Protein design and variant prediction using autoregressive generative models

The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments...

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Detalles Bibliográficos
Autores principales: Shin, Jung-Eun, Riesselman, Adam J., Kollasch, Aaron W., McMahon, Conor, Simon, Elana, Sander, Chris, Manglik, Aashish, Kruse, Andrew C., Marks, Debora S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065141/
https://www.ncbi.nlm.nih.gov/pubmed/33893299
http://dx.doi.org/10.1038/s41467-021-22732-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065141/
https://www.ncbi.nlm.nih.gov/pubmed/33893299
http://dx.doi.org/10.1038/s41467-021-22732-w

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