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T ASHOK KUMAR

cfPred

cfPred is an C language application developed using Chou & Fasman algorithm. It is a command user interface (CUI) application runs in both 32-Bit and 64-Bit Windows platforms. The algorithm basics of Chou & Fasman algorithm is bellow:

Chou-Fasman Algorithm:

The Chou-Fasman method of secondary structure prediction depends on assigning a set of prediction values to a residue and then applying a simple algorithm to those numbers. The table of numbers for 29 proteins database is as follows:

Name           P(a)   P(b)   P(turn)    f(i)    f(i+1)  f(i+2)  f(i+3)

Alanine        142     83       66      0.06    0.076   0.035   0.058
Arginine        98     93       95      0.070   0.106   0.099   0.085
Aspartic Acid  101     54      146      0.147   0.110   0.179   0.081
Asparagine      67     89      156      0.161   0.083   0.191   0.091
Cysteine        70    119      119      0.149   0.050   0.117   0.128
Glutamic Acid  151    037       74      0.056   0.060   0.077   0.064
Glutamine      111    110       98      0.074   0.098   0.037   0.098
Glycine         57     75      156      0.102   0.085   0.190   0.152
Histidine      100     87       95      0.140   0.047   0.093   0.054
Isoleucine     108    160       47      0.043   0.034   0.013   0.056
Leucine        121    130       59      0.061   0.025   0.036   0.070
Lysine         114     74      101      0.055   0.115   0.072   0.095
Methionine     145    105       60      0.068   0.082   0.014   0.055
Phenylalanine  113    138       60      0.059   0.041   0.065   0.065
Proline         57     55      152      0.102   0.301   0.034   0.068
Serine          77     75      143      0.120   0.139   0.125   0.106
Threonine       83    119       96      0.086   0.108   0.065   0.079
Tryptophan     108    137       96      0.077   0.013   0.064   0.167
Tyrosine        69    147      114      0.082   0.065   0.114   0.125
Valine         106    170       50      0.062   0.048   0.028   0.053

The actual algorithm contains a few simple steps:

  1. Assign all of the residues in the peptide the appropriate set of parameters.
  2. Scan through the peptide and identify regions where 4 out of 6 contiguous residues have
    P(a-helix) > 100. That region is declared an alpha-helix. Extend the helix in both directions until a set of four contiguous residues that have an average P(a-helix) < 100 is reached. That is declared the end of the helix. If the segment defined by this procedure is longer than 5 residues and the average P(a-helix) > P(b-sheet) for that segment, the segment can be assigned as a helix.
  3. Repeat this procedure to locate all of the helical regions in the sequence.
  4. Scan through the peptide and identify a region where 3 out of 5 of the residues have a value of
    P(b-sheet) > 100. That region is declared as a beta-sheet. Extend the sheet in both directions until a set of four contiguous residues that have an average P(b-sheet) < 100 is reached. That is declared the end of the beta-sheet. Any segment of the region located by this procedure is assigned as a beta-sheet if the average P(b-sheet) > 105 and the average P(b-sheet) > P(a-helix) for that region.
  5. Any region containing overlapping alpha-helical and beta-sheet assignments are taken to be helical if the average P(a-helix) > P(b-sheet) for that region.
    It is a beta sheet if the average P(b-sheet) > P(a-helix) for that region.
  6. To identify a bend at residue number j, calculate the following value
    p(t) = f(j)f(j+1)f(j+2)f(j+3)
    where the f(j+1) value for the j+1 residue is used, the f(j+2) value for the j+2 residue is used and the f(j+3) value for the j+3 residue is used. If: (1) p(t) > 0.000075; (2) the average value for
    P(turn) > 1.00 in the tetrapeptide; and (3) the averages for the tetrapeptide obey the inequality
    P(a-helix) < P(turn) > P(b-sheet), then a beta-turn is predicted at that location.