Abstract Unwanted side effects represent a leading cause of clinical trial failures. For nuclear receptors (NRs), a prominent family of therapeutic targets representing a $40 billion/year industry, side effects often arise via drugs that are incapable of achieving tissue selectivity. As transcription factors, nuclear receptors interact with cofactor proteins (i.e., coregulators) which modulate gene expression through chromatin remodeling and histone modifications, thus playing key roles in deciding the transcriptional outcomes of NRs. The activity of nuclear receptor drugs is controlled by the differential expression of coregulators. In a unique but poorly understood mechanism, these drugs induce conformational changes that cause the receptor to selectively bind coregulators. Unfortunately, nonselective NR drugs drive association with both intended and unintended coregulators. This promiscuity allows NR drugs to achieve both desirable therapeutic outcomes and unintended side effects. Because we do not understand how ligands drive NRs to selectively associate with coregulators, we are unable to design tissue- selective drugs that avoid the pitfall of undesirable coregulator binding. This proposal presents an innovative approach to understand and predict selective coregulator recruitment in NR ligands. First, by combining ancestral protein reconstruction with computational studies and high throughput NR-coregulator profiling, we will determine the biophysical basis by which NR-drug complexes discriminately associate with coregulators. Next, we will train machine learning models to predict the NR coregulators most likely to be recruited by various drugs. These models will be developed into a web-based bioinformatics tool that allows new ligand structures to be screened on the basis of their predicted coregulator association. As coregulator recruitment informs NR activity in different tissues, this tool will represent a powerful approach to predict tissue-specific, in vivo activity of ligands with great potential to transform in silico drug design. Finally, we will combine our mechanistic understanding of coregulator recruitment and our computational prediction tool towards a unique application: the design of drugs to treat non- alcoholic fatty liver disease in mice. In sum, these studies will develop a fundamental, molecular understanding of coregulator selectivity and bridge this knowledge to the powerful application of drug design.

Project Narrative By understanding the molecular basis by which drugs promote unintended side effects, we can reduce them and greatly increase the potential to design effective drugs. In this work, we will develop this molecular understanding along with predictive tools that allow ligands to be screened for their potential to promote undesired interactions between target and cofactor proteins. We will test and validate these tools in nuclear receptors, a critical family of drug targets for disorders ranging from cancer to metabolic diseases.