CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2.
Nat Methods
; 21(3): 477-487, 2024 Mar.
Article
in En
| MEDLINE
| ID: mdl-38326495
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Nat Methods
Journal subject:
TECNICAS E PROCEDIMENTOS DE LABORATORIO
Year:
2024
Document type:
Article
Affiliation country:
Israel