HMMSTR: a hidden Markov model for local sequence-structure correlations inproteins

We describe a hidden Markov model, HMMSTR, for general protein sequence bas ed on the I-sites library of sequence-structure motifs. Unlike the Linear h idden Markov models used to model individual protein families, HMMSTR has a highly branched topology and captures recurrent local features of protein sequences and structures that transcend protein family boundaries. The mode l extends the I-sites library by describing the adjacencies of different se quence-structure motifs as observed in the protein database and, by represe nting overlapping motifs in a much more compact form, achieves a great redu ction in parameters. The HMM attributes a considerably higher probability t o coding sequence than does an equivalent dipeptide model, predicts seconda ry structure with an accuracy of 74.3 %, backbone torsion angles better tha n any previously reported method and the structural context of beta strands and turns with an accuracy that should be useful for tertiary structure pr ediction. (C) 2000 Academic Press.