Low Identity, Low Similarity Protein Sequences: Independent Modeling of the Ordered-Series-of-Motifs and Motif-Intervening-Regions

Marcella A. McClure (mars@parvati.lv-whi.nevada.edu)
Julianna Hudak (julie@parvati.lv-whi.nevada.edu)
John Kowalski (johnmk@parvati.lv-whi.nevada.edu)

Department of Biological Sciences, UNLV
Las Vegas, NV 89129, USA


Abstract

We present a strategy for generating a multiple alignment from a hidden Markov model (HMM) for low identity, low similarity protein sequences. In this approach the ordered-series-of-motifs and the motif-intervening-regions are independently modeled. We also provide a measure of multiple alignment "goodness" called the stability function to compared one alignment to another. This strategy provides a more robust HMM representing highly divergent sequence data.

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Japanese Society for Bioinformatics