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Add PSSpred
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nickcafferry committed Sep 17, 2020
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Binary file added Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4.tar.bz2
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731 changes: 731 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/PSSpred.pl
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39 changes: 39 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/README
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################################################################
#
# This is a script program for protein secondary structure
# prediction (PSSpred) based on multiple weight neural-network
# training. It first collects multiple homologous sequences using
# PSI-BLAST. Amino-acid frequence and log-odds data with Henikoff
# weights are then used to train SS separately based on the Rumelhart
# back-propagation of erros method. The final SS prediction is a
# combination of 7 neural network SS predictors.
# Comments should be addressed to zhng@umich.edu.
#
################################################################

1, how to run 'PSSpred.pl' in Linux system?

PSSpred.pl seq.txt

Note:

a, seq.txt is fasta file at current directory (the only input file)

b, output file:
seq.dat
seq.dat.ss

c, PSSpred.pl is split into three steps:
Step 1: prepare and run PSI-BLAST
Step 2: prepare mtx, pssm.txt, profw, freqccw, freqccwG
Step 3: run PSSpred and output files

2, how to cite PSSpred?

PSSpred has not been published as an article. Please cite it as
Y. Zhang, https://zhanglab.ccmb.med.umich.edu/PSSpred

Alternatively, you can cite:
R. Yan, D. Xu, J. Yang, S. Walker, Yang Zhang. A comparative assessment
and analysis of 20 representative sequence alignment methods for protein
structure prediction. Scientific Reports, 3: 2619 (2013).
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27 changes: 27 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/BLOSUM45
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# Entries for the BLOSUM45 matrix at a scale of ln(2)/3.0.
A R N D C Q E G H I L K M F P S T W Y V B J Z X *
A 5 -2 -1 -2 -1 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -2 -2 0 -1 -1 -1 -1 -5
R -2 7 0 -1 -3 1 0 -2 0 -3 -2 3 -1 -2 -2 -1 -1 -2 -1 -2 -1 -3 1 -1 -5
N -1 0 6 2 -2 0 0 0 1 -2 -3 0 -2 -2 -2 1 0 -4 -2 -3 5 -3 0 -1 -5
D -2 -1 2 7 -3 0 2 -1 0 -4 -3 0 -3 -4 -1 0 -1 -4 -2 -3 6 -3 1 -1 -5
C -1 -3 -2 -3 12 -3 -3 -3 -3 -3 -2 -3 -2 -2 -4 -1 -1 -5 -3 -1 -2 -2 -3 -1 -5
Q -1 1 0 0 -3 6 2 -2 1 -2 -2 1 0 -4 -1 0 -1 -2 -1 -3 0 -2 4 -1 -5
E -1 0 0 2 -3 2 6 -2 0 -3 -2 1 -2 -3 0 0 -1 -3 -2 -3 1 -3 5 -1 -5
G 0 -2 0 -1 -3 -2 -2 7 -2 -4 -3 -2 -2 -3 -2 0 -2 -2 -3 -3 -1 -4 -2 -1 -5
H -2 0 1 0 -3 1 0 -2 10 -3 -2 -1 0 -2 -2 -1 -2 -3 2 -3 0 -2 0 -1 -5
I -1 -3 -2 -4 -3 -2 -3 -4 -3 5 2 -3 2 0 -2 -2 -1 -2 0 3 -3 4 -3 -1 -5
L -1 -2 -3 -3 -2 -2 -2 -3 -2 2 5 -3 2 1 -3 -3 -1 -2 0 1 -3 4 -2 -1 -5
K -1 3 0 0 -3 1 1 -2 -1 -3 -3 5 -1 -3 -1 -1 -1 -2 -1 -2 0 -3 1 -1 -5
M -1 -1 -2 -3 -2 0 -2 -2 0 2 2 -1 6 0 -2 -2 -1 -2 0 1 -2 2 -1 -1 -5
F -2 -2 -2 -4 -2 -4 -3 -3 -2 0 1 -3 0 8 -3 -2 -1 1 3 0 -3 1 -3 -1 -5
P -1 -2 -2 -1 -4 -1 0 -2 -2 -2 -3 -1 -2 -3 9 -1 -1 -3 -3 -3 -2 -3 -1 -1 -5
S 1 -1 1 0 -1 0 0 0 -1 -2 -3 -1 -2 -2 -1 4 2 -4 -2 -1 0 -2 0 -1 -5
T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -1 -1 2 5 -3 -1 0 0 -1 -1 -1 -5
W -2 -2 -4 -4 -5 -2 -3 -2 -3 -2 -2 -2 -2 1 -3 -4 -3 15 3 -3 -4 -2 -2 -1 -5
Y -2 -1 -2 -2 -3 -1 -2 -3 2 0 0 -1 0 3 -3 -2 -1 3 8 -1 -2 0 -2 -1 -5
V 0 -2 -3 -3 -1 -3 -3 -3 -3 3 1 -2 1 0 -3 -1 0 -3 -1 5 -3 2 -3 -1 -5
B -1 -1 5 6 -2 0 1 -1 0 -3 -3 0 -2 -3 -2 0 0 -4 -2 -3 5 -3 1 -1 -5
J -1 -3 -3 -3 -2 -2 -3 -4 -2 4 4 -3 2 1 -3 -2 -1 -2 0 2 -3 4 -2 -1 -5
Z -1 1 0 1 -3 4 5 -2 0 -3 -2 1 -1 -3 -1 0 -1 -2 -2 -3 1 -2 5 -1 -5
X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -5
* -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 -5 1
27 changes: 27 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/BLOSUM62
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# Entries for the BLOSUM62 matrix at a scale of ln(2)/2.0.
A R N D C Q E G H I L K M F P S T W Y V B J Z X *
A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 -1 -1 -4
R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 -2 0 -1 -4
N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 4 -3 0 -1 -4
D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 -3 1 -1 -4
C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -1 -3 -1 -4
Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 -2 4 -1 -4
E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 -3 4 -1 -4
G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -4 -2 -1 -4
H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 -3 0 -1 -4
I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 3 -3 -1 -4
L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 3 -3 -1 -4
K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 -3 1 -1 -4
M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 2 -1 -1 -4
F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 0 -3 -1 -4
P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -3 -1 -1 -4
S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 -2 0 -1 -4
T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 -1 -1 -4
W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -2 -2 -1 -4
Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -1 -2 -1 -4
V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 2 -2 -1 -4
B -2 -1 4 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4 -3 0 -1 -4
J -1 -2 -3 -3 -1 -2 -3 -4 -3 3 3 -3 2 0 -3 -2 -1 -2 -1 2 -3 3 -3 -1 -4
Z -1 0 0 1 -3 4 4 -2 0 -3 -3 1 -1 -3 -1 0 -1 -2 -2 -2 0 -3 4 -1 -4
X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -4
* -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 1
27 changes: 27 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/BLOSUM80
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# Entries for the BLOSUM80 matrix at a scale of ln(2)/2.0.
A R N D C Q E G H I L K M F P S T W Y V B J Z X *
A 5 -2 -2 -2 -1 -1 -1 0 -2 -2 -2 -1 -1 -3 -1 1 0 -3 -2 0 -2 -2 -1 -1 -6
R -2 6 -1 -2 -4 1 -1 -3 0 -3 -3 2 -2 -4 -2 -1 -1 -4 -3 -3 -1 -3 0 -1 -6
N -2 -1 6 1 -3 0 -1 -1 0 -4 -4 0 -3 -4 -3 0 0 -4 -3 -4 5 -4 0 -1 -6
D -2 -2 1 6 -4 -1 1 -2 -2 -4 -5 -1 -4 -4 -2 -1 -1 -6 -4 -4 5 -5 1 -1 -6
C -1 -4 -3 -4 9 -4 -5 -4 -4 -2 -2 -4 -2 -3 -4 -2 -1 -3 -3 -1 -4 -2 -4 -1 -6
Q -1 1 0 -1 -4 6 2 -2 1 -3 -3 1 0 -4 -2 0 -1 -3 -2 -3 0 -3 4 -1 -6
E -1 -1 -1 1 -5 2 6 -3 0 -4 -4 1 -2 -4 -2 0 -1 -4 -3 -3 1 -4 5 -1 -6
G 0 -3 -1 -2 -4 -2 -3 6 -3 -5 -4 -2 -4 -4 -3 -1 -2 -4 -4 -4 -1 -5 -3 -1 -6
H -2 0 0 -2 -4 1 0 -3 8 -4 -3 -1 -2 -2 -3 -1 -2 -3 2 -4 -1 -4 0 -1 -6
I -2 -3 -4 -4 -2 -3 -4 -5 -4 5 1 -3 1 -1 -4 -3 -1 -3 -2 3 -4 3 -4 -1 -6
L -2 -3 -4 -5 -2 -3 -4 -4 -3 1 4 -3 2 0 -3 -3 -2 -2 -2 1 -4 3 -3 -1 -6
K -1 2 0 -1 -4 1 1 -2 -1 -3 -3 5 -2 -4 -1 -1 -1 -4 -3 -3 -1 -3 1 -1 -6
M -1 -2 -3 -4 -2 0 -2 -4 -2 1 2 -2 6 0 -3 -2 -1 -2 -2 1 -3 2 -1 -1 -6
F -3 -4 -4 -4 -3 -4 -4 -4 -2 -1 0 -4 0 6 -4 -3 -2 0 3 -1 -4 0 -4 -1 -6
P -1 -2 -3 -2 -4 -2 -2 -3 -3 -4 -3 -1 -3 -4 8 -1 -2 -5 -4 -3 -2 -4 -2 -1 -6
S 1 -1 0 -1 -2 0 0 -1 -1 -3 -3 -1 -2 -3 -1 5 1 -4 -2 -2 0 -3 0 -1 -6
T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -2 -1 -1 -2 -2 1 5 -4 -2 0 -1 -1 -1 -1 -6
W -3 -4 -4 -6 -3 -3 -4 -4 -3 -3 -2 -4 -2 0 -5 -4 -4 11 2 -3 -5 -3 -3 -1 -6
Y -2 -3 -3 -4 -3 -2 -3 -4 2 -2 -2 -3 -2 3 -4 -2 -2 2 7 -2 -3 -2 -3 -1 -6
V 0 -3 -4 -4 -1 -3 -3 -4 -4 3 1 -3 1 -1 -3 -2 0 -3 -2 4 -4 2 -3 -1 -6
B -2 -1 5 5 -4 0 1 -1 -1 -4 -4 -1 -3 -4 -2 0 -1 -5 -3 -4 5 -4 0 -1 -6
J -2 -3 -4 -5 -2 -3 -4 -5 -4 3 3 -3 2 0 -4 -3 -1 -3 -2 2 -4 3 -3 -1 -6
Z -1 0 0 1 -4 4 5 -3 0 -4 -3 1 -1 -4 -2 0 -1 -3 -3 -3 0 -3 5 -1 -6
X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -6
* -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 1
30 changes: 30 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/KSat.flt
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; comments must have a semicolon in the first column or else
; unpredictable behavior after reading the file will result
; the first value is the type of filter; the second is info; then data
1 1 = filter 2 = matrix
250 if 1, window if 2, cutoff
100 { symbol "A", name "Adenine" },
33 { symbol "B" , name "G or T or C" },
0 { symbol "C", name "Cytosine" },
67 { symbol "D", name "G or A or T" },
; { symbol "", name "" },
; { symbol "", name "" },
0 { symbol "G", name "Guanine" },
67 { symbol "H", name "A or C or T" } ,
; { symbol "", name "" },
; { symbol "", name "" },
50 { symbol "K", name "G or T" },
; { symbol "", name ""},
50 { symbol "M", name "A or C" },
50 { symbol "N", name "A or G or C or T" } ,
; { symbol "", name "" },
; { symbol "", name "" },
; { symbol "", name ""},
50 { symbol "R", name "G or A"},
0 { symbol "S", name "G or C"},
100 { symbol "T", name "Thymine"},
; { symbol "", name ""},
33 { symbol "V", name "G or C or A"},
0 { symbol "W", name "A or T" },
; { symbol "", name ""},
50 { symbol "Y", name "T or C"}
29 changes: 29 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/KSchoth.flt
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;Chothia aa ; 16 character descriptor
;2 ; 1=Nucleic 2=Protein
1 ; 1=Filter 2-64=No. Pos. in Matrix
8 ; 3-256=Filter Window 0-100=Percent Cutoff Matrix Output
-0.27 A Ala
0.55 BB Asx
-0.23 C Cys Note: Columns 1-8 must contain 1 numeric value only
0.50 D Asp
0.33 E Glu
-0.55 F Phe
-0.22 G Gly
0.37 H His
-0.80 I Ile
1.17 K Lys
-0.44 L Leu
-0.31 M Met
0.61 N Asn
0.36 P Pro
1.00 Q Gln
2.00 R Arg
0.17 S Ser
0.18 T Thr
-0.65 V Val
0.05 W Trp
0.19 X- Unk
0.48 Y Tyr
0.66 ZZ Glx
0.19 ** ***
;Chothia -- dunno -- picked out of a FORTRAN file from Spouge
30 changes: 30 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/KSgc.flt
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@@ -0,0 +1,30 @@
; comments must have a semicolon in the first column or else
; unpredictable behavior after reading the file will result
; the first value is the type of filter; the second is info; then data
1 1 = filter 2 = matrix
250 if 1, window if 2, cutoff
0 { symbol "A", name "Adenine" },
67 { symbol "B" , name "G or T or C" },
100 { symbol "C", name "Cytosine" },
33 { symbol "D", name "G or A or T" },
; { symbol "", name "" },
; { symbol "", name "" },
100 { symbol "G", name "Guanine" },
33 { symbol "H", name "A or C or T" } ,
; { symbol "", name "" },
; { symbol "", name "" },
50 { symbol "K", name "G or T" },
; { symbol "", name ""},
50 { symbol "M", name "A or C" },
50 { symbol "N", name "A or G or C or T" } ,
; { symbol "", name "" },
; { symbol "", name "" },
; { symbol "", name ""},
50 { symbol "R", name "G or A"},
100 { symbol "S", name "G or C"},
0 { symbol "T", name "Thymine"},
; { symbol "", name ""},
67 { symbol "V", name "G or C or A"},
0 { symbol "W", name "A or T" },
; { symbol "", name ""},
50 { symbol "Y", name "T or C"}
31 changes: 31 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/KShopp.flt
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;Antigenicity ; 16 character descriptor
;2 ; 1=Nucleic 2=Protein
1 ; 1=Filter 2-64=No. Pos. in Matrix
6 ; 3-256=Filter Window 0-100=Percent Cutoff Matrix Output
-0.500 A Ala
1.600 BB Asx
-1.000 C Cys Note: Columns 1-8 must contain 1 numeric value only
3.000 D Asp
3.000 E Glu
-2.500 F Phe
0.000 G Gly
-0.500 H His
-1.800 I Ile
3.000 K Lys
-1.800 L Leu
-1.300 M Met
0.200 N Asn
0.000 P Pro
0.200 Q Gln
3.000 R Arg
0.300 S Ser
-0.400 T Thr
-1.500 V Val
-3.400 W Trp
-0.200 X- Unk
-2.300 Y Tyr
1.600 ZZ Glx
-0.200 ** ***
;Antigenicity - Hopp and Woods (Hydrophilicity Index)
;A window of 6 residues is the published (and recomended)
;value for calculating the moving average
31 changes: 31 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/KSkyte.flt
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;Hydropathy ; 16 character descriptor
;2 ; 1=Nucleic 2=Protein
1 ; 1=Filter 2-64=No. Pos. in Matrix
19 ; 3-256=Filter Window 0-100=Percent Cutoff Matrix Output
1.800 A Ala
-3.500 BB Asx
2.500 C Cys Note: Columns 1-8 must contain 1 numeric value only
-3.500 D Asp
-3.500 E Glu Note: This file is required for amphpathic helic
2.800 F Phe
-0.400 G Gly
-3.200 H His
4.500 I Ile
-3.900 K Lys
3.800 L Leu
1.900 M Met
-3.500 N Asn
-1.600 P Pro
-3.500 Q Gln
-4.500 R Arg
-0.800 S Ser
-0.700 T Thr
4.200 V Val
-0.900 W Trp
-0.490 X- Unk
-1.300 Y Tyr
-3.500 ZZ Glx
-0.490 ** ***
;Hydropathy - Kyte and Doolittle (Hydrophobicity Index)
;A window of 19 residues is the published (and recomended) value
;for calculating the moving average to find membrane spanning regions
25 changes: 25 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/KSpcc.mat
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; original matrix
A 1.297 1.551 1.084 2.612 0.377 1.248 0.877 ; A
B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 ; BDN
C 0.824 0.022 0.308 0.152 0.180 0.156 0.044 ; C
D 0.030 2.352 2.268 0.237 0.663 1.620 1.448 ; D
E 0.262 3.496 3.108 0.998 5.685 2.494 3.048 ; E
F 0.531 0.076 0.403 0.662 0.189 0.106 0.013 ; F
G 0.045 0.275 0.578 0.216 0.211 0.426 0.156 ; G
H 0.347 0.275 0.679 0.395 0.294 0.579 0.213 ; H
I 2.597 0.098 0.345 0.894 0.514 0.471 0.431 ; I
K 1.375 2.639 1.763 0.191 1.815 1.961 2.795 ; K
L 3.167 0.297 0.398 3.902 0.585 0.501 0.483 ; L
M 2.240 0.370 0.480 1.409 0.541 0.772 0.663 ; M
N 0.835 1.475 1.534 0.039 1.722 2.456 2.280 ; N
P 0.000 0.008 0.000 0.013 0.000 0.000 0.000 ; P
Q 0.179 2.114 1.778 0.631 2.550 1.578 2.526 ; Q
R 0.659 1.163 1.210 0.031 1.358 1.937 1.798 ; R
S 0.382 0.583 1.052 0.419 0.525 0.916 0.628 ; S
T 0.169 0.702 0.955 0.654 0.791 0.843 0.647 ; T
V 1.665 0.403 0.386 0.949 0.211 0.342 0.360 ; V
W 0.240 0.000 0.000 0.456 0.019 0.000 0.000 ; W
X 0.000 0.000 0.000 0.000 0.000 0.000 0.000 ; X-
Y 1.417 0.090 0.122 1.659 0.190 0.130 0.155 ; Y
Z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 ; ZEQ
* 0.000 0.000 0.000 0.000 0.000 0.000 0.000 ; **
30 changes: 30 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/KSpur.flt
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,30 @@
; comments must have a semicolon in the first column or else
; unpredictable behavior after reading the file will result
; the first value is the type of filter; the second is info; then data
1 1 = filter 2 = matrix
250 if 1, window if 2, cutoff
100 { symbol "A", name "Adenine" },
33 { symbol "B" , name "G or T or C" },
0 { symbol "C", name "Cytosine" },
67 { symbol "D", name "G or A or T" },
; { symbol "", name "" },
; { symbol "", name "" },
100 { symbol "G", name "Guanine" },
33 { symbol "H", name "A or C or T" } ,
; { symbol "", name "" },
; { symbol "", name "" },
50 { symbol "K", name "G or T" },
; { symbol "", name ""},
50 { symbol "M", name "A or C" },
50 { symbol "N", name "A or G or C or T" } ,
; { symbol "", name "" },
; { symbol "", name "" },
; { symbol "", name ""},
100 { symbol "R", name "G or A"},
50 { symbol "S", name "G or C"},
0 { symbol "T", name "Thymine"},
; { symbol "", name ""},
67 { symbol "V", name "G or C or A"},
50 { symbol "W", name "A or T" },
; { symbol "", name ""},
0 { symbol "Y", name "T or C"}
30 changes: 30 additions & 0 deletions Machine-Learning-for-Structural-Bioinformatics/PSSpred/PSSpred_v4/data/KSpyr.flt
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Original file line number Diff line number Diff line change
@@ -0,0 +1,30 @@
; comments must have a semicolon in the first column or else
; unpredictable behavior after reading the file will result
; the first value is the type of filter; the second is info; then data
1 1 = filter 2 = matrix
250 if 1, window if 2, cutoff
0 { symbol "A", name "Adenine" },
67 { symbol "B" , name "G or T or C" },
100 { symbol "C", name "Cytosine" },
33 { symbol "D", name "G or A or T" },
; { symbol "", name "" },
; { symbol "", name "" },
0 { symbol "G", name "Guanine" },
67 { symbol "H", name "A or C or T" } ,
; { symbol "", name "" },
; { symbol "", name "" },
50 { symbol "K", name "G or T" },
; { symbol "", name ""},
50 { symbol "M", name "A or C" },
50 { symbol "N", name "A or G or C or T" } ,
; { symbol "", name "" },
; { symbol "", name "" },
; { symbol "", name ""},
0 { symbol "R", name "G or A"},
50 { symbol "S", name "G or C"},
100 { symbol "T", name "Thymine"},
; { symbol "", name ""},
33 { symbol "V", name "G or C or A"},
0 { symbol "W", name "A or T" },
; { symbol "", name ""},
100 { symbol "Y", name "T or C"}

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