title: [Protein secondary structure prediction based on maximum entropy model].
abstract: Secondary structure prediction plays an important role in function prediction of protein. In this paper, maximum entropy model is used to predict protein secondary structure. We build feature function sets based on the influential factors which are crucial to the states of secondary structure of residues in protein sequence. Multi-factors are taken into account in the model, including charge of amino acids, conformational parameter for the states of secondary structure, short and long ranges of interaction of residues in sequence. As such, multi-source information is integrated into a single probability model by the method. Compared with the reported methods, our method gets a higher accuracy rate in predicting protein secondary structure. The results demonstrate that the proposed method is practical.
authors.affiliation: College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.
authors.forename: Tongliang
authors.initials: T
authors.lastname: Zhang
authors.forename: Yongsheng
authors.initials: Y
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D019277:Entropy; D006801:Humans; D008956:Models, Chemical; D008958:Models, Molecular; D011237:Predictive Value of Tests; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein
chemical_list: D011506:Proteins