Abstracts
AI-based protein design and validation
Presenter: Sophie Binder (sobind@mailbox.org)
Authors:
Sophie Binder¹; Bianca Broske²; Benjamin A. McEnroe²; Marie Kleinert²; Dominic Ferber¹; Julia Meßmer²; Elisabeth Tan²; Tim N. Kempchen²; Caroline I. Fandrey³; Alexander Hoch³; Peter Konopka³; Katja Blumenstock³; Prof. Dr. Matthias Geyer¹; Prof. Dr. Jonathan Schmid-Burgk³; Dr. Stephan Menzel⁴; Dr. Gregor Hagelueken¹; Prof. Dr. Michael Hoelzel²
¹ Institute of Structural Biology, University of Bonn; ² Institute of Experimental Oncology, University of Bonn; ³ Institute of Clinical Chemistry and Clinical Pharmacology, University of Bonn; ⁴ Core Facility Nanobodies, University of Bonn
AI-based prediction of protein structures is a very recent and rapidly evolving topic with high potential in the field of cancer research and development of novel therapeutic agents, in particular in settings of difficult antibody or nanobody production.
Using alpha-fold-based prediction algorithms, we generated libraries of artificial proteins designed to bind specific target structures. Through a cellular screening system, we identified a subset of binders that were validated to successfully bind their respective targets. Binding to target proteins was verified in FACS experiments using cellular systems as well as recombinantly expressed proteins. When studying the binding kinetics using SPR spectroscopy, we observed high binding affinities in the nanomolar range. Further, the AI-Binders exhibited a high thermal stability and a notable ability to refold after denaturation.
The AI-Binders are readily expressed in E. coli and can be easily purified to homogeneity. Preliminary crystallization trials revealed that the AI-Binders are highly inclined to form crystals, enabling their use as crystallization chaperones. By site-specific biotinylation and functionalization with streptavidin, we generated tetravalent target-binding scaffolds, which are promising tools for target protein structure determination using cryo-electron microscopy.