Rational Design of Molecular Glue Degraders via Computational Prediction of E3 Ligase Interactions

Victoria Mischley

doi:10.17918/00010440

Covalent modification of substrate proteins with ubiquitination is responsible for controlling the fate of proteins within the cell. Ubiquitination directs target proteins towards several paths. The path most critical for the focus of this dissertation is the proteasome-mediated degradation. Ubiquitin is added to the protein via the E1-E2-E3 ligase cascade. The final step of ubiquitin transfer, mediated by the E3 ligase, confers specificity of protein targeting and is therefore a crucial step in mechanistic regulation. Within the human proteome there are 600 known E3 ligases, each with a distinct substrate specificity that allows it to engage a prescribed subset of the proteome. Given that one of the prototypical consequences of ubiquitination is to mark a protein for destruction, it is unsurprising that dysregulation or mutation of E3 ligases can lead to a disruption of cellular homeostatic balance; accordingly, E3 ligases have been implicated in a wide variety of diseases including autoimmune disorders and cancer. Interestingly, specific mutations in the E3 ligases have been demonstrated to modulate the substrate specificity of an E3 ligase, thereby either inhibiting the degradation of target proteins within the canonical substrate pool of the E3 ligase or redirecting the E3 ligase's interactome towards novel protein substrates. These dynamics ultimately have a profound impact on cellular processes and disease pathology. Serendipitously, it was found that a small subset of small molecules, termed molecular glues, could also divert E3 ligases to new protein substrates by inducing an interaction between an E3 ligase and a neo-substrate, resulting in the neo-substrate being ubiquinated and ultimately tagged for proteasomal degradation. Thus, molecular glue degraders provide the possibility of removing disease-modifying proteins. However, the transient nature of E3 ligase's interactions with their substrates (and neo-substrates) has served as a bottleneck for identifying both endogenous and "glue-able" substrates of E3 ligases. To address this, in this work we develop a cutting-edge structure-based machine learning method, termed PPIscreenML, to (1) computationally identify endogenous substrates of E3 ligases, and (2) rationally design molecular glue degraders. Beyond the immediate scope of this dissertation, we anticipate that the methods developed herein will illuminate the underlying biology of E3 ligases and facilitate rational targeting of disease-modifying proteins via molecular glue degraders for various human diseases.

Rational Design of Molecular Glue Degraders via Computational Prediction of E3 Ligase Interactions

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Rational Design of Molecular Glue Degraders via Computational Prediction of E3 Ligase Interactions

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