Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search.
Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search.
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DOI：
10.1038/s41467-022-33729-4
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发表时间：
2022-10-12
影响因子：
16.6
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AlphaFold可以非常准确地预测单链和多链蛋白质的结构。然而，准确性随着链的数量而降低，并且可用的GPU内存限制了可以预测的蛋白质复合物的大小。在这里，我们表明，人们可以预测的结构，从预测的子组件的大型复合物。我们组装91的175复合物与10-30链预测的子组件，使用Monte Carlo树搜索，与中值TM得分为0.51。有30个高度准确的复合物（TM评分≥0.8，33%的完整组装）。我们创建了一个评分函数mpDockQ，它可以区分组装是否完整并预测其准确性。我们发现，含有对称性的复合物是准确组装的，而不对称的复合物仍然具有挑战性。该方法可免费获得，并可作为Colab笔记本访问https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb。AlphaFold的准确性随着蛋白质链的数量而降低，可用的GPU内存限制了可以预测的蛋白质复合物的大小。在这里，作者表明，10-30链的复合物可以组装从预测的子组件使用Monte Carlo树搜索。
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. 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.