![]() These algorithms allowed us to engineer active P450 enzymes that are more thermostable than any previously made by chimeragenesis, rational design, or directed evolution. We demonstrate the ability of Gaussian processes to guide the search through protein sequence space by designing, constructing, and testing chimeric cytochrome P450s. 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 develop and test two protein sequence design algorithms motivated by Bayesian decision theory. Furthermore, the explicit representation of model uncertainty allows for efficient searches through the vast space of possible sequences. Trained on experimental data, these models achieve unrivaled quantitative accuracy. Gaussian process landscapes can model various protein sequence properties, including functional status, thermostability, enzyme activity, and ligand binding affinity. We demonstrate that the protein fitness landscape can be inferred from experimental data, using Gaussian processes, a Bayesian learning technique. 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.
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