ArticleNature communications2024
Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Democratizing protein language model training, sharing and collaboration.Nature biotechnology · 2026Article
- Iterative and data-driven ortholog mining enables reliable discovery of stereoselective ketoreductases.Nature communications · 2026Article
- Data-driven pipeline enables discovery of plant protein surfactants.Communications chemistry · 2026Article
- Strategies and mechanisms of precision genome engineering: From gene editing to genome writing.iMetaOmics · 2026Review
- Enhancing Enzyme Activity With Mutation Combinations Guided by Few-Shot Learning and Causal Inference.Angewandte Chemie (International ed. in English) · 2026Article
- Recombinant Thermostable DNA Polymerases: Current Approaches to Production, Molecular Engineering, and Applications in Biotechnology and Diagnostics.International journal of molecular sciences · 2026Review
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Rapid screening of staphylokinase protein variants using an unpurified cell-free expression system.FEBS open bio · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- StabLyzeGraph: High-throughput screening of combinatorial mutations using graph neural networks.Protein science : a publication of the Protein Society · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Harnessing deep learning to accelerate the development of antibodies and aptamers.Acta pharmaceutica Sinica. B · 2026Review
- Computational evolution of poly(U) polymerase for efficient and controlled RNA oligonucleotide synthesis.Nucleic acids research · 2026Article
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- ADAPT: a programme for the advanced detection of AI-enabled pathogenic threats.Frontiers in bioengineering and biotechnology · 2026Article
- DeepVIC: modular prediction and classification of bacterial virulence factors using protein language model embeddings.Bioinformatics advances · 2026Article
- ALLM-Ab: Active Learning-Driven Antibody Optimization Using Fine-Tuned Protein Language Models.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.
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