Toward an improved discrimination of outer membrane proteins using a sequence-based approach.

This article offers a novel sequence-based approach to discriminate outer membrane proteins (OMPs). The first step is to use a new representation approach, factor analysis scales of generalized amino acid information (FASGAI) representing hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, etc., to characterize sequences of OMPs and non-OMPs. The subsequent data is then transformed into a uniform matrix by the auto cross covariance (ACC). The second step is to develop discrimination predictors of OMPs from non-OMPs using a support vector machine (SVM). The SVM predictors thus successfully produce a high Matthews correlation coefficient (MCC) of 0.916 on 208 OMPs from non-OMPs including 206 α-helical membrane proteins and 673 globular proteins by a fivefold cross validation test. Meanwhile, overall MCC values of 0.923 and 0.930 are obtained for the discrimination OMPs from the α-helical membrane proteins and the globular proteins, respectively. The results demonstrate that the FASGAI-ACC-SVM combination approach shows great prospect of application in the field of bioinformatics or proteomics studies.