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CSE182-L11

Protein sequencing and Mass Spectrometry

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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)

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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.

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Problem 3: Repeats

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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

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Repeats

• How can you detect if your fragment overlap is due to a repeat?

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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

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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

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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

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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

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Creating Super Contigs

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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.

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Supercontig merging

• Remove the top scoring pair (S1,S2) from the priority queue.

• Merge (S1,S2) to form contig T.• Remove all pairs in Q containing S1 or S2

• Find all supercontigs W that share mate-pair links with T and insert (T,W) into the priority queue.

• Detect Repeated Supercontigs and remove

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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

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Filling gaps in Supercontigs

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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.

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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

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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

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Dynamic aspects of cellular function

• Expressed transcripts– Microarrays,….

• Expressed proteins– Mass spectrometry,..

• Protein-protein interactions (protein networks)• Protein-DNA interactions• Population studies

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Mass Spectrometry

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Nobel citation ’02

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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..

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Sample Preparation

Enzymatic Digestion (Trypsin)

+Fractionation

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Single Stage MS

MassSpectrometry

LC-MS: 1 MS spectrum / second

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Tandem MS

Secondary Fragmentation

Ionized parent peptide

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The peptide backbone

H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH

Ri-1 Ri Ri+1

AA residuei-1 AA residuei AA residuei+1

N-terminus C-terminus

The peptide backbone breaks to formfragments with characteristic masses.

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Ionization

H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH

Ri-1 Ri Ri+1

AA residuei-1 AA residuei AA residuei+1

N-terminus C-terminus

The peptide backbone breaks to formfragments with characteristic masses.

Ionized parent peptide

H+

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Fragment ion generation

H...-HN-CH-CO NH-CH-CO-NH-CH-CO-…OH

Ri-1 Ri Ri+1

AA residuei-1 AA residuei AA residuei+1

N-terminus C-terminus

The peptide backbone breaks to formfragments with characteristic masses.

Ionized peptide fragment

H+

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Tandem MS for Peptide ID

147K

1166L

260

1020E

389

907D

504

778E

633

663E

762

534L

875

405F

1022

292G

1080

145S

1166

88

y ions

b ions

100

0250 500 750 1000

[M+2H]2+

m/z

% I

nte

nsit

y

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Peak Assignment

147K

1166L

260

1020E

389

907D

504

778E

633

663E

762

534L

875

405F

1022

292G

1080

145S

1166

88

y ions

b ions

100

0250 500 750 1000

y2 y3 y4

y5

y6

y7

b3b4 b5 b8 b9

[M+2H]2+

b6 b7 y9

y8

m/z

% I

nte

nsit

y Peak assignment impliesSequence (Residue tag) Reconstruction!

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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

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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

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Peptide fragmentation possibilities(ion types)

-HN-CH-CO-NH-CH-CO-NH-

RiCH-R’

ai

bici

xn-iyn-i

zn-i

yn-i-1

bi+1

R”

di+1

vn-i wn-i

i+1

i+1

low energy fragments high energy fragments

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Ion types, and offsets

• P = prefix residue mass• S = Suffix residue mass• b-ions = P+1• y-ions = S+19• a-ions = P-27

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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?

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Isotopic peaks

• Ex: Consider peptide SAM• Mass = 308.12802 • You should see:

• Instead, you see 308.13

308.13 310.13

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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?

€

11

1

⎛

⎝ ⎜

⎞

⎠ ⎟⋅0.01⋅(0.99)10

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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?

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Isotope Calculation

• Denote:– Nc : number of carbon atoms in the peptide

– Pc : probability of occurrence of C-13 (~1%)

– Then

€

Pr[Peak at M] =NC0

⎛ ⎝ ⎜

⎞ ⎠ ⎟pc

0 1− pc( )NC

Pr[Peak at M +1] =NC1

⎛ ⎝ ⎜

⎞ ⎠ ⎟pc

1 1− pc( )NC −1

+1

Nc=50

+1

Nc=200

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Isotope Calculation Example • Suppose we consider Nitrogen, and Carbon• NN: number of Nitrogen atoms• PN: probability of occurrence of N-15• Pr(peak at M)• Pr(peak at M+1)?• Pr(peak at M+2)?

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Pr[Peak at M] =NC0

⎛ ⎝ ⎜

⎞ ⎠ ⎟pc

0 1− pc( )NC NN

0 ⎛ ⎝ ⎜

⎞ ⎠ ⎟pN

0 1− pN( )NN

Pr[Peak at M +1] =NC1

⎛ ⎝ ⎜

⎞ ⎠ ⎟pc

1 1− pc( )NC −1 NN

0 ⎛ ⎝ ⎜

⎞ ⎠ ⎟pN

0 1− pN( )NN

+NC0

⎛ ⎝ ⎜

⎞ ⎠ ⎟pc

0 1− pc( )NC NN

1 ⎛ ⎝ ⎜

⎞ ⎠ ⎟pN

1 1− pN( )NN −1

How do we generalize? How can we handle Oxygen (O-16,18)?

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General isotope computation

• Definition:– Let pi,a be the abundance of the isotope with mass i Da

above the least mass– Ex: P0,C : abundance of C-12, P2,O: O-18 etc.

• Characteristic polynomial

• Prob{M+i}: coefficient of xi in (x) (a binomial convolution)

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φ(x) = p0,a + p1,ax + p2,ax2 +L( )

a∏

Na

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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 p1,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.

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φt2 (x) = φt1(x)(p0,H + p1,H )N H

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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

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Post-translational modifications

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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?

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De novo analysis basics

• Suppose all ions were prefix ions? Could you tell what the peptide was?

• Can post-translational modifications help?

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