ArticleNature methods2025
EvoAI enables extreme compression and reconstruction of the protein sequence space.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Enhancing Enzyme Activity With Mutation Combinations Guided by Few-Shot Learning and Causal Inference.Angewandte Chemie (International ed. in English) · 2026Article
- Harnessing Nature's Algorithm: From Test Tubes to Autonomous In Vivo Evolution.Biotechnology journal · 2026Review
- Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Coupling high-throughput protease enzymology with viral replication reveals biochemical constraints of viral fitness.bioRxiv : the preprint server for biology · 2026Article
- A Structural Landscape Depiction of Dynamic Stability Centers of Local Structure in Protein Thermostability Engineering.Research (Washington, D.C.) · 2026Article
- Distinct actin microfilament localization during early cell plate formation through deep learning-based image restoration.Plant cell reports · 2025Article
- Accelerated enzyme engineering by machine-learning guided cell-free expression.Nature communications · 2025Article
- Method of the Year 2024: spatial proteomics.Nature methods · 2024Article
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14 authors.
Funding
Abstract
Designing proteins with improved functions requires a deep understanding of how sequence and function are related, a vast space that is hard to explore. The ability to efficiently compress this space by identifying functionally important features is extremely valuable. Here we establish a method called EvoScan to comprehensively segment and scan the high-fitness sequence space to obtain anchor points that capture its essential features, especially in high dimensions. Our approach is compatible with any biomolecular function that can be coupled to a transcriptional output. We then develop deep learning and large language models to accurately reconstruct the space from these anchors, allowing computational prediction of novel, highly fit sequences without prior homology-derived or structural information. We apply this hybrid experimental-computational method, which we call EvoAI, to a repressor protein and find that only 82 anchors are sufficient to compress the high-fitness sequence space with a compression ratio of 10
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