Predicting contact map using Radial Basis Function Neural Network with Conformational Energy Function

Authors:
Peng Chen;De-/Shuang Huang;Xing-/Ming Zhao;Xueling Li
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China/ Department of Automation, University of Science and Technology of Chin ...;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China.;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China.;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2008

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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.