Lect12 Peptide identification

An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)

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Fa 06 CSE182 CSE182-L12 Mass Spectrometry Peptide identification
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Fa 06 CSE182 Ion mass computations Amino-acids are linked into peptide chains, by forming peptide bonds Residue mass Res.Mass(aa) = Mol.Mass(aa)-18 (loss of water)
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Fa 06 CSE182 Peptide chains MolMass(SGFAL) = resM(S)+…res(L)+18
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Fa 06 CSE182 M/ Z values for b/ y-ions Singly charged b-ion = ResMass(prefix) + 1 Singly charged y-ion= ResMass(suffix)+18+1 What if the ions have higher units of charge? R NH + 3 -CH-CO-NH-CH-COOH R R + 3 -CH-CO-NH-CH-CO R H+ R 2 -CH-CO-………-NH-CH-COOH R Ionized Peptide
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Fa 06 CSE182 De novo interpretation Given a spectrum (a collection of b-y ions), compute the peptide that generated the spectrum. A database of peptides is not given! Useful? Many genomes have not been sequenced, but are very useful. Tagging/ filtering PTMs
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Fa 06 CSE182 De Novo Interpretation: Example S G E K 0 88 145 274 402 b-ions 420 333 276 147 0 y-ions b y y 2 100 500 400 300 200 M/Z b 1 1 2 Ion Offsets b=P+1 y=S+19=M-P+19
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Fa 06 CSE182 Computing possible prefixes We know the parent mass M=401. Consider a mass value 88 Assume that it is a b-ion, or a y-ion If b-ion, it corresponds to a prefix of the peptide with residue mass 88-1 = 87. If y-ion, y=M-P+19. Therefore the prefix has mass P=M-y+19= 401-88+19=332 Compute all possible Prefix Residue Masses (PRM) for all ions.
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Fa 06 CSE182 Putative Prefix Masses Prefix Mass M=401 b y 88 87 332 145 144 275 147 146 273 276 275 144 S G E K 0 87 144 273 401 Only a subset of the prefix masses are correct. The correct mass values form a ladder of amino-acid residues
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Fa 06 CSE182 Spectral Graph Each prefix residue mass (PRM) corresponds to a node. Two nodes are connected by an edge if the mass difference is a residue mass. A path in the graph is a de novo interpretation of the spectrum 87 144 G
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CSE182 Spectral Graph Each peak, when assigned to a prefix/ suffix ion type generates a unique prefix residue mass. Spectral graph: Each node u defines a putative prefix residue M(u). (u,v) in E if M(v)-M(u) is the residue mass of an a.a. (tag) or 0. Paths in the spectral graph correspond to a interpretation 300 100 401 200 0 S G E K 273 87 146 144 275 332
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Fa 06 CSE182 Re-defining de novo interpretation Find a subset of nodes in spectral graph s.t . 0, M are included Each peak contributes at most one node (interpretation)(*) Each adjacent pair (when sorted by mass) is connected by an edge (valid residue mass) An appropriate objective function (ex: the number of peaks interpreted) is maximized 300 100 401 200 0 S G E K 273 87 146 144 275 332 87 144 G
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Two problems Too many nodes. Only a small fraction are correspond to b/ y ions (leading to true PRMs) (learning problem) Multiple Interpretations Even if the b/ y ions were correctly predicted, each peak generates multiple possibilities, only one of which is correct. We need to find a path that uses each peak only once (algorithmic problem).
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This note was uploaded on 02/14/2008 for the course CSE 182 taught by Professor Bafna during the Fall '06 term at UCSD.

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Lect12 Peptide identification - CSE182-L12 Mass...

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