Towards a generalizable drug discovery framework based on intrinsically disordered regions

项目摘要 目前的药物发现方法产生的回报正在递减，因为成本、失败 耐药率和耐药率均呈上升趋势。与此同时，新的靶点和候选药物是 跟不上整个疾病谱的需求。这项工作旨在解决 其中的几个领域。它寻求降低成本，提高成功率，并解决药物问题 耐药性的同时增加了新的靶点和潜在的候选药物。 虽然大多数药物是通过反复试验或针对特定结构蛋白而设计的 事实证明，许多疾病和耐药性都发生在与 无序的蛋白质区域。因此，虽然大多数药物设计的信息学都专注于 结构蛋白口袋，一个具有巨大潜力的领域，存在于无序的蛋白质中 以及它们的接口。为了有效地实现这一点，并在大规模地建立一个信息学框架 需要有效地利用基因组、蛋白质、结构、 化学、途径、本体论、交互建模和进化空间。 在这里，我们提出了这样一个泛化的信息学框架，它创建：1)无序 靶向文库和相应的小分子相互作用；2)小分子 分子可以模拟无序区域，从而与常见的 无序的蛋白质区域。我们将首先创建一个无序的目标库 几个生物体。然后，通过贝叶斯框架，我们将整合专家 用于预测药物的知识、序列信息/统计和交互建模 这可以：1)瞄准这些区域，2)在相互作用中模仿这些区域。最后，我们 将专注于耐药病原体，以实验验证预测的药物。

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 generalized, informatics 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.