CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2

Ben Shor, Dina Schneidman-Duhovny*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 Protein Data Bank 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.

Original languageAmerican English
JournalNature Methods
Early online date7 Feb 2024
DOIs
StatePublished - Mar 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

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