Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition
Cotet, T.-S.; Krawczuk, I.; Pacesa, M.; Nickel, L.; Correia, B. E.; Haas, N.; Qamar, A.; Challacombe, C. A.; Kidger, P.; Ferragu, C.; Naka, A.; Castorina, L. V.; Subr, K.; Kluonis, T.; Stam, M. J.; Unal, S. M.; Wood, C. W.; Stocco, F.; Ferruz, N.; Kurumida, Y.; Calia, C. N.; Paesani, F.; Machado, L. d. A.; Belot, E.; Gitter, A.; Campbell, M. J.; Hallee, L.; Adaptyv Competition Organizers,
Show abstract
In this report, we summarize and analyze the 2024 Adaptyv protein design competition. Participants used computational and Machine Learning (ML) methods of their choice to design proteins that bind the Epidermal Growth Factor Receptor (EGFR), a key drug target involved in cell growth, differentiation, and cancer development. Over 1,800 designs were submitted across two rounds. Of these, 601 proteins were selected and characterized for expression and binding affinity to EGFR, with competitors both optimizing existing binders (KD = 1.21 nM) and creating de novo binders (KD = 82 nM). All selected designs were experimentally validated using Adaptyvs automated Bio-Layer Interferometry (BLI) pipeline. This competition illustrates the potential of crowdsourcing to drive creativity and innovation in protein design. However, it also exposed key challenges, such as the lack of standardized benchmarks, experimental design targets, and robust computational metrics for method comparison. We anticipate that future competitions will address these gaps and further motivate progress in computational protein design.
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