  The flexibility in gap cost enjoyed by Hidden Markov Models (HMMs) is
expected to afford them better retrieval accuracy than position-specific
scoring matrices (PSSMs). We attempt to quantify the effect of more general gap
parameters by separately examining the influence of position- and
composition-specific gap scores, as well as by comparing the retrieval accuracy
of the PSSMs constructed using an iterative procedure to that of the HMMs
provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments.
  We found that position-specific gap penalties have an advantage over uniform
gap costs. We did not explore optimizing distinct uniform gap costs for each
query. For Pfam, PSSMs iteratively constructed from seeds based on HMM
consensus sequences perform equivalently to HMMs that were adjusted to have
constant gap transition probabilities, albeit with much greater variance. We
observed no effect of composition-specific gap costs on retrieval performance.
