kyle jensen's mit ph.d. thesis proposal

K.L. JENSEN20-Dec-01

BIOINFORMATICS AND METABOLIC ENGINEERING LABORATORY AT MIT

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Syntactic Pattern Discovery as a Generic Tool in Systems Biology

Kyle L. Jensen20 December 2001

Or: How I learned to stop worrying and love biology.



SLIDE 2/25

Outline

• Introduction– Pattern Discovery– Teireisas

• Proposed Problems– Biological Sequences– Gene Expression and Physiological Data

• Work to Date– Protein Evolution and Scoring Matrices



SLIDE 3/25

Part I: Introduction



SLIDE 4/25

primitive steams

Pattern Discovery

• Decision-Theoretic

• Syntactic

introduction → pattern discovery

ABCDEF

0 12 13 0 2 1 7 8 9 10integers -

characters -

MSKNIVLLPGDHVGPEVVAamino acids -

ATGAGCATCGATCGATCGAATCTAnucleotides -

Basic Question: When are two events the same?

patterns:

V[HDV].[ST]K

12 . . 1 . 7

TCGATCGA



SLIDE 5/25

A Little History

• Formal language theory – pattern recognition

• Biological sequence analysis– Teiresias, Blocks, Emotif, AlignACE, Prosite…– Discovery: functional, structural,

classification

introduction → syntactic pattern discovery → a little history

submedian telocentricprimitives:

a b c d ebabcbabdacad ebabcbab

RP[VI]ILDPx[DE]PT ATCATACTATACGA H…..HRD.K..N Teireisas

serine kinaseAlignACE

yeast promoterProsite

family classifier



SLIDE 6/25

lliw, recnac, poleved, elbi, ylbaborp, enummi, eugalp, setebaid, ylekil, otelbitpecsus, kcaj, nhoj, ylbaborpsi, llij, esnopsere, noos, retal, esnopserenummina, sire, polevedyl, recnacote, tonlliw, otenummi, otelbitpecsusylbaborpsi, sikcaj, sirecnac, polevedlli, lliwesnopsere, otylekilsi, setebaidotelbitpecsus, wnhoj, evah, alpo, sinhoj, elbirroh

will, cancer, develop, ible, probably, immune, plague, diabetes, likely, susceptibleto, jack, john, isprobably, jill, eresponse, soon, later, animmuneresponse, eris, lydevelop, etocancer, willnot, immuneto, isprobablysusceptibleto, jackis, canceris, illdevelop, eresponsewill, islikelyto, susceptibletodiabetes, johnw, have, opla, johnis, horrible

recnacotenummiylbaborpsikcaj • recnacotelbitpecsusylbaborpsinhoj • retalsetebaidpolevedylbaborplliwllij dabsirecnac • elbirrohsaweugalp • noosrecnacevahotylekilsikcaj • retalsetebaiddlimdepolevedllij eugalpdlimotelbitpecsusylbaborpsinhoj • wolebtonlliwesnopserenummina • retalpolevedylbaborplliwsetebaid noosrecnacpolevedylekillliwllij • eugalpdabevahlliwnhojretal • setebaidotelbitpecsussawnhoj polevedtonlliwesnopserenummina • enajnipolevedotylekilsirecnac • eugalppolevedlliwkcaj recnacotenummisienaj • setebaidotelbitpecsusebnooslliwkcaj • eugalppolevedlliwylbaborpkcaj elbirrohsirecnac • ylekiltonsiretalesnopserenummina • setebaidotelbitpecsussinhoj recnacpolevedylekilnooslliwnhoj • ylekilebtonlliwsetebaid • tceffenaevahtonlliwrecnac eugalpotenummisillij • elbirroheblliwesnopsereht

jackisprobablyimmunetocancer • johnisprobablysusceptibletocancer • marywillprobablydevelopdiabeteslater cancerisbad • plaguewashorrible • jackislikelytohavecancersoon • marydevelopedmilddiabeteslater johnisprobablysusceptibletomildplague • animmuneresponsewillnotbelow • diabeteswillprobablydeveloplater marywilllikelydevelopcancersoon • laterjohnwillhavebadplague • maryisprobablysusceptibletocancer animmuneresponseislikelytodevelopsoon • jackisprobablyimmunetoplague • johnwassusceptibletodiabetes animmuneresponsewillnotdevelop • cancerislikelytodevelopinjane • jackwilldevelopplague • janeisimmunetocancer jackwillsoonbesusceptibletodiabetes • jackprobablywilldevelopplague • cancerishorrible animmuneresponselaterisnotlikely • johnissusceptibletodiabetes • johnwillsoonlikelydevelopcancer diabeteswillnotbelikely • cancerwillnothaveaneffect • maryisimmunetoplague • therebsponsewillbehorrible

An Illustrative Example

• Patterns in sequences

introduction → syntactic pattern discovery → a quick example

Given sequences:

Strings with 4+ chars occurring 3+ times:…things that occur many times…

John is probably susceptible to cancer.

…find important features……but, what is “important”…

How do we know these are important?



SLIDE 7/25

density = 9/19

Teiresias Overview

• Finds patterns in primitive streams – L/W/K patterns

• L = minimum number of primitives in pattern

• L/W = minimum density ( % non-wildcards )

• K = number of times a pattern occursExample Output: 6/15/2 patterns

AFGLYEPC......LHQ.G.ET[ST]NSL.....A....SLKII.KA

LFPCFY wildcarddensity = 6/6

introduction → teiresias → teiresias overview



SLIDE 8/25

Teiresias Example

• Finding protein motifs>protein 0MSKNIVLLPGDHVGPEVVAEAVKVLEAVSSAIGVKFNFSKHLIGGASIDAYGVPLSDEALEAAKK>protein 1MSKQILVLPGDGIGPEIMAEAVKVLELANDRFQLGFELAEDVIGGAAIDKHGVP>protein 2MKFLILLFNILCLFPVLAADNHGVGPQGASGVDPITFDINSNQTGPAFLT

All patterns with at least 5 characters, density 5/8, and support 2

TEIRESIAS5/8/2

pattern

GPE..AEAVKVLE

IGGA.ID..GVP

MSK.I..LPGD..GPE

A.D.HGV

location

(0,13) (1,13)

(0,42) (1,42)

(0,00) (1,00)

(1,46) (2,17)

Take away point:Given sequences, Teiresias finds possibly important patterns in them.

introduction → teiresias → teiresias example



SLIDE 9/25

Part II: Proposed Problems



SLIDE 10/25

Biological Sequences

• Motivation– Protein and DNA sequences– Lots of data

• GenBank > 107 sequences, 1010 nt• Swiss-Prot/TrEMBL nrdb 600,000 proteins

– Natural language metaphor

• Many interesting problems– sequence-structure, molecular evolution,

splicing, gene-finding, alignment

proposed problems → biological sequences



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Proposed Problems

• Amino acid scoring matrix design– Model protein evolution using conserved motifs

in protein databases.– Use this model of evolution to design scoring

matrices for homology detection and sequence alignment.

• Oligonucleotide probe design– Predict hybridization kinetics from pattern based

homology– Use these prediction to choose optimal

oligonucleotide probes for DNA mircoarrays

proposed problems → biological sequences → proposed problems



SLIDE 12/25

Expression and Physiology

• Motivation– Creating associations: simple observations of

complex biological systems– Indicators for further research

• Association Discovery– Event streams are all the same length– Patterns cannot be shifted– Multiple associations possible, unlike clustering– Sensitive to local similarity and global

proposed problems → expression and physiology → motivation



SLIDE 13/25

Association Discovery Example• Heart disease clinical data

– Cleveland study of 500 patients

proposed problems → expression and physiological data → association discovery

63 1 145 233 1 2 150 0 3 0 6 067 1 160 286 0 2 108 1 2 3 3 267 1 120 229 0 2 129 1 2 2 7 137 1 130 250 0 0 187 0 3 0 3 041 0 130 204 0 2 172 0 1 0 3 0

age

sex

blood p

res.

pain ty

pe

chole

s.

blood su

g.

ekg

exer

cise

ekg d

epre

ss.

fluoros

copy

+’s

ekg a

nomaly

#>50% cl

ogged

Patients with type 2 EKG anomaly, with positive fluoroscopy results and high blood pressure are likely to have more than one critically

clogged artery.

Find conserved motifs in the

rows



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Proposed Problems

• Linking expression and phenotype– Association discovery

proposed problems → expression and physiological data

23 8 9 14

54 7 16 45

65 45 26 5

15 10 16 1

physiological1 2 3 4

A

B

C

D

samples

2 -1 3 -2

-2 5 5 1

-1 4 3 2

9 7 -2 0

Example associations:“Genes 1 and 4 are associated with pathway ”

or “Up-regulation of genes {4,6,10,…} gives rise to phenotype ”

gene expression1 2 3 4

A

B

C

D

How does the genome relate to the “physiome”?Are there any recurring motifs?

…biological significance?



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Part III: Work To Date



SLIDE 16/25

Motivation

• The sequence alignment problem– Given a protein sequence, find similar

proteins in a database.

sequence

KSDFKJSDTLKASLDKJFSLDDSLKDJFSKL SKDJFKDKSJDLKLSLKDJLKSJDLLKJDLKSJDKS

database

scoringmatrix

KSDFSDTLKASLDKJFSLDDSLKDJFSKLLKDKSJDLKLSLKDJLKSJDLLKJDLJDKS

KSDFSDDASLDKJFSLKDJFSLKDFJDKKSJDLKLSLKDJLKLKJDLJD

KSDFSDTLKASLDKJFSLDDSLKDJFSKL

LKDKSJDLKL

SLKDJLKSJDLLKJDLJDKS

sequencealignments

But what do we mean by similar?

work to date → aa scoring matrices → motivation



SLIDE 17/25

Scoring Matrix Basics

• Describe how we should align proteins– Matrix specifies a score for aligning

each pair of amino acids RKISWMEIYTGEKSTKVYGQDVWLPAETLDLIREYRVAIKGPLTTPVGGGIRSLNVALRQ::: :.:.: :::.:. : .. ::: :::....::.:.:::::::::::: :::::.::RKIEWLEVYAGEKATQMYDSETWLPEETLNILQEYKVSIKGPLTTPVGGGMSSLNVAIRQ

score for K-Q alignment

For detecting homology the matrix should capture evolutionary processes.

…but how do we describe evolution?

Highest score is the “best” alignment.

alignment

A R N D C M E G H I L K Q

ARNKCQE

5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1–1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1–1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1–1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7–4 –6 –7 –3 –4 –6 –7 –3

scoring matrix

work to date → aa scoring matrices → scoring matrix basics



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Protein Evolution

• A simple model of evolution

work to date → aa scoring matrices → protein evolution

ILHLVGPNGAGKSTLLARMAancestral protein

IVTLIGANGAGKSTLLMTLCMAFLTGHSGAGKSTLLKLICVVVIIGPSGSGKSTLVRCINNIMVVGPSGSGKSTLLRCINVTAFIGPSGCGKTTLLRTFN

MAFLTGHSGAGKSPLLKLIC

VVVIIGPSVSGKSTLVRCINnot functional

…use syntactic pattern discovery to find these conserved motifs.

The distribution of amino acids in the changing positions describes the evolutionary process…

G..G.GK.TL active site

NIMVVGQSGLGKSTLINTLFdescendant proteins



SLIDE 19/25

Discovering Patterns

• Example: four ATP-associated proteins>sp•Q07698•ABCA_AERSA ABC transporter protein

MSEPVLAVSGVNKSFPIYRSPWQALWHALNPKADVKVFQALRDIELTVYRGETIGIVGHNGAGKSTLLQLITGVMQPDCGQITRTGRVVGLLELGSGFNPEFTGRENIFFNGAILGMSQREMDDRLERILSFAAIGDFIDQPVKNYSSGMMVRLAFSVIINTDPDVLIIDEALAVGDDAFQRKCYARLKQLQSQGVTILLVSHAAGSVIELCDRAVLLDRGEVLLQGEPKAVVHNYHKLLHMEGDERARFRYHLRQTGRGDSYISDESTSEPKIKSAPGILSVDLQPQSTVWYESKGAVLSDVHIESF

>sp•Q02856•ABCX_ANTSP Probable ATP•dependent transporter MNNRILLNIKNLDVTIGETQILNSLNLSIKPGEIHAIMGKNGSGKSTLAKVIAGHPSYKI TNGQILFENQDVTEIEPEDRSHLGIFLAFQYPVEIPGVTNADFLRIAYNAKRAFDNKEEL DPLSFFSFIENKISNIDLNSTFLSRNVNEGFSGGEKKKNEILQMSLLNSKLAILDETDSG LDIDALKTIAKQINSLKTQENSIILITHYQRLLDYIKPDYIHVMQKGEIIYTGGSDTAMKLEKYGYDYLNK

ATP binding motif G..G.GK[ST]TL was “discovered” in 2500 sequences in SWISS-PROT/TrEMBL.

…how do we construct the scoring matrix?

>sp•P07655•PSTB_ECOLI ATP•BINDING PROTEIN PSTBMSMVETAPSKIQVRNLNFYYGKFHALKNINLDIAKNQVTAFIGPSGCGKSTLLRTFNKMFELYPEQRAEGEILLDGDNILTNSQDIALLRAKVGMVFQKPTPFPMSIYDNIAFGVRLFEKLSRADMDERVQWALTKAALWNETKDKLHQSGYSLSGGQQQRLCIARGIAIRPEVLLLDEPCSALDPISTGRIEELITELKQDYTVVIVTHNMQQAARCSDHTAFMYLGELIEFSNTDDLFTKPAKKQTEDYITGRYG

>sp•P10346•GLNQ_ECOLI ATP•BINDING PROTEIN GLNQGPTQVLHNIDLNIAQGEVVVIIGPSGSGKSTLLRCINKLEEITSGDLIVDGLKVNDPKVDERLIRQEAGMVFQQFYLFPHLTALENVMFGPLRVRGANKEEAKLARELLAKVGLAERAHHYPSELSGGQQQRVAIARALAVKPKMMLFDEPTSALDPELRHEVLKVMQDLAEEGMTMVIVTHEIGFAEKVASRLIFIDKGRIAEDGNPQVLIKNPPSQRLQEFLQHVS

ATP binding signature

Given a database, we can use Teiresias to find the conserved motifs…

work to date → aa scoring matrices → discovering motifs



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Patterns to Matrix

• Counting pairs of amino acidsExample Pattern: L..F.L..CI...L

IINSSLWWIIKGPILISILVNFILFICIIRILVQKLRPPDIGSeq A •

LTLITRVGLALSLFCLLLCILTFLLVRPIQGSRTTIHLHLCICLFVGSeq B •

IKTPILVSILRNFILFICIIRILVQKLHSPDVGHNESeq C •

How many AA pairs are there at each position?

pairs1 – VS1 – VR1 • SR pairs

1 – FF2 • LF

Count AA pairs for all patterns and construct a table of pair counts.


ARNKCQE

34 23 43 56 78 32 12 54 76 43 23 21 1112 54 76 43 23 21 11 12 54 76 43 23 2123 43 56 78 32 12 54 76 43 23 21 76 4376 43 23 21 76 43 23 21 76 43 23 21 4567 87 76 43 23 21 12 39 05 37 29 04 2390 76 43 23 21 76 43 23 21 87 76 43 2254 23 54 23 12 64 76 45

AA pair frequency table

work to date → aa scoring matrices → patterns to matrix



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Patterns to Matrix

• Make a Log-of-odds matrixodds that a AA pair does not occur by chance

probability of seeing AA pair in our patterns

probability of seeing AA pair by chance=


ARNKCQE

34 23 43 56 78 32 12 54 76 43 23 21 1112 54 76 43 23 21 11 12 54 76 43 23 2123 43 56 78 32 12 54 76 43 23 21 76 4376 43 23 21 76 43 23 21 76 43 23 21 4567 87 76 43 23 21 12 39 05 37 29 04 2390 76 43 23 21 76 43 23 21 87 76 43 2254 23 54 23 12 64 76 45

AA pair frequency table


ARNKCQE

5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1–1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1–1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7 5 –3 –4 –6 –7 –3 –2 –1 –1 0 –3 –2 –1–1 0 –3 –2 –1 –1 0 –3 –2 –1 –4 –6 –7–4 6 –7 –3 –4 –6 –7 –3

AA log•of•odds scoring matrix

MATH

positive values mean these pairs are more prevalent in our patterns than by chance……and negative values are less prevalent

Take away point:The evolutionary information contained in the patterns is stored in terms of the scoring matrix.

work to date → aa scoring matrices → patterns to matrix



SLIDE 22/25

Basic Idea

KSDFKJSDTLKASLDKJFSLDDSLKDJFSKL SKDJFKDKSJDLKLSLKDJLKSJDLLKJDLKSJDKS

database

TEIRESIASHQ.G.ET..STNSRP..K.TSTP.NSL.S.DF.SLKS.DKISV...EG.A..YPDVELA..YPDVEL.NSEG.A K.T

patternsscoringmatrixMATRIX

ENGINE

Take away point:Given a set of sequences, we use Teiresias to discover important patterns and construct a scoring matrix which captures the way these patterns are evolving.

BDSUM:Bio-Dictionary AA Substitution Matrices

work to date → aa scoring matrices → basic idea



SLIDE 23/25

Example Results

• Isocitrate dehydrogenase family– 100 sequences from Prosite PS00470

Experiment: Using each sequence from the family, try to detect the other 99 sequences in the Swiss-Prot/TrEMBL database.

100 0 0

Results:

BDSUM(PS00470)

win loss tie

BLOSUM62(PS00470)

work to date → aa scoring matrices → example results

BLOSUM62(Prosite)30 17 53BDSUM(PS00470)

47 9 44 BLOSUM50(Prosite)BDSUM(PS00470)



SLIDE 24/25

Current Work

• Applying to Bio-Dictionary– full SWISS-PROT/TrEMBL

• “Tweaking”– Which pattern classes are

evolutionarily meaningful?– Different “PAM-distance” matrices

• More testing

work to date → aa scoring matrices → current work

…and the oligo probes…



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Acknowledgements

• Dr. Isidore Rigoutsos• Prof. Greg Stephanopoulos

Group members:Mike, Maciek, Bill, Daehee, Jatin, Vipin, Maria, Javier, Maria, Matt, Gary, Saliya, Juan, Angelo, Chris, Dan, Giovanna, Joanne, Hyun-Tae, Patrick, Kyongbum…

kyle jensen's mit ph.d. thesis proposal

Technology

introduction teiresias

teiresias example

protein motifs protein

pattern gpe

pattern lw

important patterns

teiresias overview density

protein databases