Electronic Theses and Dissertations

Date

2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Chemistry

Committee Chair

Yongmei Wang

Committee Member

Mohamed Laradji

Committee Member

Qianyi Cheng

Committee Member

Tomoko Fujiwara

Committee Member

Xiaohua Huang

Abstract

Intrinsically Disordered Proteins (IDPs) are proteins without stable three-dimensional structures. These conformationally flexible proteins form a substantial portion of the eukaryotic proteome and are frequently associated with devastating human conditions such as Alzheimer’s and cancer. There has been significant interest in IDPs in recent decades, given the important roles they play in biological systems. Here, we examine sequences, structures, and functions of IDPs. We explore the sequence-structure-function relationships of protamines (known IDPs), specifically looking at three well-known protamine sequences (salmon, human P1 and bull P1). Using a simple scatter plot of radius of gyration (Rg) and instantaneous shape ratio (Rs), we construct a ‘map’ of the conformational landscape of each protamine. Consistent with their disordered nature, we observe broad conformational landscapes for each protamine, with conformations ranging from hairpin loops to extended coils. We observed some secondary structure (helices, hairpin loops) as well, with human P1 being the protamine with the highest propensity for secondary structure content and salmon being the lowest. Using Rg and Rs, we develop a polymer physics-based framework to ‘map’ the conformational landscape of any IDP or polymer. We conduct Monte Carlo simulations of the Gaussian Walk (GW) polymer chain model and use its conformational landscape map as a reference for those of other proteins/polymer. Our maps not only distinguish the conformational landscapes of disordered proteins from structured proteins, but also reveal differences in the conformational ensembles among disordered proteins. Additionally, we compute an fC score for protein/polymer each map that quantifies its conformational diversity and enables comparison of the conformational diversities of different proteins/polymers. We provide an easy-to-use python module (PyConforMap) complete with instructions, that generates our GW-based map for any protein.

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Data is provided by the student.

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Open access.

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