Protein Sequencing in addition to Identification by Mass Spectrometry Outline T in addition to em Mass S

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Protein Sequencing in addition to Identification by Mass Spectrometry Outline T in addition to em Mass S

Fox, Shea, Midday Host has reference to this Academic Journal, PHwiki organized this Journal Protein Sequencing in addition to Identification by Mass Spectrometry Outline T in addition to em Mass Spectrometry De Novo Peptide Sequencing Spectrum Graph Protein Identification via Database Search Identifying Post Translationally Modified Peptides Spectral Convolution Spectral Alignment Masses of Amino Acid Residues

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Protein 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 Peptide Fragmentation Peptides tend to fragment along the backbone. Fragments can also loose neutral chemical groups like NH3 in addition to H2O. H -HN-CH-CO NH-CH-CO-NH-CH-CO- OH Ri-1 Ri Ri+1 H+ Prefix Fragment Suffix Fragment Collision Induced Dissociation Breaking Protein into Peptides in addition to Peptides into Fragment Ions Proteases, e.g. trypsin, break protein into peptides. A T in addition to em Mass Spectrometer further breaks the peptides down into fragment ions in addition to measures the mass of each piece. Mass Spectrometer accelerates the fragmented ions; heavier ions accelerate slower than lighter ones. Mass Spectrometer measure mass/charge ratio of an ion.

N- in addition to C-terminal Peptides N-terminal peptides C-terminal peptides Terminal peptides in addition to ion types Peptide Mass (D) 57 + 97 + 147 + 114 = 415 Peptide Mass (D) 57 + 97 + 147 + 114 – 18 = 397 without N- in addition to C-terminal Peptides N-terminal peptides C-terminal peptides 415 486 301 154 57 71 185 332 429

N- in addition to C-terminal Peptides N-terminal peptides C-terminal peptides 415 486 301 154 57 71 185 332 429 N- in addition to C-terminal Peptides 415 486 301 154 57 71 185 332 429 N- in addition to C-terminal Peptides 415 486 301 154 57 71 185 332 429 Reconstruct peptide from the set of masses of fragment ions (mass-spectrum)

Peptide Fragmentation y3 b2 y2 y1 b3 a2 a3 HO NH3+ R1 O R2 O R3 O R4 H – N — C — C — N — C — C — N — C — C — N — C – COOH H H H H H H H b2-H2O y3 -H2O b3- NH3 y2 – NH3 Mass Spectra The peaks in the mass spectrum: Prefix Fragments with neutral losses (-H2O, -NH3) Noise in addition to missing peaks. mass 0 in addition to Suffix Fragments. Protein Identification with MS/MS

T in addition to em Mass-Spectrometry Breaking Proteins into Peptides peptides MPSER GTDIMR PAKID HPLC To MS/MS MPSERGTDIMRPAKID protein Mass Spectrometry Matrix-Assisted Laser Desorption/Ionization (MALDI) From lectures by Vineet Bafna (UCSD)

T in addition to em Mass Spectrometry Scan 1708 LC Scan 1707 MS MS/MS Protein Identification by T in addition to em Mass Spectrometry S e q u e n c e MS/MS instrument Database search Sequest de Novo interpretation Sherenga T in addition to em Mass Spectrum T in addition to em Mass Spectrometry (MS/MS): mainly generates partial N- in addition to C-terminal peptides Spectrum consists of different ion types because peptides can be broken in several places. Chemical noise often complicates the spectrum. Represented in 2-D: mass/charge axis vs. intensity axis

De Novo vs. Database Search De Novo AVGELTK Database Search Mass, Score De Novo vs. Database Search: A Paradox The database of all peptides is huge O(20n) . The database of all known peptides is much smaller O(108). However, de novo algorithms can be much faster, even though their search space is much larger! A database search scans all peptides in the database of all known peptides search space to find best one. De novo eliminates the need to scan database of all peptides by modeling the problem as a graph search. De novo Peptide Sequencing Sequence

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Theoretical Spectrum Theoretical Spectrum (cont’d) Theoretical Spectrum (cont’d)

Building Spectrum Graph How to create vertices (from masses) How to create edges (from mass differences) How to score paths How to find best path S E Q U E N C E b Mass/Charge (M/Z) a Mass/Charge (M/Z) S E Q U E N C E

Improving the Efficiency Homology Match mode: Assumes tagging (only peptides that share a tag of length 3 with de novo reconstruction are considered) in addition to extension of found hits by dynamic programming around the hits. Non-gapped homology match mode: Sequencing error in addition to homology mutations do not overlap. Segment Match mode: No homology mutations. Exact Match mode: No sequencing errors in addition to homology mutations. Experiment Result The correct peptide sequence as long as each spectrum is known. The proteins are all in Swissprot but not in Human database. SPIDER searches 144 spectra against both Swissprot in addition to human databases Example Using de novo reconstruction X=CCQWDAEACAFNNPGK, the homolog Z was found in human database. At the same time, the correct sequence Y, was found in SwissProt database.

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