Hybridized structure- and ligand- based drug discovery approaches targeting ASCT2, an amino acid transporter critical for upregulated cell proliferation in numerous cancer types

基于杂交结构和配体的药物发现方法靶向ASCT 2，一种氨基酸 在多种癌症类型中对上调细胞增殖至关重要的转运蛋白 这份提案概述了我将用来优化药物发现的协议和技术 ASCT 2是一个有前途的抗癌治疗靶点。ASCT 2在增加 谷氨酰胺流入肿瘤细胞，以维持快速增殖所需的高代谢率。 ASCT 2的第一个结构最近通过实验确定，使其成为一种新的可行的方法。 基于结构研究的靶点ASCT 2最近才被发现在癌症中起关键作用 细胞代谢和很少的药物化学工作集中在ASCT 2拮抗剂上 开发允许巨大的潜力，为进一步测试打破成新的化合物支架。 目前，还没有任何ASCT 2药物活动纳入计算药物 发现方法和这个建议概述了致力于这一点的第一个研究。 许多机构和制药公司已经实施了计算策略 作为一种手段，以更具有成本效益的方式生产可行的候选药物， 及时的方式。根据感兴趣的目标，研究人员更专注于 基于配体（LB）或基于结构（SB）的方法，但很少有这两种方法杂交在一个 精致的时尚通过利用LB-和SB-计算药物发现策略， 我打算合并这两种方法的优点，作为一种手段，采样和过滤大 化学空间更有效。我们实验室在两个计算化学领域有着积极的发展 软件套件：Rosetta主要专注于SB方法，而生物学和化学库 (BCL)包含LB方法的高级化学信息学工具集。我的项目的重点是 整合RosettaDrugDesign代码，以实现更广泛、更有效的化学品采样 空间使用基于配体的技术。我们打算将这些更先进的LB技术 在BCL中可用，包括用于定量结构的多任务人工神经网络- 活动关系预测，以在对接模拟期间过滤化合物， RosettaDrugDesign.通过将罗塞塔的结构预测能力和小- BCL的分子工具，我们期待我们的能力，有效地设计药物的特殊进展 对于ASCT 2。

Hybridized structure- and ligand- based drug discovery approaches targeting ASCT2, an amino acid transporter critical for upregulated cell proliferation in numerous cancer types This proposal outlines the protocols and techniques I will be using to optimize drug discovery of ASCT2, a promising target for anti-cancer therapeutics. ASCT2 plays a key role in increasing the glutamine influx for tumor cells to maintain such high metabolic rates required for rapid proliferation. The first structures of ASCT2 were recently determined experimentally, making this a newly viable target for structure-based studies ASCT2 was only recently discovered to play a critical role in cancer cell metabolism and little medicinal chemistry efforts have been focused on ASCT2 antagonist development allowing immense potential for breaking into new compound scaffolds for further testing. Currently, there have not been any ASCT2 drug campaigns that incorporate computational drug discovery methods and this proposal outlines the first studies dedicated to this. Many institutions and pharmaceutical companies have implemented computational strategies into drug discovery pipelines as a means to produce viable drug candidates in a more cost-efficient and timely manner. Depending on the target of interest, researchers focus more intently on either ligand-based (LB) or structure-based (SB) methods, but rarely are these two methods hybridized in a sophisticated fashion. By utilizing strategies of both LB- and SB- computational drug discovery, I intend to merge the advantages of both methodologies as a means to sample and filter large chemical space more efficiently. Our lab has active development in two computational chemistry software suites: Rosetta primarily focuses on SB methods whereas the Biology and Chemistry Library (BCL) contains advanced cheminformatics toolsets for LB methods. The focus of my project will be to integrate the RosettaDrugDesign code to allow a more extensive, yet efficient sampling of chemical space using ligand-based techniques. We intend to incorporate these more advanced LB techniques available in the BCL, including multi-tasking artificial neural networks for Quantitative Structure- Activity Relationship predictions, to filter compounds during docking simulations within the RosettaDrugDesign. By bringing together the structure prediction abilities of Rosetta and small- molecule tools of BCL, we anticipate exceptional advances in our abilities to efficiently design drugs for ASCT2.