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Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search
Bryant, Patrick Pozzati, Gabriele Zhu, Wensi Shenoy, Aditi Kundrotas, Petras Elofsson, Arne
Bryant, Patrick
Pozzati, Gabriele
Zhu, Wensi
Shenoy, Aditi
Kundrotas, Petras
Elofsson, Arne
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Abstract
AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb.
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Date
2022-10-12
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Publisher
Nature Research
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Keywords
Molecular modelling, Protein structure predictions, Proteins
Citation
Bryant, P., Pozzati, G., Zhu, W. et al. Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search. Nat Commun 13, 6028 (2022). https://doi.org/10.1038/s41467-022-33729-4