A journey into computational protein design : simulation methods, physical origins of disease, and therapeutic design for neurodegenerative diseases and COVID19

This thesis aims to advance the development pipeline of protein and peptide therapeutics from a biophysical perspective, and covers a spectrum of contributions, from methodologies to applications. For methodology contributions, an unbiased molecular dynamics (MD) simulation tool, Reservoir REMD (Res-REMD), has been developed and integrated into GROMACS. It has been benchmarked and shown to give the same results for different initial conformations, even when starting the simulation from a kinetically trapped initial state. Res-REMD and other enhanced MD methods were used to calculate the dimer binding free energy of SOD1 to study how disease-associated mutations affect homodimer binding. The results reveal that while the A4V mutation decreases the binding affinity, the D101N mutation does not. These findings challenge the hypothesis that dimer dissociation initiates SOD1 misfolding, a pathological event that is known to contribute to ALS disease progression. For application-related contributions, enhanced MD and free energy calculations were performed to guide the design of vaccine immunogens for neurodegenerative diseases and mutation-robust therapeutics for COVID19. For neurodegenerative diseases, flexible cyclic peptide immunogens were designed to mimic the conformations of accessible epitopes in toxic oligomers mode of proteins such as tau in Alzheimer's disease, or alpha-synuclein in Parkinson's disease. This approach, called "Glycindel scaffolding", can be extended to other protein misfolding diseases. For COVID19, we hypothesized that a conserved region on the spike S2 region could be scaffolded to become a mutation-robust vaccine. Free energy calculations predicted that the S2 region would be exposed under unglycosylated conditions and would be stable in the pre-fusion state. With this information, Rosetta was used to scaffold this S2 region, and design several protein constructs, which have been successfully expressed and shown to be functional in wet-lab experiments. A portion of ACE2, the human receptor to SARS-CoV-2, was engineered to develop mutation-robust protein binders for spike RBD. Several engineered ACE2 decoys have been successfully expressed, showcasing the power of integrating machine-learning tools into protein design.Science, Faculty ofPhysics and Astronomy, Department ofGraduat