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...

Descripción completa

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
_version_ 1784660984264130560
author Jindal, Akhil
Kotelnikov, Sergei
Padhorny, Dzmitry
Kozakov, Dima
Zhu, Yimin
Chowdhury, Rezaul
Vajda, Sandor
author_facet Jindal, Akhil
Kotelnikov, Sergei
Padhorny, Dzmitry
Kozakov, Dima
Zhu, Yimin
Chowdhury, Rezaul
Vajda, Sandor
author_sort Jindal, Akhil
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8887833
institution National Center for Biotechnology Information
language English
publishDate 2022
record_format MEDLINE/PubMed
spelling pubmed-88878332022-03-01 Side-chain Packing Using SE(3)-Transformer Jindal, Akhil Kotelnikov, Sergei Padhorny, Dzmitry Kozakov, Dima Zhu, Yimin Chowdhury, Rezaul Vajda, Sandor Pac Symp Biocomput Article 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. 2022 /pmc/articles/PMC8887833/ /pubmed/34890135 Text en https://creativecommons.org/licenses/by/4.0/Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License.
spellingShingle Article
Jindal, Akhil
Kotelnikov, Sergei
Padhorny, Dzmitry
Kozakov, Dima
Zhu, Yimin
Chowdhury, Rezaul
Vajda, Sandor
Side-chain Packing Using SE(3)-Transformer
title Side-chain Packing Using SE(3)-Transformer
title_full Side-chain Packing Using SE(3)-Transformer
title_fullStr Side-chain Packing Using SE(3)-Transformer
title_full_unstemmed Side-chain Packing Using SE(3)-Transformer
title_short Side-chain Packing Using SE(3)-Transformer
title_sort side-chain packing using se(3)-transformer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8887833/
https://www.ncbi.nlm.nih.gov/pubmed/34890135
work_keys_str_mv AT jindalakhil sidechainpackingusingse3transformer
AT kotelnikovsergei sidechainpackingusingse3transformer
AT padhornydzmitry sidechainpackingusingse3transformer
AT kozakovdima sidechainpackingusingse3transformer
AT zhuyimin sidechainpackingusingse3transformer
AT chowdhuryrezaul sidechainpackingusingse3transformer
AT vajdasandor sidechainpackingusingse3transformer

Ejemplares similares