Computational Modeling and Analysis of Protein Interactions at Multiple Coarse-grained Levels in Biological Systems

Proteins, composed of unique amino acid sequences, fold into compact, functional structures. These proteins often form large complexes that interact with other proteins and genes to regulate cellular processes. Studying these interactions is challenging due to their transient nature. This thesis em- ploys coarse-graining techniques to simplify protein structures while preserving their behavior to analyze protein interactions in different systems and address specific research questions. We focus on the Superoxide Dismutase 1 (SOD1) protein, which can cause aggregation and Amyotrophic Lateral Sclerosis (ALS) when mutated. Despite numerous studies on SOD1 folding, understanding the interplay between crowding, folding and aggregation in vivo remains lacking. Using an atomic- level coarse-graining approach, approximating amino acid residues as beads-on-chain, we find that electrostatics play a crucial role in SOD1’s folding pathway, with mutations potentially impacting protein aggregation. Additionally, we model protein assemblies in crowded cellular environments using molecule-level coarse-graining, approximating protein subunits as Lennard Jones spheres. While some protein complexes form structures visible with imaging, many remain unsolved due to dynamic associations. We model clusters of protein assemblies from multiple subunits, capturing the distribution of high-order clusters in the protein complex Succinate Dehydrogenase (SDH). Our findings show crowding stabilizes clusters not present in dilute conditions and the spatial arrange- ments of the subunits vary with cell types. We also explore protein interactions in gene regulatory networks. Extensive studies on these networks often require expensive simulations. Using system- level coarse-graining, we represent proteins and genes as nodes and their interactions as edges. We develop centrality measures and metrics to rank node importance and predict network properties from topologies, negating the need for network dynamics simulations. We further group nodes according to cell phenotypes governed by gene regulatory networks. Different systems require dif- ferent coarse-graining techniques based on the scientific questions being probed. Emerging research involves simulations with multiple levels of coarse-graining within the same system, tailored to the complexity needed for specific properties. This thesis builds toward that approach, applying in- creasing levels of coarse-graining across diverse systems. Doing so enhances our understanding of the complex processes underlying life, showing how various levels of simplification can reveal intricate interactions governing cellular functions.

Name thesis:degree_name

Doctor of Philosophy

Discipline thesis:degree_discipline

Physics

Grantor

University of Houston

Year dc:date.issued

2024

Author dc:creator

• Sarkar, Atrayee

Advisor dc:contributor.advisor

• Morrison, Greg

Committee members dc:contributor.committeemember

• Gunaratne, Gemunu
• Ratti, Claudia
• Sharma, Pradeep
• Varghese, Oomman K

• Physics

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10657/19961

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/19961