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A Hybridized Clustering Approach based on Rough Set and Fuzzy c-Means to Mine Cholesterol Sequence from ABC Family
Objectives: The current study is focused on design of a computational model for human ABC transporters; wherein the TM-sequences matching the CRAC/CARC motif are extracted. Methods: The postulation of cholesterol binding motif (CRAC/CARC), its presence in different proteins and validating its interaction with cholesterol has indeed established the importance of the motif in cholesterol-mediated modulation of protein/signaling pathway. Several viral proteins and membrane proteins (especially alpha-helical trans membrane proteins) such as GPCR transporters are reported to be modulated by cholesterol. The experimental studies are so far performed on only a few proteins in a family but based on an evolutionary conservation and consensus an exploration can be done confidently within a family. However, the representation of motif has a low consensus yielding several false positives thus reducing its reliability. Findings: A computational hybrid clustering method based on rough set with fuzzy c-means algorithm is used to mine the cholesterol sequence from ABC family. Higher weightage is given to those sequences based on the following parameters: motifs with more number of sub motifs, number of helices bearing the motif in a protein and compliance with the orientation of the cholesterol in the membrane for its interaction with the motif. Improvement: A detailed study in a given super family with an approach to reduce redundancy and enrichment can improve its predictability.
ABC transporter, CRAC/CARC, Fuzzy c-Means, GPCR, Motif, Rough Set.
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