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Characterizing the effect of mutations is key to understand the evolution of protein sequences and to separate neutral amino-acid changes from deleterious ones. Epistatic interactions between residues can lead to a context dependence of mutation effects. Context dependence constrains the amino-acid changes that can contribute to polymorphism in the short term, and the ones that can accumulate between species in the long term. We use computational approaches to accurately predict the polymorphisms segregating in a panel of 61,157 Escherichia coli genomes from the analysis of distant homologues. By comparing a context-aware Direct-Coupling Analysis modelling to a non-epistatic approach, we show that the genetic context strongly constrains the tolerable amino acids in 30% to 50% of amino-acid sites. The study of more distant species suggests the gradual build-up of genetic context over long evolutionary timescales by the accumulation of small epistatic contributions., (© 2022. The Author(s).)

Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 10 2 and 10 3 ). Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Within these models, we can easily estimate both the probability of a given sequence, and, using the model's entropy, the size of the functional sequence space related to a specific protein family. In the example of response regulators, we find a huge number of ca. 10 68 possible sequences, which nevertheless constitute only the astronomically small fraction 10 -80 of all amino-acid sequences of the same length. These findings illustrate the potential and the difficulty in exploring sequence space via generative sequence models., (© 2021. The Author(s).)

Genome replication is highly regulated in time and space, but the rules governing the remodeling of these programs during evolution remain largely unknown. We generated genome-wide replication timing profiles for ten Lachancea yeasts, covering a continuous evolutionary range from closely related to more divergent species. We show that replication programs primarily evolve through a highly dynamic evolutionary renewal of the cohort of active replication origins. We found that gained origins appear with low activity yet become more efficient and fire earlier as they evolutionarily age. By contrast, origins that are lost comprise the complete range of firing strength. Additionally, they preferentially occur in close vicinity to strong origins. Interestingly, despite high evolutionary turnover, active replication origins remain regularly spaced along chromosomes in all species, suggesting that origin distribution is optimized to limit large inter-origin intervals. We propose a model on the evolutionary birth, death, and conservation of active replication origins.