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

以氨基酸ASCT2为靶点的基于结构和配体的混合药物发现方法 转运蛋白在多种癌症类型中对上调细胞增殖至关重要 这份提案概述了我将用来优化药物发现的方案和技术 ASCT2，一个很有希望的抗癌治疗靶点。ASCT2在提高 谷氨酰胺的流入为肿瘤细胞维持了快速增殖所需的高代谢率。 最近通过实验确定了ASCT2的第一个结构，使其成为一种新的可行的 基于结构研究的靶点ASCT2直到最近才被发现在癌症中发挥关键作用 细胞代谢和很少的药物化学努力集中在ASCT2拮抗剂上 这一技术的开发为进一步测试新的复合支架提供了巨大的潜力。 目前，还没有任何ASCT2药物活动包含计算药物 发现方法和这项提案概述了致力于此的第一批研究。 许多机构和制药公司已经实施了计算策略 进入药物发现流水线，作为一种手段，以更具成本效益的方式生产可行的候选药物 而且是及时的。根据感兴趣的目标，研究人员更专注于其中的任何一个 基于配基(LB)或基于结构(SB)的方法，但这两种方法很少在 精致的时尚。通过利用LB计算和SB计算的药物发现策略，I 我打算将这两种方法的优点结合起来，作为一种抽样和筛选大样本的手段 更有效地利用化学空间。我们的实验室在两个计算化学方面有积极的发展 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。我的项目的重点将是 集成RosettaDrugDesign代码以实现更广泛、更高效的化学品采样 使用基于配基的技术的空间。我们打算采用这些更先进的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，以便在对接模拟期间过滤化合物 RosettaDrugDesign。通过将Rosetta和Small的结构预测能力结合起来- 作为BCL的分子工具，我们预计我们高效设计药物的能力将取得非凡的进步 对于ASCT2。

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.