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

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.