SGNet: Folding Symmetrical Protein Complex with Deep Learning
Zhaoqun Li Jingcheng Yu Qiwei Ye
Deep learning has made significant progress in protein structure prediction,
advancing the development of computational biology. However, despite the high
accuracy achieved in predicting single-chain structures, a significant number
of large homo-oligomeric assemblies exhibit internal symmetry, posing a major
challenge in structure determination. The performances of existing deep
learning methods are limited since the symmetrical protein assembly usually has
a long sequence, making structural computation infeasible. In addition,
multiple identical subunits in symmetrical protein complex cause the issue of
supervision ambiguity in label assignment, requiring a consistent structure
modeling for the training. To tackle these problems, we propose a protein
folding framework called SGNet to model protein-protein interactions in
symmetrical assemblies. SGNet conducts feature extraction on a single subunit
and generates the whole assembly using our proposed symmetry module, which
largely mitigates computational problems caused by sequence length. Thanks to
the elaborate design of modeling symmetry consistently, we can model all global
symmetry types in quaternary protein structure prediction. Extensive
experimental results on a benchmark of symmetrical protein complexes further
demonstrate the effectiveness of our method.