Supplemental Data. Lee et al. (2008). Arabidopsis nuclear-encoded plastid transit peptides contain multiple sequence subgroups with distinctive chloroplast-targeting sequence motifs.

Supplemental Figure S1. A dendrogram constructed by hierarchical clustering of 208 chloroplast transit peptides in Arabidopsis thaliana. The first 80 residues of each sequence were defined as the N-terminal transit peptide. Multiple sequence alignment was performed using CLUSTALX 1.83 with default parameter settings (pairwise alignment gap opening: 9; pairwise alignment gap extension: 0.10; protein weight matrix: BLOSUM series) (Thompson et al., 1997). The distance matrix for sequence alignment was estimated with Protdist of PHYLIP 3.66 with default parameter settings (Felsenstein, 1989). The dendrogram for hierarchical clustering was constructed by using UPGMA method. The UPGMA method was carried out using Neighbor of PHYLIP 3.66. The dendrogram was drawn by PhyloDraw 0.82 (Choi et al., 2000). The seven representative transit peptides in their subgroups are indicated by arrows.

Supplemental Figure S2. Fractionation analysis of selected alanine substitution mutants on a Percoll gradient. (A), (B) Protoplasts transformed with GFP-fused T2A and T4A of DnaJ-J8 (A) or GFP-fused T2A, T4A, and T6A of PORA (B) were gently lysed and fractionated on a Percoll gradient. Intact chloroplasts were obtained and analyzed by Western blotting using an anti-GFP antibody. The total protein extracts were included as control. Pre, precursor; Pro, processed mature form.

Supplemental Figure S3. In vitro translation of alanine substitution mutants. (A), (B) To confirm that the upper bands of GFP-fused T2A, T3A and T4A of DnaJ-J8 (A) or GFP-fused T2A, T4A and T6A of PORA (B) are precursor forms, these mutant constructs together with their wild-type counterparts were translated in vitro and their migration in SDS gels was compared. The alanine substitution mutants migrated slightly faster than their corresponding wild-types, indicating that the upper band observed in protoplasts corresponds to precursor forms.

Supplemental Figure S4. The performance of the motif discovery algorithm was degraded by removal of sequences downstream of predicted cleavage sites. (A), (B) The predicted sequence motifs (blue) of RbcS, BCCP, Cab, DnaJ-J8, and PORA are shown with the sequence motifs characterized experimentally in this study (red). The predicted motifs were obtained with 208experimentally-confirmed plastid transit peptides, where sequences downstream of the predicted cleavage sites were removed.

Supplemental Figure S5. GFP reporter constructs with the N-terminal transit peptide region upstream of the cleavage sites were not efficiently imported into chloroplasts. (A) The amino acid sequences of RbcS, BCCP, Cab, DnaJ-J8, and PORA. The regions used to construct Cab-cs:GFP, BCCP-cs:GFP, DnaJ-J8-cs:GFP, and PORA-cs:GFP are underlined. cs, cleavage site predicted by ChloroP, except the Cab transit peptide. (B) In vivo targeting of GFP fusion proteins. Protoplasts were transformed with the constructs indicated and localization was examined 8 h after transformation. To simplify labeling, GFP is omitted from the construct names. Bar, 20 mm. (C) Western blot analysis of reporter construct import. Protein extracts were prepared 8 h after transformation and analyzed by immunoblotting using an anti-GFP antibody. The targeting efficiencies of Cab-cs:GFP, BCCP-cs:GFP, DnaJ-J8-cs:GFP, and PORA-cs:GFP were compared to those of Cab-nt:GFP, BCCP-nt:GFP, DnaJ-J8-nt:GFP, and PORA-nt:GFP, respectively. Pre, precursor form; Pro, processed mature form. (D) Confirmation of import into chloroplasts. Protoplasts were transformed with the indicated constructs together with an RFP construct. Chloroplasts were purified from lysed protoplasts by Percoll gradient and chloroplast extracts (CH) were analyzed by Western blotting using an anti-GFP antibody. The total extracts (T) were included. As a control for cytosolic proteins, co-transformed RFP was detected with an anti-RFP antibody. RbcL was used as loading control.

Supplemental Figure S6. Plastid transit peptide motif predictionscheme. The model has two main steps for motif discovery: forward motif selection and backward motif elimination. In forward motif selection, a motif segment which minimizes the misclassification error bound is added stepwise into the motif set. Then, the backward motif elimination step deletes a motif segment stepwise from a motif set chosen from the forward motif selection. We eliminate the motif segment producing the smallest value of the error bound at each iteration and stop when the smallest value is larger than the previous smallest value. The output is the predicted sequence motifs of the query sequence.

Supplemental Figure S7. Overview of plastid transit peptides prediction. Prediction of plastid transit peptides has three main steps: (1) clustering plastid transit peptides, (2) extracting features from sequences, and (3) learning an SVM classifier. The first step partitions all plastid transit peptides of the training data into several groups sharing common sequence motifs. The second step converts each sequence of the training data into the corresponding feature vector based on the representative sequences from the first step. The last step learns an SVM classifier where the input vector is the one obtained from the second step. The learned SVM classifier takes an input sequence and predicts plastid transit peptides.

Supplemental Figure S8. Gaussian distribution assessment of alignment score significance. Histograms of the alignment scores of 10,000 randomly permuted RbcS transit peptide sequences with BCCP transit peptides (A) or BCCP sequence motifs (B). The red curve shows fit to Gaussian distribution.

Supplemental Table S1. The nucleotide sequences of primers used to construct various alanine substation mutants. CaMV 35S-T TTTCAgAAAgAATgCTAACC nosT-B GAACGATCGGGGAAATTC Cab-5 CAACTCAATATggCTACCACC Cab-3 AGGCATCCAGTGAGCAGCCA CabT1A-T

TAAAGAAGAACAATGGCGGCGGCCGCGGCTGCGGCCGCTGCCGCAGCCGCCGTGTACCCTTCG

CabT1A-B

CGAAGGGTACACGGCGGCTGCGGCAGCGGCCGCAGCCGCGGCCGCCGCCATTGTTCTTCTTTA

CabT2A-T AGCTGTGGCATAGCCGCCGCGGCCGCTGCGGCTGCCGCTGCTTCCAAGTCTAAATTCGTATC

CabT2A-B GATACGAATTTAGACTTGGAAGCAGCGGCAGCCGCAGCGGCCGCGGCGGCTATGCCACAGCT

CabT3A-T CCTTCGCTTCTCTCTTCTGCCGCGGCTGCAGCCGCAGCCGCCGCAGCTCCACTCCCAAACGCCGGG

CabT3A-B CCCGGCGTTTGGGAGTGGAGCTGCGGCGGCTGCGGCTGCAGCCGCGGCAGAAGAGAGAAGCGAAGG

CabT4A-T TTCGTATCCGCCGGAGTTGCAGCCGCAGCCGCCGCGGCTGCTGCTGCTATCAGAATGGCTGCTCAC

CabT4A-B

GTGAGCAGCCATTCTGATAGCAGCAGCAGCCGCGGCGGCTGCGGCTGCAACTCCGGCGGATACGAA

CabT5A-T

GCCGGGAATGTTGGTCGTGCCGCAGCGGCTGCTGCCGCGGCGGCTGCCGAGCCACGACCAGCTTAC

CabT5A-B

GTAAGCTGGTCGTGGCTCGGCAGCCGCCGCGGCAGCAGCCGCTGCGGCACGACCAACATTCCCGGC

CabT6A-T

GCTCACTGGATGCCTGGCGCGGCAGCAGCAGCTGCCGCTGCCGCTGCTGCTCCTGGTGACTTTGGG

CabT6A-B GCTCACTGGATGCCTGGCGCGGCAGCAGCAGCTGCCGCTGCCGCTGCTGCTCCTGGTGACTTTGGG

Cab6-T

GCCGCAGCGGCTGCAGCCGCGGCTGCAGCTTCTGCTCCTGGTGACTTTGG

Cab6-B

AGCTGCAGCCGCGGCTGCAGCCGCTGCGGCAGGCATCCAGTGAGCAGCCA

Cab1-1

ATGGCGGCCGCCGCGGCTGCGAGCTGTGGCATAGCCGCCG

Cab3-1T

TCTCTTCTTCCGCGGCTGCAGCGGTATCCGCCGGAGTTCCACTC

Cab3-1B CGGATACCGCTGCAGCCGCGGAAGAAGAGAGAAGCGAAGG Cab3-2T AATTCGCAGCCGCCGCAGCTCCACTCCCAAACGCCGGGAAT Cab3-2B GGGAGTGGAGCTGCGGCGGCTGCGAATTTAGACTTGGAAGAAGACab4-1T GGAGTTGCAGCCGCAGCCGCCGGGAATGTTGGTCGTATCAGA Cab4-1B ACATTCCCGGCGGCTGCGGCTGCAACTCCGGCGGATACGAATTT Cab4-2T ACGCCGCGGCAGCCGCAGCTATCAGAATGGCTGCTCACTGG Cab4-2B CATTCTGATAGCTGCGGCTGCCGCGGCGTTTGGGAGTGGAACTCCBCCP-T (Xba1) TCTAGAAAAGAGTAAAGAAGAACAATGGCGTCTTCGTCGTTC

BCCP-B (Xho1) CTCGAGCCCATCAACTTTGGCAGC BC-T2A-T CGTTCTCAGTCACATCTCCAGCAGCCGCGGCTGCAGCCGCGG

CTGCAGCTCAAACCTCCTCGCACTTCCC BC-T2A-B GGGAAGTGCGAGGAGGTTTGAGCTGCAGCCGCGGCTGCAGCCG

CGGCTGCTGGAGATGTGACTGAGAACG BC-T3A-T CTTCCGTCTATGCAGTCACTGCAGCCGCGGCTGCAGCCGCGGCTG

CAGCTCGCTCTCGCAGAGTTTCTTT BC-T3A-B AAAGAAACTCTGCGAGAGCGAGCTGCAGCCGCGGCTGCAGCCG

CGGCTGCAGTGACTGCATAGACGGAAG BC-T4A-T CGCACTTCCCAATCCAAAACGCAGCCGCGGCTGCAGCCGCGGCT

GCAGCTGCTAAGCCCAAGCTTCGCTT BC-T4A-B AAGCGAAGCTTGGGCTTAGCAGCTGCAGCCGCGGCTGCAGCCGC

GGCTGCGTTTTGGATTGGGAAGTGCG BC-T5A-T GAGTTTCTTTCCGTCTCTCTGCAGCCGCGGCTGCAGCCGCGGCTG

CAGCTCCTAGTCGCAGTAGCTACCC BC-T5A-B GGGTAGCTACTGCGACTAGGAGCTGCAGCCGCGGCTGCAGCCGC

GGCTGCAGAGAGACGGAAAGAAACTC BC-T6A-T AGCTTCGCTTTCTCTCCAAGGCAGCCGCGGCTGCAGCCGCGGCT

GCAGCTGCACAATCTAACAAGGTTAG BC-T6A-B CTAACCTTGTTAGATTGTGCAGCTGCAGCCGCGGCTGCAGCCGCG

GCTGCCTTGGAGAGAAAGCGAAGCT BC-T7A-T GTAGCTACCCTGTGGTGAAAGCAGCCGCGGCTGCAGCCGCGGCT

GCAGCTTCATCAAATGCTGCCAAAGT BC-T7A-B ACTTTGGCAGCATTTGATGAAGCTGCAGCCGCGGCTGCAGCCGC

GGCTGCTTTCACCACAGGGTAGCTAC BC-T3A+QTSSH-T

TCACTCAAACCTCCTCGCACGCTGCCGCAGCCGCGCGCTCTCGCAGAGTTTCTTT

BC-T3A+QTSSH-B

AAAGAAACTCTGCGAGAGCGCGCGGCTGCGGCAGCGTGCGAGGAGGTTTGAGTGA

BC-T3A+FPIQN-T

CTTCCGTCTATGCAGTCACTGCTGCCGCAGCCGCGTTCCCAATCCAAAACCGCTC

BC-T3A+FPIQN-B

GAGCGGTTTTGGATTGGGAACGCGGCTGCGGCAGCAGTGACTGCATAGACGGAAG

BC-T5A+AKPKL-T

TCTCTGCTAAGCCCAAGCTTGCTGCCGCAGCCGCGCCTAGTCGCAGTAGCTACCC

BC-T5A+AKPKL-B

GGGTAGCTACTGCGACTAGGCGCGGCTGCGGCAGCAAGCTTGGGCTTAGCAGAGA

BC-T5A+RFLSK-T

GAGTTTCTTTCCGTCTCTCTGCTGCCGCAGCCGCGCGCTTTCTCTCCAAGCCTAG

BC-T5A+RFLSK-B

CTAGGCTTGGAGAGAAAGCGCGCGGCTGCGGCAGCAGAGAGACGGAAAGAAACTC

BC-T4A+RSRRV-T

AAAACCGCTCTCGCAGAGTTGCTGCCGCAGCCGCGGCTAAGCCCAAGCTTCGCTT

BC-T4A+RSRRV-B

AAGCGAAGCTTGGGCTTAGCCGCGGCTGCGGCAGCAACTCTGCGAGAGCGGTTTT

BC-T4A+SFRLS CGCACTTCCCAATCCAAAACGCTGCCGCAGCCGCGTCTTTCCGTC

-T TCTCTGCTAA BC-T4A+SFRLS-B

TTAGCAGAGAGACGGAAAGACGCGGCTGCGGCAGCGTTTTGGATTGGGAAGTGCG

BC-T6A+PSRSS-T

CCAAGCCTAGTCGCAGTAGCGCTGCCGCAGCCGCGGCACAATCTAACAAGGTTAG

BC-T6A+PSRSS-B

CTAACCTTGTTAGATTGTGCCGCGGCTGCGGCAGCGCTACTGCGACTAGGCTTGG

BC-T6A+YPVVK-T

AGCTTCGCTTTCTCTCCAAGGCTGCCGCAGCCGCGTACCCTGTGGTGAAAGCACA

BC-T6A+YPVVK-B

TGTGCTTTCACCACAGGGTACGCGGCTGCGGCAGCCTTGGAGAGAAAGCGAAGCT

DnaJ-J8-T (XbaI)

TCTAGAAAAGAGTAAAGAAGAACAATGACAATTGCTTTAACG

DnaJ-J8-T (XhoI)

CTCGAGTTTAGCGAGTTGTCTGAA

J8T2A-T CTTTAACGATCGGAGGAAACGCAGCCGCGGCTGCAGCCGCGGCTGCAGCTTCTTCATCATCTTCGTCGTT

J8T2A-B AACGACGAAGATGATGAAGAAGCTGCAGCCGCGGCTGCAGCCGCGGCTGCGTTTCCTCCGATCGTTAAAG

J8T3A-T GTCTACCAGGATCGTCGTTTGCAGCCGCGGCTGCAGCCGCGGCTGCAGCTAACAGCAGAAGAAAGAACAC

J8T3A-B GTGTTCTTTCTTCTGCTGTTAGCTGCAGCCGCGGCTGCAGCCGCGGCTGCAAACGACGATCCTGGTAGAC

J8T4A-T CTTCGTCGTTTCGATTAAAAGCAGCCGCGGCTGCAGCCGCGGCTGCAGCTAACAGATCAAAAGTCGTTTG

J8T4A-B CAAACGACTTTTGATCTGTTAGCTGCAGCCGCGGCTGCAGCCGCGGCTGCTTTTAATCGAAACGACGAAG

J8T5A-T GAAAGAACACGAAGATGCTCGCAGCCGCGGCTGCAGCCGCGGCTGCAGCTTCTTCTGTAATGGATCCGTA

J8T5A-B TACGGATCCATTACAGAAGAAGCTGCAGCCGCGGCTGCAGCCGCGGCTGCGAGCATCTTCGTGTTCTTTC

J8T6A-T AAGTCGTTTGTTCTTCTTCAGCAGCCGCGGCTGCAGCCGCGGCTGCAGCTAAGATCCGACCCGATTCATC

J8T6A-B GATGAATCGGGTCGGATCTTAGCTGCAGCCGCGGCTGCAGCCGCGGCTGCTGAAGAAGAACAAACGACTT

J8T2A+GFSGL-T

GAAACGGGTTTTCGGGTCTAGCAGCCGCGGCTGCATCTTCATCATCTTCGTCGTT

J8T2A+GFSGL-B

AACGACGAAGATGATGAAGATGCAGCCGCGGCTGCTAGACCCGAAAACCCGTTTC

J8T2A+PGSSF-T

CTTTAACGATCGGAGGAAACGCAGCCGCGGCTGCACCAGGATCGTCGTTTTCTTC

J8T2A+PGSSF-B

GAAGAAAACGACGATCCTGGTGCAGCCGCGGCTGCGTTTCCTCCGATCGTTAAAG

J8T3A+SSSSS-T CGTTTTCTTCATCATCTTCGGCAGCCGCGGCTGCAAACAGCAGAAGAAAGAACAC

J8T3A+SSSSS- GTGTTCTTTCTTCTGCTGTTTGCAGCCGCGGCTGCCGAAGATGAT

B GAAGAAAACG J8T3A+SFRLK-T

GTCTACCAGGATCGTCGTTTGCAGCCGCGGCTGCATCGTTTCGATTAAAAAACAG

J8T3A+SFRLK-B

CTGTTTTTTAATCGAAACGATGCAGCCGCGGCTGCAAACGACGATCCTGGTAGAC

J8T4A+NSRRK-T

TAAAAAACAGCAGAAGAAAGGCAGCCGCGGCTGCAAACAGATCAAAAGTCGTTTG

J8T4A+NSRRK-B

CAAACGACTTTTGATCTGTTTGCAGCCGCGGCTGCCTTTCTTCTGCTGTTTTTTA

J8T4A+NTKML-T

CTTCGTCGTTTCGATTAAAAGCAGCCGCGGCTGCAAACACGAAGATGCTCAACAG

J8T4A+NTKML-B

CTGTTGAGCATCTTCGTGTTTGCAGCCGCGGCTGCTTTTAATCGAAACGACGAAG

J8T5A+NRSKV-T

TGCTCAACAGATCAAAAGTCGCAGCCGCGGCTGCATCTTCTGTAATGGATCCGTA

J8T5A+NRSKV-B

TACGGATCCATTACAGAAGATGCAGCCGCGGCTGCGACTTTTGATCTGTTGAGCA

J8T5A+VCSSS-T

TGCTCAACAGATCAAAAGTCGCAGCCGCGGCTGCATCTTCTGTAATGGATCCGTA

J8T5A+VCSSS-B

TACGGATCCATTACAGAAGATGCAGCCGCGGCTGCGACTTTTGATCTGTTGAGCA

PORA-T (XbaI) TCTAGAAAAGAGTAAAGAAGAACAATGGCCCTTCAAGCTGCTTC PORA-B (XhoI) CTCGAGTGTGACTGATGGAGTTGAAG PORA T2A-T AAGCTGCTTCTTTGGTCTCCGCAGCCGCGGCTGCAGCCGCGGCT

GCAGCTTTAAATGCTTCAGCATCATC PORA T2A-B GATGATGCTGAAGCATTTAAAGCTGCAGCCGCGGCTGCAGCCGC

GGCTGCGGAGACCAAAGAAGCAGCTT PORA T3A-T CTGTCCGCAAAGATGGAAAAGCAGCCGCGGCTGCAGCCGCGGCT

GCAGCTGAGTCTAGTCTGTTCGGTGT PORA T3A-B ACACCGAACAGACTAGACTCAGCTGCAGCCGCGGCTGCAGCCGC

GGCTGCTTTTCCATCTTTGCGGACAG PORA T4A-T CAGCATCATCATCATTCAAAGCAGCCGCGGCTGCAGCCGCGGCT

GCAGCTGAGCAAAGCAAAGCTGACTT PORA T4A-B AAGTCAGCTTTGCTTTGCTCAGCTGCAGCCGCGGCTGCAGCCGC

GGCTGCTTTGAATGATGATGATGCTG PORA T5A-T TGTTCGGTGTTTCACTTTCGGCAGCCGCGGCTGCAGCCGCGGCT

GCAGCTTCATTGAGATGCAAGAGGGA PORA T5A-B TCCCTCTTGCATCTCAATGAAGCTGCAGCCGCGGCTGCAGCCGCG

GCTGCCGAAAGTGAAACACCGAACA PORA T6A-T AAGCTGACTTTGTCTCTTCCGCAGCCGCGGCTGCAGCCGCGGCT

GCAGCTAGGAATAATAAAGCGATTAT PORA T6A-B ATAATCGCTTTATTATTCCTAGCTGCAGCCGCGGCTGCAGCCGCGG

CTGCGGAAGAGACAAAGTCAGCTT PT2A+SAFSV-T TCTCCTCTGCTTTCTCTGTCGCAGCCGCGGCTGCATTAAATGCTTC

AGCATCATC PT2A+SAFSV-B GATGATGCTGAAGCATTTAATGCAGCCGCGGCTGCGACAGAGAA

AGCAGAGGAGA PT2A+RKDGK-T

AAGCTGCTTCTTTGGTCTCCGCAGCCGCGGCTGCACGCAAAGATGGAAAATTAAA

PT2A+RKDGK-B

TTTAATTTTCCATCTTTGCGTGCAGCCGCGGCTGCGGAGACCAAAGAAGCAGCTT

PT3A+LNASA-T

GAAAATTAAATGCTTCAGCAGCAGCCGCGGCTGCAGAGTCTAGTCTGTTCGGTGT

PT3A+LNASA-B

ACACCGAACAGACTAGACTCTGCAGCCGCGGCTGCTGCTGAAGCATTTAATTTTC

PT3A+SSSFK-T CTGTCCGCAAAGATGGAAAAGCAGCCGCGGCTGCATCATCATCATTCAAAGAGTC

PT3A+SSSFK-B GACTCTTTGAATGATGATGATGCAGCCGCGGCTGCTTTTCCATCTTTGCGGACAG

PT4A+ESSLF-T TCAAAGAGTCTAGTCTGTTCGCAGCCGCGGCTGCAGAGCAAAGCAAAGCTGACTT

PT4A+ESSLF-B AAGTCAGCTTTGCTTTGCTCTGCAGCCGCGGCTGCGAACAGACTAGACTCTTTGA

PT4A+GVSLS-T

CAGCATCATCATCATTCAAAGCAGCCGCGGCTGCAGGTGTTTCACTTTCGGAGCA

PT4A+GVSLS-B

TGCTCCGAAAGTGAAACACCTGCAGCCGCGGCTGCTTTGAATGATGATGATGCTG

PT5A+EQSKA-T

TTTCGGAGCAAAGCAAAGCTGCAGCCGCGGCTGCATCATTGAGATGCAAGAGGGA

PT5A+EQSKA-B

TCCCTCTTGCATCTCAATGATGCAGCCGCGGCTGCAGCTTTGCTTTGCTCCGAAA

PT5A+DFVSS-T TGTTCGGTGTTTCACTTTCGGCAGCCGCGGCTGCAGACTTTGTCTCTTCCTCATT

PT5A+DFVSS-B

AATGAGGAAGAGACAAAGTCTGCAGCCGCGGCTGCCGAAAGTGAAACACCGAACA

PT6A+SLRCK-T

CTTCCTCATTGAGATGCAAGGCAGCCGCGGCTGCAAGGAATAATAAAGCGATTAT

PT6A+SLRCK-B

ATAATCGCTTTATTATTCCTTGCAGCCGCGGCTGCCTTGCATCTCAATGAGGAAG

PT6A+REQSL-T

AAGCTGACTTTGTCTCTTCCGCAGCCGCGGCTGCAAGGGAACAGAGCTTGAGGAA

PT6A+REQSL-B

TTCCTCAAGCTCTGTTCCCTTGCAGCCGCGGCTGCGGAAGAGACAAAGTCAGCTT

GLU2(Xba1)-T CTAG TCTAGAATGGCTCTACAGTCTCCCGG GLU2(Xho1)-B CCG CTCGAG GGCTCGGTCAGAATTAAGGA GLU2 T1-hy-5 CTAG TCTAGA ATGGCT GCT CAGTCT GCTGCT GCTACC GCT

GCTTCATCTTCCGTTTCC GLU2 T2A-T TCTCCCGGAGCTACCGGAGCAGCCGCGGCTGCAGCCGCGGCTGC

AGCT TCCGCGAAATTAAGCTCT GLU2 T2A-B AGAGCTTAATTTCGCGGAAGCTGCAGCCGCGGCTGCAGCCGCGG

CTGCTCCGGTAGCTCCGGGAGA GLU2 T3A-T GTTTCCCGGCTTCTCTCTGCAGCCGCGGCTGCAGCCGCGGCTGC

AGCTTTCTCTGTTGACTTCGTC GLU2 T3A-B GACGAAGTCAACAGAGAAAGCTGCAGCCGCGGCTGCAGCCGCG

GCTGCAGAGAGAAGCCGGGAAAC GLU2 T4A-T AGCTCTACTAAGACTATCGCAGCCGCGGCTGCAGCCGCGGCTGC

AGCTATTTCTAAAGGAACCAAA GLU2 T4A-B TTTGGTTCCTTTAGAAATAGCTGCAGCCGCGGCTGCAGCCGCGGC

TGCGATAGTCTTAGTAGAGCT GLU2 T5A-T TTCGTCAGATCCTACTGTGCAGCCGCGGCTGCAGCCGCGGCTGC

AGCTCTCTCCGGCTTTCGCGGC GLU2 T5A-B GCCGCGAAAGCCGGAGAGAGCTGCAGCCGCGGCTGCAGCCGCG

GCTGCACAGTAGGATCTGACGAA GLU2 T6A-T ACCAAACGGCGTAACGAAGCAGCCGCGGCTGCAGCCGCGGCTG

CAGCTCTCAAGTCCTCGCTGAGG GLU2 T6A-B CCTCAGCGAGGACTTGAGAGCTGCAGCCGCGGCTGCAGCCGCG

GCTGCTTCGTTACGCCGTTTGGT GLU2 T3A+SAKLS-T

TCTTCCGCGAAATTAAGCGCAGCCGCGGCTGCATTCTCTGTTGACTTCGTC

GLU2 T3A+SAKLS-B

GACGAAGTCAACAGAGAATGCAGCCGCGGCTGCGCTTAATTTCGCGGAAGA

GLU2 T3A+STKTI-T

GTTTCCCGGCTTCTCTCTGCAGCCGCGGCTGCATCTACTAAGACTATCTTC

GLU2 T3A+STKTI-B

GAAGATAGTCTTAGTAGATGCAGCCGCGGCTGCAGAGAGAAGCCGGGAAAC

GLU2 T4A+FSVDF-T

ATCTTCTCTGTTGACTTCGCAGCCGCGGCTGCAATTTCTAAAGGAACCAAA

GLU2 T4A+FSVDF-B

TTTGGTTCCTTTAGAAATTGCAGCCGCGGCTGCGAAGTCAACAGAGAAGAT

GLU2 T4A+VRSYC-T

AGCTCTACTAAGACTATCGCAGCCGCGGCTGCAGTCAGATCCTACTGTATT

GLU2 T4A+VRSYC-B

AATACAGTAGGATCTGACTGCAGCCGCGGCTGCGATAGTCTTAGTAGAGCT

GLU2 T5A+ISKGT-T

TGTATTTCTAAAGGAACCGCAGCCGCGGCTGCACTCTCCGGCTTTCGCGGC

GLU2 T5A+ISKGT-B

GCCGCGAAAGCCGGAGAGTGCAGCCGCGGCTGCGGTTCCTTTAGAAATACA

GLU2 T5A+KRRNE-T

TTCGTCAGATCCTACTGTGCAGCCGCGGCTGCAAAACGGCGTAACGAACTC

GLU2 T5A+KRRNE-B

GAGTTCGTTACGCCGTTTTGCAGCCGCGGCTGCACAGTAGGATCTGACGAA

TOCC (xba1)-T CTAG TCTAGAATGGAGATACGGAGCTTG TOCC (xho1)-B CCG CTCGAGACTGTGAGGAGTCCGGAG TOCC T1-hy-5 CTAGTCTAGAATGGAGGCGCGGAGCGCGGCAGCATCTGCGAACC

CTAATTTATCTTCC TOCC T2A-T AGCTTGATTGTTTCTATGGCAGCCGCGGCTGCAGCCGCGGCTGCA

GCTCGCCCTGTATCTCCTCTC TOCC T2A-B GAGAGGAGATACAGGGCGAGCTGCAGCCGCGGCTGCAGCCGCG

GCTGCCATAGAAACAATCAAGCT TOCC T3A-T TCTTCCTTTGAGCTCTCTGCAGCCGCGGCTGCAGCCGCGGCTGCA

GCTGTTCCGTTCCGATCGACT TOCC T3A-B AGTCGATCGGAACGGAACAGCTGCAGCCGCGGCTGCAGCCGCG

GCTGCAGAGAGCTCAAAGGAAGA TOCC T4A-T CCTCTCACTCGCTCACTAGCAGCCGCGGCTGCAGCCGCGGCTGC

AGCTCGCTCCATTTCTAGGGTT TOCC T4A-B AACCCTAGAAATGGAGCGAGCTGCAGCCGCGGCTGCAGCCGCGG

CTGCTAGTGAGCGAGTGAGAGG TOCC T5A-T TCGACTAAACTAGTTCCCGCAGCCGCGGCTGCAGCCGCGGCTGC

AGCTTCCACCCCGAATAGTGAA TOCC T5A-B TTCACTATTCGGGGTGGAAGCTGCAGCCGCGGCTGCAGCCGCGG

CTGCGGGAACTAGTTTAGTCGA TOCC T6A-T AGGGTTTCGGCGTCGATCGCAGCCGCGGCTGCAGCCGCGGCTGC

AGCTTCCGTTAAACCTGTTTAC TOCC T6A-B GTAAACAGGTTTAACGGAAGCTGCAGCCGCGGCTGCAGCCGCGG

CTGCGATCGACGCCGAAACCCT TOCC T3A+RPVSP-T

TCTCGCCCTGTATCTCCTGCAGCCGCGGCTGCAGTTCCGTTCCGATCGACT

TOCC T3A+RPVSP-B

AGTCGATCGGAACGGAACTGCAGCCGCGGCTGCAGGAGATACAGGGCGAGA

TOCC T3A+LTRSL-T

TCTTCCTTTGAGCTCTCTGCAGCCGCGGCTGCACTCACTCGCTCACTAGTT

TOCC T3A+LTRSL-B

AACTAGTGAGCGAGTGAGTGCAGCCGCGGCTGCAGAGAGCTCAAAGGAAGA

TOCC T4A+VPFRS-T

CTAGTTCCGTTCCGATCGGCAGCCGCGGCTGCACGCTCCATTTCTAGGGTT

TOCC T4A+VPFRS-B

AACCCTAGAAATGGAGCGTGCAGCCGCGGCTGCCGATCGGAACGGAACTAG

TOCC T4A+TKLVP-T

CCTCTCACTCGCTCACTAGCAGCCGCGGCTGCAACTAAACTAGTTCCCCGC

TOCC T4A+TKLVP-B

GCGGGGAACTAGTTTAGTTGCAGCCGCGGCTGCTAGTGAGCGAGTGAGAGG

TOCC T5A+RSISR-T

CCCCGCTCCATTTCTAGGGCAGCCGCGGCTGCATCCACCCCGAATAGTGAA

TOCC T5A+RSISR-B

TTCACTATTCGGGGTGGATGCAGCCGCGGCTGCCCTAGAAATGGAGCGGGG

TOCC T5A+VSASI-T

TCGACTAAACTAGTTCCCGCAGCCGCGGCTGCAGTTTCGGCGTCGATCTCC

TOCC T5A+VSASI-B

GGAGATCGACGCCGAAACTGCAGCCGCGGCTGCGGGAACTAGTTTAGTCGA

Primers for in vitro translation in wheat germ extracts T7p CabM GGC CAG TGA ATT GTA ATA CGA CTC ACT ATA GGG CGA ATT GGA

GCT CCA CCG CGG TGG CGG CCG CTCTAGAATG gcttaccttgacggttct T7p J8M GGC CAG TGA ATT GTA ATA CGA CTC ACT ATA GGG CGA ATT GGA

GCT CCA CCG CGG TGG CGG CCG CTCTAGAATG tgttcttcttcatcttct T7p BCCPM

GGC CAG TGA ATT GTA ATA CGA CTC ACT ATA GGG CGA ATT GGA GCT CCA CCG CGG TGG CGG CCG CTCTAGAATG gcacaatctaacaaggtt

T7p PORAM

GGC CAG TGA ATT GTA ATA CGA CTC ACT ATA GGG CGA ATT GGA GCT CCA CCG CGG TGG CGG CCG CTCTAGAATG tgcaagagggaacagagc

T7P GLU2M

GGC CAG TGA ATT GTA ATA CGA CTC ACT ATA GGG CGA ATT GGA GCT CCA CCG CGG TGG CGG CCG CATG gcgatccttaattctgac

T7P TOCCM

GGC CAG TGA ATT GTA ATA CGA CTC ACT ATA GGG CGA ATT GGA GCT CCA CCG CGG TGG CGG CCG CATG gcgtcgatctccaccccg

nosT 3’ ctatgacatgattacgaatt

1

Supplemental Methods

Assessment of alignment significance

The statistical significance of the observed scores of global alignments, based on either

Needleman-Wunsch (Needleman and Wunsch, 1970) or our proposed algorithm with dual gap

penalties, was assessed by calculating the p-value, where the scores of the alignments to 1000

randomly-permuted sequences were approximated by Gaussian distribution. A Gaussian

distribution with mean μ and variance 2σ has the form

2 22 1/2 2

1 1( | , ) exp ( )(2 ) 2

P x xμ σ μπσ σ

⎧ ⎫= − −⎨ ⎬⎩ ⎭

.

Maximum likelihood estimates of the two parameters were obtained from the scores of the

alignments of 10000 randomly permuted sequences. The p-value of the observed alignment

score obsx was calculated from the estimated Gaussian distribution by

p-value 1 ( )obsP X x= − ≤ .

The Gaussian score distribution of the global alignments can be justified by the adequate fit to

the score histograms of the alignments to 10,000 randomly-permuted sequences (Fig. S8).

Detailed description of prediction of plastid transit peptide sequence motifs

The upper bound of the misclassification error plays a central role in our model. According to

the Bayesian decision theory, the optimal decision rule minimizing the probability of

misclassification error is given by

1 2Decide if ( | ) ( | ); otherwise decide 1C P C P C C>x x 2 ,

where is the feature vector, is the class label for chloroplast transit peptides, and

is the class label for other proteins. If the class-conditional densities are normal, the

probability of error is given by

x 1C 2C

2

( )1 2

( ) ( | ) ( )

( ) ( ) exp (1/ 2) ,

P error P error p d

P C P C k

∞

−∞=

≤ −

∫ x x x

where is called Bhattacharyya bound of the error and is given by (1/ 2)k

1 21

T 1 22 1 2 1

1 2

1 1 2(1/ 2) ( ) ( ) ln ,8 2 2 | || |

k μ μ μ μ−

Σ + ΣΣ + Σ⎛ ⎞= − − +⎜ ⎟ Σ Σ⎝ ⎠

where 1μ is the the mean vector for , 1C 2μ is the the mean vector for , 2C 1Σ is the

covariance matrix for , and is the covariance matrix for . 1C 2Σ 2C

To evaluate the significance of the overlap between the experimentally-determined

and predicted ones, we calculated p-values under the null hypothesis, in which overlaps

occurred by chance and could be modeled using a hypergeometric distribution.

Detailed description of plastid transit peptide prediction

An empirical kernel map enables the SVM classifier to learn a classification function from

protein sequences. From the clustering module, we have a set of representative plastid transit

peptide sequences with the predicted motifs 1{ , , }MD b b= K 1, , Mb b′ ′K , where M is equal

to the number of groups. Let be an input sequence or a sequence in the training data. The

empirical kernel map finds a feature vector representation, mapping the sequence into a

vector space by

a

a

( )a aφa . Then, the feature vector for the sequence has the following form a

1 1 1( ) ( ( , ), ( , ), ( , ), , ( , ), ( , ), ( , ))TNW SW MA NW M SW M MA Ma d b a d b a d b a d b a d b a d b aφ ′ ′= K

where is the scores of global alignments (Needleman-Wunsch

algorithm) and local alignments (Smith-Waterman algorithm) between the sequence and

the chosen plastid transit peptide sequence representing the ith group, and

( , ) and ( , )NW i SW id b a d b a

a

ib ( , )MA id b a′ is

the score of the proposed global alignments with dual gap penalties with the predicted

sequence motifs ib′ . We used LIBSVM 2.84 for the SVM classifier (Chang et al., 2001).

3

Performance evaluation of predicting chloroplast transit peptides

To measure the performance of the prediction system, we calculated sensitivity, specificity,

Matthew’s correlation coefficient (MCC), and the overall accuracy using the following

equations:

( )Sensitivity( )( ) ( )

tp iitp i fn i

=+ ,

( )Specificity( )( ) ( )

tp iitp i fp i

=+ ,

( ) ( ) ( ) ( )MCC( )( ( ) ( ))( ( ) ( ))( ( ) ( ))( ( ) ( ))

tp i tn i fp i fn iitp i fn i tp i fp i tn i fp i tn i fn i

× − ×=

+ + + + ,

2

1

(Overall accuracy

)i

tp

N

i==∑

,

where N is the total number of sequences, tp(i) (true positive) is the number of correctly

predicted sequences of class i, tn(i) (true negative) is the number of correctly predicted

sequences not of class i, fp(i) (false positive) is the number of over-predicted sequences of

class i, and fn(i) (false negative) is the number of under-predicted sequences of class i. When

a classifier gives a perfect prediction result, MCC equals one. In the opposite case of a

completely random assignment, MCC is zero.

Performance comparisons of predicting chloroplast transit peptides

To compare our prediction method with four predictors including ChloroP, PCLR, TargetP,

and Predotar, we used (1) the same training set, (2) the same test set, and (3) equally sized N-

terminal sequences between our method and the comparison predictor. More detailed

information on preparing training and test sets is available in a previous publication by Schein

et al. (2001). For ChloroP and PCLR, our method was trained using the same training set (75

cTP + 75 non cTP), tested with the same test set (113 cTP + 725 non cTP), and the size of N-

4

terminal sequences was set to 100. For TargetP, we trained using the same training set (141

cTP + 799 non cTP), tested with the same test set (204 cTP + 741 non cTP), and the size of N-

terminal sequences was set to 100. The test set was generated from the 208 experimentally-

confirmed plastid proteins and 778 non-plastid proteins (see Methods section), and redundant

sequences that overlapped the training set were removed. For Predotar, we used the same

training set (588 cTP + 5400 non cTP), tested with the same test set (154 cTP + 683 non cTP),

and the size of N-terminal sequences was set to 60. The test set was generated in a similar

manner with TargetP. When we trained our method with the training sets of TargetP and

Predotar, we balanced the positive and negative ratios in the training data, where the number

of negative sequences is twice as many as the positive ones.

5

Supplemental References

Chang, C., and Lin, C. (2001). LIBSVM: a library for support vector machines. Software

available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Choi, J.H, Jung, H.Y, Kim, H.S, and Cho, H.G. (2000). PhyloDraw: a phylogenetic tree

drawing system. Bioinformatics. 16, 1056-1058.

Felsenstein J. (1989). PHYLIP - Phylogeny Inference Package (Version 3.2). Cladistics. 5,

164-166.

Needleman, S.B., and Wunsch, C.D. (1970). A general method applicable to the search for

similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 443-453.

Thompson, J.D., Gibson, T.J., Plewniak, F, Jeanmougin, F, and Higgins, D.G. (1997). The

CLUSTAL X windows interface: flexible strategies for multiple sequence alignment aided by

quality analysis tools. Nucleic Acids Res. 25, 4876-4882.