Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

Patrick Bryant (), Gabriele Pozzati, Wensi Zhu, Aditi Shenoy, Petras Kundrotas and Arne Elofsson
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Patrick Bryant: Science for Life Laboratory
Gabriele Pozzati: Science for Life Laboratory
Wensi Zhu: Science for Life Laboratory
Aditi Shenoy: Science for Life Laboratory
Petras Kundrotas: Science for Life Laboratory
Arne Elofsson: Science for Life Laboratory

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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 .

Date: 2022
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DOI: 10.1038/s41467-022-33729-4

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