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首页> 外文期刊>BMC Bioinformatics >Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques
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Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques

机译：使用基于比对和与比对无关的方法进行激酶描述以及线性和非线性数据分析技术的全基因组相互作用建模

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Background Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment of these diseases. However, current inhibitors interact with a broad variety of kinases and interfere with multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that can predict inhibitor-kinase interactions from the chemical properties of the inhibitors and the kinase macromolecules might aid in design of more selective therapeutic agents, that show better efficacy and lower toxicity. Results We applied proteochemometric modelling to correlate the properties of 317 wild-type and mutated kinases and 38 inhibitors (12,046 inhibitor-kinase combinations) to the respective combination's interaction dissociation constant (Kd). We compared six approaches for description of protein kinases and several linear and non-linear correlation methods. The best performing models encoded kinase sequences with amino acid physico-chemical z-scale descriptors and used support vector machines or partial least- squares projections to latent structures for the correlations. Modelling performance was estimated by double cross-validation. The best models showed high predictive ability; the squared correlation coefficient for new kinase-inhibitor pairs ranging P2 = 0.67-0.73; for new kinases it ranged P2kin = 0.65-0.70. Models could also separate interacting from non-interacting inhibitor-kinase pairs with high sensitivity and specificity; the areas under the ROC curves ranging AUC = 0.92-0.93. We also investigated the relationship between the number of protein kinases in the dataset and the modelling results. Using only 10% of all data still a valid model was obtained with P2 = 0.47, P2kin = 0.42 and AUC = 0.83. Conclusions Our results strongly support the applicability of proteochemometrics for kinome-wide interaction modelling. Proteochemometrics might be used to speed-up identification and optimization of protein kinase targeted and multi-targeted inhibitors.
机译：背景技术蛋白激酶在细胞生长，分化和凋亡中起关键作用。蛋白激酶的功能异常会导致许多严重的疾病，例如癌症。激酶抑制剂具有治疗这些疾病的潜力。然而，当前的抑制剂与多种激酶相互作用并干扰多种重要的细胞过程，这引起毒性作用。可以通过抑制剂和激酶大分子的化学性质预测抑制剂-激酶相互作用的生物信息学方法可能有助于设计更具选择性，表现出更好疗效和更低毒性的治疗剂。结果我们应用了蛋白质化学计量学模型，将317种野生型和突变型激酶以及38种抑制剂（12,046种抑制剂-激酶组合）的性质与各自组合的相互作用解离常数（K d ）相关联。我们比较了描述蛋白激酶的六种方法以及几种线性和非线性相关方法。表现最佳的模型使用氨基酸物理化学z尺度描述符对激酶序列进行编码，并使用支持向量机或部分最小二乘法投影到相关的潜在结构。通过双重交叉验证来估计建模性能。最好的模型具有较高的预测能力; P 2 = 0.67-0.73的新型激酶抑制剂对的平方相关系数;对于新激酶，其范围为P 2 kin = 0.65-0.70。模型还可以以高灵敏度和特异性将相互作用与非相互作用的抑制剂-激酶对分开。 ROC曲线下的面积为AUC = 0.92-0.93。我们还研究了数据集中的蛋白激酶数量与建模结果之间的关系。仅使用所有数据的10％，仍可获得有效模型，其中P 2 = 0.47，P 2 kin = 0.42和AUC = 0.83。结论我们的结果强有力地支持了蛋白化学计量学在全基因组相互作用模型中的适用性。蛋白质化学计量学可用于加速蛋白激酶靶向和多靶点抑制剂的鉴定和优化。

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