8DT0

Scaffolding protein functional sites using deep learning

Summary for 8DT0

• Entry DOI: 10.2210/pdb8dt0/pdb
• Descriptor: Scaffolding protein functional sites (1 entity in total)
• Functional Keywords: de novo design, scaffolding protein functional sites, deep learning, de novo protein
• Biological source: synthetic construct
• Total number of polymer chains: 2
• Total formula weight: 30780.29

Authors

Bera, A.K.,Watson, J.,Baker, D. (deposition date: 2022-07-24, release date: 2022-08-10, Last modification date: 2024-04-03)

Primary citation

Wang, J.,Lisanza, S.,Juergens, D.,Tischer, D.,Watson, J.L.,Castro, K.M.,Ragotte, R.,Saragovi, A.,Milles, L.F.,Baek, M.,Anishchenko, I.,Yang, W.,Hicks, D.R.,Exposit, M.,Schlichthaerle, T.,Chun, J.H.,Dauparas, J.,Bennett, N.,Wicky, B.I.M.,Muenks, A.,DiMaio, F.,Correia, B.,Ovchinnikov, S.,Baker, D.
Scaffolding protein functional sites using deep learning.
Science, 377:387-394, 2022

PubMed 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 design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

PubMed: 35862514
DOI: 10.1126/science.abn2100

Experimental method

X-RAY DIFFRACTION (2.46 Å)