ArticleNature communications2021
Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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Who cites it
41 citing papers in PubMed, 100 citations in OpenAlex.
- Microbial cell factories for gamma-linolenic acid biosynthesis: metabolic engineering, downstream processing, and future perspectives.Bioresources and bioprocessing · 2026Review
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Rapid directed evolution guided by protein language models and epistatic interactions.Science (New York, N.Y.) · 2026Article
- Enhancing CAR-T cell activity prediction via fine-tuning protein language models with generated CAR sequences.BMC bioinformatics · 2026Article
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
- Combing Directed Enzyme Evolution with Metabolic Engineering to Develop Efficient Microbial Cell Factories.Chem & bio engineering · 2025Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- ML-enhanced peroxisome capacity enables compartmentalization of multienzyme pathway.Nature chemical biology · 2025Article
- Biophysics-based protein language models for protein engineering.bioRxiv : the preprint server for biology · 2025Article
- Quantifying Protein-Nucleic Acid Interactions for Engineering Useful CRISPR-Cas9 Genome-Editing Variants.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Benchmarking uncertainty quantification for protein engineering.PLoS computational biology · 2025Article
- Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number.Chembiochem : a European journal of chemical biology · 2024Article
- Neural network extrapolation to distant regions of the protein fitness landscape.Nature communications · 2024Article
- Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering.Nature communications · 2024Article
- Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning.ACS central science · 2024Article
- Article
- Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number.bioRxiv : the preprint server for biology · 2024Article
- Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme.Nature communications · 2024Article
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. Many metabolic engineering strategies utilize FARs to produce fatty alcohols from intracellular acyl-CoA and acyl-ACP pools; however, enzyme activity, especially on acyl-ACPs, remains a significant bottleneck to high-flux production. Here, we engineer FARs with enhanced activity on acyl-ACP substrates by implementing a machine learning (ML)-driven approach to iteratively search the protein fitness landscape. Over the course of ten design-test-learn rounds, we engineer enzymes that produce over twofold more fatty alcohols than the starting natural sequences. We characterize the top sequence and show that it has an enhanced catalytic rate on palmitoyl-ACP. Finally, we analyze the sequence-function data to identify features, like the net charge near the substrate-binding site, that correlate with in vivo activity. This work demonstrates the power of ML to navigate the fitness landscape of traditionally difficult-to-engineer proteins.
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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.