P-glycoprotein Substrate Models Using Support Vector Machines Based on a Comprehensive Data set
P-glycoprotein Substrate Models Using Support Vector Machines Based on a Comprehensive Data set
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使用基于综合数据集的支持向量机的 P-糖蛋白底物模型
DOI：
10.1021/ci2001583
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发表时间：
2011-06-01
影响因子：
5.6
通讯作者：
Yan, Aixia
中科院分区：
文献类型：
--
作者：
Wang, Zhi;Chen, Yuanying;Yan, Aixia
P-糖蛋白（P-gp）是主要的ABC转运蛋白之一，参与许多重要过程，例如脂质和类固醇跨细胞膜转运，还参与HIV蛋白酶和逆转录酶抑制剂等药物的摄入。尽管它的重要性，可靠的模型预测底物的P-gp是稀缺的。在这项研究中，我们已经建立了几个计算模型来预测一种化合物是否是P-gp底物，基于迄今为止公布的最大数据集，采用332种不同的结构。每个分子由ADRIANA.代码、莫伊和ECFP_4指纹描述符表示。使用基于训练集的支持向量机计算模型，所述训练集包括通过5倍、10倍和留一法（LOO）交叉验证评估的131个底物和81个非底物。最佳模型在测试集上的马修斯相关系数为0.73，预测精度为0.88。对基于ECFP_4指纹图谱的模型的检验揭示了几个可能在分离P-gp的底物和非底物中具有意义的亚结构，如腈和亚砜官能团在非底物中的频率高于在底物中的频率。此外，发现糖的结构异构性导致化合物作为P-gp底物的可能性存在显著差异。
P-glycoprotein (P-gp) is one of the major ABC transporters and involved in many essential processes such as lipid and steroid transport across cell membranes but also in the uptake of drugs such as HIV protease and reverse transcriptase inhibitors. Despite its importance, reliable models predicting substrates of P-gp are scarce. In this study, we have built several computational models to predict whether or not a compound is a P-gp substrate, based on the largest data set yet published, employing 332 distinct structures. Each molecule is represented by ADRIANA.Code, MOE, and ECFP_4 fingerprint descriptors. The models are computed using a support vector machine based on a training set which includes 131 substrates and 81 nonsubstrates that were evaluated by 5-, 10-fold, and leave-one-out (LOO) cross-validation. The best model gives a Matthews Correlation Coefficient of 0.73 and a prediction accuracy of 0.88 on the test set. Examination of the model based on ECFP_4 fingerprints revealed several substructures which could have significance in separating substrates and nonsubstrates of P-gp, such as the nitrile and sulfoxide functional groups which have a higher frequency in nonsubstrates than in substrates. In addition structural isomerism in sugars was found to result in remarkable differences regarding the likelihood of a compound to be a substrate for P-gp.