Inter-Residue Spatial Distance Map Prediction by Using Integrating Ga with Rbfnn

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Abstract:

The spatial ordering information of amino acid residue in protein primary sequence is an important determinant of protein three-dimensional structure. In this paper, we describe a radial basis function neural network (RBFNN), whose hidden centers and basis function widths are optimized by a genetic algorithm (GA), for the purpose of predicting three dimensional spatial distance location from primary sequence information. Experimental evidence on soybean protein sequences indicates the utility of this approach.

Keywords: genetic algorithm; prediction; radial basis function neural network; soybean protein; spatial distance

Document Type: Review Article

DOI: http://dx.doi.org/10.2174/0929866043406283

Affiliations: Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences Department of Automation, University of Science & Technology of China P.O. Box 1130, Hefei, Anhui 230031,C71hina.

Publication date: December 1, 2004

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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