ArticleNature methods2025
Biophysics-based protein language models for protein engineering.
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 33 papers.
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Who cites it
33 citing papers in PubMed.
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- Barrel Shape and Chromophore Rigidity Predict Fluorescent-Protein Photophysics.Journal of chemical information and modeling · 2026Article
- New paradigm of q-CAR drug development targeting disease-specific protein conformations.npj drug discovery · 2026Review
- Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- Molecular evolution in light of regulatory-coding epistasis.EMBO reports · 2026Review
- Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations.bioRxiv : the preprint server for biology · 2026Article
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Membrane Protein Folding and Biogenesis: Insights from Single-Molecule Force Spectroscopy.Chemical reviews · 2026Review
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- INBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence in the assessment of epilepsy-related genetic mutations: Learned from GABAEpilepsia open · 2026Review
- ProSSF: integrating sequence, structure, and gene ontology for prediction of protein stability, interaction, and function.Molecular genetics and genomics : MGG · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Descent from a common ancestor restricts exploration of protein sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Overcoming extrapolation challenges of deep learning by incorporating physics in protein sequence-function modeling.PLoS computational biology · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Protein language models trained on biophysical dynamics inform mutation effects.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose mutational effect transfer learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics. We fine-tune METL on experimental sequence-function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METL's ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.
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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 Socratic graph.