De novo protein design—From new structures to programmable functions
Quantitative BioSciences · Chan Zuckerberg Initiative (United States) · +1 more institution
Abstract
Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and…
Citation impact
- FWCI
- 61.26
- Percentile
- 100%
- References
- 130
Authors
1Topics & keywords
- Biology
- Computational biology
- Controllability
- Synthetic biology
- Complementarity (molecular biology)
- Protein design
- Modularity (biology)
- Protein engineering