1. Protein Structure Prediction by A Data-level Parallel Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Speaker : Chuan-Cheng Lin
Advisor :Prof. R. C. T. Lee
CSIE National Chi Nan University

2. Outline
Concepts
Introduction
Approach
Example
Conclusions
Reference

3. polypeptides ->amino acids4
Concepts
80,000 5
Cell, Chromosome, DNA, Gene
trillions
Protein synthesis
23 pairs 6
polypeptides ->amino acids
3 billions 1
DNA words 3 2

4. Concepts
Protein Synthesis
Transcription
Enzyme
Messenger RNA

5. Concepts
Protein Synthesis
Translation

6. Concepts

8. Concepts
Amino acid

9. Concepts
Peptide bound

10. Concepts

11. Concepts Protein Primary structure Secondary structure
Tertiary structure
Quaternary structure

12. Introduction What is protein? Why do we prediction protein structure?
X-ray
NMR
Known protein structure(22-Oct-2002 )
How?

13. Introduction Method of protein structure prediction AI Neural Network
PHI-PSI
Potential Energy
Statistical method …

14. Introduction To determine the native folded state of a
protein given only the primary sequence
of amino acids is referred to as the
protein folding problem.

15. Introduction The protein folding problem is, given an
amino acid sequence, to find its correctly
folded 3D protein structure.
Protein Folding in the Hydrophobic-
Hydrophilic(HP) Model is NP-Complete
[BL98].

16. Given a test protein sequence, we want
to compare every part of it against every
part of every protein in the database, then
to select some similar parts of proteins
in the database.

17. The Basic Algorithm Step 1:
Specify the initial parameters, such as the
initial windows size W, the window weight
pattern P, and N, the number of best
matches to keep.

18. 2.The five and seven are good choices for the initial window size.
1.Large or small
2.The five and seven are good
choices for the initial window
size.
3.A smaller windows is used
in finding the best matches for
prediction of the next larger
window.

19. The Weight Pattern: 1 2 3 4 5 P

20. The Basic Algorithm Step 2:
Move the window over the test protein sequence,
And at each position, extract an amino acid
segment S of length W, and do:

21. The Basic Algorithm 2-1. set the window size in every processor to
be of length W;
2-2. send S to every processor;
2-3. match S against all si , i=1,2,..,m in all the
processors, and compute a score for each si
using a scoring function;
2-4. select the N segments from {s1,…,sm} which
have the highest N scores.

22. Compute a score:

23. Why do we bother to use the top N matches rather
than just the one with the highest score?
Among the top N matchers, the majority have a
similar structure, then the input will at least have
the tendency to form that structure as well.

24. The Basic Algorithm Step 3:
If the recursive mode is chosen, adjust the
parameters (e.g. the window size) and repeat
Step 2 unless the end conditions are met or
PHI-PSI has gone though a pre-specified number
Recursive levels.

25. Example:
Step 1:
Initial parameters
W : 5
N : 2
Recursive level=1
Sr=0

26. The Weight Pattern: 1 2 3 4 5 P

27. The layout of the known protein structure data
Step 2-1:
The layout of the known protein structure data
on the Connection Machine
A L G G P E P Y
KP1: … A L G P
-64.19
106.63
-66.44
-92.02
-33.26
8.49
0.20
163.55
-6.88 G P E Y
-66.44
-92.02
-70.98
-84.58
163.55
-6.88
140.07
141.20
120.99
PHI
PSI …
Processor 1
Processor 4

28. A L G G A S E W … … KP2: A L G G A S E W PHI PSI Processor 5
-61.48
-94.70
83.20
-28.65
3.88
22.82
-8.01
142.77 G A S E W
-61.15
-8.01
142.77
-37.26
171.98
120.48
PHI
PSI …
Processor 5
Processor 8

29. P1: A L G G P
P2: L G G P E
P3: G G P E P
P4: G P E P Y
P5: A L G G A
P6: L G G A S
P7: G G A S E
P8: G A S E W

30. Step 2-2:
Testing protein sequence : ALGGPNAWTG
A L G G P N A W T G
S : ALGGP
Send S to P1~P8

31. P1: A L G G P
P2: L G G P E
P3: G G P E P
P4: G P E P Y
P5: A L G G A
P6: L G G A S
P7: G G A S E
P8: G A S E W
ALGGP

32. Step 2-3
S: ALGGP
P1: ALGGP

34. S: ALGGP
P2: LGGPE

35. S: ALGGP
P3: GGPEP

36. S: ALGGP
P4: GPEPY

37. S: ALGGP
P5: ALGGA

38. Step 2-4: Score 1=9 Score 2=3 Score 3=1.5 Score 4=0 Score 5=7.5

39. S: ALGGP A L G P A L G P1: PHI PSI P5: PHI PSI -64.19 -100.49 106.63
-66.44
-92.02
-33.26
8.49
0.20
163.55
-6.88
PHI
PSI
P5: A L G
-61.48
-94.70
83.20
-28.65
3.88
22.82
-8.01
142.77
PHI
PSI

40. S: test protein A L G P A L G P N W T PHI PSI PHI PSI -64.19 -100.49
106.63
-66.44
-92.02
-33.26
8.49
0.20
163.55
-6.88
PHI
PSI
test protein A L G P N W T
-64.19
106.63
-66.44
-92.02
-66.18
-73.71
-85.96
-33.26
8.49
0.20
163.55
-6.88
116.38
155.62
125.74
18.36
PHI
PSI

41. Step 3:
if Sr<=recursive level then
W=W+2
Sr++
go to Step 2
else
end

42. The Weight Pattern: 1 2 3 4 5 6 7 P

43. The layout of the known protein structure data
Step 2-1:
The layout of the known protein structure data
on the Connection Machine
A L G G P E P Y
KP1: A L G P E
-64.19
106.63
-66.44
-92.02
-70.98
-33.26
8.49
0.20
163.55
-6.88
140.07
141.20
Processor 1 L G P E Y
106.63
-66.44
-92.02
-70.98
-84.58
8.49
0.20
163.55
-6.88
140.07
141.20
120.99
Processor 2

44. A L G G A S E W KP2: A L G S E L G A S E W Processor 3 Processor 4
-61.48
-94.70
83.20
-61.15
-28.65
3.88
22.82
-8.01
142.77
-37.26
171.98
Processor 3 L G A S E W
-94.70
83.20
-61.15
3.88
22.82
-8.01
142.77
-37.26
171.98
120.48
Processor 4

45. Step 2-2:
Testing protein sequence : AALGGPNA…
A L G G P N A
S : ALGGPNA
Send S to P1~P4

46. P1: A L G G P E P
P2: L G G P E P Y
P3: A L G G A S E
P4: L G G A S E W
A L G G P N A

47. Step 2-3
S: ALGGPNA
P1: ALGGPEP

49. S: ALGGPNA
P2: LGGPEPY

51. S: ALGGPNA
P3: ALGGASE

53. S: ALGGPNA
P4: LGGASEW

55. Step 2-4: Score 1=-74.9 Score 2=-970.63 Score 3=-1592.74

56. A L G P E L G A S E W Processor 1 Processor 4 -64.19 -100.49 106.63
-66.44
-92.02
-70.98
-33.26
8.49
0.20
163.55
-6.88
140.07
141.20
Processor 1 L G A S E W
-94.70
83.20
-61.15
3.88
22.82
-8.01
142.77
-37.26
171.98
120.48
Processor 4

57. test protein A L G P N PHI PSI -64.19 -100.49 106.63 -66.44 -92.02
-70.98
-33.26
8.49
0.20
163.55
-6.88
140.07
141.20
PHI
PSI

58. Prediction errors The prediction errors are measured
in terms of PHI and PSI angles.
There are several ways to measure the
errors, such as:
Residue error
Overall errors

59. Residue errors – the difference between
the real angle values computed from the
3D coordinates and the values predicted
by the algorithm for a particular residue
in a protein.
Overall errors – the average of the residue
errors of all the proteins in the database.

60. Conclusions
Secondary Structure Prediction

62. Reference Protein Structure Prediction PDB (Protein Data Bank)
[BL98] Protein Folding in the Hydrophobic-Hydrophilic(HP) Model is NP-Complete, Berger, B. and Leighton, T., Journal of Computational Biology, Vol. 5, No. 1, 1998, pp
Protein Structure Prediction
PDB (Protein Data Bank)

63. Thank you