CORE C

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CORE C – COMPUTATIONAL CHEMISTRY & BIOPHYSICS ABSTRACT APOBEC is a recently discovered enzymatic source of mutation in breast cancer. Multiple lines of evidence indicate that APOBEC mutagenesis is an ongoing source of mutation in tumor cells and that the major enzyme responsible is the single-stranded (ss)DNA cytosine deaminase APOBEC3B (A3B). Our Program is therefore united in testing the overarching hypothesis that A3B inhibition will prevent a large proportion of new mutations in estrogen receptor-positive breast cancer, thereby improving the durability of current treatments and resulting in better overall outcomes. Projects 1, 2, and 3 are focused on testing this idea through a carefully organized multidisciplinary team involving biology, chemical biology, and structural biology approaches. Core C – Computational Chemistry & Biophysics provides the computational modeling backbone to support these Projects through 2 well-integrated specific aims. Aim 1 encompasses the development of physically detailed 3D structural models of APOBEC biomolecular systems, including those that prove challenging to resolve experimentally, such as the different macromolecular regulatory complexes being explored in Project 1, or full- length, wild-type A3B in complex with ssDNA in Project 3. In these examples and others, explicitly solvated molecular dynamics (MD) simulations will be used to predict atomic-level interactions, and these dynamic 3- dimensional models will guide wet experiments by the Project teams. The resulting data will drive Core C to develop further refined models for additional testing by the Project teams. Aim 2 consists of in silico small molecule screening and lead optimization. Innovative MD analysis frameworks, such as Markov state modeling, will be used to extract long-timescale dynamics from many short-timescale simulations and elucidate the thermodynamic and kinetic landscapes of APOBEC enzymes that control molecular recognition and functional activity. A key strength of this approach is identification of cryptic pockets that are capable of binding chemical probes but are often absent from x-ray structures. A range of ligand- and receptor-based approaches will be employed in silico to increase the diversity of APOBEC inhibitors. Core C will also perform lead optimization in silico, including computational Absorption Distribution Metabolism Excretion / Pharmacokinetics (ADME/PK) optimization to help avoid potential chemical liabilities and maximize experimental efficiencies. Inhibitors and probes will be developed through continual collaboration with Projects 2 and 1 and Core D. The biochemical and biological testing of candidate molecules identified or predicted in silico will fuel additional rounds of computational refinement, ultimately leading to structural studies by Project 3 and in vivo tumor evolution experiments by Core B.