Identifying inhibitors of protein interactions using pocket optimization

使用口袋优化识别蛋白质相互作用的抑制剂

• 批准号: 8304731
• 负责人: John Karanicolas
• 金额: $ 27.6万
• 依托单位: UNIVERSITY OF KANSAS LAWRENCE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8304731
• 关键词: Address; Adopted; Allosteric Site; Binding; Binding Sites; Biochemical; Biological; Categories; Cell Proliferation; Chemicals; Complement; Complex; Computer Simulation; Computing Methodologies; DNA Sequence Rearrangement; Disease; Effectiveness; Homologous Gene; In Vitro; Learning; Libraries; Ligands; Malignant Neoplasms; Membrane Proteins; Methodology; Methods; Molecular Conformation; Mutation; Physiological; Plant Roots; Proteins; Proteus; Proxy; Research; Sampling; Screening Result; Screening procedure; Shapes; Site; Structure; Surface; Testing; Work; base; conformational conversion; cost; driving force; falls; high throughput screening; inhibitor/antagonist; innovation; insight; interest; meetings; novel; protein protein interaction; protein structure; small molecule; small molecule libraries; stem; survivin; tool

DESCRIPTION (provided by applicant): Identifying small-molecule inhibitors of protein interactions has traditionally presented a challenge for modern screening methods, despite interest stemming from the fact that such interactions comprise the underlying mechanisms for cell proliferation, differentiation, and survival. The objective of this application is to employ insights from computational methodology we have recently developed to address the distinct challenges associated with finding inhibitors of different classes of protein surface. Our central hypothesis is that exploring protein fluctuations leading to formation of surface pockets is criticl for understanding the regions of chemical space in which suitable inhibitory compounds may be found. We propose to meet our objective by pursuit of the following three specific aims: 1) Apply pocket optimization for selecting and characterizing protein targets. 2) Employ protein-ligand complementarity to build libraries enriched in protein interface inhibitors. 3) Extend these tools to allosteric inhibitors of protein interfaces. The proposed research is innovative in its ue of insight from protein fluctuations to identify binding pockets. By first confirming the ability o a target protein to form a suitable pocket and second assembling a complementary library, we collectively address the two main hurdles outlined above that have hitherto hindered identification of small molecules that directly inhibit protein-protein interactions. By combining this approach with in vitro biochemical screening, we expect to identify novel inhibitors of protei interactions involving each of three well-validated cancer targets: Bcl-xL, survivin, and b- TrCP. PUBLIC HEALTH RELEVANCE: This research is expected to have an important positive impact because it will provide new insights and tools to address the distinct challenges associated with finding small-molecule inhibitors of protein interactions. This contribution is significant because protein interactions have traditionally represented challenging targets for modern screening methods, despite interest stemming from the fact that such interactions comprise the underlying mechanisms for cell proliferation, differentiation, and survival.

描述（由申请人提供）：鉴定蛋白质相互作用的小分子抑制剂传统上对现代筛选方法提出了挑战，尽管这种相互作用包含细胞增殖、分化和存活的潜在机制这一事实引起了人们的兴趣。本申请的目的是采用我们最近开发的计算方法的见解，以解决与发现不同类别蛋白质表面抑制剂相关的独特挑战。我们的中心假设是，探索蛋白质的波动，导致形成的表面口袋是cricl了解的化学空间的区域中，合适的抑制性化合物可能会被发现，我们建议通过追求以下三个具体目标，以满足我们的目标：1）应用口袋优化选择和表征蛋白质的目标。2)利用蛋白质-配体互补性构建富含蛋白质界面抑制剂的文库。3)扩展这些 蛋白质界面的变构抑制剂的工具。 这项研究的创新之处在于它从蛋白质波动中识别结合口袋。通过首先确认靶蛋白形成合适口袋的能力，然后组装互补文库，我们共同解决了上述两个主要障碍，这两个障碍迄今为止阻碍了直接抑制蛋白质-蛋白质相互作用的小分子的鉴定。通过将这种方法与体外生物化学筛选相结合，我们期望鉴定涉及三种充分验证的癌症靶点中的每一种的蛋白质相互作用的新型抑制剂：Bcl-xL、存活素和B- TrCP。 公共卫生相关性：这项研究预计将产生重要的积极影响，因为它将提供新的见解和工具，以解决与寻找蛋白质相互作用的小分子抑制剂相关的独特挑战。这一贡献是重要的，因为蛋白质相互作用传统上代表了现代筛选方法的挑战性目标，尽管这种相互作用包括细胞增殖，分化和存活的潜在机制。