2. Fa 06CSE182 CSE182-L11 Protein sequencing and Mass Spectrometry

3. Fa 06CSE182 Whole genome shotgun Input: –Shotgun sequence fragments (reads) –Mate pairs Output: –A single sequence created by consensus of overlapping reads First generation of assemblers did not include mate-pairs (Phrap, CAP..) Second generation: CA, Arachne, Euler We will discuss Arachne, a freely available sequence assembler (2nd generation)

4. Fa 06CSE182 Arachne (also celera assembler) Overlap –Problem 1: Large all against all computation Fast overlap computation using k-mer hashing. Layout –Problem 2: Small contigs with 10X coverage –Solution 2: Use mate-pairs to build super-contigs –Problem 3: Repetitive structure of the genome.

5. Fa 06CSE182 Problem 3: Repeats

6. Fa 06CSE182 Repeats & Chimerisms 40-50% of the human genome is made up of repetitive elements. Repeats can cause great problems in the assembly! Chimerism causes a clone to be from two different parts of the genome. Can again give a completely wrong assembly

7. Fa 06CSE182 Repeats How can you detect if your fragment overlap is due to a repeat?

8. Fa 06CSE182 Repeat detection Lander Waterman strikes again! The expected number of clones in a Repeat containing island is MUCH larger than in a non-repeat containing island (contig). Thus, every contig can be marked as Unique, or non-unique. In the first step, throw away the non-unique islands. Repeat

9. Fa 06CSE182 Detecting Repeat Contigs 1: Read Density Compute the log-odds ratio of two hypotheses: H1: The contig is from a unique region of the genome. The contig is from a region that is repeated at least twice

10. Fa 06CSE182 Detecting Chimeric reads Chimeric reads: Reads that contain sequence from two genomic locations. Good overlaps: G(a,b) if a,b overlap with a high score Transitive overlap: T(a,c) if G(a,b), and G(b,c) Find a point x across which only transitive overlaps occur. X is a point of chimerism

11. Fa 06CSE182 Contig assembly Reads are merged into contigs upto repeat boundaries. (a,b) & (a,c) overlap, (b,c) should overlap as well. Also, –shift(a,c)=shift(a,b)+shift(b,c) Most of the contigs are unique pieces of the genome, and end at some Repeat boundary. Some contigs might be entirely within repeats. These must be detected

12. Fa 06CSE182 Creating Super Contigs

13. Fa 06CSE182 Supercontig assembly Supercontigs are built incrementally Initially, each contig is a supercontig. In each round, a pair of super-contigs is merged until no more can be performed. Create a Priority Queue with a score for every pair of ‘mergeable supercontigs’. –Score has two terms: A reward for multiple mate-pair links A penalty for distance between the links.

14. Fa 06CSE182 Supercontig merging Remove the top scoring pair (S 1,S 2 ) from the priority queue. Merge (S 1,S 2 ) to form contig T. Remove all pairs in Q containing S 1 or S 2 Find all supercontigs W that share mate-pair links with T and insert (T,W) into the priority queue. Detect Repeated Supercontigs and remove

15. Fa 06CSE182 Repeat Supercontigs If the distance between two super-contigs is not correct, they are marked as Repeated If transitivity is not maintained, then there is a Repeat

16. Fa 06CSE182 Filling gaps in Supercontigs

17. Fa 06CSE182 Consensus Derivation Consensus sequence is created by converting pairwise read alignments into multiple-read alignments. The final sequence is reported as a consensus for each of the super contigs. The supercontigs themselves are ordered using physical markers. Gaps are filled in using directed sequencing efforts.

18. Fa 06CSE182 Summary Whole genome shotgun is now routine: –Human, Mouse, Rat, Dog, Chimpanzee.. –Many Prokaryotes (One can be sequenced in a day) –Plant genomes: Arabidopsis, Rice –Model organisms: Worm, Fly, Yeast A lot is not known about genome structure, organization and function. –Comparative genomics offers low hanging fruit

19. Fa 06CSE182 Course Summary Sequence Comparison (BLAST & other tools) Protein Motifs: –Profiles/Regular Expression/HMMs Discovering protein coding genes –Gene finding HMMs –DNA signals (splice signals) How is the genomic sequence itself obtained? –LW statistics –Sequencing and assembly Next topic: the dynamic aspects of the cell Protein sequence analysis ESTs Gene finding

20. Fa 06CSE182 Dynamic aspects of cellular function Expressed transcripts –Microarrays,…. Expressed proteins –Mass spectrometry,.. Protein-protein interactions (protein networks) Protein-DNA interactions Population studies

21. Fa 06CSE182 Mass Spectrometry

22. Fa 06CSE182 Nobel citation ’02

23. Fa 06CSE182 The promise of mass spectrometry Mass spectrometry is coming of age as the tool of choice for proteomics –Protein sequencing, networks, quantitation, interactions, structure…. Computation has a big role to play in the interpretation of MS data. We will discuss algorithms for –Sequencing, Modifications, Interactions..

24. Fa 06CSE182 Sample Preparation Enzymatic Digestion (Trypsin) + Fractionation

25. Fa 06CSE182 Single Stage MS Mass Spectrometry LC-MS: 1 MS spectrum / second

26. Fa 06CSE182 Tandem MS Secondary Fragmentation Ionized parent peptide

27. Fa 06CSE182 The peptide backbone H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH R i-1 RiRi R i+1 AA residue i-1 AA residue i AA residue i+1 N-terminus C-terminus The peptide backbone breaks to form fragments with characteristic masses.

28. Fa 06CSE182 Ionization H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH R i-1 RiRi R i+1 AA residue i-1 AA residue i AA residue i+1 N-terminus C-terminus The peptide backbone breaks to form fragments with characteristic masses. Ionized parent peptide H+H+

29. Fa 06CSE182 Fragment ion generation H...-HN-CH-CO NH-CH-CO-NH-CH-CO-…OH R i-1 RiRi R i+1 AA residue i-1 AA residue i AA residue i+1 N-terminus C-terminus The peptide backbone breaks to form fragments with characteristic masses. Ionized peptide fragment H+H+

30. Fa 06CSE182 Tandem MS for Peptide ID 147 K 1166 L 260 1020 E 389 907 D 504 778 E 633 663 E 762 534 L 875 405 F 1022 292 G 1080 145 S 1166 88 y ions b ions 100 0 2505007501000 [M+2H] 2+ m/z % Intensity

31. Fa 06CSE182 Peak Assignment 147 K 1166 L 260 1020 E 389 907 D 504 778 E 633 663 E 762 534 L 875 405 F 1022 292 G 1080 145 S 1166 88 y ions b ions 100 0 2505007501000 y2y2 y3y3 y4y4 y5y5 y6y6 y7y7 b3b3 b4b4 b5b5 b8b8 b9b9 [M+2H] 2+ b6b6 b7b7 y9y9 y8y8 m/z % Intensity Peak assignment implies Sequence (Residue tag) Reconstruction!

32. Fa 06CSE182 Database Searching for peptide ID For every peptide from a database –Generate a hypothetical spectrum –Compute a correlation between observed and experimental spectra –Choose the best Database searching is very powerful and is the de facto standard for MS. –Sequest, Mascot, and many others

33. Fa 06CSE182 Spectra: the real story Noise Peaks Ions, not prefixes & suffixes Mass to charge ratio, and not mass –Multiply charged ions Isotope patterns, not single peaks

34. Fa 06CSE182 Peptide fragmentation possibilities (ion types) -HN-CH-CO-NH-CH-CO-NH- RiRi CH-R’ aiai bibi cici x n-i y n-i z n-i y n-i-1 b i+1 R” d i+1 v n-i w n-i i+1 low energy fragmentshigh energy fragments

35. Fa 06CSE182 Ion types, and offsets P = prefix residue mass S = Suffix residue mass b-ions = P+1 y-ions = S+19 a-ions = P-27

36. Fa 06CSE182 Mass-Charge ratio The X-axis is not mass, but (M+Z)/Z –Z=1 implies that peak is at M+1 –Z=2 implies that peak is at (M+2)/2 M=1000, Z=2, peak position is at 501 Quiz: Suppose you see a peak at 501. Is the mass 500, or is it 1000?

37. Fa 06CSE182 Isotopic peaks Ex: Consider peptide SAM Mass = 308.12802 You should see: Instead, you see 308.13 310.13

38. Fa 06CSE182 Isotopes C-12 is the most common. Suppose C-13 occurs with probability 1% EX: SAM –Composition: C11 H22 N3 O5 S1 What is the probability that you will see a single C-13? Note that C,S,O,N all have isotopes. Can you compute the isotopic distribution?

39. Fa 06CSE182 All atoms have isotopes Isotopes of atoms –O16,18, C-12,13, S32,34…. –Each isotope has a frequency of occurrence If a molecule (peptide) has a single copy of C-13, that will shift its peak by 1 Da With multiple copies of a peptide, we have a distribution of intensities over a range of masses (Isotopic profile). How can you compute the isotopic profile of a peak?

40. Fa 06CSE182 Isotope Calculation Denote: –N c : number of carbon atoms in the peptide –P c : probability of occurrence of C-13 (~1%) –Then +1 N c =50 +1 N c =200

41. Fa 06CSE182 Isotope Calculation Example Suppose we consider Nitrogen, and Carbon N N : number of Nitrogen atoms P N : probability of occurrence of N-15 Pr(peak at M) Pr(peak at M+1)? Pr(peak at M+2)? How do we generalize? How can we handle Oxygen (O-16,18)?

42. Fa 06CSE182 General isotope computation Definition: –Let p i,a be the abundance of the isotope with mass i Da above the least mass –Ex: P 0,C : abundance of C-12, P 2,O : O-18 etc. Characteristic polynomial Prob{M+i}: coefficient of x i in  (x) (a binomial convolution)

43. Fa 06CSE182 Isotopic Profile Application In DxMS, hydrogen atoms are exchanged with deuterium The rate of exchange indicates how buried the peptide is (in folded state) Consider the observed characteristic polynomial of the isotope profile  t1,  t2, at various time points. Then The estimates of p 1,H can be obtained by a deconvolution Such estimates at various time points should give the rate of incorporation of Deuterium, and therefore, the accessibility.

44. Fa 06CSE182 Quiz  How can you determine the charge on a peptide?  Difference between the first and second isotope peak is 1/Z  Proposal:  Given a mass, predict a composition, and the isotopic profile  Do a ‘goodness of fit’ test to isolate the peaks corresponding to the isotope  Compute the difference

45. Fa 06CSE182 Post-translational modifications

46. Fa 06CSE182 Tandem MS summary The basics of peptide ID using tandem MS is simple. –Correlate experimental with theoretical spectra In practice, there might be many confounding problems. –Isotope peaks, noise peaks, varying charges, post-translational modifications, no database. Recall that we discussed how peptides could be identified by scanning a database. What if the database did not contain the peptide of interest?

47. Fa 06CSE182 De novo analysis basics Suppose all ions were prefix ions? Could you tell what the peptide was? Can post-translational modifications help?

48. Fa 06CSE182