Tamposis Ioannis A, Sarantopoulou Dimitra, Theodoropoulou Margarita C, Stasi Evangelia A, Kontou Panagiota I, Tsirigos Konstantinos D, Bagos Pantelis G
Department of Computer Science and Biomedical Informatics, University of Thessaly, 35100 Lamia, Greece.
Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Comput Struct Biotechnol J. 2021 Nov 8;19:6090-6097. doi: 10.1016/j.csbj.2021.11.006. eCollection 2021.
Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.
隐马尔可夫模型（HMMs）是生物序列分析中预测蛋白质特征最成功的方法之一。然而，存在一些生物学问题，其中马尔可夫假设并不充分，因为序列上下文可以为预测目的提供有用信息。为了克服其局限性，文献中出现了几种HMMs的扩展方法。我们在此应用一种将HMMs和神经网络（NNs）相结合的混合方法，即隐神经网络（HNNs），以直接的方式进行生物序列分析。在此框架中，传统的HMM概率参数被NN输出所取代。作为一个案例研究，我们专注于α-螺旋和β-桶状膜蛋白的拓扑结构预测。与标准HMMs相比，HNNs表现出性能提升，并且各自的预测器优于该领域得分最高的方法。HNNs的实现可以在JUCHMME包中找到，可从http://www.compgen.org/tools/juchmme、https://github.com/pbagos/juchmme下载。更新后的PRED-TMBB2和HMM-TM预测服务器可在www.compgen.org上访问。
