Protein Modeling using Hidden Markov Models: Analysis of Globins

We apply Hidden Markov Models (HMMs) to the problem of statistical modeling and multiple alignment of protein families. A variant of the Expectation Maximization (EM) algorithm known as the Viterbi algorithm is used to obtain the statistical model from the unaligned sequences. In a detailed series of experiments, we have taken 400 unaligned globin sequences, and produced a statistical model entirely automatically from the primary (unaligned) sequences using no prior knowledge of globin structure. The produced model includes amino acid distributions for all the known positions in the 7 major alpha-helices, as well as the probability of and average length of insertions between these positions, and the probability that each position is not present at all. Using this model, we obtained a multiple alignment of the 400 sequences and 225 other globin sequences, that agrees almost perfectly with a structural alignment by Bashford et al. This model can also discriminate all these 625 globins fr..