Date of Award
2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Materials Science
First Advisor
Jianing J. Li
Second Advisor
Frederic F. Sansoz
Abstract
Peptide self-assembly plays an important role in biomedical and material discovery, of- fering a promising insight for development of nanostructures, serving as bio-materials for drug delivery. The tendency of peptide aggregating to certain nanostructures help in forming the basis for tissue engineering scaffolds and enabling the development of innovative diagnostic and therapeutic biologics. The designing of peptide through ex- periment is expensive and time consuming. The virtual screening of natural tendency of peptide aggregation is cost-effective to evaluate numerous designs and prioritize synthesis and characterization efforts. We have characterized the propensity of pep- tide aggregation utilizing the all-atom (AA), and coarse-grained (CG) simulations. We have studied the widely explored di-phenylalanine (FF) and di-Glycine (GG) as a toy model to benchmark the AA and CG simulation. Furthermore, we have investigated the intrinsic properties such as the system size, terminal modification and salinity and their effects in peptide aggregation. We have used the aggregation propensity (AP) metrics to differentiate between the aggregated and non-aggregated structure. We have also proposed the new AP metrics called APcontact which is also able to differentiate between the aggregated and non-aggregated structure.The second project includes the top-down approach to screen the peptideâs ten- dency to aggregate to the hydrogel like structure. We started with predicting the AP scores for each amino acid using the optimization method, L-BFGS-B, to refine our initially assigned AP values. The initial AP values was assigned based on the physio- chemical properties of the amino acids, such as hydrophobicity, aromaticity, polarity, tendency of forming beta-sheets, alpha-helix etc. This allows us to screen the vari- ous peptides of varying length and their tendency to aggregate. We further simulate those top list using CG simulation followed by all-atom simulation and the top list of the peptides from the all-atom simulation were further selected for synthesis. The final stage of this research involves the co-assembly of peptides with optimized AP scores to explore the formation of diverse nanostructures These discoveries establish a foundation for prospective virtual screening of nanostructures assembled by peptides and the computer-aided design of biologics.
Language
en
Number of Pages
97 p.
Recommended Citation
Thapa, Subhadra, "Multiscale Modeling To Study Peptide Aggregation" (2024). Graduate College Dissertations and Theses. 1876.
https://scholarworks.uvm.edu/graddis/1876