Protein Secondary Structure Prediction from Circular Dichroism Spectra Using a Self-Organizing Map with Concentration Correction
Protein Secondary Structure Prediction from Circular Dichroism Spectra Using a Self-Organizing Map with Concentration Correction
复制标题
DOI：
10.1002/chir.22338
复制
发表时间：
2014-09-01
期刊：
影响因子：
2
通讯作者：
Rodger, Alison
中科院分区：
文献类型：
--
作者：
Hall, Vincent;Sklepari, Meropi;Rodger, Alison
收集蛋白质溶液的圆二色性 (CD) 光谱是一个简单的实验，但二级结构内容的可靠提取取决于蛋白质浓度的知识，而蛋白质浓度并不总是准确的。我们之前开发了一种称为二级结构神经网络 (SSNN) 的自组织图 (SOM)，用于对 CD 谱数据库进行聚类，并使用该图来分配 CD 谱中新蛋白质的二级结构内容。 SSNN 的性能至少与其他可用的蛋白质 CD 结构拟合算法一样好。在这项工作中，我们将 SSNN 应用于怀疑标称蛋白质浓度不正确的实验样品光谱集合。我们表明，通过绘制 SSNN 预测光谱与实验光谱的归一化均方根偏差与浓度比例因子的关系，可以改进蛋白质浓度的估计，同时提供二级结构的估计。对于我们的实现（51 个数据点 240-190 nm，以 nm 增量），如果 NRMSD（归一化均方根位移，RMSE/数据范围）为
Collecting circular dichroism (CD) spectra for protein solutions is a simple experiment, yet reliable extraction of secondary structure content is dependent on knowledge of the concentration of the protein-which is not always available with accuracy. We previously developed a self-organizing map (SOM), called Secondary Structure Neural Network (SSNN), to cluster a database of CD spectra and use that map to assign the secondary structure content of new proteins from CD spectra. The performance of SSNN is at least as good as other available protein CD structure-fitting algorithms. In this work we apply SSNN to a collection of spectra of experimental samples where there was suspicion that the nominal protein concentration was incorrect. We show that by plotting the normalized root mean square deviation of the SSNN predicted spectrum from the experimental one versus a concentration scaling-factor it is possible to improve the estimate of the protein concentration while providing an estimate of the secondary structure. For our implementation (51 data points 240-190 nm in nm increments) good fits and structure estimates were obtained if the NRMSD (normalized root mean square displacement, RMSE/data range) is