Deep generative modeling for protein design. (February 2022)

• Record Type: Journal Article
• Authors: Strokach, Alexey; Kim, Philip M.
• Is Part Of: Current opinion in structural biology. Volume 72(2022)
• Page Start: 226
• Page End: 236
• Publication Date: 2022-02
• Subjects: Artificial intelligence -- Machine learning -- Representation learning -- Neural networks -- Protein optimization -- Protein design; Molecular biology -- Periodicals; 570
• DOI: 10.1016/j.sbi.2021.11.008
• Languages: English
• ISSNs: 0959-440X

Abstract

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. Highlights: Machine learning is becoming a key component of the protein design process. Deep generative models can produce novel protein sequences and structures. Conditioned generative models can produce proteins with specific properties. Discriminative oracles can be used to further fine-tune the design process.