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激酶药物研发不仅仅局限于结构——结构基因组学。

Doing more than just the structure-structural genomics in kinase drug discovery.

作者信息

Marsden Brian D, Knapp Stefan

机构信息

Structural Genomics Consortium, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford OX3 7DQ, UK.

出版信息

Curr Opin Chem Biol. 2008 Feb;12(1):40-5. doi: 10.1016/j.cbpa.2008.01.042. Epub 2008 Feb 29.

Abstract

Structural genomics (SG) has significantly increased the number of novel protein structures of targets with medical relevance. In the protein kinase area, SG has contributed >50% of all novel kinases structures during the past three years and determined more than 30 novel catalytic domain structures. Many of the released structures are inhibitor complexes and a number of them have identified new inhibitor binding modes and scaffolds. In addition, generated reagents, assays, and inhibitor screening data provide a diversity of chemogenomic data that can be utilized for early drug development. Here we discuss the currently available structural data for the kinase family considering novel structures as well as inhibitor complexes. Our analysis revealed that the structural coverage of many kinases families is still rather poor, and inhibitor complexes with diverse inhibitors are only available for a few kinases. However, we anticipate that with the current rate of structure determination and high throughput technologies developed by SG programs these gaps will be closed soon. In addition, the generated reagents will put SG initiatives in a unique position providing data beyond protein structure determination by identifying chemical probes, determining their binding modes and target specificity.

摘要

结构基因组学(SG)显著增加了具有医学相关性的靶标新蛋白质结构的数量。在蛋白激酶领域,过去三年中SG贡献了所有新激酶结构的50%以上,并确定了30多个新的催化结构域结构。许多已发布的结构是抑制剂复合物,其中一些已确定了新的抑制剂结合模式和支架。此外,生成的试剂、检测方法和抑制剂筛选数据提供了多种化学基因组数据,可用于早期药物开发。在此,我们讨论激酶家族目前可用的结构数据,包括新结构以及抑制剂复合物。我们的分析表明,许多激酶家族的结构覆盖仍然相当差,只有少数激酶有与多种抑制剂形成的复合物。然而,我们预计,以目前的结构测定速度和SG计划开发的高通量技术,这些差距将很快得到弥补。此外,生成的试剂将使SG计划处于独特的地位,通过识别化学探针、确定其结合模式和靶标特异性,提供超越蛋白质结构测定的数据。

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