PFRMAT SS
TARGET T0052
AUTHOR 1751-3146-3362
METHOD Using neural net fssp-guide-12345-IDaa13-9-6-11-9-3-8-7-ehl-seeded-trained.net
METHOD This is a 4-layer network, with amino acid frequencies, insertions,
METHOD and deletions as inputs, and the following layers:
METHOD	window	units
METHOD	    9	    6
METHOD	   11	    9
METHOD	    3	    8
METHOD	    7	    3 (EHC codes)
METHOD 
METHOD The input amino acid frequencies were determined from weighted counts
METHOD (1.3 bits/column) and the recode2.20comp Dirichlet mixture regularizer.
METHOD The input alignment (using the SAM/T98 method) had only one sequence,
METHOD so we expect this prediction to be quite poor---probably only about
METHOD Q3=65%.  The neural net used all available training data, and so
METHOD no independent assesment of its quality is available.
METHOD 
METHOD It is unclear whether the helix fragments at QTcyNSA 
METHOD should be merged into a single helix or omitted.
METHOD 
MODEL 1
L C 0.78
G C 0.80
K C 0.64
F C 0.54
S C 0.42
Q H 0.42
T H 0.41
C C 0.41
Y C 0.47
N H 0.44
S H 0.55
A H 0.50
I C 0.46
Q C 0.56
G C 0.63
S C 0.59
V E 0.47
L E 0.50
T E 0.43
S C 0.49
T C 0.57
C C 0.54
E C 0.47
R C 0.54
T C 0.74
N C 0.87
G C 0.91
G C 0.88
Y C 0.87
N C 0.83
T C 0.79
S C 0.74
S C 0.65
I C 0.60
D C 0.65
L C 0.53
N H 0.54
S H 0.69
V H 0.63
I H 0.50
E C 0.48
N C 0.73
V C 0.85
D C 0.83
G C 0.76
S C 0.67
L C 0.47
K E 0.53
W C 0.46
Q C 0.69
P C 0.77
S C 0.67
N H 0.49
F H 0.53
I H 0.61
E H 0.63
T H 0.49
C C 0.65
R C 0.70
N C 0.71
T C 0.71
N C 0.62
L C 0.53
A C 0.66
G C 0.80
S C 0.70
S H 0.74
E H 0.77
L H 0.83
A H 0.92
A H 0.94
E H 0.90
C H 0.77
K H 0.68
T H 0.74
R H 0.75
A H 0.72
Q H 0.69
Q H 0.57
F H 0.37
V E 0.39
S C 0.45
T C 0.44
K C 0.43
I C 0.55
N C 0.77
L C 0.78
D C 0.64
D H 0.53
H H 0.48
I C 0.48
A C 0.47
N C 0.58
I C 0.65
D C 0.70
G C 0.72
T C 0.71
L C 0.55
K C 0.48
Y C 0.50
E C 0.59
END