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Understanding Protein Sequencing and Mass Spectrometry

This course covers protein sequencing methods like mass spectrometry, ionization, fragmentation, and database searching. Learn about identifying peptide fragments and interpreting spectra for peptide reconstruction. Explore isotopic peaks and isotopic distributions. Gain insights into handling isotopes in peptide analysis and exploring the isotope profile to determine the charge on a peptide.

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Understanding Protein Sequencing and Mass Spectrometry

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  1. CSE182-L7 Protein sequencing and Mass Spectrometry CSE182

  2. Announcements • Midterm 1: Nov 1, in class. • Assignment 2: Online, due October 20. CSE182

  3. Trivia Quiz • What research won the Nobel prize in Chemistry in 2004? • In 2002? CSE182

  4. How are Proteins Sequenced? Mass Spec 101: CSE182

  5. Nobel Citation 2002 CSE182

  6. Nobel Citation, 2002 CSE182

  7. Mass Spectrometry CSE182

  8. Enzymatic Digestion (Trypsin) + Fractionation Sample Preparation CSE182

  9. Single Stage MS Mass Spectrometry LC-MS: 1 MS spectrum / second CSE182

  10. Tandem MS Secondary Fragmentation Ionized parent peptide CSE182

  11. The peptide backbone The peptide backbone breaks to form fragments with characteristic masses. H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH Ri-1 Ri Ri+1 C-terminus N-terminus AA residuei-1 AA residuei+1 AA residuei CSE182

  12. Ionization The peptide backbone breaks to form fragments with characteristic masses. H+ H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH Ri-1 Ri Ri+1 C-terminus N-terminus AA residuei-1 AA residuei+1 AA residuei Ionized parent peptide CSE182

  13. Fragment ion generation The peptide backbone breaks to form fragments with characteristic masses. H+ H...-HN-CH-CONH-CH-CO-NH-CH-CO-…OH Ri-1 Ri Ri+1 C-terminus N-terminus AA residuei-1 AA residuei AA residuei+1 Ionized peptide fragment CSE182

  14. Tandem MS for Peptide ID 88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions 100 % Intensity [M+2H]2+ 0 250 500 750 1000 m/z CSE182

  15. Peak Assignment 88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions y6 100 Peak assignment implies Sequence (Residue tag) Reconstruction! y7 % Intensity [M+2H]2+ y5 b3 b4 y2 y3 b5 y4 y8 b8 b9 b6 b7 y9 0 250 500 750 1000 m/z CSE182

  16. 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 CSE182

  17. 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 CSE182

  18. xn-i yn-i yn-i-1 vn-i wn-i zn-i -HN-CH-CO-NH-CH-CO-NH- CH-R’ Ri i+1 ai R” i+1 bi bi+1 ci di+1 low energy fragments high energy fragments Peptide fragmentation possibilities(ion types) CSE182

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

  20. Mass-Charge ratio • The X-axis is (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 • Suppose you see a peak at 501. Is the mass 500, or is it 1000? CSE182

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

  22. 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? CSE182

  23. 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? CSE182

  24. Nc=50 +1 Isotope Calculation • Denote: • Nc : number of carbon atoms in the peptide • Pc : probability of occurrence of C-13 (~1%) • Then Nc=200 +1 CSE182

  25. 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)? How do we generalize? How can we handle Oxygen (O-16,18)? CSE182

  26. 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) CSE182

  27. 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. CSE182

  28. 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 CSE182

  29. 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. • A toolkit that resolves some of these problems will be useful. CSE182

  30. MS Quiz: • Why aren’t all tandem MS peaks of the same intensity? • Do the intensities for a peptide vary from spectrum to spectrum? CSE182

  31. De novo interpretation of mass spectra • The so called de novo algorithms focus exclusively on the D module. • There is no database (I/F). • Limited scoring and validation CSE182

  32. 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. CSE182

  33. Putative Prefix Masses Prefix Mass M=401 b y 88 87 332 145 144 275 147 146 273 276 275 144 • Only a subset of the prefix masses are correct. • The correct mass values form a ladder of amino-acid residues S G E K 0 87 144 273 401 CSE182

  34. 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 G 144 CSE182

  35. 0 273 332 401 87 144 146 275 100 200 300 S G E K 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 CSE182

  36. 0 273 332 401 87 144 146 275 100 200 300 S G E K 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 87 G 144 CSE182

  37. 0 273 332 401 87 144 146 275 100 200 300 S G E K Two problems • Too many nodes. • Only a small fraction are correspond to b/y ions (leading to true PRMs) (learning problem) • 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). • In general, the forbidden pairs problem is NP-hard CSE182

  38. However,.. • The b,y ions have a special non-interleaving property • Consider pairs (b1,y1), (b2,y2) • If (b1 < b2), then y1 > y2 CSE182

  39. 100 0 400 200 Non-Intersecting Forbidden pairs 332 300 87 S • If we consider only b,y ions, ‘forbidden’ node pairs are non-intersecting, • The de novo problem can be solved efficiently using a dynamic programming technique. G E K CSE182

  40. The forbidden pairs method • There may be many paths that avoid forbidden pairs. • We choose a path that maximizes an objective function, • EX: the number of peaks interpreted CSE182

  41. 332 100 300 0 400 200 87 The forbidden pairs method • Sort the PRMs according to increasing mass values. • For each node u, f(u) represents the forbidden pair • Let m(u) denote the mass value of the PRM. f(u) u CSE182

  42. D.P. for forbidden pairs • Consider all pairs u,v • m[u] <= M/2, m[v] >M/2 • Define S(u,v) as the best score of a forbidden pair path from 0->u, v->M • Is it sufficient to compute S(u,v) for all u,v? 332 100 300 0 400 200 87 u v CSE182

  43. D.P. for forbidden pairs • Note that the best interpretation is given by 332 100 300 0 400 200 87 u v CSE182

  44. D.P. for forbidden pairs • Note that we have one of two cases. • Either u < f(v) (and f(u) > v) • Or, u > f(v) (and f(u) < v) • Case 1. • Extend u, do not touch f(v) 100 300 0 f(u) 400 200 u v CSE182

  45. The complete algorithm for all u /*increasing mass values from 0 to M/2 */ for all v /*decreasing mass values from M to M/2 */ if (u > f[v]) else if (u < f[v]) If (u,v)E /*maxI is the score of the best interpretation*/ maxI = max {maxI,S[u,v]} CSE182

  46. De Novo: Second issue • Given only b,y ions, a forbidden pairs path will solve the problem. • However, recall that there are MANY other ion types. • Typical length of peptide: 15 • Typical # peaks? 50-150? • #b/y ions? • Most ions are “Other” • a ions, neutral losses, isotopic peaks…. CSE182

  47. De novo: Weighting nodes in Spectrum Graph • Factors determining if the ion is b or y • Intensity • Support ions • Isotopic peaks (InsPecT’) CSE182

  48. De novo: Weighting nodes • A probabilistic network to model support ions (Pepnovo) CSE182

  49. De Novo Interpretation Summary • The main challenge is to separate b/y ions from everything else (weighting nodes), and separating the prefix ions from the suffix ions (Forbidden Pairs). • As always, the abstract idea must be supplemented with many details. • Noise peaks, incomplete fragmentation • In reality, a PRM is first scored on its likelihood of being correct, and the forbidden pair method is applied subsequently. CSE182

  50. CSE182

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