"EXPERT" A Practical and Forward-Looking Guide to Capturing Protein Expression and Purification Metadata to develop Machine Learning Models.
Christopher D O Cooper,
Evgeny Tankhilevich,
Kim Remans,
Ahmed El Marjou,
Aled M Edwards,
Andrew Leach,
Andrew Quigley,
Anja Schutz,
Anne Zemella,
Ario de Marco,
Åsa Sivertsson,
Barbara Borgonovo,
Ben Kemp,
Bjørn Voldborg,
Carlo Carolis,
Carter Mitchell,
Cecilia Wickstrand,
Dafydd R Owen,
David Drechsel,
David Jones,
Diego A Oyarzún,
Dominic Esposito,
Eric Geertsma,
Ewa Krupinska,
Frank Bernhard,
Frederico Ferreira-da-Silva,
Hanna Tegel,
Ian Hunt,
Imre Berger,
James Love,
Jan Dohnalek,
Jelena Thies,
Jerome Basquin,
Jesse Coker,
Johannes Buyel,
Joop van den Heuvel,
Jurgen Haustraete,
Katharina L Dürr,
Kelvin Lau,
Kristina Hedfalk,
Kristof Bozovicar,
Lihua Liu,
Lisa Bamber,
Luigi Angelo Scietti,
Malin Bäckström,
Maren Schubert,
Mark Elvin,
Martin Pelosse,
Matthew Todd,
Matthieu Schapira,
Miroslava Alblova,
Nicholas S Berrow,
Nikolay Marinchev Dobrev,
Oleg Brodsky,
Ondřej Vaněk,
Opher Gileadi,
Pam Dossang,
Paola Storici,
Patrick Celie,
Paul Wan,
Peter A Loppnau,
Rachel Harding,
Ray Owens,
Renaud Vincentelli,
Richard Altman,
Rick Davies,
Rob Meijers,
Robbert Kim,
Ruth Saxl,
Sabine Suppmann,
Sameer Velankar,
Sebastiano Pasqualato,
Sergio Martinez Cuesta,
Stephane Petres,
Steven Harborne,
Susanne Witt,
Svend Kjaer,
Tamar Unger,
Timothy Craig,
Tsafi Danieli,
Ugis Sarkans,
Wolfgang Knecht,
Wolfgang Kuttenlochner,
Yoav Peleg,
Lovisa Holmberg Schiavone,
Nicola A Burgess-Brown
Abstract
As machine learning (ML) becomes increasingly important in protein engineering, synthetic biology, and bioprocess optimisation, the lack of consistent, complete, and standardised experimental reporting remains a major barrier to building effective models. EXPERT (EXpression and Purification Experimental Reporting Template) defines a structured framework for capturing protein expression and purification data across the community, to increase reproducibility across laboratories and support downstream ML applications. We propose here the essential data that should be recorded during expression and purification workflows by both novice and experienced protein production scientists. Its recommendations are informed by expert contributions from across academia and industry. The reporting requirements were distilled to a focused set of parameters deemed sufficient for workflow reproducibility and constructing robust ML models aimed at improving protein production. By deploying these guidelines, we hope to enable the collection of larger data sets to support future model building.