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
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070
An analysis of approximations for maximizing submodular set functions—I
Nemhauser, G. L., Wolsey, L. A., & Fisher, M. L. (1978) · Mathematical Programming · doi:10.1007/bf01588971 · Cited by 4,412
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
Srinivas, N., Krause, A., Kakade, S. M., & Seeger, M. (2012) · IEEE Transactions on Information Theory · doi:10.48550/arxiv.0912.3995 · Cited by 2,846
Self-citationStein, M. L. (1999) · Springer Series in Statistics · doi:10.1007/978-1-4612-1494-6 · Cited by 2,348
Using confidence bounds for exploitation-exploration trade-offs
Auer, P. L. (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944941 · Cited by 1,229
De Novo Computational Design of Retro-Aldol Enzymes
Jiang, L., Althoff, E. A., Clemente, F. R., Doyle, L., Röthlisberger, D., Zanghellini, A., Gallaher, J. L., Betker, J. L., Tanaka, F., Barbas, C. F., et al. (2008) · Science · doi:10.1126/science.1152692 · Cited by 1,204
Exploring protein fitness landscapes by directed evolution
Romero, P. A., & Arnold, F. H. (2009) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2805 · Cited by 1,161
Self-citationDe Novo Protein Design: Fully Automated Sequence Selection
Dahiyat, B. I., & Mayo, S. L. (1997) · Science · doi:10.1126/science.278.5335.82 · Cited by 1,145
An analysis of approximations for maximizing submodular set functions - 1Preprint
Fisher, M. L., Nemhauser, G. L., & Wolsey, L. A. (1978) · RePEc: Research Papers in Economics · Cited by 837
Research works printed but not linked: 7
- 10.1109/TIT.2011.2182033 · DOI
- 10.1038/nrm2805 · DOI · Paper page
- 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).
- CE Rasmussen, C Williams Gaussian Processes for Machine Learning (MIT Press, Cambridge, MA, 2006).
- 10.1038/nbt1333 · DOI
- 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).
- 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).
Cited by
Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131
Machine-learning-guided directed evolution for protein engineering
Yang, K. K., Wu, Z., & Arnold, F. H. (2019) · Nature Methods · doi:10.1038/s41592-019-0496-6 · Cited by 1,130
Self-citationGlobal analysis of protein folding using massively parallel design, synthesis, and testing
Rocklin, G. J., Chidyausiku, T. M., Goreshnik, I., Ford, A., Houliston, S., Lemak, A., Carter, L., Ravichandran, R., Mulligan, V. K., Chevalier, A., et al. (2017) · Science · doi:10.1126/science.aan0693 · Cited by 501
Learning the protein language: Evolution, structure, and function
Bepler, T., & Berger, B. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.017 · Cited by 483
Low-N protein engineering with data-efficient deep learning
Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M., & Church, G. M. (2021) · Nature Methods · doi:10.1038/s41592-021-01100-y · Cited by 422
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
- Cited by
- 360
- References
- 43
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Navigating the protein fitness landscape with Gaussian processes. https://app.cochat.ai/papers/10.1073/pnas.1215251110
- The paper
- Romero, P. A., Krause, A., & Arnold, F. H. (2013). Navigating the protein fitness landscape with Gaussian processes. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1215251110
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
