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- W4376876247 abstract "Abstract Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold’s high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins." @default.
- W4376876247 created "2023-05-18" @default.
- W4376876247 creator A5020529918 @default.
- W4376876247 creator A5091965485 @default.
- W4376876247 date "2023-05-16" @default.
- W4376876247 modified "2023-09-30" @default.
- W4376876247 title "Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2" @default.
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- W4376876247 doi "https://doi.org/10.1101/2023.05.16.541003" @default.
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