Date of Award

9-6-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Bioinformatics

First Advisor

Robert Reis

Second Advisor

Srinivas Ayyadevara

Abstract

A protein’s structure is determined by its amino acid sequence and post-translational modifications, and provides the basis for its physiological functions. Roughly a third of the proteome in mammalian species comprises proteins that contain highly unstructured or intrinsically disordered regions. Proteins with extensive unstructured regions are referred to as intrinsically disordered proteins (IDPs). Recent advances in experimental and computational analyses predicted multiple interacting partners for the disordered regions of proteins, implying critical roles in signal transduction and regulation of biological processes. This dissertation has studied the connection between the misfolding of a protein and an insoluble protein using the ability of neural network analysis to predict the physiochemical properties that determine if a disordered protein has to enter into aggregates. These neural network analysis led us to uncover the functional analysis of already known disordered protein and also a novel disordered proteins like 14-3-3 and glial fibrillar acidic protein (GFAP) respectively. The 14-3-3 family of proteins are the known intrinsically disordered proteins that bind to several key transcription factor (FOXO, TFEB) and structural protein like tau, which are majorly implicated in neurodegenerative diseases. Our research focused on understanding structural and functional aspects of the protein to predict a probable drub to disrupt the toxic protein-protein interaction unique to AD conditions. A novel disordered protein GFAP, also plays a key role in progression of protein aggregation and neurotoxicity during neurodegeneration. GFAP accumulates three times more in aggregates from AD relative to AMC hippocampus and could also serve as a novel drug target for AD.

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