title: Predicting contact map using radial basis function neural network with conformational energy function.
abstract: Contact map, which is important to understand and reconstruct protein's three-dimensional (3D) structure, may be helpful to solve the protein's 3D structure. This paper presents a novel approach to predict the contact map using Radial Basis Function Neural Network (RBFNN) optimised by Conformational Energy Function (CEF) based on chemico-physical knowledge of amino acids. Finally, the results are trimmed by Short-Range Contact Function (SRCF). Consequently, it can be found that our proposed method is better than the existing methods such as PROFcon and the PE-based method. Particularly, this method can accurately predict 35% of contacts at a distance cutoff of 8 A.
authors.affiliation: Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China. bigeagle@email.ustc.edu.cn
authors.forename: Peng
authors.initials: P
authors.lastname: Chen
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
authors.forename: Xing-Ming
authors.initials: XM
authors.lastname: Zhao
authors.forename: Xueling
authors.initials: X
authors.lastname: Li
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D030562:Databases, Protein; D008968:Molecular Conformation; D016571:Neural Networks, Computer
chemical_list: D000596:Amino Acids