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.

基于杂化结构和配体的药物发现方法靶向ASCT2，一种氨基酸