Hallucinating symmetric 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

221 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 seven designs are very similar to the computational models (median root mean square deviation: 0.6 angstroms), as are three cryo–electron microscopy structures of giant 10-nanometer rings with up to 1550 residues and C33 symmetry; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be generated using deep learning and pave the way for the design of increasingly complex components for nanomachines and biomaterials. Description Deep learning takes on protein design Deep learning approaches such as Alphafold and Rosettafold have made reliable protein structure prediction broadly accessible. For the inverse problem, finding a sequence that folds to a desired structure, most approaches remain based on energy optimization. In two papers, a range of protein design problems were addressed through deep learning methods. Dauparas et al. built on recent deep learning protein design approaches to develop a method called ProteinMPNN. They validated designs experimentally and showed that ProteinMPNN can rescue previously failed designs made using Rosetta or Alphafold. Wicky et al. started from a random sequence and used Monte Carlo sequence search coupled with structure prediction by Alphafold to design cyclic homo-oligomers. Although the designs were generated to achieve stable expression, the sequences had to be regenerated using ProteinMPNN. This approach allowed for the design of a range of experimentally validated cyclic oligomers and paves the way for the design of increasingly complex assemblies. —VV A network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.

Subfields: Molecular Biology, Structural Biology, Computational Biology & Bioinformatics

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

In Molecular Biology

  1. David Baker
  2. Frank DiMaio
  3. Ivan V. Anishchenko
  4. Tommi S. Jaakkola
  5. Regina Barzilay
  6. Minkyung Baek
  7. W. Sheffler
  8. J. Dauparas

In Structural Biology

  1. Frank DiMaio
  2. David Baker

In Computational Biology & Bioinformatics

  1. David Baker
  2. Frank DiMaio
  3. Ivan V. Anishchenko
  4. J. Dauparas
  5. W. Sheffler
  6. Tommi S. Jaakkola
  7. Minkyung Baek
  8. Regina Barzilay

Citations in the corpus