ArticleNature communications2024
Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
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- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- Machine-Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: A p-Coumaric Acid Case Study.ACS synthetic biology · 2026Article
- A multi-fidelity tabular prior-data fitted network model for accurate prediction and uncertainty quantification.Nature communications · 2026Article
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- Efficient, Few-shot Directed Evolution with Energy Rank Alignment.bioRxiv : the preprint server for biology · 2026Article
- Computational design of generalist cyclopropanases with stereodivergent selectivity.Nature communications · 2026Article
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- Active learning-guided optimization of cell-free biosensors for lead testing in drinking water.Nature communications · 2025Article
- Best Practices for Machine Learning-Assisted Protein Engineering.Journal of chemical information and modeling · 2025Review
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Building a Synthetic Cell Together.Nature communications · 2025Review
- SubTuner leverages physics-based modeling to complement AI in enzyme engineering toward non-native substrates.Chem catalysis · 2025Article
- Designing diverse and high-performance proteins with a large language model in the loop.PLoS computational biology · 2025Article
- Engineering CAR-T Therapeutics for Enhanced Solid Tumor Targeting.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Insight into improved specificity and thermostability of3 Biotech · 2025Article
- Single-Walled Carbon Nanotube Probes for Protease Characterization Directly in Cell-Free Expression Reactions.Analytical chemistry · 2025Article
- Article
- Carmna: classification and regression models for nitrogenase activity based on a pretrained large protein language model.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY's potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution.
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