Dynamic membrane interfaces shape biomolecular structure and function

The dynamic interplay of proteins, lipids, and small molecules within the cellular membrane is fundamental to critical biological processes. In this dissertation, I unravel the complexities of membrane dynamics through molecular simulations and bilayer modeling, addressing the nuanced interplay between lipids, proteins, and small molecules. By combining simulations, machine learning, and enhanced sampling techniques, I offer new insights into the mechanisms of protein-lipid interactions, the formation of amyloid fibrils, antimicrobial peptide bilayer disruption, and small-molecule modulators within cellular membranes. Chapter 1 motivates this thesis by discussing the diverse roles of lipids in biological membranes and their implications for cellular functionality. I discuss how lipid composition, including the presence of cholesterol and variations in phospholipid types, impacts the physical properties of membranes and their interactions with proteins. Chapter 2 focuses on the interactions of proteins and acidic lipids in the membrane, highlighting lactadherin's binding to phosphatidylserine and the broader implications for blood coagulation. This section examines the potential for targeted therapies by modulating the membrane binding mechanism. Continuing in Chapter 3, I investigate the membrane interactions with medin, dissecting its role in aortic amyloid fibril formation and contributing to understanding cardiovascular diseases. In Chapter 4, the discussion pivots to the disruption of bacterial membranes by antimicrobial peptides (AMPs), with a comprehensive analysis correlating AMP structures and properties to membrane disruption capabilities. Next, Chapter 5 discusses an innovative approach to mitigating climate change using bromoform from red seaweed as a ruminant food additive. Through membrane simulations and unsupervised machine learning, one can develop cellular engineering strategies to increase its storage in microalgae that can reduce methane emissions. Chapter 6 employs Markov State Models to characterize the kinetics and conformational landscape of the SWEET glucose transporter. This chapter also sheds light on the evolutionary connection between these transmembrane proteins and their bacterial homologs, SemiSWEET, offering molecular insight into differences in their transport process. Lastly, Chapter 7 tackles the challenges of simulating membrane proteins over extended timescales, introducing an adaptive sampling method motivated by unsupervised machine learning principles.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biophysics & Quant Biology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Cheng, Kevin Jose

Contributors dc:contributor

• Pogorelov, Taras
• Gruebele, Martin
• Burke, Martin
• Tajkhorshid, Emad

• Molecular Dynamics
• Membranes
• Biophysics
• Simulations
• Protein Binding

Statement dc:rights

• Copyright 2024 Kevin Cheng

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124241

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124241