Abstract

this article, we provide a coherent view of the two recent models used for multiple sequence alignment --- the hidden Markov model (HMM) and the block-based motif model --- in order to develop a set of new algorithms that enjoy both the sensitivity of the block-based model and the flexibility of the HMM. In particular, we decompose the standard HMM into two components: the insertion component, which is captured by the socalled "propagation model," and the deletion component, which is described by a deletion vector. Such a decomposition serves as a basis for rational compromise between biological specificity and model flexibility. Furthermore, we introduce a Bayesian model selection criterion that --- in combination with the propagation model, genetic algorithm, and other computational aspects --- forms the core of PROBE, a multiple alignment and database search methodology (software available via anonymous ftp at ftp://ncbi.nlm.nih.gov/pub/neuwald/probe1.0). The application of our method to a GTPase family of protein sequences yields an alignment that is confirmed by comparison with known tertiary structures.

Citations