Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search
The accuracy of AlphaFold decreases with the number of protein chains and the available GPU memory limits the size of protein complexes that can be predicted. Here, the authors show that complexes with 10–30 chains can be assembled from predicted subcomponents using Monte Carlo tree search.
Main Authors: | Patrick Bryant, Gabriele Pozzati, Wensi Zhu, Aditi Shenoy, Petras Kundrotas, Arne Elofsson |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2022-10-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-022-33729-4 |
Similar Items
-
Author Correction: Improved prediction of protein-protein interactions using AlphaFold2
by: Patrick Bryant, et al.
Published: (2022-03-01) -
AlphaFold2 Update and Perspectives
by: Sébastien Tourlet, et al.
Published: (2023-05-01) -
AlphaFold and the future of structural biology
by: Randy J. Read, et al.
Published: (2023-07-01) -
Using AlphaFold Predictions in Viral Research
by: Daria Gutnik, et al.
Published: (2023-04-01) -
Real-time structure search and structure classification for AlphaFold protein models
by: Tunde Aderinwale, et al.
Published: (2022-04-01)