Marcella A. McClure (firstname.lastname@example.org)
Julianna Hudak (email@example.com)
John Kowalski (firstname.lastname@example.org)
Department of Biological Sciences, UNLV
Las Vegas, NV 89129, USA
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.