Authors: Maqsood Hayat Asifullah Khan
Publish Date: 2013/03/14
Volume: 44, Issue: 5, Pages: 1317-1328
Abstract
Membrane protein is the prime constituent of a cell which performs a role of mediator between intra and extracellular processes The prediction of transmembrane TM helix and its topology provides essential information regarding the function and structure of membrane proteins However prediction of TM helix and its topology is a challenging issue in bioinformatics and computational biology due to experimental complexities and lack of its established structures Therefore the location and orientation of TM helix segments are predicted from topogenic sequences In this regard we propose WRFTMH model for effectively predicting TM helix segments In this model information is extracted from membrane protein sequences using compositional index and physicochemical properties The redundant and irrelevant features are eliminated through singular value decomposition The selected features provided by these feature extraction strategies are then fused to develop a hybrid model Weighted random forest is adopted as a classification approach We have used two benchmark datasets including low and highresolution datasets tenfold cross validation is employed to assess the performance of WRFTMH model at different levels including per protein per segment and per residue The success rates of WRFTMH model are quite promising and are the best reported so far on the same datasets It is observed that WRFTMH model might play a substantial role and will provide essential information for further structural and functional studies on membrane proteins The accompanied web predictor is accessible at http//1116899218/WRFTMH/
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