Abstract
AbstractIt has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.
Publisher
Cold Spring Harbor Laboratory
Reference17 articles.
1. High-accuracy protein structure prediction in CASP14;Proteins,2021
2. Highly accurate protein structure prediction with AlphaFold;Nature,2021
3. The breakthrough in protein structure prediction;Biochemical Journal,2021
4. Ozden B , Kryshtafovych A , Karaca E. Assessment of the CASP14 Assembly Predictions. Proteins: Structure, Function, and Bioinformatics. 2021.
5. Egbert M , Ghani U , Ashizawa R , Kotelnikov S , Nguyen T , Desta I , et al. Assessing the binding properties of CASP14 targets and models. Proteins: Structure, Function, and Bioinformatics. 2021.
Cited by
44 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献