ArticleNature methods2021
Low-N protein engineering with data-efficient deep learning.
Article in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 198 papers.
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Who cites it
198 citing papers in PubMed.
- 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
- The fitness landscape of a form II rubisco in a photosynthetic bacterium guides engineering of oxygen tolerance.Science advances · 2026Article
- Functional customization of peptide linkers in fusion proteins through multimodal deep learning approach.Synthetic and systems biotechnology · 2026Article
- Amylosucrase as a Versatile Biocatalyst for Next-Generation Functional Ingredients: Starch Modification and Glycoside Synthesis.Comprehensive reviews in food science and food safety · 2026Review
- Enhancing Enzyme Activity With Mutation Combinations Guided by Few-Shot Learning and Causal Inference.Angewandte Chemie (International ed. in English) · 2026Article
- Evolutionary profiles for protein fitness prediction.Bioinformatics (Oxford, England) · 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
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- Article
- Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- Inferring context-specific site variation with evotuned protein language models.NAR genomics and bioinformatics · 2026Article
- Enhancing CAR-T cell activity prediction via fine-tuning protein language models with generated CAR sequences.BMC bioinformatics · 2026Article
- Efficient, Few-shot Directed Evolution with Energy Rank Alignment.bioRxiv : the preprint server for biology · 2026Article
- Screening and machine learning-based prediction of translation-enhancing peptides that reduce ribosomal stalling inRSC chemical biology · 2026Article
- Dual-use artificial intelligence and biology: upstream risk-benefit reviews.Frontiers in microbiology · 2026Article
138 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution. As demonstrated in two dissimilar proteins, GFP from Aequorea victoria (avGFP) and E. coli strain TEM-1 β-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous high-throughput efforts. By distilling information from natural protein sequence landscapes, our model learns a latent representation of 'unnaturalness', which helps to guide search away from nonfunctional sequence neighborhoods. Subsequent low-N supervision then identifies improvements to the activity of interest. In sum, our approach enables efficient use of resource-intensive high-fidelity assays without sacrificing throughput, and helps to accelerate engineered proteins into the fermenter, field and clinic.
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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.