摘要
Membrane proteins are embedded in the lipid bilayer,which creates a suitable environment for their actions. It is important to decide which tpye it belongs to because it is closely relevant to its biological function and its interaction process with other molecules in a biological system. Membrane proteins have different types. The function of a membrane protein is closely correlated with the type it belongs to. In this study,on the basis of the concept of pseudo amino acid (PseAA) composition originally introduced by Chou,the value of approximate entropy (ApEn) of the query membrane protein was used to integrate the complementary information. By fusing fifteen powerful individual fuzzy K-nearest neighbor ( FKNN) classifiers,an ensemble classifier was presented. Each basic classifier was trained in PseAA composition of membrane protein sequences with different parameters. The results of experiments demonstrate it is efficient for the structural prediction of membrane proteins.
Membrane proteins are embedded in the lipid bilaycr, which creates a suitable environment for their actions. It is important to decide which tpye it belongs to because it is closely relevant to its biological function and its interaction process with other molecules in a biological system. Membrane proteins have different types. The function of a membrane protein is closely correlated with the type it belongs to. In this study, on the basis of the concept of pseudo amino acid (PseAA) composition originally introduced by Chou, the value of approximate entropy (ApEn) of the query membrane protein was used to integrate the complementary information. By fusing fifteen powerful individual fuzzy K-nearest neighbor (FKNN) classifiers, an ensemble classifier was presented. Each basic classifier was trained in PseAA composition of membrane protein sequences with different parameters. The results of experiments demonstrate it is efficient for the structural prediction of membrane proteins.
基金
National Nature Science Foundations of China (No.60975059, No.60775052)
Specialized Research Fund for the Doctoral Program of Higher Education from Ministry of Education of China ( No.20090075110002)
Projects of the Shanghai Committee of Science and Technology (No.09JC1400900, No.08JC1400100, No.10DZ0506500)