Download PDFOpen PDF in browserTransmembrane Protein Inter-Helical Residue Contacts Prediction Using Transductive Support Vector Machines12 pages•Published: May 1, 2023AbstractProtein functions are strongly related to their 3D structure. Therefore, it is crucial to identify their structure to understand how they behave. Studies have shown that numerous numbers of proteins cross a biological membrane, called Transmembrane (TM) proteins, and many of them adopt alpha helices shape. Unlike the current contact prediction methods that use inductive learning to predict transmembrane protein inter-helical residues contact, we adopt a transductive learning approach. The idea of transductive learning can be very useful when the test set is much bigger than the training set, which is usually the case in amino acids residues contacts prediction. We test this approach on a set of transmembrane protein sequences to identify helix-helix residues contacts, compare transductive and inductive approaches, and identify conditions and limitations where TSVM outperforms inductive SVM. In addition, we investigate the performance degradation of the traditional TSVM and explore the proposed solutions in the literature. Moreover, we propose an early stop technique that can outperform the state of art TSVM and produce a more accurate prediction.Keyphrases: inductive vs transductive svm, inter helical residue contacts, transductive support vector machines, transmembrane protein In: Hisham Al-Mubaid, Tamer Aldwairi and Oliver Eulenstein (editors). Proceedings of International Conference on Bioinformatics and Computational Biology (BICOB-2023), vol 92, pages 35-46.
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