The author proposes SGNet, a protein folding framework leveraging deep learning to model symmetrical protein complexes. By addressing challenges like sequence length and label assignment ambiguity, SGNet demonstrates improved performance in predicting global symmetry types.
Proposing SGNet for modeling symmetrical protein structures, leveraging relative position maps and symmetry generators.
Deep learning framework SGNet models symmetrical protein complexes effectively, addressing challenges in structure determination.
SGNet proposes a protein folding framework to model symmetrical protein assemblies, addressing challenges in structure determination.