Scaffolding protein functional sites using deep learning
University of Washington · École Polytechnique Fédérale de Lausanne · +3 more institutions
Abstract
The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to…
Citation impact
- FWCI
- 63.47
- Percentile
- 100%
- References
- 83
Authors
24Topics & keywords
- Scaffold
- In silico
- Computational biology
- Scaffold protein
- Functional analysis
- Protein design
- Protein secondary structure
- Sequence (biology)