ArticleMolecular biology and evolution2022
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.
Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 30 citations in OpenAlex.
- 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
- Dual role of glycosylation in resistance to CD4-binding site broadly neutralizing antibodies.Journal of virology · 2026Article
- Descent from a common ancestor restricts exploration of protein sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- Unraveling Epistatic Interactions Between Sites Under Drug-Dependent Selection in the Mycobacterium tuberculosis Genome.Molecular biology and evolution · 2025Article
- Protein evolution as a complex system.Nature chemical biology · 2025Article
- Second-order allosteric control as a mechanism for compensatory mutations in B-cell translocation gene 2.Protein science : a publication of the Protein Society · 2025Article
- Reconstruction of Ancestral Protein Sequences Using Autoregressive Generative Models.Molecular biology and evolution · 2025Article
- Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis.Journal of chemical theory and computation · 2025Article
- Combination of Coevolutionary Information and Supervised Learning Enables Generation of Cyclic Peptide Inhibitors with Enhanced Potency from a Small Data Set.ACS central science · 2024Article
- Emergent time scales of epistasis in protein evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning.Nature communications · 2024Article
- Machine learning in biological physics: From biomolecular prediction to design.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Towards parsimonious generative modeling of RNA families.Nucleic acids research · 2024Article
- Article
- Kinetic coevolutionary models predict the temporal emergence of HIV-1 resistance mutations under drug selection pressure.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- In vivo functional phenotypes from a computational epistatic model of evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Coevolutionary Information Captures Catalytic Functions and Reveals Divergent Roles of Terpene Synthase Interdomain Connections.Biochemistry · 2024Article
- Inference of annealed protein fitness landscapes with AnnealDCA.PLoS computational biology · 2024Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection. Here, we build upon recent progress in reconstructing data-driven fitness landscapes for families of homologous proteins, to propose stochastic models of experimental protein evolution. These models predict quantitatively important features of experimentally evolved sequence libraries, like fitness distributions and position-specific mutational spectra. They also allow us to efficiently simulate sequence libraries for a vast array of combinations of experimental parameters like sequence divergence, selection strength, and library size. We showcase the potential of the approach in reanalyzing two recent experiments to determine protein structure from signals of epistasis emerging in experimental sequence libraries. To be detectable, these signals require sufficiently large and sufficiently diverged libraries. Our modeling framework offers a quantitative explanation for different outcomes of recently published experiments. Furthermore, we can forecast the outcome of time- and resource-intensive evolution experiments, opening thereby a way to computationally optimize experimental protocols.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.