Chronic infections can generate SARS-CoV-2-like bursts of viral evolution without epistasis.

Multiple SARS-CoV-2 variants have arisen during the first years of the pandemic, often bearing many new mutations. Several explanations have been offered for the surprisingly sudden emergence of multiple mutations that enhance viral fitness, including cryptic transmission, spillover from animal reservoirs, epistasis between mutations, and chronic infections. Here, we simulated pathogen evolution combining within-host replication and between-host transmission.

On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins.

Coevolution-based contact prediction, either directly by coevolutionary couplings resulting from global statistical sequence models or using structural supervision and deep learning, has found widespread application in protein-structure prediction from sequence. However, one of the basic assumptions in global statistical modeling is that sequences form an at least approximately independent sample of an unknown probability distribution, which is to be learned from data. In the case of protein families, this assumption is obviously violated by phylogenetic relations between protein sequences.

Global analysis of more than 50,000 SARS-CoV-2 genomes reveals epistasis between eight viral genes.

Genome-wide epistasis analysis is a powerful tool to infer gene interactions, which can guide drug and vaccine development and lead to deeper understanding of microbial pathogenesis. We have considered all complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes deposited in the Global Initiative on Sharing All Influenza Data (GISAID) repository until four different cutoff dates, and used direct coupling analysis together with an assumption of quasi-linkage equilibrium to infer epistatic contributions to fitness from polymorphic loci. We find eight interactions, of which three are between pairs where one locus lies in gene ORF3a, both loci holding nonsynonymous mutations.

Close-packed structure dynamics with finite-range interaction: computational mechanics with individual layer interaction.

This is the second contribution in a series of papers dealing with dynamical models in equilibrium theories of polytypism. A Hamiltonian introduced by Ahmad & Khan [Phys. Status Solidi B (2000), 218, 425-430] avoids the unphysical assignment of interaction terms to fictitious entities given by spins in the Hägg coding of the stacking arrangement.

Close-packed structures with finite-range interaction: computational mechanics of layer pair interaction.

The stacking problem is approached by computational mechanics, using an Ising next-nearest-neighbour model. Computational mechanics allows one to treat the stacking arrangement as an information processing system in the light of a symbol-generating process. A general method for solving the stochastic matrix of the random Gibbs field is presented and then applied to the problem at hand.