Protein Structure Prediction by A Data-level Parallel Proceedings of the 1989 ACM/IEEE conference on Supercomputing

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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

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Outline

• Concepts
• Introduction
• Approach
• Example
• Conclusions
• Reference

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Concepts
80,000
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Cell, Chromosome, DNA, Gene
trillions
Protein synthesis
23 pairs
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polypeptides -gtamino acids
3 billions
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DNA words
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2
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Concepts

• Protein Synthesis

Transcription
Enzyme
Messenger RNA
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Concepts

• Protein Synthesis

Translation
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Concepts
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(No Transcript)
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Concepts
Amino acid
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Concepts
Peptide bound
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Concepts
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Concepts

• Protein
• Primary structure
• Secondary structure
• Tertiary structure
• Quaternary structure

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Introduction

• What is protein?
• Why do we prediction protein structure?
• X-ray
• NMR
• Known 19006 protein structure(22-Oct-2002 )
• How?

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Introduction

• Method of protein structure prediction
• AI
• Neural Network
• PHI-PSI
• Potential Energy
• Statistical method

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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.
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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.
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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.
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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.
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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.
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The Weight Pattern
1 2 3 4 5
P 1 1 2 1 1
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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
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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 ,
i1,2,..,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.
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Compute a score
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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.
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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.
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Example
Step 1 Initial parameters W 5 N 2 Recursive
level1 Sr0
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The Weight Pattern
1 2 3 4 5
P 1 1 2 1 1
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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 G P
-64.19 -100.49 106.63 -66.44 -92.02
-33.26 8.49 0.20 163.55 -6.88
G P E P Y
-66.44 -92.02 -70.98 -70.98 -84.58
163.55 -6.88 140.07 141.20 120.99
PHI
PSI

Processor 1
Processor 4
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A L G G A S E W
KP2

A L G G A
-61.48 -94.70 83.20 -106.05 -136.41
-28.65 3.88 22.82 -8.01 142.77
G A S E W
-106.05 -136.41 -61.15 -153.99 -125.64
-8.01 142.77 -37.26 171.98 120.48
PHI
PSI

Processor 5
Processor 8
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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
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Step 2-2
Testing protein sequence ALGGPNAWTG
A L G G P N A W T G
S ALGGP
Send S to P1P8
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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
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Step 2-3
S ALGGP P1 ALGGP
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S ALGGP P2 LGGPE
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S ALGGP P3 GGPEP
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S ALGGP P4 GPEPY
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S ALGGP P5 ALGGA
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Step 2-4 Score 19
Score 23
Score 31.5 Score 40
Score 57.5
Score 63 Score 70
Score 80
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S ALGGP
P1
A L G G P
-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 G A
-61.48 -94.70 83.20 -106.05 -136.41
-28.65 3.88 22.82 -8.01 142.77
PHI
PSI
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S
A L G G P
-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 G P N A W T G
-64.19 -100.49 106.63 -66.44 -92.02 -108.72 -66.18 -73.71 -125.23 -85.96
-33.26 8.49 0.20 163.55 -6.88 116.38 155.62 125.74 18.36 -162.86
PHI
PSI
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Step 3 if
Srltrecursive level then WW2
Sr go to
Step 2 else end
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The Weight Pattern
1 2 3 4 5 6 7
P 1 2 2 3 2 2 1
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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 G P E P
-64.19 -100.49 106.63 -66.44 -92.02 -70.98 -70.98
-33.26 8.49 0.20 163.55 -6.88 140.07 141.20
Processor 1
L G G P E P Y
-100.49 106.63 -66.44 -92.02 -70.98 -70.98 -84.58
8.49 0.20 163.55 -6.88 140.07 141.20 120.99
Processor 2
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A L G G A S E W
KP2
A L G G A S E
-61.48 -94.70 83.20 -106.05 -136.41 -61.15 -153.99
-28.65 3.88 22.82 -8.01 142.77 -37.26 171.98
Processor 3
L G G A S E W
-94.70 83.20 -106.05 -136.41 -61.15 -153.99 -125.64
3.88 22.82 -8.01 142.77 -37.26 171.98 120.48
Processor 4
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Step 2-2
Testing protein sequence AALGGPNA
A L G G P N A
S ALGGPNA
Send S to P1P4
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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
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Step 2-3
S ALGGPNA P1 ALGGPEP
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S ALGGPNA P2 LGGPEPY
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S ALGGPNA P3 ALGGASE
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S ALGGPNA P4 LGGASEW
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Step 2-4 Score 1-74.9 Score 2-970.63 Score
3-1592.74 Score 4-860.25
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A L G G P E P
-64.19 -100.49 106.63 -66.44 -92.02 -70.98 -70.98
-33.26 8.49 0.20 163.55 -6.88 140.07 141.20
Processor 1
L G G A S E W
-94.70 83.20 -106.05 -136.41 -61.15 -153.99 -125.64
3.88 22.82 -8.01 142.77 -37.26 171.98 120.48
Processor 4
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test protein
A L G G P N A
-64.19 -100.49 106.63 -66.44 -92.02 -70.98 -70.98
-33.26 8.49 0.20 163.55 -6.88 140.07 141.20
PHI
PSI
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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

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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.
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Conclusions
Secondary Structure Prediction
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Reference

• BL98 Protein Folding in the Hydrophobic-Hydroph
  ilic(HP) Model is NP-Complete, Berger, B. and
  Leighton, T., Journal of Computational Biology,
  Vol. 5, No. 1, 1998, pp. 27-40.
• Protein Structure Prediction
• http//cmgm.stanford.edu/WWW/www_predict.html
• PDB (Protein Data Bank)
• http//www.rcsb.org/pdb/

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Thank you