Computational investigation of protein-lipid interactions and lipid modulation of membrane proteins

Lihan, Muyun

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https://hdl.handle.net/2142/115917 Copy

Description

Title

Computational investigation of protein-lipid interactions and lipid modulation of membrane proteins

Author(s)

Lihan, Muyun

Issue Date

2022-07-13

Director of Research (if dissertation) or Advisor (if thesis)

Tajkhorshid, Emad

Doctoral Committee Chair(s)

Tajkhorshid, Emad

Committee Member(s)

• Shukla, Diwakar
• Pogorelov, Taras V
• Rienstra, Chad M

Department of Study

School of Molecular & Cell Bio

Discipline

Biophysics & Computnl Biology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Membrane Proteins
• Lipids
• Protein-lipid Interactions
• Molecular Dynamics

Abstract

With rapid advancements in structural biology, an increasing number of membrane protein structures become accessible for characterization of their structures, dynamics, and functions. Over the past decades, membrane lipids have gained more appreciation for their roles in not only directly interacting with membrane proteins but also actively modulating protein structures and functions. Computational approaches, particularly molecular dynamics (MD) simulations, serve as a powerful tool for illuminating detailed protein-lipid interactions and lipid-mediated modulation in membrane proteins. This dissertation aims to investigate protein-lipid interactions and understand how lipids modulate protein dynamics in membrane protein systems with the use of MD simulations. We first present a brief review of protein-lipid interactions and recent applications of computational approaches in characterizing protein-lipid interactions and specific lipid modulation of membrane protein functions. In the following chapter, we then describe the development of an improved membrane mimetic model that facilitates the study of protein interactions with membrane lipids, especially cholesterol. The improved membrane mimetic model offers enhanced lipid diffusion and dynamics while maintaining lipid membrane distributions when compared to conventional membranes. Next, we provide example applications of our improved membrane mimetic model to several membrane proteins. These examples include four integral membrane proteins and one peripheral membrane protein that have been experimentally identified to directly interact with cholesterol. We have demonstrated that our membrane model proves overall effective in capturing cholesterol binding sites and characterizing protein-lipid interactions. Through these examples, we have also discussed the success and limitation of our model for general applications in membrane systems. In the next chapter, we investigate the membrane binding of a peripheral membrane-binding domain, the C2 domain, in the protein kinase C and how anionic lipids and calcium ions modulate this membrane targeting and binding process. Our simulations have revealed two distinct C2 domain membrane-binding modes influenced by both specific anionic lipid interactions and the calcium-binding stoichiometry. Our results have also suggested that calcium-dependent signaling of protein kinase C activation might occur through modulation at the membrane-binding configurations of the C2 domain. In the last study, we present a computational and experimental collaboration employing nuclear magnetic resonance (NMR)-guided MD simulations to reveal the protein dynamics of the extracellular domain in a membrane-anchored protein, human tissue factor. Our NMR-guided MD simulations have provided novel insights into tissue factor structures and revealed a dynamic ensemble of loop configurations by which tissue factor interacts with the coagulation factor VIIa. From our simulations, key residues have been identified in the factor VIIa binding loop with divergent backbone or side-chain configurations to account for loop dynamics observed in the structural ensemble.

Graduation Semester

2022-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Muyun Lihan

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