CSE182-L5: Position specific scoring matrices Regular Expression Matching Protein Domains

1. CSE182-L5: Position specific scoring matricesRegular Expression MatchingProtein Domains CSE 182

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

3. 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? CSE 182

4. Silly Quiz CSE 182

5. Silly Quiz CSE 182

6. 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? CSE 182

7. 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 CSE 182

8. 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 CSE 182

9. Zinc Finger domain CSE 182

10. Proteins containing zf domains How can we find a motif corresponding to a zf domain CSE 182

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

12. 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 CSE 182

13. Regular Expression MatchingProtein structure basics CSE 182

14. Zinc Finger domain CSE 182

15. 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? CSE 182

16. 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 CSE 182

17. 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? CSE 182

18. 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. CSE 182

19. Examples: Regular Expression & Automata • (A+C)*EEC* A C E E start end C CSE 182

20.     Constructing automata from R.E  • R = {} • R = {},    • R = R1 + R2 • R = R1 · R2 • R = R1*      CSE 182

21. 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? CSE 182

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

23. Alg. For matching R.E. • If D[1..c] is accepted by the automaton RA • There is a path labeled D[1]…D[c] that goes from START to END in RA • 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 u D[1] .. D[c-1] D[c] CSE 182

24. D.P. to match regular expression u  v • 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 Eps(u) CSE 182

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

26. Algorithm CSE 182

27. 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 CSE 182

28. 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. CSE 182

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

30. Scoring Profiles Scoring Matrix i k fki s CSE 182

31. 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. CSE 182

32. 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 • Multiple alignment: • Use ungapped multiple alignments only Psi-BLAST speed • Two time consuming steps. • Multiple alignment of homologs • Searching with Profiles. • Does the keyword search idea work? CSE 182

33. 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 CSE 182

34. Databases of protein domains CSE 182

35. Pfam http://pfam.wustl.edu/ Also at Sanger CSE 182

36. PROSITE http://us.expasy.org/prosite/ CSE 182

37. CSE 182

38. BLOCKS CSE 182

39. CSE 182