ArticleProceedings of the National Academy of Sciences of the United States of America2024
Emergent time scales of epistasis in protein evolution.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Mechanisms of Phenotypic Evolution from Molecules to Organisms: Integrating Evolutionary Biochemistry and Evolutionary Developmental Biology.Annual review of ecology, evolution, and systematics · 2026Article
- Mind the gap: An embedding guide to safely travel in sequence space.PLoS computational biology · 2026Article
- Epistasis plays a limited role in driving entrenchment during neutral protein evolution.Genome biology · 2026Article
- A simple model captures key characteristics of biological non-deterministic genotype-phenotype maps.PLoS computational biology · 2026Article
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- Factors controlling protein evolvability-at the molecular scale.European biophysics journal : EBJ · 2026Article
- Evolution of Plant AIG1-like Proteins: Different Modes of Sequence Divergence and Their Contributions to Functional Diversification.Plants (Basel, Switzerland) · 2026Article
- Epistatic drift in protein evolution.Current opinion in genetics & development · 2025Review
- Constrained Evolutionary Funnels Shape Viral Immune Escape.bioRxiv : the preprint server for biology · 2025Article
- Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses.Journal of molecular biology · 2025Review
- Protein evolution as a complex system.Nature chemical biology · 2025Article
- Integrating experimental feedback improves generative models for biological sequences.Nucleic acids research · 2025Article
- Inferring protein-folding mechanisms from natural sequence diversity.Biophysical journal · 2025Article
- Higher-order epistasis drives evolutionary unpredictability toward novel antibiotic resistance.bioRxiv : the preprint server for biology · 2025Article
- Robustness of Ancestral Sequence Reconstruction to Among-site and Among-lineage Evolutionary Heterogeneity.Molecular biology and evolution · 2025Article
- Reconstruction of Ancestral Protein Sequences Using Autoregressive Generative Models.Molecular biology and evolution · 2025Article
- Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions.Biophysics reviews · 2025Review
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5 authors.
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Abstract
We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. We show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments. Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations. This collective nature triggers the emergence of a long evolutionary time scale, separating fast mutational processes inside a given sequence context, from the slow evolution of the context itself. The model quantitatively reproduces epistatic phenomena such as contingency and entrenchment, as well as the loss of predictability in protein evolution observed in deep mutational scanning experiments of distant homologs. It thereby deepens our understanding of the interplay between mutation and selection in shaping protein diversity and functions, allows one to statistically forecast evolution, and challenges the prevailing independent-site models of protein evolution, which are unable to capture the fundamental importance of epistasis.
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