Deciphering protein evolution and fitness landscapes with latent space models
By Xinqiang Ding, Zhengting Zou, Charles L. Brooks III. 2019
108 citations recorded by the source corpus.
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. 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.
Subfields: Artificial Intelligence, Genetics, Molecular Biology
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- Sai Pooja Mahajan
- Alexey Strokach
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- Philip M. Kim
- Jeffrey J. Gray
- Wenhao Gao
Citations in the corpus
- Deep Learning in Protein Structural Modeling and Design (2020, background): “AEs can also provide representations for subsequent supervised tasks.32 Ding et al.92 showed that a VAE model is able to capture evolutionary relationships between sequences and stability of proteins, while Sinai et al.93 and Riesselman et al.94 showed that the latent vectors learned fromVAEs are ab”
- Fast and flexible design of novel proteins using graph neural networks (2019, methodology): “There has been growing interest in using neural network-based approaches for protein representation learning and design ( 19 , 37 – 40 ), with several new methods reported during the preparation of this manuscript ( 41 , 42 ).”