Rosetta FunFolDes – A general framework for the computational design of functional proteins
By J. Bonet, Sarah Wehrle, K. Schriever, Che Yang, Anne-Marie Billet, F. Sesterhenn, A. Scheck, Freyr Sverrisson, B. Veselkova, Sabrina S. Vollers and 5 more. 2018
36 citations recorded by the source corpus.
The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function, nevertheless, it remains a challenge for state-of-the-art methodologies. Here, we present a computational design approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins. We performed extensive benchmarks, where the most unexpected finding was that the design of function into proteins may not necessarily reside in the global minimum of the energetic landscape, which could have important implications in the field. We have computationally designed and experimentally characterized a distant structural template and a de novo “functionless” fold, two prototypical design challenges, to present important viral epitopes. Overall, we present an accessible strategy to repurpose old protein folds for new functions, which may lead to important improvements on the computational design of functional proteins.
Subfields: Molecular Biology
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In Molecular Biology
- Xingjie Pan
- Hamed Khakzad
- S. Rosset
- Bruno E. Correia
- Casper A. Goverde
- Benedict Wolf
- Tanja Kortemme
- David Baker
Citations in the corpus
- De novo protein design by inversion of the AlphaFold structure prediction network (2022, methodology): “…residues; protein G (1FCL, NMR) – a mixed alpha/beta protein with 56 residues; ubiquitin (1UBQ, 1.8 Å x-ray) – a mainly beta protein with 76 residues; 4H (Rosetta model) – a de novo design with 84 residues constructed using TopoBuilder (Yang et al. 2021) and Rosetta FunFolDes (Bonet et al. 2018).”
- Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “The coordinates are then transformed into constraints for the Rosetta FunFolDes (73) method to build all-atom models.”