Synchronized attachment and the Darwinian evolution of coronaviruses CoV-1 and CoV-2

Abstract

CoV2019 has evolved to be much more dangerous than CoV2003. Experiments suggest that structural rearrangements dramatically enhance CoV2019 activity. We identify a new first stage of infection that precedes structural rearrangements by using biomolecular evolutionary theory to identify sequence differences enhancing viral attachment rates. We find a small cluster of mutations which show that CoV-2 has a new feature that promotes much stronger viral attachment and enhances contagiousness. The extremely dangerous dynamics of human coronavirus infection is a dramatic example of evolutionary approach of self-organized networks to criticality. It may favor a very successful vaccine. The identified mutations can be used to test the present theory experimentally.

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

“…We thus posit a mechanism for the increase in infectiousness of CoV-2 based on synchronized attachment [4] . The analogy is to a network of coupled oscillators that have a transition between synchrony and asynchrony.…”

“…We conjecture that natural selection acting on a given class of proteins, such as the novel coronavirus spike, favors sequences such that the protein operates close to a thermodynamic critical point at an optimized length scale. Evidence for positive selection has been demonstrated in CoV-2 compared to CoV-1 [4] . Nearer the critical point, it can function more reversibly and at lower temperatures [6] .…”

“…This approach has been attempted many times with varying success [3] . We adopt an approach used previously that predicted that vaccines based on S would be very effective and finding four key mutations that enhance viral attachment and infectiousness [4] . This is nontrivial as vaccine efficacy is not always guaranteed.…”

“…Each S chain consists of over 1200 amino acid residues, with approximately 300 of these having mutated from SARS-CoV-1 (CoV-1, GenBank: AY278741, UniProtKB: P59594) to SARS-CoV-2 (CoV-2, GenBank: MN908947.3, UniProtKB: P0DTC2) [10] . Of the 300 mutations between CoV-1 and CoV-2, the previous application of the model predicted that four spike mutations affected its infectiousness [4] . The variants contain twelve or fewer spike mutations.…”

Relating SARS-CoV-2 Variants Using Cellular Automata Imaging

“…A protein can be represented as a primary structure formed by a sequence of long strings of characters containing all information on the protein: structure, function, hydrophobicity and different motifs. Several researchers have studied how to extract information from the sequence, such as hydrophobicity [13][14][15][16] , fractality 17,18 , geometric and thermodynamic aspects [19][20][21] . Cellular Automata have been widely used to model complex systems with simple, easy-to-understand rules 22 , and in recent years many papers were devoted study protein related problems using this approach.…”