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

Event Date:
2023-06-15T09:00:00
2023-06-15T11:00:00
Event Location:
Hennings 318
Speaker:
Shawn Hsueh(PhD student)
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Intended Audience:
Public
Event Information:

My research 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, I have developed an unbiased molecular dynamics (MD) simulation tool, Reservoir REMD, and integrated it 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 were used to calculate the dimer binding free energy of SOD1 to study how disease-associated mutations affect the 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 the SOD1 misfolding that is known to contribute to ALS disease progression. For application-related contributions, I applied enhanced MD and free energy calculations to guide the design of vaccine immunogens for neurodegenerative diseases and mutation-robust therapeutics for COVID19. For neurodegenerative diseases, we designed flexible cyclic peptide immunogens to mimic the toxic oligomers 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. Through free energy calculations, I predicted that the S2 region could be exposed under unglycosylated conditions, and that the S2 region would be stable in the pre-fusion state. With this information, I used Rosetta to scaffold this S2 region, and predicted several constructs that have been successfully expressed and functional in wet-lab experiments. I also engineered ACE2-based decoys, the human receptor for SARS-CoV-2, as 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.

Add to Calendar 2023-06-15T09:00:00 2023-06-15T11:00:00 A journey into computational protein design: Novel simulation methods development, physical origins of disease mutants, and therapeutic design for neurodegenerative diseases and COVID19 Event Information: My research 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, I have developed an unbiased molecular dynamics (MD) simulation tool, Reservoir REMD, and integrated it 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 were used to calculate the dimer binding free energy of SOD1 to study how disease-associated mutations affect the 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 the SOD1 misfolding that is known to contribute to ALS disease progression. For application-related contributions, I applied enhanced MD and free energy calculations to guide the design of vaccine immunogens for neurodegenerative diseases and mutation-robust therapeutics for COVID19. For neurodegenerative diseases, we designed flexible cyclic peptide immunogens to mimic the toxic oligomers 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. Through free energy calculations, I predicted that the S2 region could be exposed under unglycosylated conditions, and that the S2 region would be stable in the pre-fusion state. With this information, I used Rosetta to scaffold this S2 region, and predicted several constructs that have been successfully expressed and functional in wet-lab experiments. I also engineered ACE2-based decoys, the human receptor for SARS-CoV-2, as 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. Event Location: Hennings 318