Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Mais filtros

Base de dados
Ano de publicação
Tipo de documento
País de afiliação
Intervalo de ano de publicação
1.
Nat Methods ; 21(8): 1514-1524, 2024 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-38744917

RESUMO

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.


Assuntos
Modelos Moleculares , Dobramento de Proteína , Proteínas , Proteínas/química , Biologia Computacional/métodos , Software , Conformação Proteica , Algoritmos , Estrutura Secundária de Proteína
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA