ArticlePLoS computational biology2021
Rosetta design with co-evolutionary information retains protein function.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A Generative Neuro-Symbolic AI for Protein Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- In silico design of foldable lasso peptides.Biophysical journal · 2025Article
- ESM-scan-A tool to guide amino acid substitutions.Protein science : a publication of the Protein Society · 2024Article
- HyperMPNN-A general strategy to design thermostable proteins learned from hyperthermophiles.bioRxiv : the preprint server for biology · 2024Article
- Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint.ACS synthetic biology · 2024Article
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 2024Review
- Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.Frontiers in cellular and infection microbiology · 2024Review
- Design of a Protein with Improved Thermal Stability by an Evolution-Based Generative Model.Angewandte Chemie (International ed. in English) · 2022Article
- Odd one out? Functional tuning of Zymomonas mobilis pyruvate kinase is narrower than its allosteric, human counterpart.Protein science : a publication of the Protein Society · 2022Article
- VInternational journal of molecular sciences · 2022Review
- Modulating Glycoside Hydrolase Activity between Hydrolysis and Transfer Reactions Using an Evolutionary Approach.Molecules (Basel, Switzerland) · 2021Article
- Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism.Frontiers in genetics · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Computational protein design has the ambitious goal of crafting novel proteins that address challenges in biology and medicine. To overcome these challenges, the computational protein modeling suite Rosetta has been tailored to address various protein design tasks. Recently, statistical methods have been developed that identify correlated mutations between residues in a multiple sequence alignment of homologous proteins. These subtle inter-dependencies in the occupancy of residue positions throughout evolution are crucial for protein function, but we found that three current Rosetta design approaches fail to recover these co-evolutionary couplings. Thus, we developed the Rosetta method ResCue (residue-coupling enhanced) that leverages co-evolutionary information to favor sequences which recapitulate correlated mutations, as observed in nature. To assess the protocols via recapitulation designs, we compiled a benchmark of ten proteins each represented by two, structurally diverse states. We could demonstrate that ResCue designed sequences with an average sequence recovery rate of 70%, whereas three other protocols reached not more than 50%, on average. Our approach had higher recovery rates also for functionally important residues, which were studied in detail. This improvement has only a minor negative effect on the fitness of the designed sequences as assessed by Rosetta energy. In conclusion, our findings support the idea that informing protocols with co-evolutionary signals helps to design stable and native-like proteins that are compatible with the different conformational states required for a complex function.
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