EBGW_OMP: A sequence-based method for accurate prediction of outer membrane proteins

Outer membrane proteins (OMPs) play important roles in bacterial cellular processes. Discriminating OMPs from different fold types of proteins is helpful for successful prediction of their structures and for exact designs of OMP-targeted drugs. In this paper, we developed a novel prediction method based on primary sequence features and support vector machine (SVM) algorithms. For protein sequences, discriminative features were extracted by the combination of sequence encoding based on grouped weights (EBGW), amino acid compositions and biochemical properties. Feature subsets were screened using F-score algorithm for training a SVM-based classifier, namely EBGW_OMP. The performance of EBGW_OMP was examined on a benchmark dataset of 1087 proteins. The results show that EBGW_OMP can discriminate OMPs from globular proteins, α-helical membrane proteins or non-OMPs with cross-validated accuracy of 98.0%, 97.6% or 97.9%, respectively, which outperformed existing sequence-based methods. EBGW_OMP also successfully distinguished 681 out of 722 OMPs with 97.0% accuracy in another benchmark dataset of 2657 proteins. Genome-wide tests show that EBGW_OMP has excellent capability of correctly detecting OMPs and is considerable for genomic OMPs prediction. The web server implements EBGW_OMP is freely accessible at http://bioinfo.tmmu.edu.cn/EBGW_ OMP.