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Enhancing the Prediction of Transmembrane β-Barrel Segments with Chain Learning and Feature Sparse Representation | IEEE Journals & Magazine | IEEE Xplore

Enhancing the Prediction of Transmembrane β-Barrel Segments with Chain Learning and Feature Sparse Representation


Abstract:

Transmembrane β-barrels (TMBs) are one important class of membrane proteins that play crucial functions in the cell. Membrane proteins are difficult wet-lab targets of st...Show More

Abstract:

Transmembrane β-barrels (TMBs) are one important class of membrane proteins that play crucial functions in the cell. Membrane proteins are difficult wet-lab targets of structural biology, which call for accurate computational prediction approaches. Here, we developed a novel method named MemBrain-TMB to predict the spanning segments of transmembrane β-barrel from amino acid sequence. MemBrain-TMB is a statistical machine learning-based model, which is constructed using a new chain learning algorithm with input features encoded by the image sparse representation approach. We considered the relative status information between neighboring residues for enhancing the performance, and the matrix of features was translated into feature image by sparse coding algorithm for noise and dimension reduction. To deal with the diverse loop length problem, we applied a dynamic threshold method, which is particularly useful for enhancing the recognition of short loops and tight turns. Our experiments demonstrate that the new protocol designed in MemBrain-TMB effectively helps improve prediction performance.
Published in: IEEE/ACM Transactions on Computational Biology and Bioinformatics ( Volume: 13, Issue: 6, 01 November 2016)
Page(s): 1016 - 1026
Date of Publication: 11 February 2016

ISSN Information:

PubMed ID: 26887010

Funding Agency:


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