ArticleCell systems2024
Deep-learning-based design of synthetic orthologs of SH3 signaling domains.
Article in Cell systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Considering Metabolic Context in Enzyme Evolution and Design.Biochemistry · 2025Review
- Engineering Dehalogenase Enzymes Using Variational Autoencoder-Generated Latent Spaces and Microfluidics.JACS Au · 2025Article
- SyntheticmicroPublication biology · 2025Article
- How deep can we decipher protein evolution with deep learning models.Patterns (New York, N.Y.) · 2024Article
- Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.Cold Spring Harbor perspectives in biology · 2024Review
- ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.ACS synthetic biology · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can learn specialized functional constraints that control fitness in specific biological contexts. Here, we examine the ability of generative models to produce synthetic versions of Src-homology 3 (SH3) domains that mediate signaling in the Sho1 osmotic stress response pathway of yeast. We show that a variational autoencoder (VAE) model produces artificial sequences that experimentally recapitulate the function of natural SH3 domains. More generally, the model organizes all fungal SH3 domains such that locality in the model latent space (but not simply locality in sequence space) enriches the design of synthetic orthologs and exposes non-obvious amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of generative models to design ortholog-like functions in vivo opens new avenues for engineering protein function in specific cellular contexts and environments.
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Registered trials
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