ArticleAngewandte Chemie (International ed. in English)2018
Co-Evolutionary Fitness Landscapes for Sequence Design.
Article in Angewandte Chemie (International ed. in English), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Protein stability is determined by single-site bias rather than pairwise covariance.Nature chemical biology · 2026Article
- Signatures of protein fold switching in the unfolded state.Biophysical journal · 2026Article
- Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability.Scientific reports · 2026Article
- Depletion of the Protein Hydration Shell with Increasing Temperature Observed by Small-Angle X-ray Scattering and Molecular Simulations.Journal of the American Chemical Society · 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
- Inferring protein-folding mechanisms from natural sequence diversity.Biophysical journal · 2025Article
- Quaternary stabilization of a GH2 β-galactosidase from the psychrophile A. ikkensis, a flexible and unstable dimeric enzyme.Protein science : a publication of the Protein Society · 2025Article
- Reconstruction of Ancestral Protein Sequences Using Autoregressive Generative Models.Molecular biology and evolution · 2025Article
- Computational scoring and experimental evaluation of enzymes generated by neural networks.Nature biotechnology · 2025Article
- Novel Insights into Enzymatic Thermostability: The "Short Board" Theory and Zero-Shot Hamiltonian Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Emergent time scales of epistasis in protein evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Deep-learning-based design of synthetic orthologs of SH3 signaling domains.Cell systems · 2024Article
- PROSCA: an online platform for humanized scaffold mining facilitating rational protein engineering.Nucleic acids research · 2024Article
- Simultaneous enhancement of multiple functional properties using evolution-informed protein design.Nature communications · 2024Article
- The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins.Protein science : a publication of the Protein Society · 2024Article
- A systematic analysis of regression models for protein engineering.PLoS computational biology · 2024Article
- 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
- Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.Cold Spring Harbor perspectives in biology · 2024Review
- Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.National science review · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Efficient and accurate models to predict the fitness of a sequence would be extremely valuable in protein design. We have explored the use of statistical potentials for the coevolutionary fitness landscape, extracted from known protein sequences, in conjunction with Monte Carlo simulations, as a tool for design. As proof of principle, we created a series of predicted high-fitness sequences for three different protein folds, representative of different structural classes: the GA (all-α) and GB (α/β) binding domains of streptococcal protein G, and an SH3 (all-β) domain. We found that most of the designed proteins can fold stably to the target structure, and a structure for a representative of each for GA, GB and SH3 was determined. Several of our designed proteins were also able to bind to native ligands, in some cases with higher affinity than wild-type. Thus, a search using a statistical fitness landscape is a remarkably effective tool for finding novel stable protein sequences.
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