Yoon Sukjoon, Welsh William J, Jung Heeyoung, Yoo Young Do
Sookmyung Women's University, Department of Biological Sciences, Research Center for Women's Diseases (RCWD), Hyochangwongil 52, Yongsan-gu, Seoul 140-742, Republic of Korea.
Comput Biol Chem. 2007 Oct;31(5-6):373-7. doi: 10.1016/j.compbiolchem.2007.06.002. Epub 2007 Jun 16.
The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native beta-strand propensities in the core sequences of amyloidogenic proteins. Here we describe a noble energy-based CSSP method implemented on dual artificial neural networks that rapidly and accurately estimate the potential for the non-native secondary structure formation in local regions of protein sequences. In this method, we attempted to quantify long-range interaction patterns in diverse secondary structures by potential energy calculations and decomposition on a pairwise per-residue basis. The calculated energy parameters and seven-residue sequence information were used as inputs for artificial neural networks (ANNs) to predict sequence potential for secondary structure conversion. The trained single ANN using the >(i, i+/-4) interaction energy parameter exhibited 74% accuracy in predicting the secondary structure of test sequences in their native energy state, while the dual ANN-based predictor using (i, i+/-4) and >(i, i+/-4) interaction energies showed 83% prediction accuracy. The present method provides a simple and accurate tool for predicting sequence potential for secondary structure conversions without using 3D structural information.
据报道，接触依赖性二级结构倾向（CSSP）的计算能够灵敏地检测淀粉样蛋白核心序列中的非天然β链倾向。在此，我们描述了一种基于能量的新型CSSP方法，该方法通过双人工神经网络实现，能够快速准确地估计蛋白质序列局部区域形成非天然二级结构的可能性。在该方法中，我们试图通过势能计算和逐个残基的成对分解来量化不同二级结构中的长程相互作用模式。计算得到的能量参数和七个残基的序列信息被用作人工神经网络（ANN）的输入，以预测二级结构转换的序列可能性。使用>(i, i+/-4)相互作用能量参数训练的单人工神经网络在预测测试序列处于天然能量状态下的二级结构时，准确率为74%，而基于双人工神经网络的预测器使用(i, i+/-4)和>(i, i+/-4)相互作用能量时，预测准确率为83%。本方法提供了一种简单准确的工具，无需使用三维结构信息即可预测二级结构转换的序列可能性。
