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Title:Computational dissection of nitrate transport mechanism
Author(s):Feng, Jiangyan
Director of Research:Shukla, Diwakar
Doctoral Committee Chair(s):Shukla, Diwakar
Doctoral Committee Member(s):Chen, Li-Qing; Harley, Brendan; Higdon, Jonathan
Department / Program:Chemical & Biomolecular Engr
Discipline:Chemical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):nitrate transporters
nitrogen uptake efficiency
molecular dynamics simulations
machine learning
Markov state models
Abstract:Nitrogen is an essential nutrient in plants and a major driving force for crop yields and food production. Annually, about 110 million tonnes of nitrogenous fertilizers are applied globally to enhance crop yields. However, over half of the nitrogen added to the soil is lost to the environment, with an average of only 25%-50% being taken up by the plants. Excess nitrogenous fertilizers have already caused biodiversity loss, environmental pollution, and climate change. So how can we increase crop yields to match the rapidly growing global population while minimizing the environmental pollution caused by nitrogenous fertilizers? One solution is improving plant nitrogen uptake efficiency (NUE), that is, enhancing crop production per unit of added nitrogen. For plants, nitrate is one of the two major nitrogen sources, which is taken up from the soil through nitrate transporters. Despite recent breakthroughs in structural biology, our knowledge of the molecular nature and regulation of nitrate transporters is far from complete. The major reason is that structural biology only provides few "snapshots" of protein structures, whereas proteins undergo complex dynamical processes to perform their functions. Molecular dynamics (MD) simulations are a powerful tool to decipher the mechanism of nitrate transporters because they can capture the dynamical behavior of biological systems in full atomic details, which is very challenging with any experimental technique. There are two major focuses of this thesis. First, we develop computational algorithms to enhance the efficiency and accuracy of MD simulations. We leverage coevolutionary relationships conserved in protein sequences to predict the slow dynamical changes of proteins, which can be used to guide MD sampling of the protein conformational space. We also propose a method called FingerprintContacts to predict alternative protein conformations by combining coevolutionary signals and machine learning. To analyze high dimensional MD simulation data, we present a genetic algorithm-based feature selection technique, which can select relevant features in an automatic and systematic way. Second, we integrate these algorithms with MD simulations to explore the fundamental transport mechanism of two representative transporters in atomistic detail: plant dual-affinity nitrate transporter (NRT1.1) and bacterial nitrate/nitrite exchanger (NarK). We not only characterize the complete substrate translocation cycles in atomic details, but also identify key residues involved in substrate recognition, binding, exchange, and translocation. We expect the detailed molecular mechanism could guide future crop engineering strategies.
Issue Date:2021-05-20
Type:Thesis
URI:http://hdl.handle.net/2142/113114
Rights Information:Copyright 2021 Jiangyan Feng
Date Available in IDEALS:2022-01-12
Date Deposited:2021-08


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