Protein Sequence Motif Information Discovery

The close relationship between protein sequence and structure plays an important role in current analysis and prediction technologies. PROSITE, PRINTS, and BLOCKS are three of the most popular motif databases. Granular computing represents information in the form of aggregates, also called information granules. For a huge and complicated problem, it applies the divide-and-conquer concept to split the original task into several smaller subtasks to save time and space complexity. The major advantages of the Fuzzy Greedy K-Means (FGK) model are reduced spatiotemporal complexity, filtered outliers, and higher-quality granular information results. Since the major purpose of this work is to obtain protein sequence motif information across protein family boundaries, the dataset of this work is intended to represent all known protein sequences. The dataset used in this work includes 2710 PDB protein sequences obtained from the Protein Sequence Culling Server (PISCES).