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

By Bo Ni, D. L. Kaplan, Markus J. Buehler. 2023

78 citations recorded by the source corpus.

SUMMARY: 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.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Minkyung Baek
2. Ivan V. Anishchenko
3. Frank DiMaio
4. A. Kang
5. Jue Wang
6. Ryan McHugh
7. A. Bera
8. David Baker

Citations in the corpus

• Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom (2023, positive): “Diffusion-based methods can generate proteins in the context of a protein target that bind with considerable affinity and specificity ( 45 ) and can be trained to explicitly condition on structural features( 46 ).”