Detection and analysis of protein-ligand binding sites is an important area of research in drug discovery. The FTMap web server is an established computational method for detection of binding hot spots, or regions on the protein surface that contribute disproportionately to the ligand binding free energy. This body of work primarily focuses on the utilization and advancement of FTMap for the study of protein-ligand interactions and their applications to drug discovery. First, the driving forces behind why some proteins require compounds beyond Lipinski’s rule-of-five (bRo5) guidelines are evaluated for 37 protein targets. Three types of proteins are identified on the basis of their binding hot spots, described by FTMap, and their ligand binding affinity profiles. We describe the multifaceted motivations for bRo5 drug discovery for each group of targets, including increased binding affinity, improved selectivity, decreased toxicity, and decreased off-target effects. Second, the conservation of surface binding properties in protein models is evaluated, with particular emphasis on their utility in drug discovery. Here, the probe-binding locations determined by FTMap are used to generate a binding fingerprint, and the Pearson correlation between the binding fingerprint of an experimental structure and a predicted model indicates the level of surface property conservation, without any knowledge of the protein function a priori. This analysis was performed on the protein models submitted to the Critical Assessment of Techniques for Protein Structure Prediction (CASP) rounds 12 and 14, and results were correlated with well-established structure quality metrics. Third, development of the publicly-available FTMove web server (https://ftmove.bu.edu) is described for detection of binding sites and their respective strengths across multiple different conformations of a protein. FTMove was tested on 22 proteins with known allosteric binding sites, and reliably identified both the orthosteric and allosteric binding sites as highly ranked binding sites. The results yield important insight into the dynamics and druggability of such binding sites. Finally, high throughput affinity purified, mass spectrometry data is evaluated for identification of protein-metabolite interactions (PMIs) in Escherichia coli. A detailed search for known PMIs in both the Protein Data Bank and KEGG database is described, and the resulting curated sets of 21 recovered and 37 potentially novel PMIs in E. Coli are presented. Finally, high confidence novel PMIs were evaluated with the template-based small molecule docking program, LigTBM. / 2023-01-26T00:00:00Z

Protein-protein interactions (PPI) represent a challenging target class in contemporary small molecule drug discovery. The difficulty arises because PPI sites are structurally and physicochemically different from conventional drug binding sites. Moreover, we currently lack a good understanding of the druggability of PPI targets: that is, how the structure and properties of a PPI interface site relates to the properties of small molecules that can bind to that site with high affinity. Efforts to achieve potent drug-like small molecule inhibitors of PPI interfaces, involving a wide range targets, historically have largely been unsuccessful, leading to the conclusion that new inhibitor chemotypes are needed to inhibit this class of target. In this thesis, I describe the application of two approaches to identify inhibitors of the PPI interface between Kelch-like ECH associated protein 1 (KEAP1) and Nuclear factor (erythroid-derived 2)-like 2 (Nrf2): (i) screening a library of synthetic macrocycles, and (ii) fragment-based lead discovery. I validate and characterize the hit compounds obtained. In the case of the fragment hits, I investigate what features of the compounds are required for binding to the target (Chapter Two). In parallel, I investigate the structure of the hot spot ensemble at the KEAP1/Nrf2 binding interface using three complementary methods: alanine scanning mutagenesis, fragment screening, and in silico probe mapping using the FTMap algorithm (Chapter Three). This analysis brings insight into the druggability of KEAP1, and advances our understanding of the utility and limitations of those three widely used methods for characterizing the hot spot ensembles at PPI interfaces (Chapter Three). Finally, to gain additional insight into the energetics of KEAP1/Nrf2 binding, I probe the additivity of combinations of alanine mutants (Chapter Four). I use the results to propose a quantitative approach to categorizing the various degrees of additivity that can be observed at PPI interfaces, and discuss the possible structural basis for these behaviors. The model potentially provides a more general framework for understanding the binding energetics at PPI interfaces using combinations of mutations.