Deep Generative Modeling for Protein Design
By Alexey Strokach, Philip M. Kim. 2021
129 citations recorded by the source corpus.
Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. Those generative models can learn protein representations that are often more informative of protein structure and function than hand-engineered features. Furthermore, they can be used to quickly propose millions of novel proteins that resemble the native counterparts in terms of expression level, stability, or other attributes. The protein design process can further be guided by discriminative oracles to select candidates with the highest probability of having the desired properties. In this review, we discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design.
Subfields: Materials Chemistry, Molecular Biology
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Citations in the corpus
- Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models (2022, background): “Machine learning has also made an impact on sequence design both in the case of conditioning on structural information [3, 21], and without [17, 9, 37, 38, 16, 18, 31, 20].”