Abstract
This paper is a refinement and extension of the Protein modelling Cellular Automata Machine (PCAM) reported in [1] for prediction of protein structure. An efficient organization of Knowledge Base (KB) is reported in the current paper. The KB is reorganized with emphasis on the residues in the Transition Regions (TRs) between structural regions like alpha helices or beta strands and an unstructured or loop regions. The meta-knowledge derived out of the KB analysis is employed for synthesis of protein structure from the primary chain of amino acid residues. Design of synthesis algorithm ensures incorporation of probable orientation of structural parameters of residues in the TR. A few structures are finally selected based on the computation of exposed surface area and core size. The algorithm is tested for the challenging protein targets from [9] to synthesize structures with reasonable accuracy and low execution time.
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Ghosh, S., Maiti, N.S., Chaudhuri, P.P. (2014). Cellular Automata Model for Protein Structure Synthesis (PSS). In: Wąs, J., Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2014. Lecture Notes in Computer Science, vol 8751. Springer, Cham. https://doi.org/10.1007/978-3-319-11520-7_28
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DOI: https://doi.org/10.1007/978-3-319-11520-7_28
Publisher Name: Springer, Cham
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