Deciphering protein evolution and fitness landscapes with latent space models
Multiple sequence alignments of proteins carry information about evolution, the protein’s fitness landscape and its stability in the face of mutations. Here, the authors demonstrate the utility of latent space models learned using variational autoencoders to infer these properties from sequences.
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| Auteurs principaux: | , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2019
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/be97bb08e16b437b9a2c5f2e3d8550b1 |
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| Résumé: | Multiple sequence alignments of proteins carry information about evolution, the protein’s fitness landscape and its stability in the face of mutations. Here, the authors demonstrate the utility of latent space models learned using variational autoencoders to infer these properties from sequences. |
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