Liu Longlong, Ma Mingjiao, Cui Jing
1 Department of Mathematics, Ocean University of China, Qingdao 266000, P. R. China.
J Bioinform Comput Biol. 2017 Aug;15(4):1750012. doi: 10.1142/S0219720017500123. Epub 2017 Apr 25.
The prediction of protein folding rates is of paramount importance in describing the protein folding mechanism, which has broad applications in fields such as enzyme engineering and protein engineering. Therefore, predicting protein folding rates using the first-order of protein sequence, secondary structure and amino acid properties has become a very active research topic in recent years. This paper presents a new fuzzy cognitive map (FCM) model based on deep learning neural networks which uses data obtained from biological experiments to predict the protein folding rate. FCM extracts the important data features from the protein sequence which then initializes the deep neural networks effectively. It was found that the Levenberg-Marquardt (LM) algorithm for deep neural networks can improve the prediction accuracy of the protein folding rates. The correlation coefficient between the predicted values and those real values obtained from experiments reached 0.94 and 0.9 in two independent numerical tests.
预测蛋白质折叠速率对于描述蛋白质折叠机制至关重要，这在酶工程和蛋白质工程等领域有着广泛应用。因此，利用蛋白质序列的一级结构、二级结构和氨基酸性质来预测蛋白质折叠速率已成为近年来一个非常活跃的研究课题。本文提出了一种基于深度学习神经网络的新型模糊认知图（FCM）模型，该模型利用从生物学实验中获得的数据来预测蛋白质折叠速率。FCM从蛋白质序列中提取重要的数据特征，然后有效地初始化深度神经网络。研究发现，深度神经网络的Levenberg-Marquardt（LM）算法可以提高蛋白质折叠速率的预测准确性。在两次独立的数值测试中，预测值与实验获得的真实值之间的相关系数分别达到了0.94和0.9。
