island method for estimating the statistical significance of profileprofile alignment scores background in the last decade a significant improvement in detecting remote similarity between protein sequences has been made by utilizing alignment profiles in place of aminoacid strings unfortunately no analytical theory is available for estimating the significance of a gapped alignment of two profiles many experiments suggest that the distribution of local profileprofile alignment scores is of the gumbel form however estimating distribution parameters by random simulations turns out to be computationally very expensiveresults we demonstrate that the background distribution of profileprofile alignment scores heavily depends on profiles composition and thus the distribution parameters must be estimated independently for each pair of profiles of interest we also show that accurate estimates of statistical parameters can be obtained using the island statistics for profileprofile alignmentsconclusion the island statistics can be generalized to profileprofile alignments to provide an efficient method for the alignment score normalization since multiple island scores can be extracted from a single comparison of two profiles the island method has a clear speed advantage over the direct shuffling method for comparable accuracy in parameter estimates