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Functional discrimination of membrane proteins using machine learning techniques

BMC Bioinformatics
Springer (Biomed Central Ltd.)
Publication Date
DOI: 10.1186/1471-2105-9-135
  • Research Article
  • Biology
  • Chemistry
  • Computer Science

Abstract ral ss BioMed CentBMC Bioinformatics Open AcceResearch article Functional discrimination of membrane proteins using machine learning techniques M Michael Gromiha* and Yukimitsu Yabuki Address: Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan Email: M Michael Gromiha* - [email protected]; Yukimitsu Yabuki - [email protected] * Corresponding author Abstract Background: Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters. Results: We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network method discriminated the channels/pores, electrochemical potential-driven transporters and active transporters with the 5-fold cross validation accuracy of 64% in a data set of 1718 membrane proteins. The application of amino acid occurrence improved the overall accuracy to 68%. In addition, we have discriminated transporters from other α-helical and β-barrel membrane proteins with the accuracy of 85% using k-nearest neighbor method. The classificati

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