Sugunakala S, Selvaraj S
Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli-620 024, Tamilnadu, India.
Curr Comput Aided Drug Des. 2016;12(3):229-240. doi: 10.2174/1573409912666160606150323.
Epidermal Growth Factor Receptor tyrosine kinase (EGFR) is an important anticancer drug target. Series of pyridopyrimidine analogs have been reported as EGFR inhibitors and they inhibit by binding to the ATP binding pocket of the tyrosine kinase domain.
To identify key properties of pyridopyrimidine analogs involved in the inhibition of the EGFR protein tyrosine kinase by developing 2D QSAR model.
Variable selection was performed by least absolute shrinkage and selection operator (LASSO) method and multiple linear regression (MLR) method was applied by using Build QSAR software to develop QSAR model. Model validation was done by Leave One Out method (LOO). Further, based on the bioactive and structural similarity, virtual screening was performed using Pubchem database. Using the developed QSAR model and Molinspiration server, PIC50 values and kinase inhibition activity were predicted for all the virtually screened compounds respectively.
The best QSAR model consists of two descriptors namely Basak and MOE type descriptors, and has R2 = 0.8205, F= 57.129 & S = 0.308 and the validation results show significant statistics of R2/cv = 0.655, Average standard deviation = 0.416. 140 compounds were obtained from virtual screening and the predicted PIC50 of all these compounds are in the range of 4.73 - 6.78. All the compounds produce positive scores which suggest that the compounds may have good kinase inhibitory profile.
This developed model may be useful to predict EGFR inhibition activity (PIC50) for the newly synthesized pyridopyrimidines analogs.
表皮生长因子受体酪氨酸激酶(EGFR)是一个重要的抗癌药物靶点。已有一系列吡啶并嘧啶类似物被报道为EGFR抑制剂,它们通过与酪氨酸激酶结构域的ATP结合口袋结合来发挥抑制作用。
通过建立二维定量构效关系(2D QSAR)模型来确定参与抑制EGFR蛋白酪氨酸激酶的吡啶并嘧啶类似物的关键性质。
采用最小绝对收缩和选择算子(LASSO)方法进行变量选择,并使用Build QSAR软件应用多元线性回归(MLR)方法建立QSAR模型。采用留一法(LOO)进行模型验证。此外,基于生物活性和结构相似性,使用Pubchem数据库进行虚拟筛选。分别使用所建立的QSAR模型和Molinspiration服务器预测所有虚拟筛选化合物的PIC50值和激酶抑制活性。
最佳QSAR模型由两个描述符组成,即Basak描述符和MOE类型描述符,R2 = 0.8205,F = 57.129,S = 0.308,验证结果显示R2/cv = 0.655、平均标准差 = 0.416的显著统计数据。通过虚拟筛选获得了140种化合物,所有这些化合物的预测PIC50值在4.73 - 6.78范围内。所有化合物均产生正分数,表明这些化合物可能具有良好的激酶抑制活性。
所建立的该模型可能有助于预测新合成的吡啶并嘧啶类似物的EGFR抑制活性(PIC50)。