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Justas Dauparas, Ivan V. Anishchenko, Nathaniel R. Bennett, Hua Bai, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Alexis Courbet, Robbert J. de Haas, Neville P. Bethel and 12 more
While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. One-sentence summary A deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.
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Dauparas, J., Anishchenko, I. V., Bennett, N. R., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., Haas, R. J. D., Bethel, N. P., Leung, P. J. Y., Huddy, T. F., Pellock, S. J., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., … Baker, D. A. (2022). Robust deep learning–based protein sequence design using ProteinMPNN. Science, 378(6615), 49–56. https://doi.org/10.1126/science.add2187
Dauparas, Justas, et al. "Robust deep learning–based protein sequence design using ProteinMPNN." Science, vol. 378, no. 6615, 2022, pp. 49–56, https://doi.org/10.1126/science.add2187.
Dauparas, Justas, Ivan V. Anishchenko, Nathaniel R. Bennett, Hua Bai, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, et al. "Robust deep learning–based protein sequence design using ProteinMPNN." Science 378, no. 6615 (2022): 49–56. https://doi.org/10.1126/science.add2187.
Dauparas J., Anishchenko I. V., Bennett N. R. [и др.] Robust deep learning–based protein sequence design using ProteinMPNN // Science. — 2022. — Vol. 378, № 6615. — P. 49–56. — URL: https://doi.org/10.1126/science.add2187
@article{dauparas2022robust,
title = {{Robust deep learning–based protein sequence design using ProteinMPNN}},
author = {Justas Dauparas and Ivan V. Anishchenko and Nathaniel R. Bennett and Hua Bai and Robert J. Ragotte and Lukas F. Milles and Basile I. M. Wicky and Alexis Courbet and Robbert J. de Haas and Neville P. Bethel and Philip J. Y. Leung and Timothy F. Huddy and Samuel J. Pellock and Doug Tischer and F. Chan and Brian Koepnick and Hannah Nguyen and Alex Kang and Banumathi Sankaran and Asim K. Bera and Neil P. King and David A. Baker},
journal = {Science},
year = {2022},
volume = {378},
number = {6615},
pages = {49--56},
doi = {10.1126/science.add2187},
url = {https://doi.org/10.1126/science.add2187}
}TY - JOUR AU - Dauparas, Justas AU - Anishchenko, Ivan V. AU - Bennett, Nathaniel R. AU - Bai, Hua AU - Ragotte, Robert J. AU - Milles, Lukas F. AU - Wicky, Basile I. M. AU - Courbet, Alexis AU - Haas, Robbert J. de AU - Bethel, Neville P. AU - Leung, Philip J. Y. AU - Huddy, Timothy F. AU - Pellock, Samuel J. AU - Tischer, Doug AU - Chan, F. AU - Koepnick, Brian AU - Nguyen, Hannah AU - Kang, Alex AU - Sankaran, Banumathi AU - Bera, Asim K. AU - King, Neil P. AU - Baker, David A. TI - Robust deep learning–based protein sequence design using ProteinMPNN JO - Science VL - 378 IS - 6615 SP - 49 EP - 56 PY - 2022 DA - 2022/09/15 DO - 10.1126/science.add2187 UR - https://doi.org/10.1126/science.add2187 ER -
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