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Norn, Christoffer;
Wicky, Basile I. M.;
Juergens, David;
Liu, Sirui;
Kim, David;
Tischer, Doug;
Koepnick, Brian;
Anishchenko, Ivan;
Baker, David;
Ovchinnikov, Sergey;
Coral, Alan;
Bubar, Alex J.;
Boykov, Alexander;
Valle Pérez, Alexander Uriel;
MacMillan, Alison;
Lubow, Allen;
Mussini, Andrea;
Cai, Andrew;
Ardill, Andrew John;
Seal, Aniruddha;
Kalantarian, Artak;
Failer, Barbara;
Lackersteen, Belinda;
Chagot, Benjamin;
[...]
Protein sequence design by conformational landscape optimization
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- Medientyp: E-Artikel
- Titel: Protein sequence design by conformational landscape optimization
- Beteiligte: Norn, Christoffer; Wicky, Basile I. M.; Juergens, David; Liu, Sirui; Kim, David; Tischer, Doug; Koepnick, Brian; Anishchenko, Ivan; Baker, David; Ovchinnikov, Sergey; Coral, Alan; Bubar, Alex J.; Boykov, Alexander; Valle Pérez, Alexander Uriel; MacMillan, Alison; Lubow, Allen; Mussini, Andrea; Cai, Andrew; Ardill, Andrew John; Seal, Aniruddha; Kalantarian, Artak; Failer, Barbara; Lackersteen, Belinda; Chagot, Benjamin; [...]
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Erschienen:
Proceedings of the National Academy of Sciences, 2021
- Erschienen in: Proceedings of the National Academy of Sciences, 118 (2021) 11
- Sprache: Englisch
- DOI: 10.1073/pnas.2017228118
- ISSN: 0027-8424; 1091-6490
- Entstehung:
- Anmerkungen:
- Beschreibung: Significance Almost all proteins fold to their lowest free energy state, which is determined by their amino acid sequence. Computational protein design has primarily focused on finding sequences that have very low energy in the target designed structure. However, what is most relevant during folding is not the absolute energy of the folded state but the energy difference between the folded state and the lowest-lying alternative states. We describe a deep learning approach that captures aspects of the folding landscape, in particular the presence of structures in alternative energy minima, and show that it can enhance current protein design methods.
- Zugangsstatus: Freier Zugang