Contents PRO-Decoder Function Methods Results Abstract - PowerPoint PPT Presentation

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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 Transcriptional level PRO-Decoder

PRO-Decoder Abstract Methods Results Function Transcription: the binding of RNA polymeraze to promoter RNA polymerase

PRO-Decoder Abstract Methods Results Function Transcription: the binding of RNA polymeraze to promoter RNA polymerase Sigma factor

PRO-Decoder Abstract Methods Results Function Transcription: the binding of RNA polymerase to promoter

PRO-Decoder Abstract Methods Results Function Transcription: the binding of RNA polymeraze to promoter Consensus sequence Sigma factor binding site

PRO-Decoder PRO-Decoder Abstract Abstract Methods Methods Results Results Function Function Transcription: various sigma factors 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 Promoter Similarity type strength

PRO-Decoder Abstract Methods Results Function Other Transcription Factors (TF) TF similarity Consensus Trancription Factor Binding Site

PRO-Decoder Abstract Methods Results Function Sigma factor biding sites TSS TFBS Matrix S imilarity Score CTGACG TTGACA N17 TATAAT Consensus Consensus

PRO-Decoder Abstract Function Results Methods  Matrix Similarity Score Current Min  mss Position Weight Matrix ( PWM )  Max Min Posi- 1 2 3 4 5 … 13 tion L   Current I ( i ) f A 0.53 0.63 0.31 0.56 0.31 … 0.63 f 3 ， A i , bi T 0.09 0.07 0.40 0.09 0.19 … 0.21  i 1 G 0.07 0.08 0.11 0.12 0.07 … 0.02 L  L  min max Min : I ( i ) f C 0.31 0.22 0.18 0.23 0.43 … 0.14 Max : I ( i ) f i i  I(i) 0.28 0.37 0.11 0.25 0.16 … 0.41  i 1   i 1    I ( i ) f ln 4 f , i 1 , 2 ,..., L i , B i , B Position Frequency Matrix of Ada    B A , T , G , C f , Derived from RegulonDB i bi 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

PRO-Decoder Abstract Function Results Methods Recognition and location of sigma factor binding sites Similarity score=MSS(1)+MSS(2) spacer Promoter sequence -35 region -10 region MSS(1) MSS(2)

PRO-Decoder Abstract Function Results Methods Prediction of promoter strength Promoter sequence -10 region -35 region spacer MSS(1) MSS(2) Relative Strength = Similarity score Similarity score + Spacer score Spacer score

PRO-Decoder Methods Abstract Function Results Other possible TF 95% CGGATCCTAC AraC 12 0.96 CTGACGCTT TTCTCCATA BaeR 23 0.92 ATTGGCGC GTAAAGAT CpxR 44 0.85 GGGTAAA

PRO-Decoder Methods Abstract Function Results Data verification Sigma factor type: Type Sigma 70 Sigma 28 Sigma 24 Sigma 54 Sigma 38 Sigma 32 Sample size 50 10 10 10 10 10 Average 56% accuracy 92% (sigma 70) Accuracy 64% TFBS Location

PRO-Decoder Methods Abstract Function Results Promoter strength prediction - 10 Region Strength Prediction Promoter BBa _K206000 GATAGT 4437.2510 1.5214 BBa _K1070003

PRO-Decoder Methods Abstract Function Results Promoter strength prediction Experimental Results Prediction strength

PRO-Decoder Methods Abstract Function Results ATCATCCCGC 93% Position weight Similarity score+ Sigma70 matrix 15 spacer score 7 65% Ada 33 GCGCAAGATTG 0.987 TTGGTTTTTGCGT TTTCGTTTT AgaR 25 0.956 ATTTTTATCTC TAGCGGATCC AraC 56 0.924 TACCTGA

TER-Decoder Transcriptional level Translational level Transcriptional level PRO-Decoder

RBS-Decoder TER-Decoder Transcriptional level Translational level Transcriptional level PRO-Decoder

RBS-Decoder Methods Abstract 5' 3' Ribosome 3' 5' SD sequence Spacer Start codon

RBS-Decoder Abstract Methods PWM method 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 strength = MSS （ SD ） + spacer score GeneMark.hmm: new solutions for gene finding, Alexander V. Lukashin and Mark Borodovsky

RBS-Decoder Abstract Methods 0.92 Predicted Strength Actual and prediction 线性 (Predicted Strength) 0.9 strength correlation R² = 0.8039 Predicted Strength 0.88 0.86 0.84 0.82 0.8 0.03 0.05 0.07 0.09 0.11 0.13 Experimental Strength Correlation between the experimental data and prediction http://parts.igem.org/Ribosome_Binding_Sites/Prokaryotic/Constitutive/Community_Collection.

agataagatagcgataga Similarity score + spacer score AGGAG 12 ATG 100% Position weight matrix

RBS-Decoder TER-Decoder Transcriptional level Translational level Transcriptional level PRO-Decoder Synoproteiner

Synonymous Protein Analysis • Prediction •

SynoProteiner Analysis • Prediction •

SynoProteiner Extra Operation Future Theory Codon usage bias 3 1 Phe Ph UUU UUU UUC UUC Leu Leu UUG UUG UU UUA CUC CU CUU CU CUA CU CU CUG Single codon 30

SynoProteiner Extra Operation Future Theory Ideal Single point codon Best point UUU UUU UUA UU ACG GCU GC CCU CU GCG GC UUU UUU UUG UUG Codon pair Codon Pair UUC UUC UUG UUG Phe Phe Leu Leu 31

SynoProteiner Extra Operation Future Theory NSGA-II Algorithm g 1     t arg et g fit ( g ) r ( c ( k )) r ( c ( k )) sc sc sc g  k 1  g 1 1     fit ( g ) w ( c ( k ), c ( k 1 ))  cp g 1  k 1 Chung, B.; Lee, D.-Y., Computational codon China patent, 200780024670.5[P]. 2009-07-22 optimization of synthetic gene for protein expression. BMC systems biology 2012, 6 (1), 134.

SynoProteiner Extra Theory Future Operation

SynoProteiner Extra Theory Future Operation

SynoProteiner Extra Theory Future Operation

SynoProteiner Theory Operation Future Extra Analysis

SynoProteiner Theory Operation Future Extra Prediction   L 1 1      ( R R , )   1 i i 1 L 1   i 1   L 2 1      ( R R , )   2 i i 2 L 2   i 1   L 3 1        ( R R , ),( L )   3 i i 3  L 3  i 1      L 1     ( R R , )       i i L   i 1  Chou, K. C., Prediction of protein cellular attributes using pseudo ‐ amino acid composition. Proteins: Structure, Function, and Bioinformatics 2001 , 43 (3), 246-255.

SynoProteiner Theory Operation Future Extra Prediction   L 1 1      ( R R , )   1 i i 1 L 1   i 1   L 2 1      ( R R , )   2 i i 2 L 2   i 1   L 3 1        ( R R , ),( L )   3 i i 3  L 3  i 1      L 1     ( R R , )       i i L   i 1  Chou, K. C., Prediction of protein cellular attributes using pseudo ‐ amino acid composition. Proteins: Structure, Function, and Bioinformatics 2001 , 43 (3), 246-255.

SynoProteiner Theory Operation Future Extra Prediction 15   L 1 1     10  ( R R , )   1 i i 1 L 1   i 1   L 2 ln[kf(Predicted)/s] 1     5  ( R R , )   2 i i 2 L 2   i 1   L 3 1  0       ( R R , ),( L )  -15 -10 -5 0 5 10 15  3 i i 3  L 3  i 1  -5     L 1     ( R R , )  PREDICTION RESULTS      i i -10 L   i 1  THEORETICAL RESULTS -15 Chou, K. C., Prediction of protein cellular attributes using pseudo ‐ amino acid composition. ln[kf(Experimental)/s] Proteins: Structure, Function, and Bioinformatics 2001 , 43 (3), 246-255.

SynoProteiner Theory Extra Operation Future Time Accuracy Database 40

SynoProteiner Theory Extra Operation Future Optimization Analysis Prediction 41

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