Generative design of proteins based on secondary structure constraints using an attention-based diffusion model.

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Abstract

We report two generative deep learning models that predict amino acid sequences and 3D protein structures based on secondary structure design objectives via either overall content or per-residue structure. Both models are robust regarding imperfect inputs and offer de novo design capacity as they can discover new protein sequences not yet discovered from natural mechanisms or systems. The residue-level secondary structure design model generally yields higher accuracy and more diverse sequences. These findings suggest unexplored opportunities for protein designs and functional outcomes within the vast amino acid sequences beyond known proteins. Our models, based on an attention-based diffusion model and trained on a dataset extracted from experimentally known 3D protein structures, offer numerous downstream applications in conditional generative design of various biological or engineering systems. Future work may include additional conditioning, and an exploration of other functional properties of the generated proteins for various properties beyond structural objectives.