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Contents PRO-Decoder Function Methods Results Abstract Experiment Computer RBS-Decoder TER-Decoder Transcriptional level Translational level Transcriptional level PRO-Decoder Synoproteinerc Transcriptional level Translational level

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SLIDE 1
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SLIDE 2

Contents

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SLIDE 3

Experiment Computer

PRO-Decoder

Abstract Function Methods Results

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SLIDE 4

Translational level Transcriptional level Transcriptional level

PRO-Decoder RBS-Decoder TER-Decoder Synoproteinerc

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SLIDE 5

Translational level Transcriptional level Transcriptional level

PRO-Decoder

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SLIDE 6

Transcription: the binding of RNA polymeraze to promoter

RNA polymerase

PRO-Decoder

Abstract Function Methods Results

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SLIDE 7

Transcription: the binding of RNA polymeraze to promoter

RNA polymerase

Sigma factor

PRO-Decoder

Abstract Function Methods Results

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SLIDE 8

Transcription: the binding of RNA polymerase to promoter

PRO-Decoder

Abstract Function Methods Results

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SLIDE 9

Transcription: the binding of RNA polymeraze to promoter

Consensus sequence Sigma factor binding site

PRO-Decoder

Abstract Function Methods Results

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SLIDE 10

Transcription: various sigma factors Promoter strength Similarity

PRO-Decoder

Abstract Function Methods Results

Type Consensus Spacer Consensus Sigma 70 TTGACA 15-20 TATAAT Sigma 54 TGGCAC 5 TTGCW Sigma S / / CTATACT Sigma 32 CTTGAAA 11-16 CCCATNT Sigma 28 TAAA 15 GCCGATAA Sigma 24 GAACTT 16-17 TCTRA

Promoter type

PRO-Decoder

Abstract Function Methods Results

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SLIDE 11

Other Transcription Factors (TF)

Trancription Factor Binding Site Consensus

similarity

TF

PRO-Decoder

Abstract Function Methods Results

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SLIDE 12

Consensus Consensus

imilarity S Matrix Score

PRO-Decoder

Abstract Function Methods Results CTGACG TTGACA N17 TATAAT

TFBS Sigma factor biding sites TSS

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SLIDE 13

Matrix Similarity Score

Position Weight Matrix ( PWM )

Kel, A. E.; Gößling, E.; Reuter, I.; Cheremushkin, E.; Kel-Margoulis, O. V.; Wingender, E., MATCHTM: a tool for searching transcription factor binding sites in DNA sequences. Nucleic acids research 2003, 31 (13), 3576-3579

Min Max Min Current mss   

L i bi i

f i I Current

1 ,

) (

 

 

L i f f i I

C G T A B B i B i

,..., 2 , 1 , 4 ln ) (

, , , , ,

 

bi i

f , Derived from RegulonDB Posi- tion

1 2 3 4 5 … 13 A 0.53 0.63 0.31 0.56 0.31 … 0.63 T 0.09 0.07 0.40 0.09 0.19 … 0.21 G 0.07 0.08 0.11 0.12 0.07 … 0.02 C 0.31 0.22 0.18 0.23 0.43 … 0.14 I(i) 0.28 0.37 0.11 0.25 0.16 … 0.41 Position Frequency Matrix of Ada

A ， 3

f

PRO-Decoder

Abstract Function Methods Results

 L i i

f i I Max

1 max

) ( :

 L i i

f i I Min

1 min

) ( :

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SLIDE 14

Recognition and location of sigma factor binding sites

Similarity score=MSS(1)+MSS(2)

MSS(1) MSS(2)

  • 35 region
  • 10 region

spacer

Promoter sequence

PRO-Decoder

Abstract Function Methods Results

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SLIDE 15

spacer

MSS(1) MSS(2)

  • 35 region
  • 10 region

Promoter sequence

Relative Strength =

Prediction of promoter strength

Similarity score Spacer score

PRO-Decoder

Abstract Function Methods Results

+ Similarity score Spacer score

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SLIDE 16

Other possible TF

44 12 BaeR CpxR 23

CGGATCCTAC CTGACGCTT

AraC

TTCTCCATA ATTGGCGC GTAAAGAT GGGTAAA

0.96 0.92 0.85

PRO-Decoder

Abstract Function Methods Results

95%

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SLIDE 17

Sigma factor type:

TFBS Location (sigma 70) Accuracy 64%

Type Sigma 70 Sigma 28 Sigma 24 Sigma 54 Sigma 38 Sigma 32 Sample size 50 10 10 10 10 10 Average accuracy

92%

56%

PRO-Decoder

Abstract Function Methods Results

Data verification

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SLIDE 18

Promoter strength prediction Promoter BBa _K206000 GATAGT

  • 10 Region

BBa _K1070003 4437.2510 1.5214

PRO-Decoder

Abstract Function Methods Results

Strength Prediction

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SLIDE 19

Experimental Results Prediction strength

Promoter strength prediction

PRO-Decoder

Abstract Function Methods Results

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SLIDE 20

93% Sigma70 Ada AgaR AraC 33 25 56 7 15 65% ATCATCCCGC

GCGCAAGATTG TTGGTTTTTGCGT TTTCGTTTT ATTTTTATCTC TAGCGGATCC TACCTGA

0.987 0.956 0.924

PRO-Decoder

Abstract Function Methods Results

Position weight matrix Similarity score+ spacer score

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SLIDE 21

Translational level Transcriptional level Transcriptional level

PRO-Decoder TER-Decoder

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SLIDE 22

Translational level Transcriptional level Transcriptional level

PRO-Decoder RBS-Decoder TER-Decoder

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SLIDE 23

Spacer Start codon SD sequence 3' 5' 5' 3'

Ribosome

RBS-Decoder

Abstract Methods

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SLIDE 24

PWM method

RBS strength = MSS（SD）+ spacer score

GeneMark.hmm: new solutions for gene finding, Alexander V. Lukashin and Mark Borodovsky

Table 1. Nucleotide frequencies for the RBS model

Nucleotide Position

1 2 3 4 5 T 0.161 0.050 0.012 0.071 0.115 C 0.077 0.037 0.012 0.025 0.046 A 0.681 0.105 0.105 0.861 0.164 G 0.077 0.808 0.960 0.043 0.659

RBS-Decoder

Abstract Methods

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SLIDE 25

Correlation between the experimental data and prediction

http://parts.igem.org/Ribosome_Binding_Sites/Prokaryotic/Constitutive/Community_Collection.

RBS-Decoder

Abstract Methods

R² = 0.8039 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.03 0.05 0.07 0.09 0.11 0.13 Predicted Strength 线性 (Predicted Strength)

Predicted Strength Experimental Strength

Actual and prediction strength correlation
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SLIDE 26

AGGAG 12 ATG 100%

agataagatagcgataga

Position weight matrix Similarity score + spacer score

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SLIDE 27

Translational level Transcriptional level Transcriptional level

PRO-Decoder RBS-Decoder TER-Decoder Synoproteiner

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SLIDE 28
  • Analysis
  • Prediction

Protein Synonymous

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SLIDE 29
  • Analysis
  • Prediction

SynoProteiner

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SLIDE 30 30

UUU UUU UUC UUC UU UUA UUG UUG CU CUU CU CUC CU CUA CU CUG

Ph Phe Leu Leu

Codon usage bias

3 1

Single codon

SynoProteiner

Theory Operation Extra Future

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SLIDE 31 31

UUU UUU UU UUA UUC UUC UUG UUG UUU UUU UUG UUG

Phe Phe Leu Leu

Codon Pair

GC GCG CCU CU GC GCU ACG Best point

Codon pair Single codon Ideal point

SynoProteiner

Theory Operation Extra Future

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SLIDE 32

NSGA-II Algorithm

  

g k g sc et t sc sc

k c r k c r g g fit

1 arg

)) ( ( )) ( ( 1 ) (

 

   

1 1

)) 1 ( ), ( ( 1 1 ) (

g k cp

k c k c w g g fit

Chung, B.; Lee, D.-Y., Computational codon

  • ptimization of synthetic gene for protein expression.

BMC systems biology 2012, 6 (1), 134. China patent, 200780024670.5[P]. 2009-07-22

SynoProteiner

Theory Operation Extra Future

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SLIDE 33

SynoProteiner

Theory Operation Extra Future

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SLIDE 34

SynoProteiner

Theory Operation Extra Future

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SLIDE 35

SynoProteiner

Theory Operation Extra Future

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SLIDE 36

SynoProteiner

Theory Operation Extra Future

Analysis

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SLIDE 37

SynoProteiner

Theory Operation Extra Future

Prediction

1 1 1 1 2 2 2 1 3 3 3 1 1

1 ( , ) 1 1 ( , ) 2 1 ( , ),( ) 3 1 ( , )

L i i i L i i i L i i i L i i i

R R L R R L R R L L R R L

  

     

           

                           

   

Chou, K. C., Prediction of protein cellular attributes using pseudo‐amino acid composition. Proteins: Structure, Function, and Bioinformatics 2001, 43 (3), 246-255.

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SLIDE 38

SynoProteiner

Theory Operation Extra Future

Prediction

1 1 1 1 2 2 2 1 3 3 3 1 1

1 ( , ) 1 1 ( , ) 2 1 ( , ),( ) 3 1 ( , )

L i i i L i i i L i i i L i i i

R R L R R L R R L L R R L

  

     

           

                           

   

Chou, K. C., Prediction of protein cellular attributes using pseudo‐amino acid composition. Proteins: Structure, Function, and Bioinformatics 2001, 43 (3), 246-255.

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SLIDE 39

SynoProteiner

Theory Operation Extra Future

1 1 1 1 2 2 2 1 3 3 3 1 1

1 ( , ) 1 1 ( , ) 2 1 ( , ),( ) 3 1 ( , )

L i i i L i i i L i i i L i i i

R R L R R L R R L L R R L

  

     

           

                           

   

Chou, K. C., Prediction of protein cellular attributes using pseudo‐amino acid composition. Proteins: Structure, Function, and Bioinformatics 2001, 43 (3), 246-255.

  • 15
  • 10
  • 5
5 10 15
  • 15
  • 10
  • 5
5 10 15

ln[kf(Predicted)/s] ln[kf(Experimental)/s]

PREDICTION RESULTS THEORETICAL RESULTS

Prediction

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SLIDE 40 40

Time Accuracy Database

SynoProteiner

Theory Operation Extra Future

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SLIDE 41 41

Analysis Prediction Optimization

SynoProteiner

Theory Operation Extra Future

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SLIDE 42

Contents

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SLIDE 43

The sequence The process of recording experiment

Optimize

E ' NOTE

Abstract Templates Future Tools

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SLIDE 44

Hard Waste Boring

E ' NOTE

Abstract Templates Future Tools

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SLIDE 45
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SLIDE 46

Web app

E ' NOTE

Abstract Templates Future Tools

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SLIDE 47

Web app Multi-users

E ' NOTE

Abstract Templates Future Tools

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SLIDE 48

Web app Multi-users Templates

E ' NOTE

Abstract Templates Future Tools

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SLIDE 49

Web app Multi-users Templates

E ' NOTE

Abstract Templates Future Tools

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SLIDE 50

Auto-calculate?

User E' NOTE

Yes, I can!

E' NOTE

E ' NOTE

Abstract Templates Future Tools

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SLIDE 51

Auto-filling?

User E' NOTE

Yes, I can!

E' NOTE

E ' NOTE

Abstract Templates Future Tools

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SLIDE 52

Can you connect a plasmid database to it?

User E' NOTE

Yes, I can!

E' NOTE

Abstract Templets Future Tools

E ' NOTE

Abstract Templates Future Tools

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SLIDE 53

Can you organize the plasmid database?

User E' NOTE

Yes, I can!

E' NOTE

E ' NOTE

Abstract Templates Future Tools

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SLIDE 54

Templates may not be enough…

User E' NOTE

It doesn’t matter.

E' NOTE

E ' NOTE

Abstract Templates Future Tools

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SLIDE 55

Templates may not be enough…

User E' NOTE

It doesn’t matter.

E' NOTE

E ' NOTE

Abstract Templates Future Tools

slide-56
SLIDE 56

Templates may not be enough…

User E' NOTE

It doesn’t matter.

E' NOTE

E ' NOTE

Abstract Templates Future Tools

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SLIDE 57

EASIER！

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SLIDE 58

Data-output & wiki-build

I am sick of writing experiment to the wiki.

User

E ' NOTE

Abstract Templates Future Tools

E' NOTE

Don’t worry

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SLIDE 59

XMU-China has used it.

I am sick of writing experiment to the wiki.

User

E ' NOTE

Abstract Templates Future Tools

E' NOTE

Don’t worry

slide-60
SLIDE 60

XMU-China has used it.

I am sick of writing experiment to the wiki.

User

E ' NOTE

Abstract Templates Future Tools

E' NOTE

Don’t worry

slide-61
SLIDE 61

E ' NOTE

Abstract Templates Future Tools

slide-62
SLIDE 62

E ' NOTE

Abstract Templates Future Tools

From Internet:

slide-63
SLIDE 63

E ' NOTE

Abstract Templates Future Tools

From Wellesley:

slide-64
SLIDE 64

E ' NOTE

Abstract Templates Future Tools

slide-65
SLIDE 65

E ' NOTE

Abstract Templates Future Tools

slide-66
SLIDE 66

Image-upload Web-app Multi-user E-mail sender Templates Plasmid database Record Picture Text Table Wiki Experiment record XML Data output Tools PHP HTML5 Javascript CSS3

E ' NOTE, designed for iGEMers.

E ' NOTE

Abstract Templates Future Tools

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SLIDE 67

Contents

slide-68
SLIDE 68

Human Practice

Lecture Party Communication

slide-69
SLIDE 69

Human Practice

Lecture Party Communication

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SLIDE 70

Safety prombles on information The warm scene of lecture

Human Practice

Lecture Party Communication

slide-71
SLIDE 71 2013/11/2
slide-72
SLIDE 72

He Help lp th them em st stud udy Co Conv nvenient enient Br Brea eak k th the e bar arric ricad ades es No No id idea ea Yes es No No

Interest In E'NOTE Reasons

Human Practice

Lecture Party Communication

slide-73
SLIDE 73

Lantern riddles Finding differences in DNA Foldit Protein Game Synthetic-biology-kill

Human Practice

Lecture Party Communication

slide-74
SLIDE 74
slide-75
SLIDE 75
slide-76
SLIDE 76

Xiamen and Peiking iGEMers iGEMers from Xiamen University and Nanjing University

Human Practice

Lecture Party Communication

slide-77
SLIDE 77

Review

PRO-Decoder

Predict the strength of promoter and RBS

Best

slide-78
SLIDE 78

Review

Synoproteiner

Analysis and opitimize the protein

Best

slide-79
SLIDE 79

Review

E‘ NOTE

Best

Record and simplify experiments

slide-80
SLIDE 80

Analysis and opitimize the protein

Synoproteiner

Review

Predict the strength of promoter and RBS

PRO- Decoder

E‘ NOTE

Record and simplify experiments

slide-81
SLIDE 81 2013/11/2
slide-82
SLIDE 82
  • Prof. Zhiliang Ji
  • Prof. Shoufa Han

Hongchun Li Tina Zhang Team XMU China Qiang Kou Wenjun Rao

Acknowledge

slide-83
SLIDE 83
slide-84
SLIDE 84
slide-85
SLIDE 85
slide-86
SLIDE 86

China patent, 200780024670.5[P]. 2009-07-22

Appendix

Fitness:

))) , (( )), , (( max( )) , (( )) , (( )) , ((

exp exp j i combi j i high

  • bs

j i high

  • bs

j i combi j i

c c n c c n c c n c c n c c w  

 

  

) ( ) ( exp

)) , (( ) ( ) ( )) , ((

j l i k

c syn c c syn c j i high

  • bs

j all sc i all sc j i combi

c c n c r c r c c n

) ( ) ( 2 ) (

arg k all sc k high sc k et t sc

c r c r c r   

slide-87
SLIDE 87 87

Prediction:

1 1 1 1 2 2 2 1 3 3 3 1 1

1 ( , ) 1 1 ( , ) 2 1 ( , ),( ) 3 1 ( , )

L i i i L i i i L i i i L i i i

R R L R R L R R L L R R L

  

     

           

                           

   

  • 1. Chou, K. C., Prediction of protein cellular attributes using pseudo‐amino acid composition. Proteins: Structure, Function, and Bioinformatics 2001, 43 (3), 246-255.
  • 2. Galzitskaya, O. V.; Garbuzynskiy, S. O.; Ivankov, D. N.; Finkelstein, A. V., Chain length is the main determinant of the folding rate for proteins with three‐state folding
  • kinetics. Proteins: Structure, Function, and Bioinformatics 2003, 51 (2), 162-166.

i i

R , ) ( ) ( )

j j

R H R H R    （

Appendix