Computer-guided binding mode identification and affinity improvement of an LRR protein binder without structure determination

by Yoonjoo Choi, Sukyo Jeong, Jung-Min Choi, Christian Ndong, Karl E. Griswold, Chris Bailey-Kellogg, Hak-Sung Kim

Precise binding mode identification and subsequent affinity improvement without structure determination remain a challenge in the development of therapeutic proteins. However, relevant experimental techniques are generally quite costly, and purely computational methods have been unreliable. Here, we show that integrated computational and experimental epitope localization followed by full-atom energy minimization can yield an accurate complex model structure which ultimately enables effective affinity improvement and redesign of binding specificity. As proof-of-concept, we used a leucine-rich repeat (LRR) protein binder, called a repebody (Rb), that specifically recognizes human IgG₁ (hIgG₁). We performed computationally-guided identification of the Rb:hIgG₁ binding mode and leveraged the resulting model to reengineer the Rb so as to significantly increase its binding affinity for hIgG₁ as well as redesign its specificity toward multiple IgGs from other species. Experimental structure determination verified that our Rb:hIgG₁ model closely matched the co-crystal structure. Using a benchmark of other LRR protein complexes, we further demonstrated that the present approach may be broadly applicable to proteins undergoing relatively small conformational changes upon target binding.