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All About Allostery: A study of AAA nanomachines responsible for microtubule severing using molecular modelling, bioinformatics, and machine learning

Macke, Amanda
http://rave.ohiolink.edu/etdc/view?acc_num=ucin171291144530672

Abstract Details

2024, PhD, University of Cincinnati, Arts and Sciences: Chemistry.
The cytoskeleton, a key feature of the cell, acts as scaffolding that is responsible for maintaining the cell shape as well as forming a highway system for intra-cellular transportation. Thus, the cell must maintain strict regulation of its cytoskeleton to undergo deliberate change. Microtubules, an essential biopolymer of the cytoskeleton, are routinely severed by specific AAA (ATPases Associated with cellular Activities) nanomachines. Severing is required for a variety of significant cellular functions including, but not limited to, cellular division and neurogenesis. Changes to microtubules themselves, their various regulatory processes, and these proteins would have far reaching, serious implications on the viability and health of the cell and its organism. The microtubule severing enzymes are katanin, spastin, and fidgetin. Recent structural studies have solved hexameric structures for katanin and spastin in the presence of cofactors indicating they operate via a global conformational change induced by ATP hydrolysis. Simulations were previously used to study the functional states of both severing enzymes where it was identified that in long time-scales, at least one conformation will disassemble in the absence of cofactors. To further understand this observed disassembly process and the influence of the cofactors, a similar study of the resulting lower order oligomers was designed in part one. Through machine learning and in-house developed analyses, we recognized significant allosteric shifts due to the presence of ligands and neighboring protomers. During this study we also identified a particular region of katanin that is highly correlated with ligand binding from the helical bundle domain (HBD). We developed StELa, an in-house clustering algorithm, to characterize observed structural changes from simulation which identified a specific local conformational change due to ligand binding. In part two, this method was compared with other available algorithms to evaluate its capabilities. We found StELa to be more descriptive than other current methods for classifying local structural changes and particularly useful in the classification of intrinsically disordered proteins or regions. Characterizing the allosteric networks of these systems became our obsession. We turned to bioinformatics, network graph theory, and classification machine learning to identify and track allosteric changes due to cofactor binding in the tertiary structure as well as the full quaternary assemblies of spastin in part three. We identified residue R591 to be an allosteric center for the quaternary assembly of spastin, in alignment with recent cryo-EM structural studies. Then, in part four, we sought to provide the same characterization of the allosteric network of katanin. We discovered that the R591 allosteric center in spastin does not have the same functional role in katanin. Through further bioinformatics studies of katanin, a dramatic evolutionary shift was identified from metazoa to chordata that affected a divergence in the HBD. In addition, we identified a highly conserved residue in this region which is a known point mutation in lung cancer. This prompted us to model the human katanin quaternary structure for further studies in part five so that we can identify how the cancerous mutation affects the overall stability and behavior of misregulated katanin.
Ruxandra Dima, Ph.D. (Committee Chair)
Ryan White, Ph.D. (Committee Member)
Anna Gudmundsdottir, Ph.D. (Committee Member)
287 p.
Chemistry
Microtubule Severing; Machine Learning; Bioinformatics; ATPase Associated with Cellular Activities; Allostery; All Atomistic Molecular Dynamics

Recommended Citations

Citations

  • Macke, A. (2024). All About Allostery: A study of AAA nanomachines responsible for microtubule severing using molecular modelling, bioinformatics, and machine learning [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin171291144530672

    APA Style (7th edition)

  • Macke, Amanda. All About Allostery: A study of AAA nanomachines responsible for microtubule severing using molecular modelling, bioinformatics, and machine learning. 2024. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin171291144530672.

    MLA Style (8th edition)

  • Macke, Amanda. "All About Allostery: A study of AAA nanomachines responsible for microtubule severing using molecular modelling, bioinformatics, and machine learning." Doctoral dissertation, University of Cincinnati, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ucin171291144530672

    Chicago Manual of Style (17th edition)

ucin171291144530672
© 2024, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.