Scaffolding protein functional sites using deep learning
Jue Wang1,2, Sidney Lisanza1,2,3, David Juergens1,2,4
1Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
Science (New York, N.Y.)
|July 21, 2022
Summary
Deep learning designs novel protein scaffolds by optimizing sequences for functional sites. These methods create diverse proteins like enzymes and immunogens, validated computationally and experimentally.
Area of Science:
- Protein engineering
- Computational biology
- Biotechnology
Background:
- Protein function relies on specific residues within a stable structure.
- Designing novel proteins with desired functions is a significant challenge.
- Current methods often require predefined protein folds or structures.
Purpose of the Study:
- To develop deep learning methods for de novo protein scaffold design.
- To create functional protein sites without prespecifying scaffold architecture.
- To generate diverse protein designs including immunogens, enzymes, and binding proteins.
Main Methods:
- Introduced 'constrained hallucination' to optimize sequences for desired functional sites.
- Developed 'inpainting' to build scaffolds around functional sites using RoseTTAFold.
- Applied these methods to design various functional proteins.
Main Results:
- Successfully designed candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins.
- Validated designs through a combination of in silico predictions and experimental testing.
- Demonstrated the ability to create functional protein scaffolds without predefined folds.
Conclusions:
- Deep learning offers powerful tools for de novo protein scaffold design.
- These methods enable the creation of novel proteins with tailored functions.
- The developed approaches have broad applications in biotechnology and protein engineering.
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