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

针对 ASCT2 的基于杂交结构和配体的药物发现方法，ASCT2 是一种氨基酸转运蛋白，对于多种癌症类型的细胞增殖上调至关重要

基本信息

批准号：
10333203
负责人：
Shannon Talli Smith
金额：
$ 3.01万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2022-12-31
项目状态：
已结题

项目摘要

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.

针对 ASCT2（一种氨基酸）的基于杂交结构和配体的药物发现方法转运蛋白对于多种癌症类型的细胞增殖上调至关重要该提案概述了我将用于优化药物发现的方案和技术 ASCT2 是一个有前景的抗癌治疗靶点。 ASCT2 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。 ASCT2 的第一个结构最近通过实验确定，使其成为新的可行的基于结构的研究目标 ASCT2 最近才被发现在癌症中发挥着关键作用细胞代谢和药物化学研究很少集中在 ASCT2 拮抗剂上开发为开发新的复合支架以进行进一步测试提供了巨大的潜力。目前，还没有任何 ASCT2 药物活动包含计算药物发现方法和该提案概述了致力于此的第一个研究。许多机构和制药公司已经实施了计算策略进入药物发现管道，作为以更具成本效益的方式生产可行的候选药物的手段并及时进行。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，但这两种方法很少混合使用精致时尚。通过利用 LB 和 SB 计算药物发现策略，我打算融合两种方法的优点作为采样和过滤大数据的手段化学空间更有效。我们实验室在两个计算化学领域积极发展软件套件：Rosetta 主要关注 SB 方法，而生物和化学图书馆 (BCL) 包含用于 LB 方法的高级化学信息学工具集。我的项目的重点是集成 RosettaDrugDesign 代码，以实现更广泛、更高效的化学品采样使用基于配体的技术的空间。我们打算整合这些更先进的 LB 技术 BCL 中提供了这些功能，包括用于定量结构的多任务人工神经网络 - 活动关系预测，在对接模拟期间过滤化合物罗塞塔药物设计。通过将 Rosetta 和 Small- 的结构预测能力结合在一起 BCL 的分子工具，我们预计我们有效设计药物的能力将取得非凡的进步对于 ASCT2。