Construction of a neural network for detecting novel domains from amino acid sequence information only

高通量的结构确定通常需要将多结构域蛋白质分解成能够独立折叠并且其结构容易确定的结构域。通常通过与域数据库如Pfam、CD或SMART的序列相似性来识别结构域。然而，可以检测的方法需要检测新的结构域。这种方法只需要根据感兴趣蛋白质的氨基酸序列中包含的信息来预测结构域区域。在这个项目中，我们报告了一个神经网络和一个用于识别多结构域蛋白质中结构域边界的支持向量机(SVM)的初步结果：1)结构域连接序列。我们构建了一个基于SCOP和CATH结构域边界定义的多域蛋白质数据库。首先，我们选择了不形成域间相互作用的结构域，如域间VDW、Hond和SS-键的存在所定义的那样，并且是独立可折叠的。从这个集合中，我们进一步选择形成由DSSP定义的环路的域边界。满足这两个条件的结构域边界称为连接子，用于训练和测试神经网络和支持向量机。2)我们开发了一个识别结构域连接子的神经网络。交叉验证表明，我们的神经网络的预测效率为50%~60%，而随机猜测的预测效率为10%。3)在上述神经网络的基础上，我们开发了基于SVMlight的结构域连接子预测。我们观察到预测效率与神经网络相似，但训练时间是神经网络所需时间的五分之一。

High throughput structure determination often requires to dissect multi-domain proteins into structural domains that are able to fold independently and their structures readily determinable. Domains are usually identified by sequence similarity to domain databases such as Pfam, Cd or SMART. However, methods that can detect required for detecting novel domains. Such methods, need to predict domains regions solely from the information contained in the amino acid sequence of the protein of interest.. In this project, we report a neural network and preliminary results for an SVM (Support Vector Machine) that identifies domain boundaries in multi-domain proteins :1) Domain linker sequences. We constructed a multi-domain protein database based on SCOP and CATH domain boundary definition. First, we selected domains that do not form inter-domain interactions, as defined by presence of inter-domain VdW, Hbonds and SS-bonds, and are independently foldable. From this set, we further selected domain boundaries that form loops as defined by DSSP. Domain boundaries that fulfilled both conditions were called linkers and used for training and testing the neural network and the SVM.2) We developed a neural network recognizing domain linkers. Cross validation indicates that the prediction efficiency of our neural network is 50〜60%, compared to a random guess that yields a〜10% prediction efficiency.3) In addition to the above neural network, we developed a domain linker prediction based on SVMlight. We observed prediction efficiencies similar to that of the neural network, but the training time was one fifth of that needed for the neural network..