SRPVS: A New Motif Searching Algorithm for Protein Analysis

  • Authors:

  • Xiaolu Huang;Hesham Ali;Anguraj Sadanandam;Rakesh Singh

  • Affiliations:

  • University of Nebraska at Omaha;University of Nebraska at Omaha;University of Nebraska Medical Center;University of Nebraska Medical Center

  • Venue:
  • CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
  • Year:
  • 2004

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Abstract

In some protein sequence regions, when two sequences share similar amino acid composition, they also share the same biological structure regardless of the sequence order. Traditional protein analysis tools, since they are sequence order dependent, cannot detect such a sequence order relaxing similarity. In this study, a more flexible protein comparison algorithm, the Similar enRiched Parikh Vector Searching (SRPVS) algorithm is designed to detect sequence similarity in a local-sequence-order-flexible manner. In SRPVS, a peptide sequence is broken into a group of Parikh vectors of predefined word sizes, and then Similar enRiched Parikh Vectors (SRPV) are searched between the two sequences and an Order Score is assigned to each pair of SRPV to reflect the order difference between the two sequences. A test has shown that SRPVS can detect shuffled protein sequence regions that share biological structure between two protein sequences.