Deciphering protein evolution and fitness landscapes with latent space models.

ABSTRACT: 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 prot

SUBMITTER: Ding X 

PROVIDER: S-EPMC6904478 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature