USING EVOLUTIONARY TREES IN PROTEIN SECONDARY STRUCTURE PREDICTION AND OTHER COMPARATIVE SEQUENCE ANALYSES

Previously proposed methods for protein secondary structure prediction from multiple sequence alignments do not efficiently extract the evol utionary information that these alignments contain. The predictions of these methods are less accurate than they could be, because of their failure to consider explicitly the phylogenetic tree that relates alig ned protein sequences. As an alternative, we present a hidden Markov m odel approach to secondary structure prediction that more fully uses t he evolutionary information contained in protein sequence alignments, A representative example is presented, and three experiments are perfo rmed that illustrate how the appropriate representation of evolutionar y relatedness can improve inferences. We explain why similar improveme nt can be expected in other secondary structure prediction methods and indeed any comparative sequence analysis method, (C) 1996 Academic Pr ess Limited