(Georgia Institute of Technology, 2022-05)
Lutin, David J.
Allylic functionality, often taking the form of terpenes, is a mainstay of marine natural products. The aryl-allyl connection is particularly common, appearing in several product classes derived from Callophycus serratus, many of which exhibit promising initial biological activities. In pursuit of several total syntheses, which leverage a copper-catalyzed Grignard cross-coupling between a 3-halo-4-alkoxybenzoate and geranylgeranyl bromide, we explored the effects of alkoxybenzoate and halogen substitutions on the coupling. By scrutinizing the constraints and influences of these protecting groups on cross-coupling success, we lay out the potential for downstream deprotection chemistry. Here, we present the results of this investigation, along with a theoretical basis for the observations.
(Georgia Institute of Technology, 2021-05)
Watson, Chandler Avery
Tandem mass spectrometric methods revolutionized the chemical identification landscape, allowing serums and molecules to be separated in two or more dimensions. Ion Mobility Mass Spectrometry workflows combined with liquid or gas chromatographic separation have continued to progress chemical identification and further increase the amount and confidence of these identities. Such advancements have also given birth to a new molecular descriptor: the Collision Cross Section, sparking heavy interest in the analytical-computational chemistry to compile these values for known molecules. The main shortcoming has been predicting the CCS value for new molecules such as Poly-Fluorinated Alkyl Sub-stances. Preliminary prediction software has revealed that predicting CCS values for this molecular class is possible, but it can prove temporally, computationally, and financially expensive between different licenses and genetic algorithm. This work combines open-source Python modules (NumPy, Mordred, Pandas, etc.) to construct an alternative workflow that is completely free and capable of running on a mid-specification laptop within a half hour. Using the M-H and combined M+H and M-H datasets taken from the McClean CCS Compendium, median prediction errors of 2.07% and 1.84%, respectively, were found using Support Vector Regression within 5 minutes on a mid-spec laptop, satisfying the 2.50% benchmark. This overall success illustrates the power and versatility of this workflow to produce low errors with datasets as large as 1300+ molecules and as few as 37. This script can be distributed on file-sharing sites like GitHub where other users may customize the free source code to fit their experimental needs.
(Georgia Institute of Technology, 2021-05)
Schroeder, Michelle
Monoclonal antibodies (mAbs) are highly specific antigen binding proteins that are used as biological reagents, therapeutics, and in rapid diagnostics. While mAbs have extensive potential applications, their means production for small molecules and conformationally specific peptides is difficult. Here, we use a method of mAb production in which we pair conjugate virus-like particle (VLP) vaccine with hybridoma technology to produce high-affinity mAbs against three classes of molecules 1) fentanyl derivatives, 2) SARS-CoV-2 peptides, and 3) α-amanitin and microcystin LR cyclic peptide toxins. We successfully produced broad and derivative-selective mAbs against eight fentanyl derivatives. We also showed early signs of success targeting neutralizing and mutant SARS-CoV-2 peptides with conformational specificity using a heterologous prime-boost strategy. Lastly, we produced high affinity mAbs for both α-amanitin and microcystin LR, two highly toxic cyclic peptides. The early success of mAb production against the variety of targets presented in this thesis shows the viability and exceptional versatility of conjugate VLP vaccines as a means to producing mAbs.