Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques.
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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DOI：
10.1186/1471-2105-11-339
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发表时间：
2010-06-22
影响因子：
3
通讯作者：
Wikberg JE
中科院分区：
文献类型：
--
作者：
Lapins M;Wikberg JE
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可导致许多严重的疾病，如癌症。激酶抑制剂具有治疗这些疾病的潜力。然而，目前的抑制剂与多种激酶相互作用，干扰多种重要的细胞过程，从而导致毒性作用。生物信息学方法，可以预测的抑制剂和激酶大分子的化学性质的激酶相互作用，可能有助于设计更具选择性的治疗药物，显示更好的疗效和更低的毒性。我们应用蛋白化学计量学建模将317种野生型和突变型激酶以及38种抑制剂（12，046种激酶组合）的性质与各自组合的相互作用解离常数（Kd）相关联。我们比较了六种蛋白激酶的描述方法和几种线性和非线性相关方法。表现最好的模型用氨基酸物理化学z尺度描述符编码激酶序列，并使用支持向量机或部分最小二乘投影到潜在结构以进行相关性。通过双重交叉验证估计建模性能。最佳模型显示出较高的预测能力;新激酶-抑制剂对的平方相关系数范围为P2 = 0.67-0.73;新激酶的平方相关系数范围为P2 kin = 0.65-0.70。模型还可以以高灵敏度和特异性分离相互作用的与非相互作用的激酶对; ROC曲线下的面积范围为AUC = 0.92-0.93。我们还研究了数据集中蛋白激酶的数量与建模结果之间的关系。仅使用所有数据的10%仍然获得有效模型，其中P2 = 0.47，P2 kin = 0.42和AUC = 0.83。我们的研究结果强烈支持蛋白质化学计量学的适用性激酶组范围内的相互作用建模。蛋白质化学计量学可用于蛋白激酶靶向和多靶向抑制剂的快速鉴定和优化。
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. 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. 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.
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影响因子：
2.9
作者：
Illergard, Kristoffer;Ardell, David H.;Elofison, Arne
通讯作者：
Elofison, Arne
影响因子：
3
作者：
Freyhult E;Prusis P;Lapinsh M;Wikberg JE;Moulton V;Gustafsson MG
通讯作者：
Gustafsson MG
影响因子：
56.9
作者：
Cohen, MS;Zhang, C;Taunton, J
通讯作者：
Taunton, J
影响因子：
7.3
作者：
Bamborough, Paul;Drewry, David;Schneider, Klaus
通讯作者：
Schneider, Klaus
DOI：
10.1073/pnas.92.19.8700
发表时间：
1995-09-12
影响因子：
11.1
作者：
DUBCHAK, I;MUCHNIK, I;KIM, SH
通讯作者：
KIM, SH