High Throughput DNA Sequencing. 30,000 Shotgun Sequencing Isolate Chromosome ShearDNA into Fragments Clone into Seq. Vectors Sequence.

High Throughput DNA Sequencing

30,000

Shotgun Sequencing

IsolateChromosome

ShearDNAinto Fragments

Clone intoSeq. Vectors Sequence

Principles of DNA Sequencing

Primer

PBR322

Amp

Tet

Ori

DNA fragment

Denature withheat to produce

ssDNA

Klenow + ddN + dN+ primers

The Secret to Sanger Sequencing

Principles of DNA Sequencing

5’

5’ Primer

3’ TemplateG C A T G C

dATPdCTPdGTPdTTPddATP

dATPdCTPdGTPdTTPddCTP

dATPdCTPdGTPdTTPddTTP

dATPdCTPdGTPdTTP

ddCTP

GddC

GCATGddC

GCddA GCAddT ddG

GCATddG

Principles of DNA SequencingG

C

T

A

+

_

+

_

G

C

A

T

G

C

Capillary Electrophoresis

Separation by Electro-osmotic Flow

Multiplexed CE with Fluorescent detection

ABI 3700 96x700 bases

Shotgun Sequencing

SequenceChromatogram

Send to Computer AssembledSequence

Shotgun Sequencing

• Very efficient process for small-scale (~10 kb) sequencing (preferred method)

• First applied to whole genome sequencing in 1995 (H. influenzae)

• Now standard for all prokaryotic genome sequencing projects

• Successfully applied to D. melanogaster• Moderately successful for H. sapiens

The Finished Product

GATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTAGAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGATTACAGAT

Sequencing Successes

T7 bacteriophagecompleted in 198339,937 bp, 59 coded proteins

Escherichia colicompleted in 19984,639,221 bp, 4293 ORFs

Sacchoromyces cerevisaecompleted in 199612,069,252 bp, 5800 genes

Sequencing Successes

Caenorhabditis eleganscompleted in 199895,078,296 bp, 19,099 genes

Drosophila melanogastercompleted in 2000116,117,226 bp, 13,601 genes

Homo sapiens1st draft completed in 20013,160,079,000 bp, 31,780 genes

Scoring Matrices• An empirical model of evolution, biology and

chemistry all wrapped up in a 20 X 20 table of integers

• Structurally or chemically similar residues should ideally have high diagonal or off-diagonal numbers

• Structurally or chemically dissimilar residues should ideally have low diagonal or off-diagonal numbers

A Better Matrix - PAM250A R N D C Q E G H I L K M F P S T W Y V

A 2

R -2 6

N 0 0 2

D 0 -1 2 4

C -2 -4 -4 -5 4

Q 0 1 1 2 -5 4

E 0 -1 1 3 -5 2 4

G 1 -3 0 1 -3 -1 0 5

H -1 2 2 1 -3 3 1 -2 6

I -1 -2 -2 -2 -2 -2 -2 -3 -2 5

L -2 -3 -3 -4 -6 -2 -3 -4 -2 2 6

K -1 3 1 0 -5 1 0 -2 0 -2 -3 5

M -1 0 -2 -3 -5 -1 -2 -3 -2 2 4 0 6

F -4 -4 -4 -6 -4 -5 -5 -5 -2 1 2 -5 0 9

P 1 0 -1 -1 -3 0 -1 -1 0 -2 -3 -1 -2 -5 6

S 1 0 1 0 0 -1 0 1 -1 -1 -3 0 -2 -3 1 3

T 1 -1 0 0 -2 -1 0 0 -1 0 -2 0 -1 -2 0 1 3

W -6 2 -4 -7 -8 -5 -7 -7 -3 -5 -2 -3 -4 0 -6 -2 -5 17

Y -3 -4 -2 -4 0 -4 -4 -5 0 -1 -1 -4 -2 7 -5 -3 -3 0 10

V 0 -2 -2 -2 -2 -2 -2 -1 -2 4 2 -2 2 -1 -1 -1 0 -6 -2 4

Prokaryotic Gene Prediction

Initiationcodon (ATG)

Stop codon(TAG,TAA, TGA)

Shine Delgarno sequenceRBS (G-rich)

Terminator (stemloop structure)

Promoter Region -35 (TTGAC)

-10 (TATAAT)

Prokaryotic Gene Prediction

• ORF finder – http://www.ncbi.nlm.nih.gov/gorf/gorf.html

• GLIMMER– http://www.tigr.org/softlab/glimmer/glimmer.html

• GeneMark– exon.biology.gatech.edu/GeneMark/– www.ebi.ac.uk/genemark

Hidden Markov Model

Eukaryotic Gene Prediction

5’site 3’site

branchpoint site

exon 1 intron 1 exon 2 intron 2

AG/GT CAG/NT

Gene Predictors

• GRAIL2 (http://compbio.ornl.gov/Grail-1.3/)

• Neural Network Model CC=0.47

• HMMgene (http://www.cbs.dtu.dk/services/HMMgene/)

• Hidden Markov Model CC=0.91

• GENSCAN (http://genes.mit.edu/GENSCAN.html)

• Probabilistic Model CC=0.91

• GRPL (GeneTool)• Reference Point Logistics CC=0.94

Evaluation Statistics

TP FP TN FN TP FN TN

Actual

Predicted

Sensitivity Fraction of actual coding regions that are correctly predicted as codingSn=TP/(TP + FN)

Specificity Fraction of the prediction that is actually correctSp=TP/(TP + FP)

Correlation Combined measure of sensitivity and specificityCC=(TP*TN-FP*FN)/[(TP+FP)(TN+FN)(TP+FN)(TN+FP)]0.5

Gene PredictionA

ccu

racy

(%

)A

ccu

racy

(%

)

MethodMethod

61 63

7276

8694 97

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Xpound GeneID GRAIL 2 Genie GeneP3 RPL RPL+

What Works Best?

• Expect only a single exon…– BLASTN vs. dbEST– BLASTX vs. nr(protein)

• Fully sequenced data– Run GENSCAN + HMMgene + GRPL– Combine predictions (CC > 0.95)– BLAST vs. nr(protein)– Combine BLAST result with prediction

The Genetic Code

Translation

ATGCGTATAGCGATGCGCATTTACGCATATCGCTACGCGTAA

Frame3 A Y S D A HFrame2 C V * R C AFrame1 M R I A M R I

Frame-1 H T Y R H A NFrame-2 R I A I R MFrame-3 A Y L S A C

From Sequence to Function (and Structure)

ACEDFHIKNMFACEDFHIKNMFSDQWWIPANMCSDQWWIPANMCASDFDPQWEREASDFDPQWERELIQNMDKQERTLIQNMDKQERTQATRPQDS...QATRPQDS...

PROSITE Pattern Expressions

C - [ACG] - T - Matches CAT, CCT and CGT only

C - X -T - Matches CAT, CCT, CDT, CET, etc.

C - {A} -T - Matches every CXT except CAT

C - (1,3) - T - Matches CT, CCT, CCCT

C - A(2) - [TP] - Matches CAAT, CAAP

[LIV] - [VIC] - X(2) - G - [DENQ] - X - [LIVFM] (2) -G

PepTool Pattern Expressions

C[ACG]T - Matches CAT, CCT and CGT only

C*T - Matches CAT, CCT, CDT, CET, etc.

C!AT - Matches every C * T except CAT

C{1,3}T - Matches C*T, C * * T, C * * * T

$CAA(TP] - Matches CAAT, CAAP at N terminus

[LIV][VIC] * * G[DENQ] *[LIVFM][LIVFM]* G

Protein Sequence Motifs

• Pattern-Based (PQL) Sequence Motifs• >*TCP&NLGT*• DOOLITTLE, R.F., OF URFS AND ORFS (1986)• GUANIDINE KINASE ACTIVE SITE

• Profiles or Position Scoring Matrix (PSSM)• A C D E F G H I K L M N P Q R

• 1 W G V L V 3 -2 3 4 0 4 -1 3 -1 4 4 1 1 1 -2

• 2 L L S P L 2 -2 -2 -1 3 0 -1 3 -1 6 5 -1 3 0 -1

• 3 V V V V V 2 2 -2 -2 2 2 -3 11 -2 8 6 -2 1 -2 -2

• 4 K E A T A 6 -2 5 6 -5 4 1 0 5 -2 0 3 3 3 1

Phosphorylation Sites

CCOOHH2N

H

PO4

CCOOHH2N

H

PO4CH3

CCOOHH2N

H

PO4

pY pT pS

Phosphorylation SitesPhopshorylation Sites>*KRKQI[ST]VR* CHAN K.F. et al., J. BIOL. CHEM. 257:3655-3659 (1982) PHOSPHORYLASE KINASE PHOSPHORYLATION SITE

>*KKR**R**[ST]* KEMP B.E. et al., PNAS 72:3448-3452 (1975) MYOSIN LIGHT CHAIN KINASE PHOSPHORYLATION SITE

>*NYLRRL[ST]DSNF* CZERNIK A.J. et al. PNAS 84:7518-7522 (1987)

CALMODULIN DEPENDENT PROTEIN KINASE I PHOSPHORYLATION SITE

Glycosylation

Glycosylation Sites

Glycosylation Sites>*N!P[ST]!P* MARSHALL, R.D.W. ANN. REV. BIOCHEM. 41:673-702 (1972) GLYCOSYLATION SITE (S AND/OR T ARE GLYCOSYLATED)

>*G*K*R* MARSHALL, R.D.W. ANN. REV. BIOCHEM. 41:673-702 (1972) GLYCOSYLATION SITE (K IS GLYCOSYLATED)

>*G*K**R* MARSHALL, R.D.W. ANN. REV. BIOCHEM. 41:673-702 (1972) GLYCOSYLATION SITE (K IS GLYCOSYLATED)

Signaling

Signaling SitesSignaling Sites>*[KRH][DEN]EL$ SMITH M.J. et al., EMBO J. 8:3581-3586 (1989) ENDOPLASMIC RETICULUM DIRECTING SEQUENCE

>*P***KKRKAV* KALDERON, D. et al., CELL 39:400-509 (1984) NUCLEAR TRANSPORT SIGNAL OF SV40 LARGE T ANTIGEN

>${3,20}[LIVFTA][LIVFTA][LIVFTA]{3,6}[LIV]*[GA]C* VON HEIJNE, G. PROT. ENG. 2:531-534 (1989)

SIGNAL PEPTIDASE II CLEAVAGE SITE

Protease Cut Sites

Protease Cut Sites

Protease Cut Sites>*[KR]* *[KR]/* TRYPSIN CLEAVAGE SITE (CUTS AFTER [KR])

>*[FLY]![VAG] */[FLY]![VAG] PEPSIN CLEAVAGE SITE (CUTS BEFORE [FY])

>*[FWY]* *[FWY]/* CHYMOTRYPSIN CLEAVAGE SITE (CUTS AFTER [FWY])

Binding SitesBinding Sites>*RGD* RUOSLAHTII E. et al., CELL 44:517-518 (1986) FIBRONECTIN ADHESION SITE

>*CDPGYIGSR* GRAF, J. et al., CELL 48:989-996 (1987) MAMMAL LAMNIN DOMAIN III B1 CHAIN CELL ATTACHMENT SITE

>*[VIL]**[TS][DN]Y**[FY][AL]* GODOVAC-ZIMMERMANN, J., TIBS 13:64-66 (1988)

BINDING SITE FOR HYDROPHOBIC MOLECULE TRANSPORT PROTEINS

Family Signature Sequences

Protein Family Signature Sequences>*[FY]CRNPD* NAKAMURA T. et al., NATURE 342:441-445 (1989) KRINGLE DOMAIN SIGNATURE

>*[LIVM][LIVM]DEAD*[LIVM][LIVM]* CHANG T.H. et al., PNAS 87:1571-1575 (1990) EIF 4A FAMILY ATP DEPENDENT HELICASE SIGNATURE

>*C*C*****G**C* BLOMQUIST M.C. et al., PNAS 81:7363-7362 (1984)

EGF/TGF SIGNATURE SEQUENCE

Enzyme Active SitesEnzyme Active Sites>*[MAFILV]DTG[STA][STAN]* DOOLITTLE, R.F., OF URFS AND ORFS, 1986 ACID OR ASPARTYL PROTEASE ACTIVE SITE

>*TCP&NLGT* DOOLITTLE, R.F., OF URFS AND ORFS, 1986 GUANIDINE KINASE ACTIVE SITE

>*F*[LIVFMY]*S**K****[AG]*[LIVM]L*JORIS, B. ET AL., BIOCHEM. J. 250:313-324 (1989)BETA LACTAMASE (TYPE A) ACTIVE SITE

Sequence Pattern Databases

• PROSITE - http://www.expasy.ch/

• BLOCKS - http://www.blocks.fhcrc.org/

• DOMO - http://www.infobiogen.fr/~gracy/domo

• PFAM - http://pfam.wustl.edu

• PRINTS - http://www.biochem.ucl.ac.uk/bsm/dbrowser/PRINTS

• SEQSITE - PepTool

Sequence Profiles

PI

HAA K

AGT AA

L

AC

QRVN

A

AY

F

AG

A

Defining Sequence Profiles 1 50

P43871-1 .......... ...IKKLDSN SIHAIISDIP YGIDYDDWDI LHSNTNSALGS18997-1 LMSKIYQMDA VDWLKTLENC SVDLFITDPP YESL.EKYRQ IGTTTRLKESP23192-1 EINKIHQMNC FDFLDQVENK SVQLAVIDPP YNL....... ..........P29538-1 MDQRLICSNA IKALKNLEEN SIDLIITDPP YNLG.KDY.. ..........P14751-1 TRHVYDVCDC LDTLAKLPDD SVQLIICDPP YNI....... ..........P34721-1 KNFNIYQGNC IDFMSHFQDN SIDMIFADPP YFLS.NDG.L TFKNSIIQ..P50178-1 ENAILVHADS FKLLEKIKPE SMDMIFADPP YFLS.NGG.M SNSGGQIV..P20590-1 FLNTILKGDC IEKLKTIPNE SIDLIFADPP YFMQ.TEGKL LRTNGDEF..S43876-1 GPETIIHGDC IEQMNALPEK SVDLIFADPP YNLQ.LGGDL LRPDNSKV..P28638-1 EAKTIIHGDA LAELKKIPAE SVDLIFADPP YNIG.KNF.. ..........P23941-1 DLGKLYNGDC LELFKQVPDE NVDTIFADPP FNLD.KEY.. ..........P14230-1 RSCKIIVGDA REAVQGLDSE IFDCVVTSPP YWGL.RDY.. ..........P14243-1 NGATLFEGDA LSVLRRLPSG SVRCIVTSPP YWGL.RDY.. ..........Q04845-1 LNNMLLQGNC AETLKKLPDE SVNLVFTSPP YY........ ..........S53866-1 WVNDIHEGDA EEVLAELPES SVHMVMTSPP YFGL.RDY.. ..........P29568-1 .......... ...MNELKDK SINLVVTSPP YPMV.EIWDR LFSELNPKIE

Signature Sequence: DPP Y

A Sample Sequence Profile

A C D E F G H I K L M N P Q R S T V W Y

1 W G V L V 3 -2 3 4 0 4 -1 3 -1 4 4 1 1 1 -2 1 2 6 -6 -2

2 L L S P L 2 -2 -2 -1 3 0 -1 3 -1 6 5 -1 3 0 -1 3 1 4 1 -1

3 V V V V V 2 2 -2 -2 2 2 -3 11 -2 8 6 -2 1 -2 -2 0 2 15 -9 -1

4 K E A T A 6 -2 5 6 -5 4 1 0 5 -2 0 3 3 3 1 3 6 0 -6 -4

5 A P L P P 6 -1 0 1 -2 2 0 1 0 2 2 0 8 2 0 2 2 3 -5 -4

6 G G G G G 7 1 7 5 -6 15 -1 -3 0 -4 -3 4 3 6 1 6 2 -1 -6 -5

7 S S Q E D 4 -1 7 7 -6 7 2 -2 2 -3 -2 4 3 6 1 6 2 -1 -6 -5

8 S S T P S 4 4 2 2 -4 4 -1 0 2 -3 -2 2 7 0 1 10 6 0 -2 -4

<>i = log2(qi/pi)

Calculating a Profile Score

VLVAPGDS = 6+6+15+6+8+15+7+10=66LVLGPGLA = 4+4+8+4+8+15-3+4= 44

A C D E F G H I K L M N P Q R S T V W Y

1 W G V L V 3 -2 3 4 0 4 -1 3 -1 4 4 1 1 1 -2 1 2 6 -6 -2

2 L L S P L 2 -2 -2 -1 3 0 -1 3 -1 6 5 -1 3 0 -1 3 1 4 1 -1

3 V V V V V 2 2 -2 -2 2 2 -3 11 -2 8 6 -2 1 -2 -2 0 2 15 -9 -1

4 K E A T A 6 -2 5 6 -5 4 1 0 5 -2 0 3 3 3 1 3 6 0 -6 -4

5 A P L P P 6 -1 0 1 -2 2 0 1 0 2 2 0 8 2 0 2 2 3 -5 -4

6 G G G G G 7 1 7 5 -6 15 -1 -3 0 -4 -3 4 3 6 1 6 2 -1 -6 -5

7 S S Q E D 4 -1 7 7 -6 7 2 -2 2 -3 -2 4 3 6 1 6 2 -1 -6 -5

8 S S T P S 4 4 2 2 -4 4 -1 0 2 -3 -2 2 7 0 1 10 6 0 -2 -4

Profiles & Motifs are Useful

• Helped identify active site of HIV protease

• Helped identify SH2/SH3 class of STP’s

• Helped identify important GTP oncoproteins

• Helped identify hidden leucine zipper in HGA

• Used to scan for lectin binding domains

• Regularly used to predict T-cell epitopes

Membrane Spanning Regions

Predicting via Hydrophobicity

-3

-2

-1

0

1

2

3

4

1

Bacteriorhodopsin

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2OmpA

Bacteriorhodoposin OmpA

Predicting via Hydrophobicity

Protein Technique Predicted #helices Actual #helicesEngelman et al. 10

Microsomal cytochrome Chou & Fasman 8p 450 Rao & Argos 5

AMP07 1Eisenberg et al. 8Kyte-Doolittle 5Rao & Argos 4AMP07 4Jahnig 6Eisenberg et al. 1

Photosynthetic Reaction Rose 4 Centre (M chain) Kyte-Doolittle 4

Klein et al. 5Jahnig 7Kyte-Doolittle 4Engelman et al. 7Klein et al. 7

Quality of Membrane Helix Prediction of Membrane Proteins.

Fo-F1 ATPase (subunit A)

Bacteriorhodopsin

4

1

5

67

Predicting via Sequence Similarity

Protein Family No. of Membrane Segments

Cytokine/growth factor receptors (EGF, interleukin, Insulin receptors)G-coupled receptors (rhodopsin, bacteriorhodopsin etc.)Extracellular activated gated channels (Glutamate, GABA, ACh sensitive)

5) photosynthetic proteins (H chain) 5 (helices)6) photosynthetic proteins (L chain) 5 (helices)7) porins 17 (-strands)8) microsomal cytochrome p450 1 (helix)9) cytochrome b 1 (helix)10) Fo ATPases 4 (helices)

Intracellular activated gated channels 6 (helices)

1 (helix)

Table 6

7 (helices)

4 (helices)

Predicting via Neural Nets and PSSM’s

• PHDhtm http://dodo.cpmc.columbia.edu/predictprotein/

• TMAP http://www.mbb.ki.se/tmap/index.html

• TMPred http://www.ch.embnet.org/software/TMPRED_form.html

ACDEGF...

Secondary Structure Prediction

Secondary Structure Prediction

• Statistical (Chou-Fasman, GOR)• Homology or Nearest Neighbor (Levin)• Physico-Chemical (Lim, Eisenberg)• Pattern Matching (Cohen, Rooman)• Neural Nets (Qian & Sejnowski, Karplus)• Evolutionary Methods (Barton, Niemann)• Combined Approaches (Rost, Levin, Argos)

Chou-Fasman Statistics

P P Pc P P Pc

A 1.42 0.83 0.75 M 1.45 1.05 0.5C 0.7 1.19 1.11 N 0.67 0.89 1.44D 1.01 0.54 1.45 P 0.57 0.55 1.88E 1.51 0.37 1.12 Q 1.11 1.1 0.79F 1.13 1.38 0.49 R 0.98 0.93 1.09G 0.57 0.75 1.68 S 0.77 0.75 1.48H 1 0.87 1.13 T 0.83 1.19 0.98I 1.08 1.6 0.32 V 1.06 1.7 0.24K 1.16 0.74 1.1 W 1.08 1.37 0.45L 1.21 1.3 0.49 Y 0.69 1.47 0.84

Chou & Fasman Secondary Structure Propensity of the Amino Acids

Table 8

Chou-Fasman Algorithm

• Assign each residue a P, P, Pc value• Take a window of 7 residues and calculate

a window-averaged value for all P, P, Pc

• Assign the average value for each of the secondary structures to the middle residue

• Move down one residue and repeat steps 2 thru 3 until finished

• Scan and assign SS to the highest P/residue

The PhD Approach

PRFILE...

The PhD Algorithm• Search the SWISS-PROT database and

select high scoring homologues• Create a sequence “profile” from the

resulting multiple alignment• Include global sequence info in the profile• Input the profile into a trained two-layer

neural network to predict the structure and to “clean-up” the prediction

Prediction Performance

45505560657075

CF

GO

R I

LIM

LEV

IN

PTI

T

JAS

EP

7

GO

R II

I

ZHA

NG

PH

D

Sco

res

(%)

Best of the Best

• PredictProtein-PHD (72%)– http://cubic.bioc.columbia.edu/predictprotein

• Jpred (73-75%)– http://jura.ebi.ac.uk:8888/

• PREDATOR (75%)– http://www.embl-heidelberg.de/cgi/predator_serv.pl

• PSIpred (77%)– http://insulin.brunel.ac.uk/psipred