The SAM-T02 human predictions start with the same method as the
SAM-T02 server:
   
    Use the SAM-T2K method for finding homologs of the target and
      aligning them.

    Make local structure predictions using neural nets and the
      multiple alignment.  We currently have 5 local-structure
      alphabets: 
        DSSP
	STRIDE
	STR	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)
        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 4 networks (each
      probability vector output is multiplied by conditional
      probability matrix P(E|letter) P(H|letter) P(L|letter)).  The
      weights for the averaging are the mutual information between the
      local structure alphabet and the DSSP_EHL2 alphabet in a large
      training set. 
 
    
    We make four 2-track HMMs (1.0 amino acid + 0.3 local structure)
      and use them to score a template library of about 6200 templates.
      We also used a single-track HMM to score not just the template
      library, but a non-redundant copy of the entire PDB.

    [Difference from server:  the web server did not include the ALPHA
      alphabet in either the DSSP_EHL2 computation or the 2-track HMMS.]

    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 did not have time to
       optimize them), and the best templates ranked.
    
    Alignments of the target to the top templates were made using
      several different alignment methods (all using the SAM hmmscore
      program). 

After the large set of alignments were made the "human" methods and
the server diverge significantly.  The server just picks the
best-scoring templates (after removing redundancy) and reports the
local posterior-decoding alignments made with the 2-track AA+STR
target HMM.

The hand method used SAM's "fragfinder" program and the 2-track AA+STR
HMM to find short fragments (9 residues long) for each position in the
sequence (6 fragments were kept for each position).

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,
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
pike the set of alignments and fragments that undertaker would see.

A genetic algorithm with about 16 different operators were used to
optimize a score function.  The score function was hand-tweaked for
each target (mainly by adding constraints to keep beta sheets
together, but also by adjusting what terms were included in the score
function and what weights were used).  Undertaker was undergoing
extensive modification during CASP season, so may have had quite
different features available for different targets.

Bower and Dunbrack's SCWRL was run on some of the intermediate
conformations generated by undertaker, but the final conformation was
chosen entirely by the undertaker score function.

Optimization was generally done in many passes, with hand inspection
of the best conformation after each pass, followed (often) by tweaking
the score function to move the conformation in a direction we desired.

In a few cases, when we started getting a decent structure that did
not correspond well to our input alignments, we submitted the
structure to VAST to get structure-structure alignments, to try to
find some other possible templates to use as a base.

In some cases, when several conformations had good parts, different
conformations were manually cut-and-pasted, with undertaker run to try
to smooth out the transitions.

Because undertaker does not (yet) handle multimers, we often added
"scaffolding" constraints by hand to try to retain structure in
dimerization interfaces.  This is a crude hack that we hope to get rid
of when we have multimers implemented.

Because undertaker does not (yet) have a hydrogen-bond scoring
function, we often had to add constraints to hold beta sheets
together.  In some cases where the register was not obvious, we had to
guess or try several different registers.

In some cases, when we got desperate for initial starting points, we
threw the Robetta ab-initio models into the undertaker pool, and
optimized from them as well as the ones undertaker started with.

For multiple-domain models, we generally broke the sequence into
chunks (often somewhat arbitrary overlapping chunks), and did the full
SAM-T02 method for each subchain.  The alignments found were all
tossed into the undertaker conformation search.  In some cases, we
performed undertaker runs for the subchains, and cut-and-pasted the
pieces into one PDB file (with bad breaks) and let undertaker try to
assemble the pieces.