Exploiting reactivity of peptide aldehydes in Genetically-Encoded libraries: from chemical activity relationship to ligand discoveries
Creators: Yan, Kejia
Publisher: name: University of Alberta Library
Year: 2024

<p>Peptide-derived, genetically-encoded (GE) libraries are versatile tools with vast applications such as developing therapeutic drugs, studying chemical reactions, and training machine learning models. GE libraries can be easily accessed through bio-synthetic production from DNA and are massively decoded using next generation sequencing (NGS). Through post-translational modification, the chemical space of natural amino acids can be expanded to fulfill different purposes. In this thesis, I aim to focus on aldehyde-bearing peptide libraries and explore their applications. These include studying the structure-activity relationship of linear peptide library, developing new methodology for chemical modification, and discovering covalent macrocyclic ligands for therapeutically important proteins. Additionally, I will utilize the biopanning and NGS data to develop new machine learning models.In Chapter 1, I briefly review the current development of covalent peptide ligands, specifically focusing on examples of developing these ligands de novo using peptide libraries. I describe two approaches for developing covalent ligands: modifying existing ligands and screening libraries that carry covalent warheads. With the advancement of new chemical methods and the expansion of the genetic code, there are emerging examples of incorporating covalent warheads onto phage-displayed, mRNA-displayed, or synthetic peptide libraries to discover covalent peptide ligands.In Chapter 2, I explored the structure-activity relationship of aldehyde bearing linear tetrapeptide library when performing the Wittig reaction. After oxidation of N-terminal serine, the Wittig reaction was performed using an ylide with biotin tag. Reactivity predicted by NGS sequencing results of the peptide library matched with synthetic peptide kinetics and showed certain structural activity relationship. To explain the sequential dependence of reactivity, I performed density functional theory (DFT) calculations on the transition states of the Wittig reaction and demonstrated that intermolecular hydrogen bonds can stabilize transition states, thereby accelerating the reaction. Furthermore, NGS data were used to train machine learning models to predict the reactivity of peptides in the Wittig reaction. I selected peptides and measured the kinetics to confirm the machine learning predictions.Chapter 3 focuses on discovering reactivity of benzaldehyde linchpin modified macrocyclic peptides. The versatile linchpin can form monocyclic, bicyclic, and pharmacophore-bearing macrocycles. I first explored the reaction of this linchpin on synthetic peptides, aiming to form two types of bicycles with the N-terminus and can ligate with amine-containing pharmacophores. Then, I investigated the ability of this linchpin to modify phage peptide libraries, discussing the bicyclization reaction and its ability to react with exogenous pharmacophores. To further demonstrate the linchpin&#39;s capability, I performed selections using bicyclic peptide libraries on the hepatitis C protease variant NS3aH1 to discover ligands that tolerate chemical modification.In Chapter 4, I describe the attempt to use a natural amino acid derivative to develop covalent inhibitors for glutaminase. By applying the known covalent glutaminase inhibitor 6-diazo-5-oxo-L-norleucine (DON) as a fragment, I demonstrated the viability of modifying DON by forming an amine bond. I further explored different methods to ligate DON onto peptides. The challenge was to find suitable chemistry to modify this costly and reactive molecule and I was unable to determine a methodology to successfully incorporate the fragment onto peptide.Chapter 5 reflects on reusing existing NGS data to train machine learning (ML) models. With the availability of large quantities of NGS data related to target affinity, it presents a goldmine for training new ML models to predict binders. To be able to improve the accuracy of peptide affinity reflected from NGS to further enhance the performance of ML models, I inserted a non-binding library into biopanning as global negative control baseline. Validation data showed that models trained with baseline data performed better than those trained without it. To further enhance the models, new data processing and embedding methods need to be explored.</p>