Protein sequence design by conformational landscape optimization
Authors: Norn Christoffer , Wicky Basile I. M. , Juergens David , Liu Sirui , Kim David , Tischer Doug , Koepnick Brian , Anishchenko Ivan ,   Foldit Players, 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 , Haight Beverly R. , Taştan Bora , Uitham Boris , Roy Brandon G. , de Melo Cruz Breno Renan , Echols Brian , Lorenz Brian Edward , Blair Bruce , Kestemont Bruno , Eastlake C. D. , Bragdon Callen Joseph , Vardeman Carl , Salerno Carlo , Comisky Casey , Hayman Catherine Louise , Landers Catherine R , Zimov Cathy , Coleman Charles David , Painter Charles Robert , Ince Christopher , Lynagh Conor , Malaniia Dmitrii , Wheeler Douglas Craig , Robertson Douglas , Simon Vera , Chisari Emanuele , Kai Eric Lim Jit , Rezae Farah , Lengyel Ferenc , Tabotta Flavian , Padelletti Franco , Boström Frisno , Gross Gary O. , McIlvaine George , Beecher Gil , Hansen Gregory T. , de Jong Guido , Feldmann Harald , Borman Jami Lynne , Quinn Jamie , Norrgard Jane , Truong Jason , Diderich Jasper A. , Canfield Jeffrey Michael , Photakis Jeffrey , Slone Jesse David , Madzio Joanna , Mitchell Joanne , Stomieroski John Charles , Mitch John H. , Altenbeck Johnathan Robert , Schinkler Jonas , Weinberg Jonathan Barak , Burbach Joshua David , Sequeira da Costa João Carlos , Bada Juarez Juan Francisco , Gunnarsson Jón Pétur , Harper Kathleen Diane , Joo Keehyoung , Clayton Keith T. , DeFord Kenneth E. , Scully Kevin F. , Gildea Kevin M. , Abbey Kirk J. , Kohli Kristen Lee , Stenner Kyle , Takács Kálmán , Poussaint LaVerne L. , Manalo Larry C. , Withers Larry C. , Carlson Lilium , Wei Linda , Fisher Luke Ryan , Carpenter Lynn , Ji-hwan Ma , Ricci Manuel , Belcastro Marcus Anthony , Leniec Marek , Hohmann Marie , Thompson Mark , Thayer Matthew A. , Gaebel Matthias , Cassidy Michael D. , Fagiola Michael , Lewis Michael , Pfützenreuter Michael , Simon Michael , Elmassry Moamen M. , Benevides Noah , Kerr Norah Kathleen , Verma Nupur , Shannon Oak , Yin Owen , Wolfteich Pascal , Gummersall Paul , Tłuścik Paweł , Gajar Peter , Triggiani Peter John , Guha Rajarshi , Mathew Innes Renton Braden , Buchanan Ricky , Gamble Robert , Leduc Robert , Spearing Robert , dos Santos Gomes Rodrigo Luccas Corrêa , Estep Roger D. , DeWitt Ryan , Moore Ryan , Shnider Scott G. , Zaccanelli Scott J. , Kuznetsov Sergey , Burillo-Sanz Sergio , Mooney Seán , Vasiliy Sidoruk , Butkovich Slava S. , Hudson Spencer Bruce , Pote Spencer Len , Denne Stephen Phillip , Schwegmann Steven A. , Ratna Sumanth , Kleinfelter Susan C. , Bausewein Thomas , George Thomas J. , de Almeida Tobias Scherf , Yeginer Ulas , Barmettler Walter , Pulley Warwick Robert , Wright William Scott , Willyanto  , Lansford Wyatt , Hochart Xavier , Gaiji Yoan Anthony Skander , Lagodich Yuriy , Christian Vivier 
Published: March 2021

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.