Deciphering protein evolution and fitness landscapes with latent space models
Open accessAbstract
Protein sequences contain rich information about protein evolution, fitness landscapes, and stability. Here we investigate how latent space models trained using variational auto-encoders can infer these properties from sequences. Using both simulated and real sequences, we show that the low dimensional latent space representation of sequences, calculated using the encoder model, captures both evolutionary and ancestral relationships between sequences. Together with experimental fitness data and Gaussian process regression, the latent space representation also enables learning the protein fitness landscape in a continuous low dimensional space. Moreover, the model is also useful in predicting protein mutational stability landscapes and quantifying the importance of stability in shaping protein evolution. Overall, we illustrate that the latent space models learned using variational auto-encoders provide a mechanism for exploration of the rich data contained in protein sequences regarding evolution, fitness and stability and hence are well-suited to help guide protein engineering efforts.
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- Authors
- Ding, X., Zou, Z., & Brooks III, C. L.
- Affiliations
- University of Michigan (US)
- Venue
- Nature Communications
- Published
- 2019-12-10
- Volume
- 10
- Issue
- 1
- Pages
- 5644-5644
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6904478
- OpenAlex
- W2995361046
- Cited by
- 106
- References
- 71
- Citations by year
- 2026: 19 · 2025: 15 · 2024: 17 · 2023: 16 · 2022: 15 · 2021: 18 · 2020: 6
- FWCI
- 5.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (1506273)
- University of Michigan
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM037554, R01GM120093, R35GM130587)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- protein evolution, fitness landscape, latent space models, variational autoencoder, ancestral sequence reconstruction, protein stability, Gaussian process regression, protein engineering, protein sequences
- MeSH terms
- Algorithms, Models, Genetic, Mutation, Phylogeny, Proteins, Sequence Alignment, Evolution, Molecular, Protein Stability, Genetic Fitness
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