School of Applied Mathematics, University of Electronic Science and Technology of China, 610054 Chengdu, People's Republic of China.
Mol Cell Biochem. 2010 Jun;339(1-2):127-33. doi: 10.1007/s11010-009-0376-y. Epub 2010 Jan 7.
HIV-1 protease has a broad and complex substrate specificity. The discovery of an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease would greatly expedite the search for inhibitors of HIV protease. During the last two decades, various methods have been developed to explore the specificity of HIV protease cleavage activity. However, because little advancement has been made in the understanding of HIV-1 protease cleavage site specificity, not much progress has been reported in either extracting effective methods or maintaining high prediction accuracy. In this article, a theoretical framework is developed, based on the kernel method for dimensionality reduction and prediction for HIV-1 protease cleavage site specificity. A nonlinear dimensionality reduction kernel method, based on manifold learning, is proposed to reduce the high dimensions of protease specificity. A support vector machine is applied to predict the protease cleavage. Superior performance in comparison to that previously published in literature is obtained using numerical simulations showing that the basic specificities of the HIV-1 protease are maintained in reduction feature space, and by combining the nonlinear dimensionality reduction algorithm with a support vector machine classifier.
HIV-1 蛋白酶具有广泛而复杂的底物特异性。如果能够发现一种准确、稳健、快速的方法，用于预测 HIV 蛋白酶对蛋白质的切割位点，将极大地促进 HIV 蛋白酶抑制剂的研究。在过去的二十年中，已经开发出了多种方法来探索 HIV 蛋白酶切割活性的特异性。然而，由于对 HIV-1 蛋白酶切割位点特异性的理解几乎没有进展，因此在提取有效方法或保持高预测准确性方面都没有取得太大进展。本文提出了一种基于核方法的理论框架，用于 HIV-1 蛋白酶切割位点特异性的降维和预测。基于流形学习，提出了一种非线性降维核方法，用于降低蛋白酶特异性的高维性。应用支持向量机来预测蛋白酶的切割。数值模拟表明，与之前文献中的结果相比，该方法具有更好的性能，这表明在降维特征空间中保留了 HIV-1 蛋白酶的基本特异性，并将非线性降维算法与支持向量机分类器相结合。
