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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Detalles Bibliográficos
Autores principales: Xinqiang Ding, Zhengting Zou, Charles L. Brooks III
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
Materias:
Q
Acceso en línea:https://doaj.org/article/be97bb08e16b437b9a2c5f2e3d8550b1
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