Matching Problems in Bioinformatics

1. Matching Problems in Bioinformatics Charles Yan Fall 2008

2. Matching Problem Given a string P (pattern) and a long string T (text), find all occurrences, if any, of P in T. Example T: Given a string P (pattern) and a long string T (text), find all occurrences, if any, of P in T. P: any Exact matching: Does not allow any mismatch Inexact matching: Allow up to k mismatches

3. Matching Problem Unix: grep MS word: find Genbank: http://www.ncbi.nlm.nih.gov/Genbank/ Human genome: http://www.ncbi.nlm.nih.gov/projects/mapview/map_search.cgi?taxid=9606 Given “TTGTTCCGGTTAAAGATGGTGAAAATTTTT”, does it appear in human genome? Where? How about “ACCCCCAGGCGAGCATCTGACAGCCTGGAGCAGCACACACAACCCCAGGCGAG”?

4. Motifs • A motif is a conserved element corresponding to a certain function (or structure). Occurrence of a motifin a protein is likely to indicate that the protein has the corresponding function. • Motifs are usually represented using alignment or regular expression

5. Motifs

6. Motifs Protein function prediction using motifs • Each protein function is characterized by one single motif or multiple motifs . • If a protein contain the motif(s), it probably has the function that the motif(s)corresponds to. • A pertinent analogy is the use of fingerprints by the police for identification purposes. A fingerprint is generally sufficient to identify a given individual. Similarly, motif(s) can be used to formulate hypotheses about the function ofa newly discovered protein.

7. PROSITE • PROSITE (http://ca.expasy.org/prosite/) is a database of protein families and domains. (Starting in 1988). • PROSITE currently contains patterns (motifs) and profiles specific for more than a thousand protein families or domains. Release 20.36, of 22-Jul-2006 (contains 1528 documentation entries). • Each of these signatures comes with documentation providing background information on the structure and function of these proteins.

8. PROSITE

9. PROSITE

10. PROSITE

11. PROSITE

12. PROSITE Steps in the development of a new motif • Select a set of sequences that belong to a function family. Make a multiple alignment. • Find a short (not more than four or five residues long) conserved sequence (core motif) which is part of a region known to be important or which include biologically significant residue(s).

13. PROSITE Steps in the development of a new motif (cont.) • The most recent version of the Swiss-Prot knowledgebase is then scanned with these core pattern(s). If a core motif will detect all the proteins in the family and none (or very few) of the other proteins, we can stop at this stage. • In most cases we are not so lucky and we pick up a lot of extra sequences which clearly do not belong to the group of proteins under consideration. A further series of scans, involving a gradual increase in the size of the motif, is then necessary. In some cases we never manage to find a good motif.

14. PROSITE The motif are described using the following conventions: • The standard IUPAC one-letter codes for the amino acids are used. • The symbol 'x' is used for a position where any amino acid is accepted. • Ambiguities are indicated by listing the acceptable amino acids for a given position, between square parentheses '[ ]'. For example: [ALT] stands for Ala or Leu or Thr. • Ambiguities are also indicated by listing between a pair of curly brackets '{ }' the amino acids that are not accepted at a given position. For example: {AM} stands for any amino acid except Ala and Met. • Each element in a pattern is separated from its neighbor by a '-'.

15. PROSITE The motif are described using the following conventions (Cont.): • Repetition of an element of the pattern can be indicated by following that element with a numerical value or a numerical range between parenthesis. Examples: x(3) corresponds to x-x-x, x(2,4) corresponds to x-x or x-x-x or x-x-x-x. • When a pattern is restricted to either the N- or C-terminal of a sequence, that pattern either starts with a '<' symbol or respectively ends with a '>' symbol. In some rare cases (e.g. PS00267 or PS00539), '>' can also occur inside square brackets for the C-terminal element. 'F-[GSTV]-P-R-L-[G>]' means that either 'F-[GSTV]-P-R-L-G' or 'F-[GSTV]-P-R-L>' are considered. • A period ends the pattern. Examples: [AC]-x-V-x(4)-{ED}.This pattern is translated as: [Ala or Cys]-any-Val-any-any-any-any-{any but Glu or Asp}

16. PROSITE

17. PROSITE

18. PROSITE A profile or weight matrix is a table of position-specific amino acid weights and gap costs. These numbers (also referred to as scores) are used to calculate a similarity score for any alignment between a profile and a sequence, or parts of a profile and a sequence. An alignment with a similarity score higher than or equal to a given cut-off value constitutes a motif occurrence.

19. PROSITE

20. Motifs and Matching • Motif Finding: Given a set of protein sequences, to find the motif(s) that are shared by these proteins. • Motif Scanning Given a motif and a protein sequence, to find the occurrences (not necessary identical) of the motif on the protein sequences. –--The Matching Problem!

21. From Single Motif to Multiple Motifs One single motif is not sufficient to predict a protein function. Multiple motifs have stronger predicting power.

22. Multiple Motifs Protein function prediction using multiple motifs • Each protein function is characterized by a set of motifs (in stead of a single one). • If a protein contain a set of motifs, it probably has the function that the set of motifs correspond to.

23. PRINTS • PRINTS(http://umber.sbs.man.ac.uk/dbbrowser/PRINTS/ ) is a database of protein fingerprints. • A fingerprint is a group of conserved motifs used to characterize a protein family; • ftp.bioinf.man.ac.uk/pub/prints • PRINTS is now maintained at the University of Manchester • PRINTS VERSION 38.1 (25 May, 2007) • 1904 FINGERPRINTS, encoding11,451 single motifs

24. PRINTS • Two types of fingerprint are represented in the database, i.e. they are either simple or composite, depending on their complexity: simple fingerprints are essentially single-motifs; while composite fingerprints encode multiple motifs. The bulk of the database entries are of the latter type because discrimination power is greater for multi-component searches. • Usually the motifs do not overlap, but are separated along a sequence, though they may be contiguous in 3D-space. • Fingerprints can encode protein folds and functionalities more flexibly and powerfully than can single motifs, full diagnostic potency deriving from the mutual context provided by motif neighbors.

25. PRINTS

26. PRINTS

27. PRINTS a) General field

28. PRINTS FPScan Submitting a PROTEIN sequence find the closest matching PRINTS fingerprint/s.

29. PRINTS

30. PRINTS

31. PRINTS

32. PRINTS

33. Related Projects • InterPro - Integrated Resources of Proteins Domains and Functional Sites • BLOCKS - BLOCKS db • Pfam - Protein families db (HMM derived) [Mirror at St. Louis (USA)] • PRINTS - Protein Motif fingerprint db • ProDom - Protein domain db (Automatically generated) • PROTOMAP - An automatic hierarchical classification of Swiss-Prot proteins • SBASE - SBASE domain db • SMART - Simple Modular Architecture Research Tool • TIGRFAMs - TIGR protein families db

34. Motifs and Matching • Motif Finding: Given a set of protein sequences, to find the motif(s) that are shared by these proteins. • Motif Scanning Given a motif and a protein sequence, to find the occurrences (not necessary identical) of the motif on the protein sequences. –--The Matching Problem!