Prediction of outer membrane proteins by combining the position- and composition-based features of sequence profiles

Abstract

Locating the transmembrane regions of outer membrane proteins (OMPs) is highly important for deciphering their biological functions at both molecular and cellular levels. Here, we propose a novel method to predict the transmembrane regions of OMPs by employing the position- and composition-based features of sequence profiles. Furthermore, a simple probability-based prediction model, which is estimated by the secondary structures of structurally known OMPs, is also developed. Considering that these two methods are both effective and well complementary, we integrate them into a method called TransOMP, which is also capable of identifying OMPs. Furthermore, we develop an OMP identification measure I_CScore by considering transmembrane regions by TransOMP and secondary structural topology by SSEA-OMP. Our methods were benchmarked against state-of-the-art methods and assessed in the genome of Escherichia coli. Benchmark results confirmed that our methods were reliable and useful. Meanwhile, we constructed an OMP prediction web server, which can be used for OMP identification, transmembrane region location, and 3D model building.