Uni-Fold Symmetry: Harnessing Symmetry in Folding Large Protein Complexes
Li, Z.; Yang, S.; Liu, X.; Chen, W.; Wen, H.; Shen, F.; Ke, G.; Zhang, L.
Show abstract
Deep folding models have revolutionized the conventional methods of protein complex prediction. However, applying them to large protein oligomers is not easy. These models generally require copying the sequences of identical subunits to capture the in-between relationships. Accordingly, the scales of target protein complexes are strictly limited due to the cubic complexity of these models. To address this issue, we propose UF-Symmetry (Uni-Fold Symmetry), which is extricated from the need of sequence copying via harnessing the intrinsic symmetry of large protein oligomers. Taking the sequences of the asymmetric unit (AU) and a pre-specified symmetry group, UF-Symmetry learns to fold the AU and to assemble the complex structure in an end-to-end manner. By reducing the input scales from entire assemblies to AUs, UF-Symmetry allows to predict much larger assemblies with significant acceleration: for a complex of 4-fold cyclic symmetry (C4) and AU size of 512, UF-Symmetry achieves approximately 20 times acceleration to current methods. On a benchmark of recently released PDB multimers, UF-Symmetry approximately halves the failure rate of current methods and achieves approaching accuracy on commonly successful cases.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- GraphGPSM: a global scoring model for protein structure using graph neural networks 96%
- SPRI: Structure-Based Pathogenicity Relationship Identifier for Predicting Effects of Single Missense Variants and Discovery of Higher-Order Cancer Susceptibility Clusters of Mutations 96%
- A Unified Protein Embedding Model with Local and Global Structural Sensitivity 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.