The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time. A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results. Chou-Fasman algorithm is an empirical algorithm developed for the prediction of protein secondary structure

2. Contents…
• Importance of the Structures of proteins
• Prediction of 2D Structures
• Chou-Fasman Algorithm
• How it works!
Chou-Fasman Algorithm for Protein Prediction 2

3. What is chou-fasman algorithm?
• The experimental methods used by biotechnologists
to determine the structures of proteins demand
sophisticated equipment and time.
• A host of computational methods are developed to
predict the location of secondary structure elements
in proteins for complementing or creating insights
into experimental results.
• Chou-Fasman algorithm is an empirical algorithm
developed for the prediction of protein secondary
structure
Chou-Fasman Algorithm for Protein Prediction 3

4. Before we go…..
• Structures of proteins……
• Why study of structures are important….
• What is the need of an algorithm ….
Chou-Fasman Algorithm for Protein Prediction 4

5. Chou-Fasman Algorithm for Protein Prediction 5

6. Secondary structure prediction
• In either case, amino acid propensities should be
useful for predicting secondary structure
• Two classical methods that use previously
determined propensities:
• Chou-Fasman
• Garnier-Osguthorpe-Robson
Chou-Fasman Algorithm for Protein Prediction 6

7. Goal…
• Take primary structure (sequence) and, using rules
derived from known structures, predict the
secondary structure that is most likely to be
adopted by each residue
• Major classes are a-helices, b-sheets and loops
Chou-Fasman Algorithm for Protein Prediction 7

8. Structural Propensities
• Due to the size, shape and charge of its side chain,
each amino acid may “fit” better in one type of
secondary structure than another
• Classic example: The rigidity and side chain angle of
proline cannot be accomodated in an a-helical
structure
Chou-Fasman Algorithm for Protein Prediction 8

9. Structural Propensities
• Two ways to view the significance of this
preference (or propensity)
• It may control or affect the folding of the protein in its
immediate vicinity (amino acid determines structure)
• It may constitute selective pressure to use particular
amino acids in regions that must have a particular
structure (structure determines amino acid)
Chou-Fasman Algorithm for Protein Prediction 9

10. Chou-Fasman method
• Uses table of conformational parameters
(propensities) determined primarily from
measurements of secondary structure by CD
spectroscopy
• Table consists of one “likelihood” for each structure
for each amino acid
Chou-Fasman Algorithm for Protein Prediction 10

11. Chou-Fasman Algorithm for Protein Prediction 11
Chou-Fasman Algorithm
• Conformational parameters
for every amino acid (AA):
P(a) = propensity in an alpha helix P(b) = propensity in a beta
sheet P(turn) = propensity in a turn
Based on observed propensities in proteins of known structure

12. Chou-Fasman propensities
(partial table)
Amino Acid Pa Pb Pt
Glu 1.51 0.37 0.74
Met 1.45 1.05 0.60
Ala 1.42 0.83 0.66
Val 1.06 1.70 0.50
Ile 1.08 1.60 0.50
Tyr 0.69 1.47 1.14
Pro 0.57 0.55 1.52
Gly 0.57 0.75 1.56
Chou-Fasman Algorithm for Protein Prediction 12

13. Chou-Fasman method
• A prediction is made for each type of structure for
each amino acid
• Can result in ambiguity if a region has high propensities
for both helix and sheet (higher value usually chosen,
with exceptions)
Chou-Fasman Algorithm for Protein Prediction 13

14. Chou-Fasman method
• Calculation rules are somewhat ad hoc
• Example: Method for helix
• Search for nucleating region where 4 out of 6 a.a. have
Pa > 1.03
• Extend until 4 consecutive a.a. have an average Pa < 1.00
• If region is at least 6 a.a. long, has an average Pa > 1.03,
and average Pa > average Pb consider region to be helix
Chou-Fasman Algorithm for Protein Prediction 14

15. • Scan the peptide and identify regions where 3 out
of 5 contiguous residues have P(β)>100.
• These residues nucleate β- strands. Extend these in
both directions until a set of four contiguous
residues have an average P(β)<100.
• This ends β- strand.
Chou-Fasman Algorithm for Protein Prediction 15

16. • region containing overlapping α and β Any
assignment are taken to be helical or β depending
on if the average P(α) and P(β) for that region is
largest.
• If this residues an α or β- region so that it
becomes less than 5 residues, the α or β
assignment for that region is removed.
Chou-Fasman Algorithm for Protein Prediction 16

17. Chou-Fasman Algorithm for Protein Prediction 17
SPASEASDGQSVSV
P(a) P(b)
S: 77 75
P: 55 55
A: 142 83
S: 77
SPASEASDGQFETTY
P(a) P(b)
E: 151 37
A: 142 83
S: 77 75
D: 101 54
G: 57
Q: 111 1) 4 of 6, P(a) > 100
2) Extend RIGHT until 4 contiguous
Residues have P(a) < 100
3) Calculate SP(a) and SP(b). Is SP(a) >
SP(b)? (Do Not Include last 4 in
sum)
Find potential alpha
helix:
MFCTYYGNNGEHIELMM
MFCTYYGNNGEHIELMM

18. Accuracy of Chou-Fasman predictions
• Sequences whose 3D structures are known are processed so
that each residue is “assigned” to a given secondary
structure class by looking at the backbone angles
• Three classes most often used (helix=H, sheet=E, turn=C)
but sometimes use four classes (helix, sheet, turn, loop)
Chou-Fasman Algorithm for Protein Prediction 18
Conclusion…..

19. Confusion matrix for Chou-Fasman method
on 78 proteins
Predicted
True
H E C Unknown
H 47.5 3.0 4.3 45.2
E 20.8 16.8 7.1 55.4
C 6.4 3.6 38.0 52.0
Data from Z-Y Zhu, Protein Engineering 8:103-109, 1995
Average accuracy =54.4

21. Thank You!