The amino acid sequence of a protein encodes its folding, the reaction by which a peptide self-assembles into its native functional shape. A folded protein will then go on to carry on biological functions such as ligand binding, signaling, mechanical functions, or biomolecular catalysis. While much experimental work has been done to elucidate protein structure and conformational dynamics, Molecular Dynamics (MD) simulations have been necessary to provide atomic-level details of biomolecular dynamics. A challenge in using MD is the the large computational cost required to reach biologically relevant timescales when integration steps are typically limited to a few femtoseconds. To address this challenge, specialized hardware such as the ANTON supercomputer or distributed computing platforms like Folding@home can be used to collect milliseconds of aggregate trajectory data. From these datasets, kinetic network models called Markov state models (MSMs) can be constructed to infer long timescale dynamics from ensembles of short trajectories. These models can be analyzed in a human interpretable way and make physics-based connections to experimental observables. This dissertation describes how we have used MD simulations and MSMs to model protein folding reactions and protein-protein binding reactions to better understand mutation effects on our systems of interest.
The first chapter of this dissertation describes MD simulations FOXO1 FKH domain folding, which we used MSMs to characterize at atomic resolution. To predict how mutations found in diffuse large B-cell lymphoma (DLBCL) cell lines effect protein stability, we developed an MSM-based hydrophobic free energy of transfer (HT) model to estimate mutation effects. Our HT model results agree better with experiment than other state-of-the-art computational methods. Chapter two describes how we have used approximately 43000 relative binding free energy calculations via the expanded ensemble (EE) method to perform in silico site saturation mutagenesis on miniprotein binders to the highly conserved influenza A H1 hemagglutinin stem region (HA2) de novo designed by the Baker [66]. These miniproteins were selected through an exhaustive design process with iterations of computational design, high throughput affinity screens, and site saturation mutagenesis. We compare our EE SSM method with inferred relative affinities from Chevalier et al.[66], as well as with the state-of-the-art Rosetta method Flex ddG. While Flex ddG predictions are more accurate on average, they are highly conservative. In contrast, EE predictions can better classify stabilizing and destabilizing mutations. We also use a Shannon entropy based method to identify residue positions that are more susceptible to mutation. This work suggests that simulation-based free energy methods can provide predictive information for in silico affinity maturation of designed miniproteins, with many feasible improvements to the efficiency and accuracy within reach. In the final chapter, we atttempt to model the complete binding reactions of the 6 miniproteins mentioned above. We used unbiased simulations to build standard msms and , in combination with biased simulations, multiensemble markov models (MEMMs) of binding for each wild type and affinity matured pair. The unbiased MSMs show that the affinity matured miniproteins prefer different bound states than the wild type miniproteins. Additionally, they provide physically realistic k_on_s and a macroscopic 3 state pathway through an encounter complex. We characterize each of those states and use an contact map based structural similarity index measure (SSIM) and residue-wise Kullback-Leibler divergence method to better understand the differences in the bound states between affinity matured and wild type construct. Interestingly, while our biased simulations do see unbinding transitions, in estimating the MEMM, they overweight the unbinding reaction and unbound state, leading to models that do not make physical sense. This demonstrates that more sensitive enhances sampling techniques may be necessary for building MEMMs. The final two chapters of this dissertation present new methodologies for computational protein design, making great strides towards a dynamic understanding of how proteins bind their targets and how mutations effect those reactions. / Chemistry
| Identifer | oai:union.ndltd.org:TEMPLE/oai:scholarshare.temple.edu:20.500.12613/10652 |
| Date | 06 1900 |
| Creators | Novack, Dylan, 0000-0003-1434-0316 |
| Contributors | Voelz, Vincent, Carnevale, Vincenzo, Schafmeister, Christian, Dunbrack, Roland L. |
| Publisher | Temple University. Libraries |
| Source Sets | Temple University |
| Language | English |
| Detected Language | English |
| Type | Thesis/Dissertation, Text |
| Format | 275 pages |
| Rights | IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available., http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | http://dx.doi.org/10.34944/dspace/10614, Theses and Dissertations |
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