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中文摘要
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描述(由申请人提供):识别蛋白质相互作用的小分子抑制物传统上对现代筛选方法提出了挑战,尽管人们感兴趣的是这样的相互作用构成了细胞增殖、分化和生存的潜在机制。这个应用程序的目的是利用我们最近开发的计算方法的洞察力来解决与寻找不同类别的蛋白质表面抑制剂相关的独特挑战。我们的中心假设是,探索蛋白质波动导致表面口袋的形成对于理解化学空间中可能找到合适的抑制性化合物的区域至关重要。我们建议通过追求以下三个具体目标来实现我们的目标:1)将口袋优化应用于选择和表征蛋白质靶标。2)利用蛋白质-配体互补性构建富含蛋白质界面抑制物的文库。3)延长这些期限 蛋白质界面变构抑制剂的工具。这项拟议的研究在从蛋白质波动中识别结合口袋的洞察力方面具有创新性。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,我们共同解决了上面概述的两个主要障碍,这两个障碍迄今阻碍了直接抑制蛋白质-蛋白质相互作用的小分子的识别。通过将这种方法与体外生化筛选相结合,我们希望识别涉及三个已得到充分验证的癌症靶点:bclxl、Survivin和b-TrCP的蛋白质相互作用的新抑制剂。 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,以应对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着现代筛选方法的具有挑战性的靶点,尽管人们对这种相互作用构成了细胞增殖、分化和生存的潜在机制感兴趣。
英文摘要
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.
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Designing selective kinase inhibitors via deep learning
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