The SAM-T06 hand predictions use methods similar to SAM_T04 in CASP6 and the SAM-T02 method in CASP5. We start with a fully automated method (implemented as the SAM_T06 server): Use the SAM-T2K and SAM-T04 methods for finding homologs of the target and aligning them. The hand method also uses the experimental new SAM-T06 alignment method, which we hope is both more sensitive and lass prone to contamination by unrelated sequences. Make local structure predictions using neural nets and the multiple alignments. We currently use 10 local-structure alphabets: DSSP STRIDE STR2 an extended version of DSSP that splits the beta strands into multiple classes (parallel/antiparallel/mixed, edge/center) ALPHA an discretization of the alpha torsion angle: CA(i-i), CA(i), CA(i+1), CA(i+2) BYS a discretization of Ramachandran plots, due to Bystroff CB_burial_14_7 a 7-state discretization of the number of C_beta atoms in a 14 Angstrom radius sphere around the C_beta. near-backbone-11 an 11-state discretization of the number of residues (represented by near-backbone points) in a 9.65 Angstrom radius sphere around the sidechain proxy spot for the residue. DSSP_EHL2 CASP's collapse of the DSSP alphabet DSSP_EHL2 is not predicted directly by a neural net, but is computed as a weighted average of the other backbone alphabet predictions. O_NOTOR2 an alphabet for predicting characteristics of hydrogen bonds from the carbonyl oxygen N_NOTOR2 an alphabet for predicting characteristics of hydrogen bonds from the amide nitrogen We hope to add more networks for other alphabets over the summer. We make 2-track HMMs with each alphabet (1.0 amino acid + 0.3 local structure) and use them to score a template library of about 8000 (t06), 10000 (t04), or 15000 (t2k) templates. The template libraries are expanded weekly, but old template HMMs are not rebuilt. We also used a single-track HMM to score not just the template library, but a non-redundant copy of the entire PDB. One-track HMMs built from the template library multiple alignments were used to score the target sequence. All the logs of e-values were combined in a weighted average (with rather arbitrary weights, since we still have not taken the time to optimize them), and the best templates ranked. Alignments of the target to the top templates were made using several different alignment methods (mainly using the SAM hmmscore program, but a few alignments were made with Bob Edgar's MUSCLE profile-profile aligner). Generate fragments (short 9-residue alignments for each position) using SAM's "fragfinder" program and the 3-track HMM which tested best for alignment. Residue-residue contact predictions are made using mutual information, pairwise contact potentials, joint entropy, and other signals combined by a neural net. The contact prediction method is expected to evolve over the summer, as new features are selected and new networks trained. Then the "undertaker" program (named because it optimizes burial) is used to try to combine the alignments and the fragments into a consistent 3D model. No single alignment or parent template was used as a frozen core, though in many cases one had much more influence than the others. The alignment scores were not passed to undertaker, but were used only to pick the set of alignments and fragments that undertaker would see. Helix and strand constraints generated from the secondary-structure predictions are passed to undertaker to use in the cost function, as are the residue-residue contact prediction. One important change in this server over previous methods is that sheet constraints are extracted from the top few alignments and passed to undertaker. After the automatic prediction is done, we examine it by hand and try to fix any flaws that we see. This generally involves rerunning undertaker with new cost functions, increasing the weights for features we want to see and decreasing the weights where we think the optimization has gone overboard. Sometimes we will add new templates or remove ones that we think are misleading the optimization process. New this year, we are also occasionally using ProteinShop to manipulate proteins by hand, to produce starting points for undertaker optimization. We expect this to be most useful in new-fold all-alpha proteins, where undertaker often gets trapped in poor local minima by extending helices too far. Another new trick is to optimize models with gromacs to knock them out of a local minimum. The gromacs optimization does terrible things to the model (messing up sidechains and peptide planes), but is good at removing clashes. The resulting models are only a small distance from the pre-optimization models, but score much worse with the undertaker cost functions, so undertaker can move them more freely than models it has optimized itself. The most successful models for T0372 were all based on the automatic server model ROBETTA_TS4. This server model was judged by us to be the most likely out of any of our automatically generated models. In fact, even our automatic server prediction SAM_T06_server_TS1 was judged to be better than our initial automatic predictions. Admittedly, more work should have been performed on the SAM_T06_server_TS1 model instead of focusing all attention on ROBETTA_TS4 as we did. Model 1 is try12-opt2, our highest scoring model using the unconstrained cost function. try12-opt2 was a polishing run of try11-opt2, which was an optimization of the chimera made from try9-opt2/M1-D179 and try7-opt2/D180-W305. This model is not too different from try7-opt2, but scores significantly better. Model 2 is try9-opt2, our second highest scoring model using the unconstrained cost function. try9-opt2 was a polishing optimization of try8-opt2 to reduce breaks and clashes and improve hydrophobic packing. try8-opt2 was itself an optimization of try7-opt2, a model based on an optimization of the automatic server model ROBETTA_TS4. Model 3 is try11-opt2.gromacs0.repack-nonPC, which is the best scoring model using the Rosetta cost function. This model was made by optimizing a chimera made from the C-terminal domain of try9-opt2 (M1-D179) and the N-terminal domain of try7-opt2 (D180-W305). The model was then re-optimized with gromacs then repacking the sidechains (except for prolines and cysteines) by rosetta. Model 4 is try7-opt2, which has the same C-terminal domain as try9-opt2, but has a different N-terminal domain. This model was a direct optimization of the automatic server model ROBETTA_TS4, the best server model as judged by Undertaker's cost function. Model 5 is try5-opt2, our highest scoring model that was not based on the server model ROBETTA_TS4. This model was made by optimizing try4-opt2 and trying to form some longer helices. try4-opt2 came from try3-opt2, which resulted from an optimization of our automatic server prediction SAM_T06_server_TS1.