cfPred is a protein secondary structure prediction program, designed using C language. This program is redesigned using PREDICT.c program of Chou-Fasman-Prevelige Algorithm, published by Peter Prevelige, Jr., and Gerald D. Fasman in the book "Prediction of Protein Structure and the Principles of Protein Conformation". An online tool (MIX) of this program is available at http://cib.cf.ocha.ac.jp/bitool/MIX/.
cfPred runs in both 32-Bit and 64-Bit windows operating systems. cfPred is released in same flavor of the PREDICT program. The input of cfPred program must be in space delimited file format. Example:
E A L L K Q S W E V L K Q N I P G H S L C L F A L I I E A A P E S K Y V F S F L K D S N E I P E N N P K L K A H A A V I F K T I C E S A T E L R Q K G Q A V W D N N T L K R L G S I H L K N K I T D P H F E V M K G A L L G T I K E A V K E N W S D E M C C A W T E A Y N Q L V A T I K A E M K E
The current version of cfPred supports only space delimited file formatted input. FastaA file supported version of cfPred will be released soon.
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(α) P(β) 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:
- Assign all of the residues in the peptide the appropriate set of parameters.
- Scan through the peptide and identify regions where 4 out of 6 contiguous residues have P(α-helix) > 100. That region is declared an α-helix. Extend the helix in both directions until a set of four contiguous residues that have an average P(α-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(α-helix) > P(β-sheet) for that segment, the segment can be assigned as a helix.
- Repeat this procedure to locate all of the helical regions in the sequence.
- Scan through the peptide and identify a region where 3 out of 5 of the residues have a value of P(β-sheet) > 100. That region is declared as a β-sheet. Extend the sheet in both directions until a set of four contiguous residues that have an average P(β-sheet) < 100 is reached. That is declared the end of the β-sheet. Any segment of the region located by this procedure is assigned as a β-sheet if the average P(β-sheet) > 105 and the average P(β-sheet) > P(α-helix) for that region.
- Any region containing overlapping α-helical and β-sheet assignments are taken to be helical if the average P(α-helix) > P(β-sheet) for that region. It is a beta sheet if the average P(β-sheet) > P(α-helix) for that region.
- 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(α-helix) < P(turn) > P(β-sheet), then a β-turn is predicted at that location.