dc.contributor.authorCang, Zixuan
dc.contributor.authorMu, Lin
dc.contributor.authorWu, Kedi
dc.contributor.authorOpron, Kristopher
dc.contributor.authorXia, Kelin
dc.contributor.authorWei, Guo-Wei
dc.identifier.citationCang, Z., Mu, L., Wu, K., Opron, K., Xia, K., & Wei, G.-W. (2015). A topological approach for protein classification. Molecular Based Mathematical Biology, 3(1), 140-162.en_US
dc.description.abstractProtein function and dynamics are closely related to its sequence and structure.However, prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done through measuring the similarity between proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics. Persistent homology is a new branch of algebraic topology that has found its success in the topological data analysis in a variety of disciplines, including molecular biology. The present work explores the potential of using persistent homology as an independent tool for protein classification. To this end, we propose a molecular topological fingerprint based support vector machine (MTF-SVM) classifier. Specifically,we construct machine learning feature vectors solely fromprotein topological fingerprints,which are topological invariants generated during the filtration process. To validate the presentMTF-SVMapproach, we consider four types of problems. First, we study protein-drug binding by using the M2 channel protein of influenza A virus. We achieve 96% accuracy in discriminating drug bound and unbound M2 channels. Secondly, we examine the use of MTF-SVM for the classification of hemoglobin molecules in their relaxed and taut forms and obtain about 80% accuracy. Thirdly, the identification of all alpha, all beta, and alpha-beta protein domains is carried out using 900 proteins.We have found a 85% success in this identification. Finally, we apply the present technique to 55 classification tasks of protein superfamilies over 1357 samples and 246 tasks over 11944 samples. Average accuracies of 82% and 73% are attained. The present study establishes computational topology as an independent and effective alternative for protein classification.en_US
dc.format.extent23 p.en_US
dc.relation.ispartofseriesMolecular Based Mathematical Biologyen_US
dc.rights© 2015 Zixuan Cang et al., licensee De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.en_US
dc.subjectpersistent homologyen_US
dc.subjectmachine learningen_US
dc.titleA topological approach for protein classificationen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.versionPublished versionen_US

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