, et al., Imprinted sars-cov-2 humoral immunity induces convergent omicron rbd evolution. Nature 614, 521–529 (2023)

    work page 2023

  31. [31]

    Virus Evol

    JD Bloom, RA Neher, Fitness effects of mutations to sars-cov-2 proteins. Virus Evol . 9, vead055 (2023)

    work page 2023

  32. [32]

    bioRxiv (2023)

    Y Kaku, et al., Virological characteristics of the sars-cov-2 jn.1 variant. bioRxiv (2023)

    work page 2023

  33. [33]

    MC Chan, KK Chan, E Procko, D Shukla, Machine learning guided design of high-affinity ace2 decoys for sars-cov-2 neutralization. The J. Phys. Chem. B 127, 1995–2001 (2023)

    work page 1995

  34. [34]

    Acta Pharmacol

    Y Huang, C Y ang, Xf Xu, W Xu, Sw Liu, Structural and functional properties of sars-cov-2 spike protein: potential antivirus drug development for covid-19. Acta Pharmacol. Sinica 41, 1141–1149 (2020)

    work page 2020

  35. [35]

    C Chen, et al., Computational prediction of the effect of amino acid changes on the binding affin- ity between sars-cov-2 spike rbd and human ace2. Proc. Natl. Acad. Sci . 118, e2106480118 (2021)

    work page 2021

  36. [36]

    T rends Pharmacol Sci 43, 838–851 (2022)

    T Arimori, et al., Engineering ace2 decoy receptors to combat viral escapability. T rends Pharmacol Sci 43, 838–851 (2022)

    work page 2022

  37. [37]

    Nature 581, 215–220 (2020)

    J Lan, et al., Structure of the sars-cov-2 spike receptor-binding domain bound to the ace2 receptor. Nature 581, 215–220 (2020)

    work page 2020

  38. [38]

    protein folding: Differing questions, different challenges

    SJ Chen, et al., Protein folds vs. protein folding: Differing questions, different challenges. Proc. Natl. Acad. Sci . 120, e2214423119 (2023)

    work page 2023

  39. [39]

    Nature 596, 583–589 (2021)

    J Jumper, et al., Highly accurate protein structure prediction with alphafold. Nature 596, 583–589 (2021)

    work page 2021

  40. [40]

    PLOS Comput

    R Sathyapriya, MS Vijayabaskar, S Vishveshwara, Insights into protein–dna interactions through structure network analysis. PLOS Comput. Biol . 4, 1–15 (2008)

    work page 2008

  41. [41]

    K Brinda, S Vishveshwara, S Vishveshwara, Random network behaviour of protein structures. Mol. BioSystems 6, 391–398 (2010) Copyright: Copyright 2010 Elsevier B.V., All rights reserved

    work page 2010

  42. [42]

    ?a" for residues that contribute to aromaticity,

    D Deb, S Vishveshwara, S Vishveshwara, Understanding protein structure from a percolation perspective. Biophys. journal 97, 1787–1794 (2009). Fig. 1. Mesoscopic view of global connectivity maps of spike protein – ACE2 receptor complexes reveal stronger percolation behavior in SARS-CoV2 compared to SARS-CoV1 : (a-c) Percolation analyses of the largest clus...

    work page 2009