SATCHMO: Simultaneous Alignment and Tree Construction using Hidden Markov mOdels

1. SATCHMO: Simultaneous Alignment and Tree Construction using Hidden Markov mOdels Edgar, R., and Sjölander, K., Bioinformatics 2003

2. SATCHMO algorithm • Input: unaligned sequences, each forming a separate subtree (of a single sequence each) • Initialize: a profile HMM is constructed for each sequence using Dirichlet mixture densities. • Dirichlet mixture densities avoid the problems of small counts • While (#subtrees > 1) { • Use profile-profile scoring to select closest pair to join • Relative entropy between columnar distributions • Align pair to each other, keeping columns fixed within each subtree • Mask columns with many gaps or high positional relative entropy. • Construct a profile HMM for the new masked MSA • Use Dirichlet mixture densities. } • Output: Tree and MSA

6. SATCHMO performance evaluation • Evaluating the phylogenetic tree accuracy is difficult • Simulation studies are used to evaluate evolutionary tree methods • These rarely attempt to model the effects of duplication and structural and functional changes • We don’t know the evolutionary history of multi-gene families, so benchmark datasets of real protein family phylogenies are not available • However, we can directly assess the alignment accuracy by way of 3D structure • The structural alignment of two proteins is accepted as “ground truth” by the computational structural biology community • We can also assess the functional predictive power of a phylogenetic tree against what is known about the functions of proteins • This approach is not universally accepted

7. SATCHMO is more robust to extreme structural divergence than other methods

8. SATCHMO succeeds at alignment of proteins with different overall folds MAFFT SATCHMO