Phase transitions may explain why SARS-CoV-2 spreads so fast and why new variants are spreading faster

Phillips, J. C.; Moret, Marcelo A.; Zebende, Gilney Figueira; Chow, C. C.

doi:10.1016/j.physa.2022.127318

Cited by 5 publications

(14 citation statements)

References 30 publications

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“…Coronaviruses that cause MERS, SARS and COVID-19 diseases are all closely related, and it is natural to expect that the same coding scheme will be a good representation of the SARS-CoV-2 proteins based in the same molecules. This is reinforced by the discussion in 35 (see particularly Figure 3 of this paper) that shows that the Spike proteins of these viruses have very similar characteristics. Different binary codes were used to distinguish SARS-CoV viruses from other coronaviruses, such as the one used by Xiao et al 34 , which is a simpler code and does not take into account physicochemical amino acids.…”

Section: Methodssupporting

confidence: 54%

Relating SARS-CoV-2 variants using cellular automata imaging

Souza

Filho

Moret

2022

Sci Rep

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We classify the main variants of the SARS-CoV-2 virus representing a given biological sequence coded as a symbolic digital sequence and by its evolution by a cellular automata with a properly chosen rule. The spike protein, common to all variants of the SARS-CoV-2 virus, is then by the picture of the cellular automaton evolution yielding a visible representation of important features of the protein. We use information theory Hamming distance between different stages of the evolution of the cellular automaton for seven variants relative to the original Wuhan/China virus. We show that our approach allows to classify and group variants with common ancestors and same mutations. Although being a simpler method, it can be used as an alternative for building phylogenetic trees.

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Section: Methodssupporting

confidence: 54%

Relating SARS-CoV-2 variants using cellular automata imaging

Souza

Filho

Moret

2022

Sci Rep

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“…1. Both MZ and KD scale values [7] are nearly equal for these two aa. For the hydrophobic extrema we can look at the rst 40 aa of Prot1, which contains two closely spaced TM segments.…”

Section: Discussionmentioning

confidence: 91%

“…Our earlier analysis relied on Ψ(aa,W) hydropathic pro les, where Ψ(aa) measures the hydropathicity of each amino acid [13,14,7]. Protein shapes are economically described by structural domains, which are known in detail for static structures.…”

Section: Methodsmentioning

confidence: 99%

“…Proteins are the most important examples of self-organized networks, and the mechanical properties of such networks near critical points, have been extensively studied in primarily covalently bonded network glass alloys [12]. The high accuracy of the biophysical results for functional evolution of CoV spikes has resulted from the long, slender spike structures' immersion in water [6][7][8][9]. Similarly several of the functional differences between Piezo1 and 2 can be explained by pro ling differences in their interactions with water, as we shall see.…”

Section: Introductionmentioning

confidence: 99%

“…A new biophysical method of sequence analysis has recently quanti ed the connection between sequence mutations of the Coronavirus (CoV) spike and its evolving contagiousness with high accuracy [6][7][8][9]. The method is based on a conjectured connection between living systems and evolution by natural selection towards a thermodynamic critical point [10,11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Biophysical Sequence Analysis of Functional Differences of Piezo1 and Piezo2

Phillips

2023

Preprint

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Because of their large size and widespread mechanosensitive interactions the only recently discovered titled transmembrane proteins have attracted much attention. Here we present and discuss their hydropathic profiles using a new method of sequence analysis. We find large-scale similarities and differences not obtainable by conventional sequence or structural studies. These differences support the evolution-towards-criticality conjecture popular among physicists. In this case the elastic properties are critical.

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Why and how did the COVID pandemic end abruptly?

Moret,

Phillips

2024

Eur. Phys. J. B

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Phase transition theory, implemented quantitatively by thermodynamic scaling, has explained the evolution of Coronavirus’ extremely high contagiousness caused by a few key mutations from CoV2003 to CoV2019 identified among hundreds, as well as the later 2021 evolution to Omicron caused by 30 mutations. It also showed that the 2022 strain BA.5 with five mutations began a new path. Here we show that the early 2023 strains BKK with one stiffening mutation confirm that path, and the single flexing mutation of a later 2023 variant EG.5 strengthens it further. The few mutations of the new path have greatly reduced pandemic deaths, for mechanical reasons proposed here. Graphical abstract

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Phase transitions may explain why SARS-CoV-2 spreads so fast and why new variants are spreading faster

Cited by 5 publications

References 30 publications

Relating SARS-CoV-2 variants using cellular automata imaging

Relating SARS-CoV-2 variants using cellular automata imaging

Biophysical Sequence Analysis of Functional Differences of Piezo1 and Piezo2

Why and how did the COVID pandemic end abruptly?

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