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MOTIFS MOTIFSMARTIFAMORIFSMOOTIFSMICIFC

MOTIFS MOTIFSMARTIFAMORIFSMOOTIFSMICIFC. A sequence motif is a nucleotide or amino-acid sequence pattern that is widespread (repeated) and has or is conjectured to have a biological significance. Sequence motifs may be identical to each other or they may vary to a greater or lesser extent.

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MOTIFS MOTIFSMARTIFAMORIFSMOOTIFSMICIFC

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  1. MOTIFSMOTIFSMARTIFAMORIFSMOOTIFSMICIFC

  2. A sequence motif is a nucleotide or amino-acid sequence pattern that is widespread (repeated) and has or is conjectured to have a biological significance. Sequence motifs may be identical to each other or they may vary to a greater or lesser extent.

  3. Domains, Patterns, Motifs, Repeats?

  4. For proteins, a sequence motif is distinguished from a structural motif, i.e., a motif formed by the three dimensional arrangement of amino acids, which may not be adjacent.Example: N-glycosylation site motifAsn, followed by anything but Pro, followed by either Ser or Thr, followed by anything but Pro.

  5. When a sequence motif appears in protein-coding regions, it may specify a "structural motif" of a protein. Short coding motifs in proteins include sites that label proteins for delivery to particular parts of a cell, or mark them for phosphorylation. Noncoding sequences contain functional (i.e., regulatory) sequence motifs and motifs that are just "junk," such as satellite DNA. Functional motifs in DNA play different roles, such as binding sites for proteins. The discipline of bioinformatics concerns itself with the finding and the sequence characterization of motifs through computer-based techniques of sequence analysis.

  6. Motif notation Consider the N-glycosylation site motif: Asn, followed by anything but Pro, followed by either Ser or Thr, followed by anything but Pro. This pattern may be written as: N{P}[ST]{P} where N = Asn, P = Pro, S = Ser, T = Thr; {X} means any amino acid except X; and [XY] means either X or Y. The notation [XY] does not give any indication of the probability of X or Y occurring in the pattern.

  7. Identifying motifs: The challenge • A microarray experiment showed that when gene X is knocked out, 20 other genes are not expressed • How can one gene have such drastic effects?

  8. Identifying motifs: The challenge • Gene X encodes regulatory protein, such as a transcription factor(TF) • The 20 unexpressed genes rely on gene product (TF) to induce transcription • A single TF may regulate multiple genes

  9. Identifying motifs: The challenge • Every gene contains a regulatory region (RR) typically stretching 100-1000 bp upstream of the transcriptional start site • Located within the RR are the Transcription Factor Binding Sites (TFBS), also known as motifs, specific for a given transcription factor • TFs influence gene expression by binding to a specific location in the TFBS of the gene.

  10. Identifying motifs: The challenge • A motif can be located anywhere within the Regulatory Region. • Motifs may vary across different regulatory regions.

  11. Motifs and Transcriptional Start Sites ATCCCG gene TTCCGG gene ATCCCG gene ATGCCG gene ATGCCC gene

  12. Why finding motifs is difficult?Step 1: Start with random sequence atgaccgggatactgataccgtatttggcctaggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccgacccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaatactgggcataaggtacatgagtatccctgggatgacttttgggaacactatagtgctctcccgatttttgaatatgtaggatcattcgccagggtccgagctgagaattggatgaccttgtaagtgttttccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggagatcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaatggcccacttagtccacttataggtcaatcatgttcttgtgaatggatttttaactgagggcatagaccgcttggcgcacccaaattcagtgtgggcgagcgcaacggttttggcccttgttagaggcccccgtactgatggaaactttcaattatgagagagctaatctatcgcgtgcgtgttcataacttgagttggtttcgaaaatgctctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgtattggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcatttcaacgtatgccgaaccgaaagggaagctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttctgggtactgatagca

  13. Why finding motifs is difficult?Step 2: Implant motif AAAAAAAGGGGGGG atgaccgggatactgatAAAAAAAAGGGGGGGggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccgacccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaataAAAAAAAAGGGGGGGatgagtatccctgggatgacttAAAAAAAAGGGGGGGtgctctcccgatttttgaatatgtaggatcattcgccagggtccgagctgagaattggatgAAAAAAAAGGGGGGGtccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggagatcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaatAAAAAAAAGGGGGGGcttataggtcaatcatgttcttgtgaatggatttAAAAAAAAGGGGGGGgaccgcttggcgcacccaaattcagtgtgggcgagcgcaacggttttggcccttgttagaggcccccgtAAAAAAAAGGGGGGGcaattatgagagagctaatctatcgcgtgcgtgttcataacttgagttAAAAAAAAGGGGGGGctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgtattggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcatAAAAAAAAGGGGGGGaccgaaagggaagctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttAAAAAAAAGGGGGGGa

  14. Where is the implanted motif? atgaccgggatactgataaaaaaaagggggggggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccgacccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaataaaaaaaaagggggggatgagtatccctgggatgacttaaaaaaaagggggggtgctctcccgatttttgaatatgtaggatcattcgccagggtccgagctgagaattggatgaaaaaaaagggggggtccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggagatcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaataaaaaaaagggggggcttataggtcaatcatgttcttgtgaatggatttaaaaaaaaggggggggaccgcttggcgcacccaaattcagtgtgggcgagcgcaacggttttggcccttgttagaggcccccgtaaaaaaaagggggggcaattatgagagagctaatctatcgcgtgcgtgttcataacttgagttaaaaaaaagggggggctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgtattggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcataaaaaaaagggggggaccgaaagggaagctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttaaaaaaaaggggggga

  15. Implanting Motif AAAAAAGGGGGGGwith Four Mutations atgaccgggatactgatAgAAgAAAGGttGGGggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccgacccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaatacAAtAAAAcGGcGGGatgagtatccctgggatgacttAAAAtAAtGGaGtGGtgctctcccgatttttgaatatgtaggatcattcgccagggtccgagctgagaattggatgcAAAAAAAGGGattGtccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggatcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaatAtAAtAAAGGaaGGGcttataggtcaatcatgttcttgtgaatggatttAAcAAtAAGGGctGGgaccgcttggcgcacccaaattcagtgtgggcgagcgcaacggttttggcccttgttagaggcccccgtAtAAAcAAGGaGGGccaattatgagagagctaatctatcgcgtgcgtgttcataacttgagttAAAAAAtAGGGaGccctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgtattggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcatActAAAAAGGaGcGGaccgaaagggaagctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttActAAAAAGGaGcGGa

  16. Why Finding (15,4) Motif is Difficult? atgaccgggatactgatAgAAgAAAGGttGGGggcgtacacattagataaacgtatgaagtacgttagactcggcgccgccgacccctattttttgagcagatttagtgacctggaaaaaaaatttgagtacaaaacttttccgaatacAAtAAAAcGGcGGGatgagtatccctgggatgacttAAAAtAAtGGaGtGGtgctctcccgatttttgaatatgtaggatcattcgccagggtccgagctgagaattggatgcAAAAAAAGGGattGtccacgcaatcgcgaaccaacgcggacccaaaggcaagaccgataaaggagatcccttttgcggtaatgtgccgggaggctggttacgtagggaagccctaacggacttaatAtAAtAAAGGaaGGGcttataggtcaatcatgttcttgtgaatggatttAAcAAtAAGGGctGGgaccgcttggcgcacccaaattcagtgtgggcgagcgcaacggttttggcccttgttagaggcccccgtAtAAAcAAGGaGGGccaattatgagagagctaatctatcgcgtgcgtgttcataacttgagttAAAAAAtAGGGaGccctggggcacatacaagaggagtcttccttatcagttaatgctgtatgacactatgtattggcccattggctaaaagcccaacttgacaaatggaagatagaatccttgcatActAAAAAGGaGcGGaccgaaagggaagctggtgagcaacgacagattcttacgtgcattagctcgcttccggggatctaatagcacgaagcttActAAAAAGGaGcGGa AgAAgAAAGGttGGG ..|..|||.|..||| cAAtAAAAcGGcGGG

  17. Discovery of Motifs 1. consensus sequencesThe notation [XYZ] means X or Y or Z, but does not indicate the likelihood of any particular match. For this reason, two or more patterns are often associated with a single motif. It is sometimes advisable to look and consensus sequences and refine the definition of a motif.

  18. Discovery of Motifs 1. consensus sequencesRigorously, the IQ motif is: [FILV]Qxxx[RK]Gxxx[RK]xx[FILVWY]where x = any amino acid, and the square brackets indicate alternatives.Usually, the first amino acid is I, the two [RK] choices are R, and xx[FILVWY] is so undefined that it can be ignored. Thus, the consensus is: IQxxxRGxxxR

  19. Discovery of Motifs 2. Discovery through evolutionary conservation Motifs may be discovered by comparing homologous genes from different species. For example, by aligning the amino acid sequences specified by the GCM (glial cells missing) gene in man, mouse and D. melanogaster, a pattern was discovered (the GCM motif) that spans about 150 amino acids, and begins as follows: WDIND*.*P..*...D.F.*W***.**.IYS**...A.*H*S*WAMRNTNNHN Here each . signifies a single amino acid or a gap, and each * indicates one member of a closely-related amino-acid family. Subsequently, it was shown that the motif has DNA binding activity.

  20. Motif Logo • Motifs can mutate on non important bases • The five motifs in five different genes have mutations in position 3 and 5 • Representations called motif logosillustrate the conserved and variable regions of a motif TGGGGGA TGAGAGA TGGGGGA TGAGAGA TGAGGGA

  21. Motif Logos: an Example (http://www-lmmb.ncifcrf.gov/~toms/sequencelogo.html)

  22. Measure of Conservation • Relative heights of letters reflect their abundance in the alignment. • Total height = entropy-based measurement of conservation. • Entropy(i) = -SUM{ f(base, i)* ln[f(base, i)] } over all bases • Entropy measures variability/disorder. • Highly conserved = low entropy = tall stack • Highly variable = high entropy = low stack

  23. Identifying Motifs: Complications • We do not know the motif sequence • We do not know where it is located relative to some genomic landmark (say, gene start) • Motifs can differ from one another • The pattern may not be an exact sequence or an approximate sequence but something like “4-8 hydrophobic amino acids, followed by 2-3 leucines or isoleucines, followed by 2 phenylalanines and an aspartic acid or 1 spartic acid and two glycines.

  24. Discovery of Motifs 3. De novo computational discovery of motifs

  25. A Motif Finding Analogy • The Motif Finding Problem is similar to the problem posed by Edgar Allan Poe (1809–1849) in The Gold Bug

  26. "The Gold-Bug" is a story of a man named William Legrand who seemingly goes mad after being bitten by a bug thought to be made of pure gold. He notifies his closest friend, the narrator, telling him to immediately come visit him at his home on Sullivan's Island in South Carolina. The two embark upon a search for lost treasure along with a servant named Jupiter. The narrator doubts Legrand’s sanity. However, after following several clues, they find a treasure buried by the infamous pirate "Captain Kidd," that is estimated to be worth about fourteen million dollars. Among the clues, there is a secret message.

  27. The Gold Bug Problem • Given a secret message: 53++!305))6*;4826)4+.)4+);806*;48!8`60))85;]8*:+*8!83(88)5*!; 46(;88*96*?;8)*+(;485);5*!2:*+(;4956*2(5*-4)8`8*; 4069285);)6 !8)4++;1(+9;48081;8:8+1;48!85;4)485!528806*81(+9;48;(88;4(+?3 4;48)4+;161;:188;+?; • Decipher the message encrypted in the fragment

  28. Hints for The Gold Bug Problem • Additional hints: • The encrypted message is in English • Each symbol correspond to one letter in the English alphabet • No punctuation marks are encoded

  29. The Gold Bug Problem: Symbol Counts • Naive approach to solving the problem: • Count the frequency of each symbol in the encrypted message • Find the frequency of each letter in the alphabet in the English language • Compare the frequencies of the previous steps, try to find a correlation and map the symbols to a letter in the alphabet

  30. Gold Bug Message: English Language: e t a o i n s r h l d c u m f p g w y b v k x j q z Most frequentLeast frequent Symbol Frequencies in the Gold Bug Message

  31. The Gold Bug Message Decoding: First Attempt • By simply mapping the most frequent symbols to the most frequent letters of the alphabet: sfiilfcsoorntaeuroaikoaiotecrntaeleyrcooestvenpinelefheeosnltarhteenmrnwteonihtaesotsnlupnihtamsrnuhsnbaoeyentacrmuesotorleoaiitdhimtaecedtepeidtaelestaoaeslsueecrnedhimtaetheetahiwfataeoaitdrdtpdeetiwt • The result does not make sense

  32. The Gold Bug Problem: l-tuple count • A better approach: • Examine frequencies of l-tuples, combinations of 2 symbols, 3 symbols, etc. • “The” is the most frequent 3-tuple in English and “;48” is the most frequent 3-tuple in the encrypted text • Make inferences of unknown symbols by examining other frequent l-tuples

  33. The Gold Bug Problem: the ;48 clue • Mapping “the” to “;48” and substituting all occurrences of the symbols: 53++!305))6*the26)h+.)h+)te06*the!e`60))e5t]e*:+*e!e3(ee)5*!th6(tee*96*?te)*+(the5)t5*!2:*+(th956*2(5*h)e`e*th0692e5)t)6!e)h++t1(+9the0e1te:e+1the!e5th)he5!52ee06*e1(+9thet(eeth(+?3hthe)h+t161t:1eet+?t

  34. The Gold Bug Message Decoding: Second Attempt • Make inferences: 53++!305))6*the26)h+.)h+)te06*the!e`60))e5t]e*:+*e!e3(ee)5*!th6(tee*96*?te)*+(the5)t5*!2:*+(th956*2(5*h)e`e*th0692e5)t)6!e)h++t1(+9the0e1te:e+1the!e5th)he5!52ee06*e1(+9thet(eeth(+?3hthe)h+t161t:1eet+?t • “thet(ee” most likely means “the tree” • Infer “(“ = “r” • “th(+?3h” becomes “thr+?3h” • Can you guess “+”, “?”, and “3”? oug

  35. The Gold Bug Problem: The Solution • The final message is: AGOODGLASSINTHEBISHOPSHOSTELINTHEDEVILSSEATWENYONEDEGREESANDTHIRTEENMINUTESNORTHEASTANDBYNORTHMAINBRANCHSEVENTHLIMBEASTSIDESHOOTFROMTHELEFTEYEOFTHEDEATHSHEADABEELINEFROMTHETREETHROUGHTHESHOTFIFTYFEETOUT

  36. The Solution (cont’d) • Punctuation (akin to annotation) is important: A GOOD GLASS IN THE BISHOP’S HOSTEL IN THE DEVIL’S SEA, TWENY ONE DEGREES AND THIRTEEN MINUTES NORTHEAST AND BY NORTH, MAIN BRANCH SEVENTH LIMB, EAST SIDE, SHOOT FROM THE LEFT EYE OF THE DEATH’S HEAD A BEE LINE FROM THE TREE THROUGH THE SHOT, FIFTY FEET OUT.

  37. Solving the Gold Bug Problem • Prerequisites to solve the problem: • Need to know the relative frequencies of single letters, and combinations of two and three letters in English. • Knowledge of all the words in the English dictionary is highly desirable.

  38. Motif Finding and The Gold Bug Problem: Similarities • Nucleotides in motifs encode for a message in the “genetic” language. Symbols in “The Gold Bug” encode for a message in English. • In order to solve the problem, we analyze the frequencies of patterns in DNA/Gold Bug message. • Knowledge of established regulatory motifs makes the Motif Finding problem simpler. Knowledge of the words in the English dictionary helps to solve The Gold Bug problem.

  39. Similarities (cont’d) • Motif Finding: • In order to solve the problem, we analyze the frequencies of patterns in the nucleotide sequences • In order to solve the problem, we analyze the frequencies of patterns in the nucleotide sequences • The Gold Bug Problem: • In order to solve the problem, we analyze the frequencies of patterns in the text written in English

  40. Similarities (cont’d) • Motif Finding: • Knowledge of established motifs reduces the complexity of the problem • The Gold Bug Problem: • Knowledge of the words in the dictionary is highly desirable

  41. Motif Finding and The Gold Bug Problem: Differences Motif Finding is harder than the Gold Bug problem: • We don’t have the complete dictionary of motifs • The “genetic” language does not have a standard “grammar” • Only a small fraction of nucleotide sequences encode for motifs; the size of data is enormous

  42. So, what do we do?We use whatever knowledge we have, and teach the computer program to look for elements that abide by these rules.

  43. Similarity to something known • Strand specificity (cis to the gene) • Knowledge of length distribution • May have known folds • Taxonomic distribution • Position specificity

  44. Founded by Amos Bairoch • 1988 First release in the PC/Gene software • 1990 Synchronisation with Swiss-Prot • 1994 Integration of « profiles » • 1999 PROSITE joins InterPro • Release 20.57, of 23-Nov-2009

  45. Contains biological annotation in addition to sequences. • catalytic, metal binding, S-S bridge, cofactor binding, prosthetic group, PTM

  46. PROSITE Format (Pattern) Regular Expression Language (REGEXP) • Pattern: <A-x-[ST](2)-x(0,1)-{V} • Regexp: ^A.[ST]{2}.?[^V] • Text: The sequence must start with an alanine, followed by any amino acid, followed by a serine or a threonine, two times, followed by any amino acid or nothing, followed by any amino acid except a valine.

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