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

• 批准号: 10003012
• 负责人: Shannon Talli Smith
• 金额: $ 3.82万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10003012
• 关键词: Alanine; Algorithms; Amino Acid Transporter; Amino Acids; Binding; Biological; Biology; Cell Proliferation; Cells; Chemicals; Chemistry; Code; Communities; Complex; Computational Technique; Computer Assisted; Computer software; Computing Methodologies; Cryoelectron Microscopy; Cysteine; Data; Development; Docking; Drug Design; Education; Eligibility Determination; Facility Accesses; Formulation; Glutamine; Homologous Protein; Hybrids; Individual; Institution; Laboratories; Libraries; Ligands; Literature; Machine Learning; Malignant Neoplasms; Metabolic; Methodology; Methods; Modeling; Mutation; Paper; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacologic Substance; Play; Proliferating; Proteins; Protocols documentation; Publications; Pythons; Quantitative Structure-Activity Relationship; Research; Research Personnel; Resources; Role; Sampling; Schools; Serine; Structure; Techniques; Testing; Therapeutic; Time; Two-Hybrid System Techniques; Universities; Validation; anti-cancer therapeutic; artificial neural network; base; cancer type; cheminformatics; computational chemistry; computing resources; cost efficient; design; drug candidate; drug discovery; experience; high throughput screening; improved; in silico; inhibitor/antagonist; insight; interest; member; metabolic rate; method development; multitask; neoplastic cell; novel; predictive modeling; protein structure; protein structure prediction; scaffold; screening; simulation; small molecule; structural biology; therapeutic development; tool; tumor; tumor metabolism

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.

基于氨基酸的杂交结构和配体的药物发现方法ASCT2（ASCT2） 转运蛋白对于多种癌症类型的上调细胞增殖至关重要 该建议概述了我将使用的协议和技术来优化药物发现 ASCT2，一个有希望的抗癌治疗靶标。 ASCT2在增加 肿瘤细胞的谷氨酰胺涌入以维持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，使其成为新的可行性 基于结构的研究ASCT2的目标直到最近才发现在癌症中起关键作用 细胞代谢和几乎没有药物化学工作的重点是ASCT2拮抗剂 开发允许闯入新的复合支架进行进一步测试。 目前，尚无任何ASCT2毒品活动来纳入计算药物 发现方法和该提案概述了专门针对此的第一批研究。 许多机构和制药公司已经实施了计算策略 进入药物发现管道，作为一种以更具成本效益生产可行的毒品的手段 和及时的方式。根据感兴趣的目标，研究人员更专注于任何一个 基于配体（LB）或基于结构（SB）的方法，但很少在A中杂交这两种方法 精致的时尚。通过利用LB-和SB-计算药物发现的策略，I 打算合并两种方法的优点，以作为样品和过滤大的方法 化学空间更有效。我们的实验室在两种计算化学中都有积极的发展 软件套件：Rosetta主要关注SB方法，而生物学和化学库 （BCL）包含用于LB方法的高级化学信息学工具集。我项目的重点将是 整合Rosettadrugdesign代码，以允许对化学物质进行更广泛而有效的采样 使用基于配体的技术空间。我们打算结合这些更先进的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，以在对接模拟过程中过滤化合物 Rosettadrugdesign。通过将Rosetta和小型的结构预测能力汇总在一起 BCL的分子工具，我们预计我们有效设计药物的能力会取得出色的进步 对于ASCT2。