De novo computational design of bone morphogenetic protein-2 knuckle binders

Abstract

Acute or long-term symptoms of a bone fracture have increased globally at a rate of 70.1% from 1990-2019.[1] Bone morphogenetic protein 2 (BMP-2) is an important driving force in osteogenesis and could be an essential therapeutic for mitigating long-term symptoms associated with nonunion fractures. Recombinant human BMP-2 (rhBMP2) has been approved by the Food and Drug Administration (FDA) as a growth factor for therapeutic use against subtypes of nonunion fractures, but research has shown that required supraphysiological amounts from predominant BMP-2 delivery methods lead to overgrowth of bone among other adverse side effects. Therefore, there is a current heightened research interest on controlling the release kinetics of BMP-2 into a fracture site for safer and more efficient bone regeneration. Computational protein design (CPD) is a promising technique for creating de novo binders to BMP-2. Engineered protein-protein interactions can be utilized to participate in a lower risk, affinity-modulated, delivery system to increase the efficacy and decrease adverse side effects of BMP-2. Using PyRosetta[3] design scripts, and the UO Talapas and Franklin computers, I produced several potential de novo protein binding candidates. I have experimentally tested and validated the top designs while increasing the rate of success with RFDiffusion[4] and ProteinMPNN[5] computational techniques. This thesis overviews the successful de novo design of a protein binder with an equilibrium dissociation constant of 771 nM as measured by biolayer interferometry.