NOT-GM-21-028:Towards a generalizable drug discovery framework based on intrinsically disordered regions

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Project Summary Current approaches to drug discovery are yielding diminishing returns as costs, failure rate, and drug resistance all increase. Meanwhile, novel targets and drug candidates are not keeping up with demand across the disease spectrum. This work seeks to address several of these areas. It seeks to lower cost, increase success rate, and address drug resistance while increasing novel targets and potential drug candidates. While most drugs are found by trial-and-error or designed for specific structured protein pockets, it turns out that many diseases and drug resistance occur at interfaces involving disordered protein regions. So, while most informatics for drug design has focused on structured protein pockets, an area with tremendous potential lies in disordered proteins and their interfaces. To do so effectively, and at a large-scale, an informatics framework is needed that effectively uses information across genomic, proteomic, structural, chemical, pathway, ontological, interaction modeling, and evolutionary space. Here, we present such a framework that creates: 1) disordered target libraries and corresponding small molecules to interact them and 2) small molecules that can mimic disordered regions and thus interact with the usual partners of the disordered protein regions. We will first create a disordered target library across several organisms. Then, through a Bayesian framework, we will integrate expert knowledge, sequence information/statistics, and interaction modeling to predict drugs that can: 1) target these regions and 2) mimic these regions in interactions. Finally, we will focus on drug resistant pathogens to validate predicted drugs experimentally.