Development of novel chloride-selective membrane carriers

Thesis

Synthetic ion transporters attempt to achieve a monumental task - carry out the role of massive, kDa- to MDa-sized protein transporters. They hold promise as both chemical probes and potential therapeutics for diseases linked to malfunctioning biological ion transport, but face several stumbling blocks in their translation to the clinic. One of these is the cytotoxicity arising from unselective ion transport. The aim of this thesis is to address this issue - by developing novel selective transporters, as well as the methods which can be used to study them, and predict novel classes. In Chapters 2 and 3, it is demonstrated that highly active and selective anionophores can be accessed by combining halogen bonding anion recognition with macrocyclic anion encapsulation. In chapter 2, their synthesis is demonstrated and transport experiments in large unilamellar vesicles (LUVs) performed, revealing record selectivity. The mechanism underpinning selectivity is then dissected to its mechanistic underpinnings in Chapter 3 through Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations at the membrane interface, demonstrating exactly how the cyclic structure imposes an energetic preference for chloride binding over hydroxide, as well as a greater desolvation of hydroxide, which further disfavours its transport. In Chapter 4, this methodology is extended to the study of stimuli-responsive transporters. The geometric and energetic components of transporter activation are decomposed, highlighting the crucial balance of explicit solvent and quality of energetic treatment. The gaps in the respective MD and DFT treatments which inhibit its applications in predictive studies are addressed by developing novel machine learning interatomic potential (MLIP) simulation methods in Chapter 5. To do this, new components are added to the mlp-train software package, which enable completely novel sampling strategies. These are evaluated on custom datasets to benchmark sampling quality and efficiency. This allows for binding energy predictions to be produced using umbrella sampling simulations performed using a machine-learned potential trained on only 600 training set geometries. We anticipate that the combination of results and methods presented in this work has the potential accelerate the transition toward the use of artificial chloride transport in biology across the entire pipeline - from modelling, to synthesis and assay.

Authors: Flerin Maver, MO

• DOI: 10.5287/ora-vg7bjpqze
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: membrane transporters; vesicle assays; synthetic organic chemistry; chloride transport; ionophores; supramolecular chemistry; molecular dynamics; density functional theory; selective transport; computational modelling; machine learning interatomic potentials
• Subjects: Organic compounds--Synthesis; Supramolecular chemistry; Computational chemistry