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 8 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. 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. Our first model had a serious flaw---it included the HIS tag in a sheet. Under the assumption that the structure is formed without the HIS tag, we made two subdomain predictions M1-E63 and M1-G66. HIS tags were pasted onto the automatically generated subdomain models, and the resulting chimeras optimized. Closing the gaps was a bit hard, as the subdomain models had not necessarily left the C-terminus on the surface. We also tried taking the sheet from one of the optimizations, and adding the HIS tag from one of the others. history to keep track of where models came from: M1-G66 -> chimera1-> try2 M1-G66 -> chimera2 -> try3 M1-E63 -> chimera3 -> try4, try5 try4 -> chimera4 -> try6 -> try7 None of the HIS tags are very convincing, but HIS tags are often disordered, so there is not much point in trying to optimize the prediction of their structure. Model 1 is try7-opt2 which twice had pieces from other optimizations stuck onto the M1-E63 base model. It scores best with our cost functions, and is the backbone that rosetta finds easiest to repack (though we are not submitting the rosetta repacking). Model 2 is try5-opt2, the best scoring with only one round of cut-and-paste. Model 3 is try3-opt2, which uses a different alignment of the strands. Model 4 is try4-opt2, yet another optimization of from chimera3. It is the base model which had N- and C-termini replaced to make chimera4, which was optimized to form our best-scoring model. Model 5 is try2-opt2, which is an optimization of chimera1, based on the same underlying model as try3-opt2 (model 3), but with different HIS tag attached.