Modeling and Characterizing BMP-2 Protein Binders for Fracture Regeneration
| dc.contributor.advisor | Hosseinzadeh, Parisa | |
| dc.contributor.advisor | , | |
| dc.contributor.advisor | , | |
| dc.contributor.author | Fear, Karly | |
| dc.date.accessioned | 2022-07-12T20:19:38Z | |
| dc.date.available | 2022-07-12T20:19:38Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Critically-sized bone defects experience delayed regeneration correlated to deficient protein signaling at the injury site. Delivering the potent growth factor bone morphogenetic protein-2 (BMP-2) to the defect is a promising clinical intervention to augment fracture healing in such cases. The release kinetics of BMP-2 from a hydrogel scaffold play a crucial role in the success of therapeutic delivery in terms of improved healing outcome. For instance, rapid BMP-2 release causes suboptimal bone formation. Affinity interactions between BMP-2 and binder proteins offer a mechanism to fine-tune the spatial placement and timing of exogenous BMP-2 bioactivity over the course of bone healing. Experimental selection techniques may identify such binder proteins from large protein libraries. However, experimental pipelines fail to reveal structural details of the protein-protein interaction and explore only a small subset of the possible sequence landscape for binding proteins. The computational design route is therefore an attractive strategy to rationally engineer a BMP-2 delivery control mechanism. In order to slow BMP-2 release, I first sought to directionally modify experimentally selected binding proteins with computational modeling and mutagenesis. Next, I generated de novo protein binders hypothesized to retain BMP-2 via affinity interactions when conjugated to the hydrogel scaffold. I approached this challenge with well-established computational protein design methodologies to yield binding proteins with target surface site-specificity. I then developed a machine learning model to predict binding affinity for computationally modeled proteins. Finally, I sought to experimentally characterize my designed binders using yeast surface display and flow cytometry to validate in vitro that my computational pipeline generates functional designs, appropriate for controlled BMP-2 delivery. | en_US |
| dc.identifier.orcid | 0000-0002-4189-9919 | |
| dc.identifier.uri | https://hdl.handle.net/1794/27304 | |
| dc.language.iso | en_US | |
| dc.publisher | University of Oregon | |
| dc.rights | CC BY-NC-ND 4.0 | |
| dc.subject | Protein engineering | en_US |
| dc.subject | Protein design | en_US |
| dc.subject | Fracture regeneration | en_US |
| dc.subject | Bone healing | en_US |
| dc.subject | Machine Learning | en_US |
| dc.title | Modeling and Characterizing BMP-2 Protein Binders for Fracture Regeneration | |
| dc.type | Thesis/Dissertation |