In this research, we present TMexpo, a method to predict rotational preferences of transmembrane helices to facilitate structural modeling. TMexpo first predicts lipid accessibility (the relative accessible surface area in lipid) by Support Vector Regression and predicts the classification of burial and exposed status of transmembrane helices (TMHs) by Support Vector Machine; and both models use evolutionary profiles, sequence conservation, helix insertion energy and biochemical properties as features. Then TMexpo calculates rotational angles of TMHs based on the predicted relative accessible surface area.
Lai, J.S., Cheng, C.W., Lo, A., Sung, T.Y., and Hsu, W.L. (2013) Lipid exposure prediction enhances the inference of rotational preferences of transmembrane helices. (submitted)