Natural products have provided humanity with powerful tools for human health, including combating infections, curing diseases, and helping humans to understand the world around us. However, natural product discovery is complicated by the challenges in robust annotation and sample comparison from complex samples. New utilities which integrate orthogonal experimental data together could better describe the constitution of natural product extracts, allowing for functional annotation of individual components and streamlining the discovery process. MADByTE, an NMR processing platform designed for metabolomics and dereplication using 2D NMR data was designed to integrate data from HSQC and TOCSY experiments to create contextual networks to annotate structural characteristics of unknowns in complex samples. Addition of bioactivity profiling to the MADByTE network allows for prediction and targeted isolation of bioactive constituents, demonstrated through the isolation of collismycin A from an actinobacterial extract. When coupled to a molecular recognition platform (SMART) and an NMR prediction utility (NMRShiftDB2), substructures of molecules within complex samples can be proposed based on similarities in spectral profiles. Integration of MADByTE with DOSY experiments allows for the refinement of features based on molecular descriptors such as diffusion rates. Taken together, MADByTE represents a valuable utility for the untargeted analysis of natural products contained in complex samples and provides a new viewpoint of chemical diversity across an extract library.
Copyright is held by the author(s).
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Linington, Roger
Member of collection