ArticleProceedings of the National Academy of Sciences of the United States of America2024
In vivo functional phenotypes from a computational epistatic model of 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 14 papers.
What it found
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Who cites it
14 citing papers in PubMed, 24 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
- Phylogenetic corrections and higher-order sequence statistics in protein families: Potts vs multiple sequence alignment transformer machine learning models.Physical review research · 2025Article
- Generative Landscapes and Dynamics to Design Functional Multidomain Artificial Transmembrane Transporters.ACS central science · 2025Article
- Integrating experimental feedback improves generative models for biological sequences.Nucleic acids research · 2025Article
- A Cyanobacterial Screening Platform for Rubisco Mutant Variants.ACS synthetic biology · 2025Article
- Higher-order epistasis drives evolutionary unpredictability toward novel antibiotic resistance.bioRxiv : the preprint server for biology · 2025Article
- Reconstruction of Ancestral Protein Sequences Using Autoregressive Generative Models.Molecular biology and evolution · 2025Article
- Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025Review
- Emergent time scales of epistasis in protein evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Dissecting Allosteric Mutations for Antibiotic Resistance by Time-Dependent Linear Response Theory.Journal of chemical theory and computation · 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
- Simultaneous enhancement of multiple functional properties using evolution-informed protein design.Nature communications · 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
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in
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Registered trials
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.