Zobrazeno 1 - 4
of 4
pro vyhledávání: '"L. F. Milles"'
Autor:
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker
Publikováno v:
Science (New York, N.Y.), 378(6615), 49-56
Science (New York, N.Y.) 378 (2022) 6615
Science (New York, N.Y.) 378 (2022) 6615
Although 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 p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33f1b74fd5a848f8c6eb893b22937977
https://doi.org/10.1126/science.add2187
https://doi.org/10.1126/science.add2187
Autor:
B. I. M. Wicky, L. F. Milles, A. Courbet, R. J. Ragotte, J. Dauparas, E. Kinfu, S. Tipps, R. D. Kibler, M. Baek, F. DiMaio, X. Li, L. Carter, A. Kang, H. Nguyen, A. K. Bera, D. Baker
Publikováno v:
Science (New York, N.Y.). 378(6615)
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-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::967f15c15c4e20f66a07b796e7d9f6d4
https://pubmed.ncbi.nlm.nih.gov/36108048
https://pubmed.ncbi.nlm.nih.gov/36108048
Autor:
B. I. M. Wicky, L. F. Milles, A. Courbet, R. J. Ragotte, J. Dauparas, E. Kinfu, S. Tipps, R. D. Kibler, M. Baek, F. DiMaio, X. Li, L. Carter, A. Kang, H. Nguyen, A. K. Bera, D. Baker
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-o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4831eefa7ca48efff9697873053bdf3
https://doi.org/10.1101/2022.06.09.493773
https://doi.org/10.1101/2022.06.09.493773
Autor:
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a453f779ef6c751419eee0041c3dd84d
https://doi.org/10.1101/2022.06.03.494563
https://doi.org/10.1101/2022.06.03.494563