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. All dimers were based on the PDB template 1mk4, our highest scoring template from our automatic run. Unfortunately, Undertaker had a hard time maintaining the end-to-end orientation of the monomers found in template 1mk4. However, despite this, the interface between the monomers in the submitted dimeric models are surprisingly tight. Model 1 is try4-opt2, the best scoring model with our unconstrained.costfcn cost function. It was optimized by Undertaker from a model based on try2-opt2. try2-opt2 was made using ProteinShop and our original dimer try1-opt2. The monomers of try1-opt2 were rotated by 90 degrees to form a "flatter" dimer. This orientation improved hydrophobic packing moderately. The try1-opt2 dimer was made by taking our monomer prediction, try9-opt2, and placing it according to the orientation of the monomers in the dimeric PDB templated, 1mk4 using Undertaker. Model 2 is try2-opt2, the second best scoring dimeric model using the unconstrained.costfcn cost function. As described above, it originated from try1-opt2 after re-orientating the monomers using ProteinShop. The ProteinShop model was optimized by Undertaker to reduce breaks and improve the packing. Model 3 is try3-opt2, an optimization of try1-opt2, and our third highest scoring dimeric model. This model has the monomers orientated in "cross" with a fairly tight interface at their center. However, large hydrophobic patches were left exposed, leading us to the creation of a second dimer with the monomers situated in a better way. Model 4 is try1-opt2, which was a dimer made by taking our monomeric model try9-opt2 and dimerizing it according to the dimeric template 1mk4. This initial dimer was optimized by Undertaker to make try1-opt2. Interestingly, the start and final positions of these monomers were very different. Initially, the monomers were found end-to-end with a fairly weak interface. After the optimization, the monomers were located in a cross-like formation, with the center of each monomer providing the interface. Model 5 is try5-opt2, the poorest scoring dimeric model among this group. It was made by dimerizing the monomer model try7-opt2, a direct optimization of the server model ROBETTA_TS4. The N-terminal domain of try7-opt2 differed significantly enough from try9-opt2 to warrant a second dimeric model based on try7-opt2. The dimer was again created by Undertaker using the dimeric template 1mk4.