Key Laboratory of Dependence Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, China ; College of Computer Science, Chongqing University, Chongqing, China.
PLoS One. 2013 Sep 9;8(9):e74506. doi: 10.1371/journal.pone.0074506. eCollection 2013.
Prediction of proteasomal cleavage sites has been a focus of computational biology. Up to date, the predictive methods are mostly based on nonlinear classifiers and variables with little physicochemical meanings. In this paper, the physicochemical properties of 14 residues both upstream and downstream of a cleavage site are characterized by VHSE (principal component score vector of hydrophobic, steric, and electronic properties) descriptors. Then, the resulting VHSE descriptors are employed to construct prediction models by support vector machine (SVM). For both in vivo and in vitro datasets, the performance of VHSE-based method is comparatively better than that of the well-known PAProC, MAPPP, and NetChop methods. The results reveal that the hydrophobic property of 10 residues both upstream and downstream of the cleavage site is a dominant factor affecting in vivo and in vitro cleavage specificities, followed by residue's electronic and steric properties. Furthermore, the difference in hydrophobic potential between residues flanking the cleavage site is proposed to favor substrate cleavages. Overall, the interpretable VHSE-based method provides a preferable way to predict proteasomal cleavage sites.
蛋白酶体切割位点的预测一直是计算生物学的研究重点。迄今为止，预测方法大多基于非线性分类器和具有较少物理化学意义的变量。在本文中，通过 VHSE（疏水性、空间位阻和电子特性的主成分得分向量）描述符对切割位点上下游的 14 个残基的物理化学性质进行了特征化。然后，通过支持向量机（SVM）将得到的 VHSE 描述符用于构建预测模型。对于体内和体外数据集，基于 VHSE 的方法的性能均优于知名的 PAProC、MAPPP 和 NetChop 方法。结果表明，切割位点上下游 10 个残基的疏水性是影响体内和体外切割特异性的主要因素，其次是残基的电子和空间位阻特性。此外，提出了切割位点侧翼残基之间疏水性势能的差异有利于底物的切割。总的来说，这种可解释的基于 VHSE 的方法为预测蛋白酶体切割位点提供了一种更好的方法。
