“Cracking the Molecular Recognition Code: Capturing Dynamic and Disordered Protein Structures with Covalent Ligands”
Bio: Brittany Morgan is the John V. O’Connor Assistant Professor of Cancer Drug Discovery in the Department of Chemistry and Biochemistry at the University of Notre Dame. Her research group aims to discover the molecular recognition rules for targeting dynamic and/or disordered proteins with covalent ligands. Of particular interest are RNA-binding proteins; a class of proteins enriched in dynamic and disordered structures, genetically mutated in over 200 diseases, yet lack selective probes and therapeutics.
Brittany earned her Bachelor of Science Degree in Biochemistry from Western Kentucky University, followed by her Ph.D. in Chemistry at Duke University. During her Ph.D. with Prof. Amanda Hargrove, she elucidated RNA privileged small molecule features and utilized the properties to rationally design several first-in-kind RNA-targeted libraries. She then moved to the University of Michigan as the Michigan May-Walt Life Sciences and NIH Ruth L. Kirchstein Postdoctoral Fellow with Prof. Anna Mapp. There, she developed one of the first molecular recognition frameworks for the small molecule targeting of transcription factors.
Brittany has received many awards, including most recently the Burroughs Wellcome Career Award at the Scientific Interface, a faculty transition award. She is also the recipient of the Barry Goldwater Scholarship, NIH T32 and F32, and Michigan Life Sciences Fellowship. In addition to her research accomplishments, Brittany has been recognized for her dedication to mentoring, outreach, and service.
Abstract: Dynamic and disordered protein structures are essential for an array of cellular functions and are often drivers of biological dysfunction and disease; these structures, however, are currently considered unligandable, as traditional small molecule strategies often identify ligands that are non-specific and bind with weak affinity. The goal of our work is to covalently capture cryptic and/or hidden pockets formed by dynamic and disordered structures and elucidate principles that describe their recognition. During my postdoctoral work, I used the transcriptional coactivator, Med25, as a model system. I captured a cryptic pocket formed between two dynamic loops, and I built a first-in-kind framework that uncovered ligand features critical for binding. One of those features, ligand shape, was key to specific recognition and the differential regulation of protein-protein interactions and protein conformation. In addition to shape, features such as chemical architecture, pre-organization, and chirality further contributed to the affinity and selectivity of ligands. Building upon this framework, my lab is currently synthesizing novel ligands that were predicted by machine learning to bind the Med25 dynamic loop with higher affinity. We are also expanding to additional proteins and loops, assessing the generalizability of our framework described herein. In the future, the ligands identified will be used to elucidate the functional, structural, and therapeutic potential of “unligandable” proteins. The latter is of particular importance, as many dynamic macromolecules underpin humankind’s most challenging and currently intractable diseases.
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