8DT0

Scaffolding protein functional sites using deep learning

8DT0 の概要

• エントリーDOI: 10.2210/pdb8dt0/pdb
• 分子名称: Scaffolding protein functional sites (1 entity in total)
• 機能のキーワード: de novo design, scaffolding protein functional sites, deep learning, de novo protein
• 由来する生物種: synthetic construct
• タンパク質・核酸の鎖数: 2
• 化学式量合計: 30780.29

構造登録者

Bera, A.K.,Watson, J.,Baker, D. (登録日: 2022-07-24, 公開日: 2022-08-10, 最終更新日: 2024-04-03)

主引用文献

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

実験手法

X-RAY DIFFRACTION (2.46 Å)