Abstract
This paper reports the theory of a special class of Cellular Automata (CA) referred to as Restricted 5 Neighborhood CA (R5NCA). Its application deals with identification of Protein Structure. Each amino acid of a protein chain is modeled with a R5NCA rule. In the process, a protein gets modeled as a R5NCA. CA Evolution models the evolution of the protein to its minimum energy folded configuration. The physical domain parameters are next mapped to CA model parameters from the analysis of known structural data available in Protein Data Base (PDB). The process of reverse mapping is implemented to identify the structure of a protein in blind test from its model parameters. The CA model achieves close to 99% correct prediction for secondary structure prediction, while for tertiary structure prediction, the average RMSD (Root Mean Square Deviation) value has been found to 1.82.
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Ghosh, S., Maiti, N.S., Chaudhuri, P.P. (2012). Theory and Application of Restricted Five Neighborhood Cellular Automata (R5NCA) for Protein Structure Prediction. In: Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2012. Lecture Notes in Computer Science, vol 7495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33350-7_37
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DOI: https://doi.org/10.1007/978-3-642-33350-7_37
Publisher Name: Springer, Berlin, Heidelberg
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