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The movement of ions across the cell membrane is an essential for many biological processes. This study is focused on ion channels and ion transporters (pumps) as types of border guards control the incessant traffic of ions across cell membranes. Ion channels and ion transporters function to regulate membrane potential and electrical signaling and play important roles in cell proliferation, migration, apoptosis, and differentiation. In their behaviors, it is found that ion channels differ significantly from ion transporters. Therefore, a method for automatically classifying ion transporters and ion channels from membrane proteins is proposed by training deep neural networks and using the position-specific scoring matrix profile as an input. The key of novelty is the three-stage approach, in which five techniques for data normalization are used; next three imbalanced data techniques are applied to the minority classes and then, six classifiers are compared with the proposed method. © 2019 Wiley Periodicals, Inc., (© 2019 Wiley Periodicals, Inc.)

In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

Prediction of membrane spanning segments in beta-barrel outer membrane proteins (OMP) and their topology is an important problem in structural and functional genomics. In this work, we propose a method based on radial basis networks for predicting the number of beta-strands in OMPs and identifying their membrane spanning segments. Our method showed a leave-one-out cross validation accuracy of 96% in a set of 28 OMPs, which have the range of 8-22 beta-strand segments. The beta-strand segments in OMPs and the residues in membrane spanning segments are correctly predicted with the accuracy of 96% and 87%, respectively. We have developed a web server, TMBETAPRED-RBF for predicting the transmembrane beta-strands from amino acid sequence and it is available at http://rbf.bioinfo.tw/~sachen/tmrbf.html. We suggest that our method could be an effective tool for predicting the membrane spanning regions and topology of beta-barrel membrane proteins., (Copyright 2009 Wiley Periodicals, Inc.)