Finding Patterns Gopalan Vivek Lee Teck Kwong Bernett

Recap Multiple Sequence Alignment....|....|....|....|....|....|....|....|....|....| Sp1 ACTCPYCKDS EGRGSG---- DPGKKKQHIC HIQGCGKVYG KTSHLRAHLR Sp2 ACTCPNCKDG EKRS GEQGKKKHVC HIPDCGKTFR KTSLLRAHVR Sp3 ACTCPNCKEG GGRGTN---- -LGKKKQHIC HIPGCGKVYG KTSHLRAHLR Sp4 ACSCPNCREG EGRGSN---- EPGKKKQHIC HIEGCGKVYG KTSHLRAHLR DrosBtd RCTCPNCTNE MSGLPPIVGP DERGRKQHIC HIPGCERLYG KASHLKTHLR DrosSp TCDCPNCQEA ERLGPAGV-- HLRKKNIHSC HIPGCGKVYG KTSHLKAHLR CeT22C8.5 RCTCPNCKAI KHG DRGSQHTHLC SVPGCGKTYK KTSHLRAHLR Y40B1A.4 PQISLKKKIF FFIFSNFR-- GDGKSRIHIC HL--CNKTYG KTSHLRAHLR

Introduction Terms used in pattern finding is quite loose. Terms may be used differently by different authors. Thus there is a need to know the context in which the terms are used.

....|....|....|....|....|....|....|....|....|....|....|....| Sp1 ACTCPYCKDS EGRGSG---- DPGKKKQHIC HIQGCGKVYG KTSHLRAHLR WHTGERPFMC Sp2 ACTCPNCKDG EKRS GEQGKKKHVC HIPDCGKTFR KTSLLRAHVR LHTGERPFVC Sp3 ACTCPNCKEG GGRGTN---- -LGKKKQHIC HIPGCGKVYG KTSHLRAHLR WHSGERPFVC Sp4 ACSCPNCREG EGRGSN---- EPGKKKQHIC HIEGCGKVYG KTSHLRAHLR WHTGERPFIC DrosBtd RCTCPNCTNE MSGLPPIVGP DERGRKQHIC HIPGCERLYG KASHLKTHLR WHTGERPFLC DrosSp TCDCPNCQEA ERLGPAGV-- HLRKKNIHSC HIPGCGKVYG KTSHLKAHLR WHTGERPFVC CeT22C8.5 RCTCPNCKAI KHG DRGSQHTHLC SVPGCGKTYK KTSHLRAHLR KHTGDRPFVC Y40B1A.4 PQISLKKKIF FFIFSNFR-- GDGKSRIHIC HL--CNKTYG KTSHLRAHLR GHAGNKPFAC C 2 H 2 Zinc finger motif Prosite pattern C-x(2,4)-C-x(12)-H-x(3)-H

Motif –Common sequence elements shared by a group of sequences. Indicative of functional or evolutionary relationship. –N-Glycosylation site, N-{P}-[ST]-{P}

Pattern –“A consistent, characteristic form, style, or method, as a composite of traits or features characteristic of an individual or a group.” (dictionary.com) –A physical expression of a motif. –Many forms of expression.

Signature/Print –A set of patterns that defines a group of sequences having a certain common characteristic. –Bacterial Rhodopsin (2 patterns) R-Y-x-[DT]-W-x-[LIVMF]-[ST]-T-P-[LIVM](3) [FYIV]-x-[FYVG]-[LIVM]-D-[LIVMF]-x-[STA]-K- x(2)-[FY]

A single point is not indicative of identity. But many points allow for identification.

Why pattern finding and not sequence comparison? Useful in event of low sequence similarity to infer function or family –Certain motifs are characteristic of function or family. –Zinc finger motif, indicative of DNA binding. –Avidin motif, indicative of Avidin family of proteins.

Detection of specific motifs or signals –Example: Restriction Endonuclease sites –EcoRI »5’-G^AATT C-3’ (Sense strand) »3’–C TTAA^G-3’ (Antisense strand) Transcription factor binding sites –GAL4 »CCCCAGaTTTTC Protein motifs –Zinc finger

Usually faster than sequence comparison –Blast has to search using many fragments. –Pattern searching just search once

Types of Patterns DNA –Restriction Endonuclease sites –DNA binding motifs –Transcription Factor binding sites –Splicing site motifs –Other signals

Protein –Sequence motifs Zinc finger SH2 domains –Structural patterns

Representations Regular Expression (RE) Prosite Patterns Profiles (PSSM) Hidden Markov Models (HMM)

Sp1 CHIQGCGKVYGKTSHLRAHLRWH Sp2 CHIPDCGKTFRKTSLLRAHVRLH Sp3 CHIPGCGKVYGKTSHLRAHLRWH Sp4 CHIEGCGKVYGKTSHLRAHLRWH DrosBtd CHIPGCERLYGKASHLKTHLRWH DrosSp CHIPGCGKVYGKTSHLKAHLRWH CeT22C8.5 CSVPGCGKTYKKTSHLRAHLRKH Y40B1A.4 CHL--CNKTYGKTSHLRAHLRGH Sequences containing zinc finger motif

Regular Expression Used in computer science Syntax: CharacterMeaning ^Match the beginning of the line $Match the end of the line *Match 0 or more repetitions of preceding character +Match 1 or more repetitions of preceding character

CharacterMeaning ?Match 0 or 1 occurrence of preceding character {m}Match m repetition of preceding character {m,n}Match range m to n repetition of preceding character CharMatch character.Match any character []Match any character within bracket [^Char]Not character Zinc finger motif C.{2,4}C.{12}H.{3}H

Sp1 CHIQGCGKVYGKTSHLRAHLRWH Sp2 CHIPDCGKTFRKTSLLRAHVRLH Sp3 CHIPGCGKVYGKTSHLRAHLRWH Sp4 CHIEGCGKVYGKTSHLRAHLRWH DrosBtd CHIPGCERLYGKASHLKTHLRWH DrosSp CHIPGCGKVYGKTSHLKAHLRWH CeT22C8.5 CSVPGCGKTYKKTSHLRAHLRKH Y40B1A.4 CHL--CNKTYGKTSHLRAHLRGH C.{2,4}C.{12}H.{3}H Example

Prosite Patterns Very similar to RE Patterns encoded in Prosite style or RE style can be switched easily between these two styles More familiar to biologist

REProsite ^< $> ?(0,1) {m}(m) {m,n}(m,n) Char.x [] [^char]{} Zinc finger motif RE C.{2,4}C.{12}H.{3}H Prosite C-x(2,4)-C-x(12)-H-x(3)-H

Profiles Similar to scoring matrices used in sequence comparison The outcome of applying the matrices is a score A threshold is used to determine whether it is a hit

Sp1 C H I Q G C G K VYGKTSHLRAHLRWH Sp2 C H I P D C G K TFRKTSLLRAHVRLH Sp3 C H I P G C G K VYGKTSHLRAHLRWH Sp4 C H I E G C G K VYGKTSHLRAHLRWH DrosBtd C H I P G C E R LYGKASHLKTHLRWH DrosSp C H I P G C G K VYGKTSHLKAHLRWH CeT22C8.5 C S V P G C G K TYKKTSHLRAHLRKH Y40B1A.4 C H L - - C N K TYGKTSHLRAHLRGH Profile Pos A C D E F G H I K L M N P Q R S T V W X –

Pos A C D E F G H I K L M N P Q R S T V W X – seq – C H I Q G C G K – = 49

Sp1 CHIQGCGK = = 49 Sp2 CHIPDCGK = = 48 Sp3 CHIPGCGK = = 53 Sp4 CHIEGCGK = = 49 DrosBtd CHIPGCER = = 42 DrosSp CHIPGCGK = = 53 CeT22C8.5 CSVPGCGK = = 42 Y40B1A.4 CHL--CNK = = 34 <- lowest Since all the sequences are known to contain the zinc finger motif, the threshold can be set at 34. Thus any sequence having a lower score than the threshold will be rejected and any sequence having a higher score is likely to have the zinc finger motif. Example Unrelated seq – CADEGCEK – = 31 REJECT

The unrelated sequence was rejected due to a low score. However if one was using a Prosite pattern, one would have accepted it. –C-x(2,4)-C-x(2) <= Prosite motif Advantage of profile –More expressive, details are included –More sensitive –Provides a quantitative value

Example provided is very simple It is possible to include –Evolutionary distance –Amino acid frequency –Substitution matrix This makes the profile even more accurate

Hidden Markov Models (HMM) Profiles are a special case of HMM HMM have a number of states Transitions from one state to another is based on a set or probabilities called transitional probabilities At each state an observation is generated

It is known as HMM as only the observations are visible and the states hidden. The probabilities are first determined using MSA. The determined probabilities are then used to determine whether a sequence has the pattern or not.

I1 M1 D2 M2 I1 M1 D2 A Short Profile HMM I represents insertion states, M represents match states and D represents deletion state. Both I and M emits amino acids.

Sources and Creation of Patterns Source of patterns –The source of patterns is mainly MSA. Creation of patterns –Manually as in Prosite –Automatically through machine learning Meme Pratt

Considerations Sensitivity/Recall –How much of the patterns were discovered –TP / (TP + FN) Specificity/Precision –How many of the discovered patterns are correct –TP / (TP + FP) It is usually a balance between these two measures.

Ideal situation Threshold

False Positive False Negative The real situation

Other points: –A literature search can be done to identify potential conserved/functional regions suitable for use in pattern creation. For example, Alanine Scanning may indicate a region of functional importance. –All calculations of Sensitivity and Specificity is based on current state of database. –Need to consider the coverage of existing database.

Summary Definition of patterns and motifs Why use pattern finding Types of patterns Sources and Creation of Patterns