1. Profile Searches Revised 07/11/06

2. Overview Introduction Motif representation Motif screening Motif Databases Exercise

3. Features characteristic for the whole family Multiple sequence alignment Introduction How to represent the characteristic features? Motif model: captures the family characteristic features regular expression, weight matrix, HMM profile

4. Introduction Multiple sequence alignment Construct model Scan new sequence with the model Unaligned sequences model: captures the family characteristic features used to detect remote homologs of a family

5. Overview Introduction Motif representation –String based representation Consensus Regular expression –Probabilistic representation PSSM HMM Profile Motif screening Motif Databases Exercise

6. HMM Multiple sequence alignment Construct model Scan new sequence with the model I.II. Unaligned sequences III.

7. Consensus sequence: –Reductionistic representation of a motif –Most frequent instance is used as a representative –Loss of information Regular expression: –More complex representation allowing motif degeneracy String Based Representation

8. CTTAATATTAACTTAAT Consensus CTTAAKRTTMAYTTAAT Regular expression String Based Representation

9. cell signal motif Gene 1Gene 2Gene 3Gene 4 signal ? translation transcription mRNA protein gene chromosome DNA motifs String Based Representation

10. Sequences involved in enzymatic reactions (PROSITE) String Based Representation

11. Overview Introduction Motif representation –String based representation Consensus Regular expression –Probabilistic representation PSSM HMM Profile Motif screening Motif Databases Exercise

12. Probabilistic PSSM Frequency matrix G A A T T C A T G T C A C T T C A T T G Pseudo Counts Frequency matrix Alignment

13. Probabilistic PSSM G A A T T C A T G T C A C T T C A T T G Convert into PSSM Alignment PSSM p(A)=p(C)=p(G)=p(T)=0.25 Motif logo

14. PSSM msa Regular expression Weight matrix Motif logo

15. Motif Representation CTTAATATTAACTTAAT Consensus CTTAAKRTTMAYTTAAT Regular expression PSSM (motif logo)

16. Definition HMM State sequence path p: –Probability of a state depends only on the previous state –Transition probability from state l to state k –emission probability: probability that symbol b is seen when in state k a kl e k (b) State lState k A HIDDEN Markov model: it is not possible to tell what state the system is in by looking at the corresponding symbol Finding the possible paths = decoding beginMjMj IjIj DjDj end HMM

17. Probabilistic model that represents the alignment of the family –Gapped multiple alignment –Distinct states separated by transition probabilities (i.e. the probability of moving from one state to the next) –The current state  is only dependent on the previous state (first order Markov process) –The sequence of states followed in the model is called the path  –Each state has the probability of emitting a certain symbol of the alphabet (A,C,T,G for DNA) or one of the 20 amino acids for proteins: emission probability

18. HMM can model any possible sequence It defines a probability distribution over the whole space of sequences Training a HMM: search for the parametrisation that makes this distribution peak around members of the family Parametrisation –Determine model structure Length of alignment Number of insert states –Determine the probability parameters HMM

19. Training a HMM –Determine structure of the model –Determine emission and transition probabilities E.g. the first column: e 1 (A) = 4/5; e 1 (T) = 1/5; e 1 (C) = 0; e 1 (G) = 0; E.g. the second column: e 2 (A) = 0; e 2 (T) = 0; e 2 (C) = 4/5; e 2 (G) = 1/5; E.g. the third column: e 3 (A) = 4/5; e 3 (T) = 0; e 3 (C) = 1/5; e 3 (G) = 0; ACA---ATG TCAACTATC ACAC--AGC AGA---ATC ACCG--ATC A 0.8 C PC G PC T 0.2 A PC C 0.8 G 0.2 T PC A 0.8 C G T 0.2 A 1 C G T 1 1 0.4 A 0.2 C 0.4 G 0.2 T 0.2 0.6 0.4

20. Profile representation Suppose I (amino acid b) is the ancestor What is the probability of observing a T (amino acid a) in the first column (position p) of the alignment This probability is reflected by the score M M(p,a)= W(p,b) X Y(a,b) M(1,I)= W(1,T) X Y(I,T) M is dependent on The observed frequency of T in the first position of the alignment (W) The probability of mutating I => T (according to PAM) (Y) I A … I S T V A I L T V I A I V b

21. Profile representation gaps

22. Overview Introduction Motif representation Motif screening Motif Databases Exercise

23. HMM Multiple sequence alignment Construct profile HMM Scan new sequence with the profile I.II. Unaligned sequences III.

24. Screening

25. The multiple alignment of the family is known (Clustal W) The motif to be detected is known but the multiple alignment does not yet exist –Motifs already described in literature –Construct the multiple alignment, derive the model Neither the motif nor the multiple alignment exist –Probabilistic motif detection

26. Obtained Motif Model used for genome wide screening (Motif Scanner) Identification of putative additional targets Use sliding window Attribute to each sequence within the sliding window a score Rank the hits based on their score and select the most promising candidates Genome wide screening Screening

27. Distinct methods differ in the motif representation and the scoring system used Consensus Sequence or Regular expression (pattern match) –Very conservative –Do not allow mismatches PSSM / HMM: more complicated scoring schemes –based on information content –Log likelihood –Less conservative –Difficult choice of threshold score –Tradeoff between sensitivity and selectivity

28. Screening FDR (1-Precision) FP/(TP+FP) Precision TP/(TP+FP) Specificity (related to the false positive fraction= 1-spec) TN/(TN+FP) Sensitivity (true positive fraction = recall) TP/(TP+FN)

29. Screening E- value: corresponds to the probability of finding a score equal or better than the one observed, by chance alone.

30. Screening with Regular Expression Simple perl scripts

31. Screening with PSSM Background frequency of each of the four nucleotides: Slide a window of length W over a sequence Calculate for each subsequence within the window a log odds-score The highest scoring positions correspond to the most likely locations of the motif 9.4 = log2(720) (0.6*0.9*0.8*0.97*0.6*0.7)/(0.25^6)

32. Screening with HMM Belongs a sequence to a family of proteins? Scoring a sequence with a HMM –aligning the sequence to the HMM –finding the hidden path that generates the sequence A sequence can be generated by different paths Enumerate all paths and calculate for each path the probability that is generates the sequence Viterbi Path: most likely path Total probability that sequence is generated by HMM = sum of probabilities of all possible paths

33. Screening with HMM Example for 1 path ATCAGT

34. Screening with HMM Calculate the probability of the sequence being generated by the HMM profile of a protein family versus a random model = align the unknown sequence with the HMM –The sequence can be generated by different paths Impossible to enumerate all possibilities –What is the most probable path? (Viterbi, backtracking) –What is the total probability? (Forward) Bits score ATAT A-A- -T-T ATT and TTC

35. Screening with HMM Hidden Markov model because if we observe a sequence, the path of states that was followed by the Markov model to generate the observed sequence is unknown or hidden. This hidden path contains the information on how the observed sequence should be aligned with the profile. Usually a sequence can be generated in multiple ways by the Markov model and more hidden paths (corresponding to distinct alignments) are possible. Usually not all possible paths have an equal probability. Indeed some transitions are not very likely (low transition probability). Usually the path with the highest probability (highest score = most likely path) corresponds to the best alignment.

36. Screening with HMM Detecting the underlying sequence of states allows to uncover the most probable path of transitions (decoding) –VITERBI Algorithm: most probable path (backtracking) Start at first position (state k) Move to next most probable state l –V k (i) is the probability of the most probable path ending in state k –Calculate probability –Viterbi algorithm allows to detect the most probable path and the probability of this most probable path begin MjMj IjIj DjDj end

38. begin MjMj IjIj DjDj end -ACA---ATG -TCAACTATC -ACAC--AGC -AGA---ATC -ACCG--ATC A ACAC Calculate Score state 1: S(1)= a(BM) +e(A) S(2)= a(BI) + e(A) S(3)= a(BD) - Maximal score state M: S(1)= a(BM) +e(A) S(1)= a(BI) + e(A) + a(IM)+e(C) HMM ACAAG

39. Conclusion Distinct methods differ in the motif representation and the scoring system used Consensus Sequence or Regular expression (pattern match) –Very conservative –Do not allow mismatches PSSM / HMM: more complicated scoring schemes –based on information content –Log likelihood –Less conservative –Difficult choice of threshold score –Tradeoff between sensitivity and selectivity

40. Overview Introduction Motif representation Motif screening Motif Databases –Prosite –Blocks –pFAM Exercise

41. Pfam Pfam starts from a set of automatically generated domain alignments (generated by PsiBlast). From these alignments a HMM is calculated Subsequently all sequences in the SwissProt database of proteins are classified in protein families –By scoring them with the representative HMMs –Ranking sequences according to their score –separate class members from the other sequences in the database based on a suitable threshold Pfam 7.0 is such a database that contains a total of 3360 families. Pfam contains multiple protein alignments and profile-HMMs of these families.

42. Pfam

43. Full: alignment on which the Pfam HMM was based HMMs for global and fragment search

44. Pfam Screening an new sequence against Pfam HMMs to classify the novel sequence

45. Pfam Each Pfam family: "trusted cutoff" and a "noise cutoff“ TC1 is the lowest score for sequences included in the family NC1 is the highest score for sequences not included in the Full alignment The probability that the sequence was generated by the HMM and the probability that the sequence was generated by a null model E-value is the number of hits that would be expected to have a score equal or better than this by chance alone Raw score: bitscore Scores in Pfam

46. Pfam

47. PROSITE Patterns (regular expressions) (ScanProsite) –Shorter than Pfam Enzyme catalytic sites Prosthetic group attachment sites (heme, pyridoxal- phosphate, biotin, etc) Amino acids involved in binding a metal ion Cysteines involved in disulfide bonds Regions involved in binding a molecule (ADP/ATP, GDP/GTP, calcium, DNA, etc.) or another protein

48. PROSITE Profiles (Profile representation)

49. PROSITE Aminael renew

50. BLOCKS Database of ungapped alignments Motif models represented as PSSMs

51. Example sequence >gi|1071819|pir||B54759 ba-type ubiquinol oxidase Paracoccus denitrificans MATFSNETTFLLGRLNWDAIPKEPIVWATFVVVAIGGIAALAALTKYRLWGWLWREWFTSVDHKKIGIMYIVLALIMFVRGFA DAIMMRLQQVWAFGGSEGYLNSHHYDQIFTAHGVIMIFFVAMPFITGLMNYVVPLQIGARDVSFPFLNNFSFWMTVGGAVITM ASLFLGEFAQTGWLAFPPLSGIGYSPWVGVDYYIWGLQVAGVGTTLSGINLLVTILKMRAPGMTMMRMPIFTWTSFCANILIV ASFPVLTMTLILLTLDRYVGTNFFTNDLGGNPMMYINLIWIWGHPEVYILILPLFGVFSEVTSTFSGKRLFGYSSMVYATVCITVL SYLVWLHHFFTMGSGASVNSFFGITTMIISIPTGAKLFNWLFTMYRGRIRYELPMMWTIAFMLTFVIGGMTGVLLAVPPADFVL HNSLFLIAHFHNVIIGGVLFGLFAAINFWWPKAFGFKLDVFWGKVSFWFWVVGFWAAFMPLYILGLMGVTRRLRVFDDPDLRI WFAIAAFGAVLIACGIAAMFVQFGVSILRRDRPEYRDVSGDPWDGRTLEWATSSPPPAYNFAFNPISHGLDTWWEMKQQGATR PTGGYMPIHMPKNTGTGVILAALATVCGMALVWYVWWLAALSFLGIIAVSIAHTFNYNRDYYIPVSEIEATEDARTRQLAQGV http://www.expasy.org/prosite/ http://www.sanger.ac.uk/Software/Pfam/search.shtml http://blocks.fhcrc.org/ Scan sequence with prosite, Pfam, Blocks

52. PSI-BLAST

53. Overview Query Sequence Unknown Blast Sequence to search for close homologs Search pFAM, Prosite for conserved motifs You detected homology with an annotated protein family Make a multiple sequence alignment Generate profile or HMM Search database for remote homologs Blast ClustalW PFAM PROSITE HMMer, PSSM Profile Search PSI-blast

55. exercises

56. Bits score (log odd score Bayesian) –Posterior: HMM model: is this a globin domain? –Likelihood calculated: probability of the sequence being generated by the HMM model –Prior probability: p(model) –Bayes M R 1 2 3 4