Predicting contact map using Radial Basis Function Neural Network with Conformational Energy Function Online publication date: Sat, 17-May-2008
by Peng Chen, De-Shuang Huang, Xing-Ming Zhao, Xueling Li
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 4, No. 2, 2008
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 Å.
Online publication date: Sat, 17-May-2008
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