ArticleNature communications2025
Integrating protein language models and automatic biofoundry for enhanced protein evolution.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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Who cites it
31 citing papers in PubMed.
- Engineering peroxygenases for pharmaceutical applications: recent advances and future perspectives.Chemical science · 2026Review
- EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology.Patterns (New York, N.Y.) · 2026Article
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- Automated synthetic cell-based screening for designed proteins with emergent functions.Nature communications · 2026Article
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- From Prediction to Creation: Generative Plant Design.Plants (Basel, Switzerland) · 2026Review
- Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations.bioRxiv : the preprint server for biology · 2026Article
- Predicting protein-protein interaction sites based on dynamic perception mechanism within a hierarchical E(n)-equivariant graph.Briefings in bioinformatics · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Single-round evolution of RNA aptamers with GRAPE-LM.Nature biotechnology · 2026Article
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
- Artificial Intelligence in Proteins and Peptides: A Comprehensive Overview from Hybrid Modeling to Deep Learning.Current protein & peptide science · 2026Article
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- AI-Driven De Novo Binder Design: From Structure Prediction to Closed-Loop Optimization.Computational and structural biotechnology journal · 2026Review
- Automated Laboratory Security Tiers: a framework for evaluating and mitigating biosecurity risks from latent capabilities.Frontiers in microbiology · 2026Article
- Active learning-guided optimization of cell-free biosensors for lead testing in drinking water.Nature communications · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Traditional protein engineering methods, such as directed evolution, while effective, are often slow and labor-intensive. Advances in machine learning and automated biofoundry present new opportunities for optimizing these processes. This study devises a protein language model-enabled automatic evolution platform, a closed-loop system for automated protein engineering within the Design-Build-Test-Learn cycle. The protein language model ESM-2 makes zero-shot prediction of 96 variants to initiate the cycle. The biofoundry constructs and evaluates these variants, and feeds the results back to a multi-layer perceptron to train a fitness predictor, which then makes prediction of second round of 96 variants with improved fitness. With the tRNA synthetase as a model enzyme, four-rounds of evolution carried out within 10 days lead to mutants with enzyme activity improved by up to 2.4-fold. Our system significantly enhances the speed and accuracy of protein evolution, driving faster advancements in protein engineering for industrial applications.
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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.