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Cited by 360

The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.

Navigating the protein fitness landscape with Gaussian processes

Romero, Philip A., Krause, Andreas, Arnold, Frances H.·2013·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1215251110
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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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Research works printed but not linked: 7

  1. 10.1109/TIT.2011.2182033 · DOI
  2. 10.1038/nrm2805 · DOI · Paper page
  3. CKI Williams CE Rasmussen Advances in Neural Information Processing Systems eds Touretzky DS Mozer MC Hasselmo ME (MIT Press Cambridge MA) pp 514–520. (1996).
  4. CE Rasmussen, C Williams Gaussian Processes for Machine Learning (MIT Press, Cambridge, MA, 2006).
  5. 10.1038/nbt1333 · DOI
  6. N Srinivas A Krause SM Kakade M Seeger Gaussian process optimization in the bandit setting: No regret and experimental design. Proceedings of the 27th International Conference on Machine learning eds Furnkranz J Joachims T (Omnipress Madison WI) pp 1015–1022. (2010).
  7. T Desautels A Krause J Burdick Parallelizing exploration-exploitation tradeoffs with Gaussian process bandit optimization. Proceedings of the 29th International Conference on Machine Learning eds Langford J Pineau J (Omnipress Madison WI). (2012).

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Metadata

Authors
Romero, P. A., Krause, A., & Arnold, F. H.
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2012-12-31
Volume
110
Issue
3
ISSN
  • 0027-8424
  • 1091-6490
DOI
10.1073/pnas.1215251110
Cited by
360
References
43
Language
en

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