Generalized biomolecular modeling and design with RoseTTAFold All-Atom.
Science
; 384(6693): eadl2528, 2024 Apr 19.
Article
em En
| MEDLINE
| ID: mdl-38452047
ABSTRACT
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Engenharia de Proteínas
/
Proteínas
/
Aprendizado Profundo
Idioma:
En
Revista:
Science
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos