CORE C - COMPUTATIONAL BIOCHEMISTRY & BIOINFORMATICS

University Of Texas Hlth Science Center, San Antonio TX

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Abstract

CORE C – COMPUTATIONAL CHEMISTRY, BIOPHYSICS & BIOINFORMATICS ABSTRACT APOBEC is a well-established enzymatic source of on-going mutation in cancer. The major enzymes responsible are the cytosine deaminases APOBEC3A (A3A) and APOBEC3B (A3B), which attack the single- stranded (ss)DNA. Our Program is therefore united in testing the overarching hypothesis that A3A/B inhibition will prevent a large proportion of new mutations in 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 structural biology, chemical biology, and cancer biology approaches, respectively. Core C – Computational Chemistry, Biophysics & Bioinformatics provides the computational modeling and informatics backbone to support these Projects through three 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 full-length A3B and different macromolecular complexes being explored in Projects 1 and 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. Innovative MD analysis frameworks, such as multiscale modeling and residence time optimization, 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. 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 optimization efforts against A3A and A3B. Key strengths of this approach are identification of cryptic pockets that are capable of binding chemical probes, but are often absent from X-ray structures, and innovation of advanced simulation methods to predict free energies of binding to drive lead optimization efforts. A range of ligand- and receptor-based approaches will be employed in silico to increase the structural diversity of APOBEC inhibitors. Aim 3 provides bioinformatics support through analyzing large-scale next-generation sequencing datasets generated by the program. We will analyze pan-cancer human genomic, translational, and clinical data in support of Projects 1-3 and Cores A-B to further our understanding of APOBEC’s roles in cancer. In addition, we will provide integrative and comparative analysis of genomic sequence data generated by the Program and relevant publicly available human cancer datasets.