title: Accurate prediction of the burial status of transmembrane residues of α-helix membrane protein by incorporating the structural and physicochemical features.
abstract: Predicting the burial status (the residue exposure to the lipid bilayer or buried within the protein core) of transmembrane (TM) residues of α-helix membrane protein (αHMP) is of great importance for genome-wide annotation and for experimental researchers to elucidate diverse physiological processes. In this work, we developed a new computational model that can be used for predicting the burial status of TM residues of αHMP. By incorporating physicochemical scales and conservation index, an efficient prediction model using least squares support vector machine (LS-SVM) was developed. The model was developed from 43 protein chains and its prediction ability was evaluated by an independent test set of other non-redundant ten protein chains. The prediction accuracy of our method was much better than the results of the reported works. Our results demonstrate that the LS-SVM prediction model incorporating structural and physicochemical features derived from sequence information could greatly improve the prediction accuracy.
authors.affiliation: Department of Chemistry, State Key Laboratory of Applied Organic Chemistry, Lanzhou University, Lanzhou, China.
authors.forename: Chengqi
authors.initials: C
authors.lastname: Wang
authors.forename: Shuyan
authors.initials: S
authors.lastname: Li
authors.forename: Lili
authors.initials: L
authors.lastname: Xi
authors.forename: Huanxiang
authors.initials: H
authors.lastname: Liu
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Yao
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D055598:Chemical Phenomena; D005544:Forecasting; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D008969:Molecular Sequence Data; D017510:Protein Folding; D017433:Protein Structure, Secondary
chemical_list: D008565:Membrane Proteins