Hallucinating protein assemblies

By B. Wicky, L. Milles, A. Courbet, R. Ragotte, J. Dauparas, E. Kinfu, S. Tipps, R. Kibler, Minkyung Baek, Frank DiMaio and 6 more. 2022

3 citations recorded by the source corpus.

Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of 7 designs are very close to the computational models (median RMSD: 0.6 Å), as are 3 cryoEM structures of giant rings with up to 1550 residues, C33 symmetry, and 10 nanometer in diameter; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be created using deep learning, and pave the way for the design of increasingly complex nanomachines and biomaterials. One-sentence summary Deep network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.

Subfields: Molecular Biology, Structural Biology

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In Molecular Biology

1. Ali Madani
2. Jeffrey A. Ruffolo
3. Erik Nijkamp
4. Nikhil Vijay Naik
5. Eli N. Weinstein

Citations in the corpus

• ProGen2: Exploring the Boundaries of Protein Language Models (2022, background): “Extensive experimental validation demonstrated that sequences generated by ProteinMPNN dramatically improved soluble expression and folded into their intended structures (Wicky et al., 2022).”