
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.


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sgnet-protein-folding-with-deep-learning-for-symmetrical-complexes


SGNet: Protein Folding with Deep Learning for Symmetrical Complexes


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Proposing SGNet for modeling symmetrical protein structures, leveraging relative position maps and symmetry generators.


sgnet-deep-learning-for-symmetrical-protein-complex-folding


SGNet: Deep Learning for Symmetrical Protein Complex Folding



Deep learning framework SGNet models symmetrical protein complexes effectively, addressing challenges in structure determination.


sgnet-protein-folding-framework-with-deep-learning


SGNet: Protein Folding Framework with Deep Learning



SGNet proposes a protein folding framework to model symmetrical protein assemblies, addressing challenges in structure determination.


sgnet-protein-folding-in-symmetrical-complexes-with-deep-learning


SGNet: Protein Folding in Symmetrical Complexes with Deep Learning
