Booth Id:
CBIO086
Category:
Computational Biology and Bioinformatics
Year:
2021
Finalist Names:
Pairikh, Khushi (School: Gilbert Classical Academy)
Abstract:
Background:
Alzheimer’s Disease pathogenesis is associated with the deposition of amyloid-beta (AB) protein aggregates in the brain. No treatment has been proposed to degrade pre-oligomeric AB peptides via the ubiquitin-proteasome pathway. The objective of this study is to design a ubiquitin substrate de novo which binds pre-oligomeric AB, thereby marking aberrantly folded peptides for degradation.
Methods:
Folding, packing, and docking algorithms were applied to a preliminary antiparallel beta-sheet backbone to fold the desired conformation. Monte Carlo sampling was applied during folding and packing to optimize backbone and side chain dihedral angles. The protein-protein docking protocol was driven by three processes: 1) initial perturbation, 2) low-resolution search, 3) high-resolution refinement. All models were evaluated against the Rosetta all-atom energy function.
Results:
Upon interface formation, favorable changes in Lennard-Jones energy, hydrogen bond energy, disulfide bond energy, and electrostatic energy were observed. The overall interaction energy for the native conformation (RMSD=0) is consistently negative, which indicates that protein binding is an energetically favorable process. Approximately 250 kJ/mole is released from interactions between the de novo protein and AB.
Conclusion:
The de novo ubiquitin substrate designed in this study has clinical potential in Alzheimer’s Disease treatments. This study presents a proof of concept for protein design to facilitate ubiquitination of proteins associated with the pathology of other neurodegenerative diseases.
Awards Won:
University of Arizona: Renewal Tuition Scholarship