Predicting protein solubility by the general form of Chou's pseudo amino acid composition: approached from chaos game representation and fractal dimension.
Predicting protein solubility by the general form of Chou's pseudo amino acid composition: approached from chaos game representation and fractal dimension.
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DOI：
10.2174/092986612802084492
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发表时间：
2012-08
影响因子：
1.6
通讯作者：
Xiao-hui Niu;Xue-Hai Hu;Feng Shi;Jingbo Xia
中科院分区：
文献类型：
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作者：
Xiao-hui Niu;Xue-Hai Hu;Feng Shi;Jingbo Xia
获得足够浓度的可溶性蛋白质是各种实验研究中的主要障碍。如何预测大规模蛋白质组学项目中靶标的可溶性倾向是一个重要但尚未完全解决的科学问题。混沌游戏表示（CGR）可以研究隐藏在蛋白质序列中的模式，并可以直观地揭示以前未知的结构。分形维数是测量复杂、高度不规则的几何物体尺寸的好工具。在本文中，我们通过CGR算法和分形维数将每个蛋白质序列转换为高维向量，然后利用这些分形特征结合Chou的伪氨基酸组成特征和支持向量机（SVM）来预测蛋白质溶解度。我们提取并研究直接从主序列计算的六组特征，并且每组都通过 10 倍交叉验证测试进行评估。比较结果显示，445维向量组的结果最好，平均准确度为0.8741，平均MCC为0.7358。得到的预测器还与现有方法进行了比较，并显示出显着的改进。
Obtaining soluble proteins in sufficient concentrations is a major obstacle in various experimental studies. How to predict the propensity of targets in large-scale proteomics projects to be soluble is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) can investigate the patterns hiding in protein sequences, and can visually reveal previously unknown structure. Fractal dimensions are good tools to measure sizes of complex, highly irregular geometric objects. In this paper, we convert each protein sequence into a high-dimensional vector by CGR algorithm and fractal dimension, and then predict protein solubility by these fractal features together with Chou's pseudo amino acid composition features and support vector machine (SVM). We extract and study six groups of features computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test. As the results of comparisons, the group of 445-dimensional vector gets the best results, the average accuracy is 0.8741 and average MCC is 0.7358. The resulting predictor is also compared with existing methods and shows significant improvement.