Date of Award
8-7-2024
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Intrinsically disordered proteins and protein regions (IDPs) compose a significant portion of eukaryotic proteomes yet remain relatively understudied. Unlike folded domains, IDPs lack a single fixed three-dimensional structure and instead are defined by a dynamic conformational ensemble. Despite this structural heterogeneity, IDPs carry out vital roles within the cell. IDPs are particularly important in cellular signaling and regulation, owing to their ability to mediate molecular interactions with a variety of affinities and specificities. Additionally, some IDPs drive the formation of higher-order non-stoichiometric assemblies called biomolecular condensates. This dissertation focuses on the development of a variety of computational and experimental tools to explore the mechanisms and functions of IDPs and biomolecular condensates. First, we investigate mechanisms of IDP interaction for three distinct systems. In this section, we use all-atom molecular simulations, coarse-grained simulations, and bioinformatics to understand how peptide motifs and amino acid patterning relate to binding for these specific systems. Second, we present three related machine learning tools for directly predicting the properties of IDPs from amino acid sequence. Last, we describe an experimental toolkit for assembling in vivo synthetic condensates with controllable material state, localization, and composition. This system can serve as a framework for exploring the cellular functions of condensate-forming IDPs. Collectively, the tools presented in this work enhance our ability to connect the protein sequences of IDPs to their functions within the cell.
Language
English (en)
Chair and Committee
Alex Holehouse
Committee Members
Barak Cohen; Keren Lasker; Natalie Niemi; Shankar Mukherji
Recommended Citation
Griffith, Daniel, "Developing Computational and Experimental Tools for Investigating Intrinsically Disordered Proteins and Biomolecular Condensates" (2024). Arts & Sciences Electronic Theses and Dissertations. 3294.
https://openscholarship.wustl.edu/art_sci_etds/3294