Protein Sequence Motif Detection using Novel Rough Granular Computing Model

Protein sequence motifs information is essential for the analysis of biologically significant regions. Discovering sequence motifs is a key task to realize the connection of sequences with their structures. Protein sequence motifs have the potential to determine the function and activities of the proteins. Many algorithms or techniques are used to determine motifs which require a predefined fixed window size. Our input dataset is extremely large as a result, an efficient technique is demanded. So we apply three different granular computing models to find protein motif information which transcend protein family boundaries. The constructed segments from 3000 protein sequences are divided into granules using Rough K-Means and then K-Means has been applied on each granule. The highly structured clusters are further considered to find motif patterns. This approach is compared with Adaptive Fuzzy Granular model. The proposed Rough Granular computing model generates more number of highly structured motif patterns. Keywords-Protein Sequence Motifs, DBI, HSSP-BLOSUM62, Granular Computing, K-Means, Adaptive Fuzzy C-Means, Rough K-Means.