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Protein sequence design by conformational landscape optimization

2021; National Academy of Sciences; Volume: 118; Issue: 11 Linguagem: Inglês

10.1073/pnas.2017228118

1091-6490

Christoffer Norn, Basile I. M. Wicky, David Juergens, Sirui Liu, David E. Kim, Doug Tischer, Brian Koepnick, Ivan Anishchenko, David Baker, Sergey Ovchinnikov, Alan Coral, Alex J. Bubar, Alexander Boykov, Alexander Uriel Valle Pérez, Alison MacMillan, Allen Lubow, Andrea Mussini, Andrew Cai, Andrew John Ardill, Aniruddha Seal, Artak Kalantarian, Barbara Failer, Belinda Lackersteen, Benjamin Chagot, Beverly R. Haight, Bora Taştan, Boris Uitham, Brandon G. Roy, Breno Renan de Melo Cruz, Brian Echols, Brian Edward Lorenz, Bruce G. Blair, Bruno Kestemont, Charles Eastlake, Callen Joseph Bragdon, Carl Vardeman, Carlo Salerno, Casey Comisky, Catherine Louise Hayman, Catherine R. Landers, Cathy Zimov, Charles D. Coleman, Charles Robert Painter, Christopher Ince, Conor Lynagh, Dmitrii Malaniia, Douglas Craig Wheeler, Douglas Robertson, Vera Simon, Emanuele Chisari, E. Kai, Farah Rezae, Ferenc Lengyel, Flavian Tabotta, Franco Padelletti, Frisno Boström, G. Gross, George Victor McIlvaine, Gil Beecher, Gregory Hansen, Guido de Jong, Harald Feldmann, Jami Lynne Borman, Jamie Quinn, Jane Norrgard, Jason Truong, Jasper A. Diderich, Jeffrey M. Canfield, Jeffrey Photakis, Jesse Slone, Joanna Madzio, Joanne Mitchell, John Charles Stomieroski, John H. Mitch, Johnathan Robert Altenbeck, Jonas Schinkler, Jonathan Barak Weinberg, Joshua David Burbach, João C. Sequeira, Juan F. Bada Juarez, Jón Pétur Gunnarsson, Kathleen Diane Harper, Keehyoung Joo, Keith Clayton, Kenneth E. DeFord, Kevin F. Scully, Kevin M. Gildea, Kirk J. Abbey, K. L. Kohli, Kyle Stenner, Kálmán Takács, LaVerne Poussaint, Larry C. Manalo, Larry C. Withers, Lilium Carlson, Linda Wei, Luke Ryan Fisher, L. A. Carpenter, Ma Ji-hwan, Manuel Ricci, Marcus Belcastro, Marek Leniec, M. Hohmann, Mark Thompson, Matthew A. Thayer, Matthias Gaebel, Michael D. Cassidy, Michael Fagiola, Michael R. Lewis, Michael Pfützenreuter, M. Simon, Moamen M. Elmassry, Noah Benevides, Norah Kathleen Kerr, Nupur Verma, Oak Shannon, Owen Yin, Pascal Wolfteich, Paul Gummersall, Paweł Tłuścik, Peter Gajar, Peter John Triggiani, Rajarshi Guha, Renton Braden Mathew Innes, Ricky Buchanan, Robert Gamble, Robert Leduc, Robert Spearing, Rodrigo Luccas Corrêa dos Santos Gomes, Roger D. Estep, Ryan DeWitt, Ryan M. Moore, Scott Shnider, Scott J. Zaccanelli, Sergey Kuznetsov, Sergio Burillo‐Sanz, S. Mooney, Sidoruk Vasiliy, Slava Butkovich, Spencer Bruce Hudson, Spencer Len Pote, Stephen Phillip Denne, Steven A. Schwegmann, Sumanth Ratna, Susan C. Kleinfelter, Thomas Bausewein, Thomas J. George, Tobias Scherf de Almeida, Ulas Yeginer, Walter Barmettler, Warwick Pulley, William Scott Wright, Willyanto, Wyatt Lansford, Xavier Hochart, Yoan Anthony Skander Gaiji, Yuriy Lagodich, Vivier Christian,

Enzyme Structure and Function

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.