ArticleNature communications2021
ECNet is an evolutionary context-integrated deep learning framework for protein engineering.
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 84 papers.
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84 citing papers in PubMed, 178 citations in OpenAlex.
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- Overcoming extrapolation challenges of deep learning by incorporating physics in protein sequence-function modeling.PLoS computational biology · 2026Article
- CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation.Interdisciplinary sciences, computational life sciences · 2026Article
- BEST: Basic Embedding Search Tool Enhancing Discovery of Novel Enzyme.Interdisciplinary sciences, computational life sciences · 2026Article
- Deconvolving mutation effects on protein stability and function with disentangled protein language models.bioRxiv : the preprint server for biology · 2026Article
- Advancing NanoLuc Luciferase Stability beyond Directed Evolution and Rational Design through Expert-Guided Deep Learning.ACS catalysis · 2026Article
- Efficient, Few-shot Directed Evolution with Energy Rank Alignment.bioRxiv : the preprint server for biology · 2026Article
- Optimized Quantitative Bacterial Two-Hybrid (qB2H) for Protein-Protein Interaction Assessment.Computational and structural biotechnology journal · 2026Article
- Quantum-topological simulation of Berry phase-induced fentanyl-μ-opioid receptor dissociation via terahertz vortex fields.Journal of molecular modeling · 2025Article
- Overcoming Extrapolation Challenges of Deep Learning by Incorporating Physics in Protein Sequence-Function Modeling.bioRxiv : the preprint server for biology · 2025Article
- Quantified Dynamics-Property Relationships: Data-Efficient Protein Engineering with Machine Learning of Protein Dynamics.Journal of chemical information and modeling · 2025Article
- Insights from deep mutational scanning in the context of an emerging pathogen.Biochemical Society transactions · 2025Review
- ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- LassoESM a tailored language model for enhanced lasso peptide property prediction.Nature communications · 2025Article
- Article
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
- Decoding Protein Stabilization: Impact on Aggregation, Solubility, and Unfolding Mechanisms.Journal of chemical information and modeling · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Designing diverse and high-performance proteins with a large language model in the loop.PLoS computational biology · 2025Article
24 more citing papers are in PubMed but not listed here.
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10 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited. Here, we report ECNet (evolutionary context-integrated neural network), a deep-learning algorithm that exploits evolutionary contexts to predict functional fitness for protein engineering. This algorithm integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe. As such, it enables accurate mapping from sequence to function and provides generalization from low-order mutants to higher-order mutants. We show that ECNet predicts the sequence-function relationship more accurately as compared to existing machine learning algorithms by using ~50 deep mutational scanning and random mutagenesis datasets. Moreover, we used ECNet to guide the engineering of TEM-1 β-lactamase and identified variants with improved ampicillin resistance with high success rates.
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