ArticleNature biotechnology2022
Learning protein fitness models from evolutionary and assay-labeled data.
Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.
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Who cites it
120 citing papers in PubMed.
- Machine learning-assisted directed evolution of plant Rubisco.Science advances · 2026Article
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- Leveraging latent space models for enzyme discovery and sampling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The fitness landscape of a form II rubisco in a photosynthetic bacterium guides engineering of oxygen tolerance.Science advances · 2026Article
- Gaugefixer: overcoming parameter non-identifiability in models of sequence-function relationships.BMC bioinformatics · 2026Article
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- Engineering selective amyloid precursor protein inhibitors by machine learning and deep mutational scanning.Protein science : a publication of the Protein Society · 2026Article
- A Generative Neuro-Symbolic AI for Protein Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Property guidance for protein sequence generative models with ProteinGuide.Nature biotechnology · 2026Article
- Deep learning-guided engineering of SpuFz1 and rational miniaturization of ωRNA enables efficient genome editing.Nature communications · 2026Article
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- Accurate protein stability prediction for small domains using mega-scale experiments.bioRxiv : the preprint server for biology · 2026Article
- GaugeFixer: overcoming parameter non-identifiability in models of sequence-function relationships.bioRxiv : the preprint server for biology · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- Supervised learning of protein variant effects across large-scale mutagenesis datasets.Protein science : a publication of the Protein Society · 2026Article
- ProteinMCP: An agentic AI framework for autonomous protein engineering.Protein science : a publication of the Protein Society · 2026Article
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- Nested birth-death processes are competitive with parameter-heavy neural networks as time-dependent models of protein evolution.bioRxiv : the preprint server for biology · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Article
60 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent work has suggested methods for combining both sources of information. Toward that goal, we propose a simple combination approach that is competitive with, and on average outperforms more sophisticated methods. Our approach uses ridge regression on site-specific amino acid features combined with one probability density feature from modeling the evolutionary data. Within this approach, we find that a variational autoencoder-based probability density model showed the best overall performance, although any evolutionary density model can be used. Moreover, our analysis highlights the importance of systematic evaluations and sufficient baselines.
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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.