Investigation into biological and biomimetic transmembrane systems
Keywords: computational chemistry
membrane
nanopore
molecular dynamics
machine learning
molecular modelling
anionophore

Membranes are essential components of living organisms, which serve as effective barriers that separate distinct chemical environments on either side of the membrane. Chemists have designed biological and synthetic systems to functionalise membrane-embedded systems for a variety of applications such as sensing, sequencing, reaction mechanistic studies, and therapeutics. The continuous interest in functionalising membranes, combined with incomplete understanding of the underlying factors determining their mechanisms inspired the investigations undertaken in this work. This Thesis employs both experimental and computational methods to explore two distinct applications for sequencing and therapeutics, respectively.
(1) Engineered biological nanopores have found great success in DNA sequencing. The Bayley group previously reported a molecular hopper, which makes sub-nanometer steps by thiol-disulfide interchange along a track with cysteine footholds within a protein nanopore. In Chapter 2, the hopping rate was optimized with a view towards rapid enzymeless biopolymer characterization during translocation within nanopores. I first used a nanopore approach to systematically profile the reactivity of individual cysteine footholds along an engineered protein track at the single-molecule level. Using this approach, I calculated the pKa of cysteine thiols and the pH-independent rate constants for the reaction between thiolates and a disulfide molecule. This reactivity profile guided site-specific mutagenesis. Together with the optimization of experimental conditions, the overall stepping rate of a DNA cargo along a five-cysteine track was accelerated. This work extends the practical application of this enzymeless system as a sequencing method for biopolymers beyond DNA.
(2) Synthetic anion transporters have attracted significant attention as promising therapeutics for ion channel diseases. In Chapter 3, I use computational modelling to investigate the chloride binding and transmembrane transport mechanisms of E-/Z-switchable synthetic transporters. Using a model system,I developed a workflow to construct full energy profiles for the transmembrane transport process. These results revealed the importance of pre-organization of the Z-isomer and the balance between the energy barrier of transport and the solubility of the transporter. Additionally, in Chapter 4, I present a predictive machine-learning (ML) approach for estimating the chloride transport activity of a variety of synthetic chloride transporters. The ML models, employing both classification and regression frameworks, exhibited remarkable performance across a diverse range of systems. Moreover, they offered insights crucial for future design efforts, e.g., identifying key structural features and experimental conditions that influence the observed transport activity.
Overall, this work bridges biological and molecular design, computational modelling and data-driven approaches to advance the development of two applications to functionalise membranes for sequencing and therapeutics. It provides interpretable molecular models as well as structure-activity relationships that will aid hypothesis generation and contribute to synthetic advances in both fields.