ArticleProceedings of the National Academy of Sciences of the United States of America2019
Machine learning-assisted directed protein evolution with combinatorial libraries.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 235 papers.
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Who cites it
235 citing papers in PubMed, 600 citations in OpenAlex.
- Multidimensional maturation of antibody variable domains with machine-learning assistance.mAbs · 2026Article
- Engineering peroxygenases for pharmaceutical applications: recent advances and future perspectives.Chemical science · 2026Review
- Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry.bioRxiv : the preprint server for biology · 2026Article
- The fitness landscape of a form II rubisco in a photosynthetic bacterium guides engineering of oxygen tolerance.Science advances · 2026Article
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- Protein turnover in plants-cost of synthesis, repair, and crop engineering opportunities.Plant physiology · 2026Review
- Sequence-free landscape inference for directed evolution.PLoS computational biology · 2026Article
- Adaptive model-guided protein evolution with sparse data optimizes compact eukaryotic genome editors.Nature biotechnology · 2026Article
- Combinatorial Optimization of Protein Systems in Synthetic Cells.ACS synthetic biology · 2026Article
- Accelerating protein design by scaling experimental characterization.Nature communications · 2026Article
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction.Briefings in bioinformatics · 2026Article
- Machine-learning prediction of affinity and epistasis in the bovine pancreatic trypsin inhibitor-chymotrypsin complex.Protein science : a publication of the Protein Society · 2026Article
- Elucidating the catalytic mechanism of a previously undescribed carbohydrate esterase family 7 acetyl xylan esterase (LaCE7A).The FEBS journal · 2026Article
- Substrate scope of ancestral versus modern family-1 glycosidases.Protein science : a publication of the Protein Society · 2026Article
- Directed evolution of enzymes at the crossroads of tradition and innovation.FEBS open bio · 2026Review
- Harnessing Nature's Algorithm: From Test Tubes to Autonomous In Vivo Evolution.Biotechnology journal · 2026Review
- Descent from a common ancestor restricts exploration of protein sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
175 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine-learning models trained on tested variants provide a fast method for testing sequence space computationally. We validated this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (i.e., stereodivergence) of a new-to-nature carbene Si-H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79%
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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.