An In Silico Approach to Cluster CAM Kinase Protein Sequences

As we are ushering in new age of data driven world, we face an enormous challenge of deriving information from heaps of data available. The amount of data being generated is overwhelming and this calls for exploring novel and effective methods for clustering and classification of such data. CAM kinase family is known to contain many enzymes involved in impor tant physiological processes. In the present study, 13 important physicochemical parameters were calculated for 56 sequences of CAM kinase family in silico . Self organizing Maps (SOM) were employed for the classifying and clustering similar sequences and visualization of high dimensional data spaces as they are known for their capability to maintain the essence of topological relationships between the features. SOM effectively yielded 4 clusters which were distinct from each other and marked by characteristi c features.

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