Cascading classifier application for topology prediction of transmembrane beta-barrel proteins

Kazemian, Hassan and Grimaldi, Cedric Maxime (2020) Cascading classifier application for topology prediction of transmembrane beta-barrel proteins. Journal of bioinformatics and computational biology, 18 (6). pp. 1-15. ISSN 1757-6334

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Official URL: https://doi.org/10.1142/S0219720020500341

Abstract / Description

Membrane proteins are a major focus for new drug discovery. Transmembrane beta-barrel proteins play key roles in the translocation machinery, pore formation, membrane anchoring and ion exchange. Given their key roles and the difficulty in membrane protein structure determination, the use of computational modelling is essential. This paper focuses on the topology prediction of transmembrane beta-barrel proteins. In the field of bioinformatics, many years of research has been spent on the topology prediction of transmembrane alpha-helices. The efforts to TMB (transmembrane beta-barrel) proteins topology prediction have been overshadowed and the prediction accuracy could be improved with further research. Various methodologies have been developed in the past for the prediction of TMB proteins topology, however the use of cascading classifier has never been fully explored. This research presents a novel approach to TMB topology prediction with the use of a cascading classifier. The MATLAB computer simulation results show that the proposed methodology predicts transmembrane beta-barrel proteins topologies with high accuracy for randomly selected proteins. By using the cascading classifier approach the best overall accuracy is 76.3% with a precision of 0.831 and recall or probability of detection of 0.799 for TMB topology prediction. The accuracy of 76.3% is achieved using a two-layers cascading classifier.

Item Type: Article
Uncontrolled Keywords: Support vector machine; deep learning; neural networks; K-nearest neighbors; cascading classifier; beta-barrel; topology prediction
Subjects: 000 Computer science, information & general works > 020 Library & information sciences
600 Technology > 610 Medicine & health
Department: School of Computing and Digital Media
Depositing User: Hassan Kazemian
Date Deposited: 06 Jan 2021 12:32
Last Modified: 06 Jan 2021 12:32
URI: http://repository.londonmet.ac.uk/id/eprint/6280

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