Malekpour Seyed Amir, Naghizadeh Sima, Pezeshk Hamid, Sadeghi Mehdi, Eslahchi Changiz
University of Tehran, Enghelab Square, Tehran, Iran.
Math Biosci. 2009 Feb;217(2):145-50. doi: 10.1016/j.mbs.2008.11.001. Epub 2008 Nov 18.
Prediction of protein secondary structure is an important step towards elucidating its three dimensional structure and its function. This is a challenging problem in bioinformatics. Segmental semi Markov models (SSMMs) are one of the best studied methods in this field. However, incorporating evolutionary information to these methods is somewhat difficult. On the other hand, the systems of multiple neural networks (NNs) are powerful tools for multi-class pattern classification which can easily be applied to take these sorts of information into account. To overcome the weakness of SSMMs in prediction, in this work we consider a SSMM as a decision function on outputs of three NNs that uses multiple sequence alignment profiles. We consider four types of observations for outputs of a neural network. Then profile table related to each sequence is reduced to a sequence of four observations. In order to predict secondary structure of each amino acid we need to consider a decision function. We use an SSMM on outputs of three neural networks. The proposed SSMM has discriminative power and weights over different dependency models for outputs of neural networks. The results show that the accuracy of our model in predictions, particularly for strands, is considerably increased.
蛋白质二级结构预测是阐明其三维结构和功能的重要一步。这是生物信息学中的一个具有挑战性的问题。分段半马尔可夫模型（SSMMs）是该领域研究得最好的方法之一。然而，将进化信息纳入这些方法存在一定困难。另一方面，多个神经网络（NNs）系统是用于多类模式分类的强大工具，可以轻松地应用于考虑这类信息。为了克服SSMMs在预测方面的弱点，在这项工作中，我们将一个SSMM视为基于三个使用多序列比对图谱的神经网络输出的决策函数。我们考虑神经网络输出的四种类型的观测值。然后，与每个序列相关的图谱表被简化为一个由四个观测值组成的序列。为了预测每个氨基酸的二级结构，我们需要考虑一个决策函数。我们在三个神经网络的输出上使用一个SSMM。所提出的SSMM对神经网络输出的不同依赖模型具有判别能力和权重。结果表明，我们模型的预测准确率，特别是对于链结构，有显著提高。
