Computational investigation of molecular recognition in plant hormone signal transduction

Zhao, Chuankai

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https://hdl.handle.net/2142/108617 Copy

Description

Title

Computational investigation of molecular recognition in plant hormone signal transduction

Author(s)

Zhao, Chuankai

Issue Date

2020-07-15

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

Shukla, Diwakar

Doctoral Committee Chair(s)

Shukla, Diwakar

Committee Member(s)

• Schroeder, Charles M
• Zhao, Huimin
• Leakey, Andrew

Department of Study

Chemical & Biomolecular Engr

Discipline

Chemical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Plant hormone
• Perception
• Signal transduction
• Molecular dynamics
• Protein-ligand binding
• Protein-protein association

Abstract

Plant hormones are small molecules derived from natural metabolites that regulate plant growth, development and responses to biotic and abiotic stresses. There are nine major classes of plant hormones that have been identified, including abscisic acid, auxin, brassinosteroid, cytokinin, ethylene, giberellin, jasmonic acid, salicylic acid and strigolactone. Plant hormones are specifically recognized by their receptor proteins, and then initiate downstream protein-protein interactions to trigger a variety of hormone responses. Detailed understanding of the molecular mechanism of specific molecular recognition in plant hormone perception and signal transduction will create exciting opportunities for targeting hormone signaling pathway to control plant activities. While substantial experimental studies have provided insights into plant signaling mechanism, it remains, in many cases, challenging to fully understand those protein-ligand and protein-protein interactions at molecular level. Molecular dynamics (MD) simulation is a powerful technique for studying protein dynamics and function at an unparalleled spatial-temporal resolution. In this thesis, I have utilized MD simulations as the primary tool to study a series of protein-ligand and protein-protein interactions involved in early stage of plant hormone signal transduction. I have focused on abscisic acid (ABA) for the major part of the thesis, and also investigated six other classes of plant hormones, including auxin, brassinosteroid, cytokinin, giberellin, jasmonic acid, and strigolactone. By performing microsecond-long timescale MD simulations (aggregate 1.06 milliseconds) and Markov state model analysis, I have elucidated the molecular basis for the binding of the seven major classes of plant hormones to their receptors and the subsequent activation of plant receptors. I have reported the free energy landscapes that quantitatively characterize the thermodynamics and kinetics of receptor-hormone binding and receptor activation processes. I have also identified the key intermediate states in their binding pathways. For ABA, I have also studied the molecular mechanism of negative regulation of ABA signaling by post-translational modification of ABA receptor. To better understand the thermodynamic driving forces involved in binding affinity of plant hormones, I have investigated the role of water reorganization around the binding cavity of plant receptors on plant hormone perception. I have characterized the solvation structural and thermodynamic properties of plant receptors using MD simulations and inhomogeneous solvation theory-based hydration site analysis. Importantly, I have shown that water thermodynamics can potentially be exploited for rational agrochemical design to target plant hormone receptors. For the rest of this thesis, I have focused on the investigation of protein-protein interactions involved in plant signaling. In order to improve computational efficiency of unbiased molecular simulations of complex biomolecule dynamics such as protein-protein association, I have developed an adaptive sampling strategy that combines low-resolution experimental information with MD simulations. I have demonstrated the utility of this method in predicting the structures of dimeric ABA receptor and several other single domain proteins and complexes. Also, I have established a computational workflow for protein complex structure prediction via large-scale coarse grained MD simulations. Using this method, I have predicted a complex structure of protein phosphatase and small GTPase that play an essential role in negative regulatory network of ABA signaling. Overall, this thesis has provided fundamental insights into specific molecular recognition involved in plant hormone signal transduction, which can create new avenues for agrochemical control of plant growth and development. Furthermore, this thesis has established computational framework for understanding and engineering plant signaling.

Graduation Semester

2020-08

Type of Resource

Thesis

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Copyright and License Information

Copyright 2020 Chuankai Zhao

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