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Exploring sequence-structure relationships in the tyrosine kinome space: functional classification of the binding specificity mechanisms for cancer therapeutics

机译:探索酪氨酸激酶组空间中的序列-结构关系:癌症治疗药物结合特异性机制的功能分类

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摘要

Motivation: Evolutionary and structural conservation patterns shared by more than 500 of identified protein kinases have led to complex sequence-structure relationships of cross-reactivity for kinase inhibitors. Understanding the molecular basis of binding specificity for protein kinases family, which is the central problem in discovery of cancer therapeutics, remains challenging as the inhibitor selectivity is not readily interpreted from chemical proteo-mics studies, neither it is easily discernable directly from sequence or structure information. We present an integrated view of sequence-structure-binding relationships in the tyrosine kinome space in which evolutionary analysis of the kinases binding sites is combined with computational proteomics profiling of the inhibitor-protein interactions. This approach provides a functional classification of the binding specificity mechanisms for cancer agents targeting protein tyrosine kinases.Results: The proposed functional classification of the kinase binding specificities explores mechanisms in which structural plasticity of the tyrosine kinases and sequence variation of the binding-site residues are linked with conformational preferences of the inhibitors in achieving effective drug binding. The molecular basis of binding specificity for tyrosine kinases may be largely driven by conformational adaptability of the inhibitors to an ensemble of structurally different conformational states of the enzyme, rather than being determined by their phylogenetic proximity in the kinome space or differences in the interactions with the variable binding-site residues. This approach provides a fruitful functional linkage between structural bioinformatics analysis and disease by unraveling the molecular basis of kinase selectivity for the prominent kinase drugs (Imatinib, Dasatinib and Erlotinib) which is consistent with structural and proteomics experiments.
机译:动机:超过500种已识别的蛋白激酶共有的进化和结构保守模式已导致激酶抑制剂交叉反应的复杂序列结构关系。了解蛋白质激酶家族结合特异性的分子基础是癌症治疗方法发现中的主要问题,因为难以从化学蛋白质组学研究中轻松解释抑制剂的选择性,也无法直接从序列或结构中辨别出抑制剂的选择性,因此仍然具有挑战性信息。我们提出了酪氨酸激酶组空间中的序列结构结合关系的综合视图,其中激酶结合位点的进化分析与抑制剂-蛋白质相互作用的计算蛋白质组学分析相结合。该方法为靶向蛋白质酪氨酸激酶的癌症药物的结合特异性机制提供了功能分类。结果:拟议的激酶结合特异性功能分类探讨了酪氨酸激酶的结构可塑性和结合位点残基序列变异的机制。与抑制剂的构象偏好有关,以实现有效的药物结合。酪氨酸激酶结合特异性的分子基础可能主要是由抑制剂对酶在结构上不同构象状态的构象适应性驱动的,而不是由它们在激酶组空间上的系统发育接近性或与它们之间相互作用的差异所决定的。可变的结合位点残基。通过揭示主要激酶药物(伊马替尼,达沙替尼和厄洛替尼)的激酶选择性的分子基础,该方法在结构生物信息学分析和疾病之间提供了卓有成效的功能联系,这与结构和蛋白质组学实验一致。

著录项

  • 来源
    《Bioinformatics》 |2007年第15期|1919-1926|共8页
  • 作者

    Gennady M. Verkhivker;

  • 作者单位

    Department of Pharmaceutical Chemistry, School of Pharmacy, Center for Bioinformatics, The University of Kansas,2030 Becker Drive, Lawrence, KS 66047-1620;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物科学;生物工程学(生物技术);
  • 关键词

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