2. Fa05CSE 182 CSE182-L5: Position specific scoring matrices Regular Expression Matching Protein Domains

3. Fa05CSE 182 Class Mailing List fa05_182@cs.ucsd.edu To subscribe, send email to –fa05_182-subscribe@cs.ucsd.edufa05_182-subscribe@cs.ucsd.edu You can subscribe from the course web page Please subscribe with a UCSD email address if possible.

4. Fa05CSE 182 Protein Sequence Analysis What can you do if BLAST does not return a hit? –Sometimes, homology (evolutionary similarity) exists at very low levels of sequence similarity. A: Accept hits at higher P-value. –This increases the probability that the sequence similarity is a chance event. –How can we get around this paradox? –Reformulated Q: suppose two sequences B,C have the same level of sequence similarity to sequence A. If A& B are related in function, can we assume that A& C are? If not, how can we distinguish?

5. Fa05CSE 182 Silly Quiz

6. Fa05CSE 182 Silly Quiz

7. Fa05CSE 182 Protein sequence motifs Premise: The sequence of a protein sequence gives clues about its structure and function. Not all residues are equally important in determining function. How can we identify these key residues?

8. Fa05CSE 182 Prosite In some cases the sequence of an unknown protein is too distantly related to any protein of known structure to detect its resemblance by overall sequence alignment. However, relationships can be revealed by the occurrence in its sequence of a particular cluster of residue types, which is variously known as a pattern, motif, signature or fingerprint. These motifs arise because specific region(s) of a protein which may be important, for example, for their binding properties or for their enzymatic activity are conserved in both structure and sequence. These structural requirements impose very tight constraints on the evolution of this small but important portion(s) of a protein sequence. The use of protein sequence patterns or profiles to determine the function of proteins is becoming very rapidly one of the essential tools of sequence analysis. Many authors ( 3,4) have recognized this reality. Based on these observations, we decided in 1988, to actively pursue the development of a database of regular expression-like patterns, which would be used to search against sequences of unknown function. Kay Hofmann,Philipp Bucher, Laurent Falquet and Amos Bairoch The PROSITE database, its status in 1999

9. Fa05CSE 182 Basic idea It is a heuristic approach. Start with the following: –A collection of sequences with the same function. –Region/residues known to be significant for maintaining structure and function. Develop a pattern of conserved residues around the residues of interest Iterate for appropriate sensitivity and specificity

10. Fa05CSE 182 Zinc Finger domain

11. Fa05CSE 182 Proteins containing zf domains How can we find a motif corresponding to a zf domain

12. Fa05CSE 182 From alignment to regular expressions * ALRDFATHDDF SMTAEATHDSI ECDQAATHEAS ATH-[DE] Search Swissprot with the resulting pattern Refine pattern to eliminate false positives Iterate

13. Fa05CSE 182 The sequence analysis perspective Zinc Finger motif –C-x(2,4)-C-x(3)-[LIVMFYWC]-x(8)-H-x(3,5)-H –2 conserved C, and 2 conserved H How can we search a database using these motifs? –The motif is described using a regular expression. What is a regular expression? –How can we search for a match to a regular expression? Not allowed to use Perl :-) The ‘regular expression’ motif is weak. How can we make it stronger

14. Fa05CSE 182 Regular Expression Matching Protein structure basics

15. Fa05CSE 182 Zinc Finger domain

16. Fa05CSE 182 The sequence analysis perspective Zinc Finger motif –C-x(2,4)-C-x(3)-[LIVMFYWC]-x(8)-H-x(3,5)-H –2 conserved C, and 2 conserved H How can we search a database using these motifs? –The motif is described using a regular expression. What is a regular expression?

17. Fa05CSE 182 Regular Expressions Concise representation of a set of strings over alphabet . Described by a string over R is a r.e. if and only if

18. Fa05CSE 182 Regular Expression Q: Let  ={A,C,E} –Is (A+C)*EEC* a regular expression? –*(A+C)? –AC*..E? Q: When is a string s in a regular expression? –R =(A+C)*EEC* –Is CEEC in R? –AEC? –ACEE?

19. Fa05CSE 182 Regular Expression & Automata  Every R.E can be expressed by an automaton (a directed graph) with the following properties: –The automaton has a start and end node –Each edge is labeled with a symbol from , or   Suppose R is described by automaton A  S  R if and only if there is a path from start to end in A, labeled with s.

20. Fa05CSE 182 Examples: Regular Expression & Automata (A+C)*EEC* CA C startend EE

21. Fa05CSE 182 Constructing automata from R.E R = {  } R = {  },    R = R 1 + R 2 R = R 1 · R 2 R = R 1 *      

22. Fa05CSE 182 Regular Expression Matching Given a database D, and a regular expression R, is a substring of D in R? Is there a string D[l..c] that is accepted by the automaton of R? Simpler Q: Is D[1..c] accepted by the automaton of R?

23. Fa05CSE 182 Alg. For matching R.E. If D[1..c] is accepted by the automaton R A –There is a path labeled D[1]…D[c] that goes from START to END in R A D[1] D[2]  D[c]

24. Fa05CSE 182 Alg. For matching R.E. If D[1..c] is accepted by the automaton R A –There is a path labeled D[1]…D[c] that goes from START to END in R A –There is a path labeled D[1]..D[c-1] from START to node u, and a path labeled D[c] from u to the END D[1].. D[c-1] D[c] u

25. Fa05CSE 182 D.P. to match regular expression Define: –A[u,  ] = Automaton node reached from u after reading  –Eps(u): set of all nodes reachable from node u using epsilon transitions. –N[c] = subset of nodes reachable from START node after reading D[1..c] –Q: when is v  N[c]  u v  u Eps(u)

26. Fa05CSE 182 Q: when is v  N[c]? A: If for some u  N[c-1], w = A[u,D[c]], v  {w}+ Eps(w) D.P. to match regular expression

27. Fa05CSE 182 Algorithm

28. Fa05CSE 182 The final step We have answered the question: –Is D[1..c] accepted by R? –Yes, if END  N[c] We need to answer –Is D[l..c] (for some l, and some c) accepted by R

29. Fa05CSE 182 Profiles versus regular expressions Regular expressions are intolerant to an occasional mis-match. The Union operation (I+V+L) does not quantify the relative importance of I,V,L. It could be that V occurs in 80% of the family members. Profiles capture some of these ideas.

30. Fa05CSE 182 Profiles Start with an alignment of strings of length m, over an alphabet A, Build an |A| X m matrix F=(f ki ) Each entry f ki represents the frequency of symbol k in position i 0.71 0.14 0.28

31. Fa05CSE 182 Scoring Profiles k i s f ki Scoring Matrix

32. Fa05CSE 182 Psi-BLAST idea Multiple alignments are important for capturing remote homology. Profile based scores are a natural way to handle this. Q: What if the query is a single sequence. A: Iterate: –Find homologs using Blast on query –Discard very similar homologs –Align, make a profile, search with profile.

33. Fa05CSE 182 Psi-BLAST speed Two time consuming steps. 1.Multiple alignment of homologs 2.Searching with Profiles. 1.Does the keyword search idea work? Multiple alignment: –Use ungapped multiple alignments only Pigeonhole principle again: –If profile of length m must score >= T –Then, a sub-profile of length l must score >= lT/m –Generate all l-mers that score at least lT|/M –Search using an automaton

34. Fa05CSE 182 Databases of Motifs Functionally related proteins have sequence motifs. The sequence motifs can be represented in many ways, and different biological databases capture these representations –Collection of sequences (SMART) –Multiple alignments (BLOCKS) –Profiles (Pfam (HMMs)/Impala)) –Regular Expressions (Prosite) Different representations must be queried in different ways

35. Fa05CSE 182 Databases of protein domains

36. Fa05CSE 182 Pfam http://pfam.wustl.edu/ Also at Sanger

37. Fa05CSE 182 PROSITE http://us.expasy.org/prosite/

38. Fa05CSE 182

39. Fa05CSE 182 BLOCKS

40. Fa05CSE 182