In protein design, generative deep learning frameworks now support backbone generation, sequence optimization, and joint sequence-structure co-design with unprecedented accuracy.
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generative deep learning frameworks for protein design
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Protein design approaches have facilitated applications including cyclic peptide engineering, non-natural fold engineering, small-molecule sensing, catalytic center scaffolding, allosteric switching, intracellular logic circuits, and targeting of intrinsically disordered proteins.
Generative deep learning frameworks for protein design support backbone generation, sequence optimization, and joint sequence-structure co-design.
Computational design has translational potential as illustrated by immune cell engineering, GPCR-targeted miniproteins, receptor-degrading binders, and thermostable antitoxins.