Enhancing the Prediction of Transmembrane β-barrel Segments

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

Introduction

Transmembrane β-barrels (TMBs) are one important class of membrane proteins that play crucial functions in the cell. 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. The pipeline of the MemBrain-TMB is shown in the Figure 1.

Figure 1. A flow chart of MemBrain-TMB to predict the spanning segments of transmembrane β-barrels.

Code and dataset

The data and code are contained in the following compressed files:
Click here to download the datasets, and click here to download the source code. The code package has been tested using Matlab 2014a under Windows 7 in a 64 bit architecture.

Reference

Xi Yin, Ying-Ying Xu and Hong-Bin Shen, Enhancing the Prediction of Transmembrane β-barrel Segments with Chain Learning and Feature Sparse Representation, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015 (in revised).