ArticleProceedings of the National Academy of Sciences of the United States of America2013
Navigating the protein fitness landscape with Gaussian processes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers.
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Who cites it
158 citing papers in PubMed.
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- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- High yield strategies for triterpenoid biosynthesis in cell factories.Journal of microbiology (Seoul, Korea) · 2026Review
- Thermostable ancestors enabled evolutionary diversification of promiscuous chemical defence enzymes.The EMBO journal · 2026Article
- Harnessing Nature's Algorithm: From Test Tubes to Autonomous In Vivo Evolution.Biotechnology journal · 2026Review
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- A Guide to Bayesian Optimization in Bioprocess Engineering.Biotechnology and bioengineering · 2026Review
- Automation-Assisted Photoinduced Atom Transfer Radical Polymerization.ACS polymers Au · 2026Article
- Efficient, Few-shot Directed Evolution with Energy Rank Alignment.bioRxiv : the preprint server for biology · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.Communications medicine · 2026Article
- Multiobjective Design of Growth Media with Genome-Scale Metabolic Models and Bayesian Optimization.Computational and structural biotechnology journal · 2026Article
- Engineering mammalian protein secretion: Toward the convergence of high-throughput biology and computational methods.Cell systems · 2025Review
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- SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles.Biophysical journal · 2025Article
- Active site diversification of a non-canonical amino acid decarboxylase by merging substrate multiplexed screening with computationally guided recombination.Protein science : a publication of the Protein Society · 2025Article
- Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.ACS central science · 2025Review
- Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE.Bioinformatics (Oxford, England) · 2025Article
98 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
Knowing how protein sequence maps to function (the "fitness landscape") is critical for understanding protein evolution as well as for engineering proteins with new and useful properties. We demonstrate that the protein fitness landscape can be inferred from experimental data, using Gaussian processes, a Bayesian learning technique. Gaussian process landscapes can model various protein sequence properties, including functional status, thermostability, enzyme activity, and ligand binding affinity. Trained on experimental data, these models achieve unrivaled quantitative accuracy. Furthermore, the explicit representation of model uncertainty allows for efficient searches through the vast space of possible sequences. We develop and test two protein sequence design algorithms motivated by Bayesian decision theory. The first one identifies small sets of sequences that are informative about the landscape; the second one identifies optimized sequences by iteratively improving the Gaussian process model in regions of the landscape that are predicted to be optimized. We demonstrate the ability of Gaussian processes to guide the search through protein sequence space by designing, constructing, and testing chimeric cytochrome P450s. These algorithms allowed us to engineer active P450 enzymes that are more thermostable than any previously made by chimeragenesis, rational design, or directed evolution.
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