Side-chain Packing Using SE(3)-Transformer

Predicting protein side-chains is important for both protein structure prediction and protein design. Modeling approaches to predict side-chains such as SCWRL4 have become one of the most widely used tools of its type due to fast and highly accurate predictions. Motivated by the recent success of Al...

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Detalles Bibliográficos
Autores principales: Jindal, Akhil, Kotelnikov, Sergei, Padhorny, Dzmitry, Kozakov, Dima, Zhu, Yimin, Chowdhury, Rezaul, Vajda, Sandor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8887833/
https://www.ncbi.nlm.nih.gov/pubmed/34890135
Descripción
Sumario:Predicting protein side-chains is important for both protein structure prediction and protein design. Modeling approaches to predict side-chains such as SCWRL4 have become one of the most widely used tools of its type due to fast and highly accurate predictions. Motivated by the recent success of AlphaFold2 in CASP14, our group adapted a 3D equivariant neural network architecture to predict protein side-chain conformations, specifically within a protein-protein interface, a problem that has not been fully addressed by AlphaFold2.

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