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Fa05CSE 182 CSE182-L4: Scoring matrices, Dictionary Matching.

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Presentation on theme: "Fa05CSE 182 CSE182-L4: Scoring matrices, Dictionary Matching."— Presentation transcript:


2 Fa05CSE 182 CSE182-L4: Scoring matrices, Dictionary Matching

3 Fa05CSE 182 Class Mailing List To subscribe, send to You can subscribe from the course web page Use the list for all course related queries, discussions,…

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 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?

6 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

7 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

8 Fa05CSE 182 Zinc Finger domain

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

10 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

11 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

12 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

13 Fa05CSE 182 Scoring Profiles k i s f ki Scoring Matrix

14 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.

15 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? 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

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17 Fa05CSE 182 CSE182-L6 Regular Expression Matching Protein structure basics

18 Fa05CSE 182 Zinc Finger domain

19 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?

20 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

21 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?

22 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.

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

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

25 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?

26 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]

27 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

28 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)

29 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

30 Fa05CSE 182 Algorithm

31 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

32 Fa05CSE 182 A structural view of proteins

33 Fa05CSE 182 CS view of a protein >sp|P00974|BPT1_BOVIN Pancreatic trypsin inhibitor precursor (Basic protease inhibitor) (BPI) (BPTI) (Aprotinin) - Bos taurus (Bovine). MKMSRLCLSVALLVLLGTLAASTPGCDTSNQAKAQ RPDFCLEPPYTGPCKARIIRYFYNAKAGLCQTFVYGG CRAKRNNFKSAEDCMRTCGGAIGPWENL

34 Fa05CSE 182 Protein structure basics

35 Fa05CSE 182 Side chains determine amino-acid type The residues may have different properties. Aspartic acid (D), and Glutamic Acid (E) are acidic residues

36 Fa05CSE 182 Bond angles form structural constraints

37 Fa05CSE 182 Various constraints determine 3d structure Constraints –Structural constraints due to physiochemical properties –Constraints due to bond angles –H-bond formation Surprisingly, a few conformations are seen over and over again.

38 Fa05CSE 182 Alpha-helix 3.6 residues per turn H-bonds between 1st and 4th residue stabilize the structure. First discovered by Linus Pauling

39 Fa05CSE 182 Beta-sheet Each strand by itself has 2 residues per turn, and is not stable. Adjacent strands hydrogen-bond to form stable beta-sheets, parallel or anti-parallel. Beta sheets have long range interactions that stabilize the structure, while alpha-helices have local interactions.

40 Fa05CSE 182 Domains The basic structures (helix, strand, loop) combine to form complex 3D structures. Certain combinations are popular. Many sequences, but only a few folds

41 Fa05CSE 182 3D structure Predicting tertiary structure is an important problem in Bioinformatics. Premise: Clues to structure can be found in the sequence. While de novo tertiary structure prediction is hard, there are many intermediate, and tractable goals.

42 Fa05CSE 182 Protein Domains An important realization (in the last decade) is that proteins have a modular architecture of domains/folds. Example: The zinc finger domain is a DNA-binding domain. What is a domain? –Part of a sequence that can fold independently, and is present in other sequences as well

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

44 Fa05CSE 182 Domain review What is a domain? How are domains expressed –Motifs (Regular expression & others) –Multiple alignments –Profiles –Profile HMMs

45 Fa05CSE 182 Databases of protein domains

46 Fa05CSE 182 Also at Sanger

47 Fa05CSE 182 PROSITE

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51 Fa05CSE 182 HMMER programs Hmmalign –Align a sequence to an HMM Hmmbuild –Build a model from a multiple alignment Hmmemit –Emits a probabilistic sequence from an HMM Hmmpfam –Search PFAM with a sequence query Hmmsearch –Search a sequence database with an HMM query

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53 Fa05CSE 182 Post-translational modification Residues undergo modification, usually by addition of a chemical group. Key mechanism for signal transduction, and many other cellular functions Some modifications might require single residues (Ex: phosphorylation). Others might require a pattern

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55 Fa05CSE 182 Protein targeting

56 Fa05CSE 182 Protein targeting In 1970, Gunter Blobel showed that proteins have an N-terminal signal sequence which directs proteins to the membrane. Proteins have to be transported to other organelles: nucleus, mitochondria,… Can we computationally identify the ‘signal’ which distinguishes the cellular compartment?

57 Fa05CSE 182 For transmembrane proteins, can we predict the transmembrane, outer, and inner regions?

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59 Fa05CSE 182 Multiple alignment tools

60 Fa05CSE 182 Tools for secondary structure prediction Each residue must be given a state: Helix, Loop, Strand HMMs/Neural networks are used to predict

61 Fa05CSE 182 Next topic: Gene finding

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