Turning high-throughput structural biology into predictive inhibitor design
Authors: Saar Kadi L. , McCorkindale William , Fearon Daren , Boby Melissa , Barr Haim , Ben-Shmuel Amir ,   The COVID Moonshot Consortium, London Nir , von Delft Frank , Chodera John D. , Lee Alpha A. , Robinson Matthew C. , London Nir , Resnick Efrat , Zaidmann Daniel , Gehrtz Paul , Reddi Rambabu N. , Gabizon Ronen , Barr Haim , Duberstein Shirly , Zidane Hadeer , Shurrush Khriesto , Cohen Galit , Solmesky Leonardo J. , Lee Alpha , Jajack Andrew , Cvitkovic Milan , Pan Jin , Pai Ruby , Ripka Emily Grace , Nguyen Luong , Shafeev Mikhail , Matviiuk Tatiana , Michurin Oleg , Chernyshenko Eugene , Bilenko Vitaliy A. , Kinakh Serhii O. , Logvinenko Ivan G. , Melnykov Kostiantyn P. , Huliak Victor D. , Tsurupa Igor S. , Gorichko Marian , Shaikh Aarif , Pinjari Jakir , Swamy Vishwanath , Pingle Maneesh , BVNBS Sarma , Aimon Anthony , Delft Frank von , Fearon Daren , Dunnett Louise , Douangamath Alice , Dias Alex , Powell Ailsa , Neto Jose Brandao , Skyner Rachael , Thompson Warren , Gorrie-Stone Tyler , Walsh Martin , Owen David , Lukacik Petra , Strain-Damerell Claire , Mikolajek Halina , Horrell Sam , Koekemoer Lizbé , Krojer Tobias , Fairhead Mike , MacLean Elizabeth M. , Thompson Andrew , Wild Conor Francis , Smilova Mihaela D. , Wright Nathan , Delft Annette von , Gileadi Carina , Rangel Victor L. , Schofield Chris , Salah Eidarus , Malla Tika R. , Tumber Anthony , John Tobias , Vakonakis Ioannis , Kantsadi Anastassia L. , Zitzmann Nicole , Brun Juliane , Kiappes J. L. , Hill Michelle , Witt Karolina D , Alonzi Dominic S , Makower Laetitia L , Varghese Finny S. , Overheul Gijs J. , Miesen Pascal , van Rij Ronald P. , Jansen Jitske , Smeets Bart , Tomésio Susana , Weatherall Charlie , Vaschetto Mariana , Macdonald Hannah Bruce , Chodera John D. , Rufa Dominic , Wittmann Matthew , Boby Melissa L. , Henry Michael , Glass William G. , Eastman Peter K. , Coffland Joseph E. , Dotson David L. , Griffen Ed J. , McCorkindale Willam , Morris Aaron , Glen Robert , Cole Jason , Foster Richard , Foster Holly , Calmiano Mark , Tennant Rachael E. , Heer Jag , Shi Jiye , Jnoff Eric , Hurley Matthew F.D. , Lefker Bruce A. , Robinson Ralph P. , Giroud Charline , Bennett James , Fedorov Oleg , Reid St Patrick , Morwitzer Melody Jane , Cox Lisa , Morris Garrett M. , Ferla Matteo , Moustakas Demetri , Dudgeon Tim , Pšenák Vladimír , Kovar Boris , Voelz Vincent , Carbery Anna , Contini Alessandro , Clyde Austin , Ben-Shmuel Amir , Sittner Assa , Vitner Boaz Politi Einat B. , Bar-David Elad , Tamir Hadas , Achdout Hagit , Levy Haim , Glinert Itai , Paran Nir , Erez Noam , Puni Reut , Melamed Sharon , Weiss Shay , Israely Tomer , Yahalom-Ronen Yfat , Smalley Adam , Oleinikovas Vladas , Spencer John , Kenny Peter W. , Ward Walter , Cattermole Emma , Ferrins Lori , Eyermann Charles J. , Milne Bruce F. , Godoy Andre S. , Noske Gabriela D. , Oliva Glaucius , Fernandes Rafaela S. , Nakamura Aline M. , Gawriljuk Victor O. , White Kris M. , McGovern Briana L. , Rosales Romel , Garcia-Sastre Adolfo , Carney Daniel , Chang Edcon , Saikatendu Kumar Singh , Neyts Laura Vangeel Johan , Donckers Kim , Jochmans Dirk , Jonghe Steven De , Bowman Gregory R. , Borden Bruce , Singh Sukrit , Volkamer Andrea , Rodriguez-Guerra Jaime , Fate Gwen , Hart Storm Hassell , Bilenko Vitaliy A. , Kinakh Serhii O. , Logvinenko Ivan G. , Melnykov Kostiantyn P. , Huliak Victor D. , Tsurupa Igor S. , Saar Kadi L , Perry Benjamin , Fraisse Laurent , Sjö Peter , Boulet Pascale , Hahn Sophie , Mowbray Charles , Reid Lauren , Rees Paul , Huang Qiu Yu Judy , Zvornicanin Sarah N , Shaqra Ala M. , Yilmaz Nese Kurt , Schiffer Celia A. , Zhang Ivy , Pulido Iván , Tomlinson Charlie , Taylor Jenny C. , Croll Tristan Ian , Brwewitz Lennart 
Published: March 2023

A common challenge in drug design pertains to finding chemical modifications to a ligand that increases its affinity to the target protein. An underutilized advance is the increase in structural biology throughput, which has progressed from an artisanal endeavor to a monthly throughput of hundreds of different ligands against a protein in modern synchrotrons. However, the missing piece is a framework that turns high-throughput crystallography data into predictive models for ligand design. Here, we designed a simple machine learning approach that predicts protein–ligand affinity from experimental structures of diverse ligands against a single protein paired with biochemical measurements. Our key insight is using physics-based energy descriptors to represent protein–ligand complexes and a learning-to-rank approach that infers the relevant differences between binding modes. We ran a high-throughput crystallography campaign against the SARS-CoV-2 main protease (MPro), obtaining parallel measurements of over 200 protein–ligand complexes and their binding activities. This allows us to design one-step library syntheses which improved the potency of two distinct micromolar hits by over 10-fold, arriving at a noncovalent and nonpeptidomimetic inhibitor with 120 nM antiviral efficacy. Crucially, our approach successfully extends ligands to unexplored regions of the binding pocket, executing large and fruitful moves in chemical space with simple chemistry.