Chou–Fasman Method - 16 August 2011 - BioInformatics Pakistan
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Chou–Fasman Method


Chou–Fasman Method

1. Description:
   The Chou–Fasman method are an empirical technique for the prediction of secondary structures in proteins, originally developed in the 1970s.The method is based on analyses of the relative frequencies of each amino acid in alpha helices, beta sheets, and turns based on known protein structures solved with X-ray crystallography. From these frequencies a set of probability parameters were derived for the appearance of each amino acid in each secondary structure type, and these parameters are used to predict the probability that a given sequence of amino acids would form a helix, a beta strand, or a turn in a protein. The method is at most about 50–60% accurate in identifying correct secondary structures,which is significantly less accurate than the modern machine learning–based techniques.

2. Amino Acid Propensities:
   The original Chou–Fasman parameters found some strong tendencies among individual amino acids to prefer one type of secondary structure over others. Alanine, glutamate, leucine, and methionine were identified as helix formers, while proline and glycine, due to the unique conformational properties of their peptide bonds, commonly end a helix. The original Chou–Fasman parameters were derived from a very small and non-representative sample of protein structures due to the small number of such structures that were known at the time of their original work. These original parameters have since been shown to be unreliable and have been updated from a current dataset, along with modifications to the initial algorithm.
   The Chou–Fasman method takes into account only the probability that each individual amino acid will appear in a helix, strand, or turn. Unlike the more complex GOR method, it does not reflect the conditional probabilities of an amino acid to form a particular secondary structure given that its neighbors already possess that structure. This lack of cooperativity increases its computational efficiency but decreases its accuracy, since the propensities of individual amino acids are often not strong enough to render a definitive prediction.

3. Propensity Table
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

4. Algorithm
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: 
      • p(t) > 0.000075; 
      • The average value for P(turn) > 1.00 in the tetrapeptide
      • 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.



Category: Algorithms | Views: 740 | Added by: Ansari | Rating: 0.0/0
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