Deep Generative Modeling for Protein Design

• URL: http://arxiv.org/abs/2109.13754v1
• Date: Tue, 31 Aug 2021 14:38:26 GMT
• Title: Deep Generative Modeling for Protein Design
• Authors: Alexey Strokach, Philip M. Kim
• Abstract summary: Deep learning approaches have produced breakthroughs in fields such as image classification and natural language processing. generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. We discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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