Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14

The trRosetta structure prediction method employs deep learning to generate predicted residue‐residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to sequence information) both language model embeddings...

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Detalles Bibliográficos
Autores principales: Anishchenko, Ivan, Baek, Minkyung, Park, Hahnbeom, Hiranuma, Naozumi, Kim, David E., Dauparas, Justas, Mansoor, Sanaa, Humphreys, Ian R., Baker, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2021
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616808/
https://www.ncbi.nlm.nih.gov/pubmed/34331359
http://dx.doi.org/10.1002/prot.26194

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616808/
https://www.ncbi.nlm.nih.gov/pubmed/34331359
http://dx.doi.org/10.1002/prot.26194

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