ArticleACS synthetic biology2024
Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint.
Article in ACS synthetic biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Discovering naturally occurring antifreeze peptides from microbiome by integrating protein language models and molecular dynamics simulation.Journal of materials chemistry. B · 2026Article
- Rational protein engineering using an omni-directional multipoint mutagenesis generation pipeline.iScience · 2025Article
- Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.Science advances · 2025Article
- Integrating Protein Language Model and Molecular Dynamics Simulations to Discover Antibiofouling Peptides.Langmuir : the ACS journal of surfaces and colloids · 2025Article
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3 authors.
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Abstract
Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.
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