Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current...
Transcript of Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current...
![Page 1: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/1.jpg)
Proteomics
November 13, 2007
![Page 2: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/2.jpg)
Acknowledgement
• Slides presented here have been borrowed from presentations by :– Dr. Mark A. Knepper (LKEM, NHLBI, NIH)– Dr. Nathan Edwards (Center for
Bioinformatics and Computational Biology, University of Maryland, College Park)
– Dr. Catherine Fenselau (Dept. of Chemistry, University of Maryland, College Park)
![Page 3: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/3.jpg)
What Is “Proteomics”?
Simultaneous analysis of all members of a defined set of proteins.
![Page 4: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/4.jpg)
Identification & Analysis of Proteins
• Sample preparation and handling• Determination of amino acid sequence• Protein identification and quantification• Cell mapping
![Page 5: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/5.jpg)
What is Mass Spectrometry?
• Measures masses of individual molecules• Molecules are converted into ions in the
gas phase• Molecules must be charged (+ or -)• Measure mass-to-charge ratio (m/z) of
ions and its charged fragments• Mass-to-charge ratio (m/z) of ions
detected
![Page 6: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/6.jpg)
Mass Spectrometry Components
Inlet
Ion Source Mass Analyzer Detector
![Page 7: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/7.jpg)
Mass Spectrometry Components
Inlet
Ion Source Mass Analyzer Detector
Vacuum
![Page 8: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/8.jpg)
Mass Spectrometry Components
Inlet
Ion Source Mass Analyzer Detector
Vacuum
Electronics
![Page 9: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/9.jpg)
Example Mass Spectrum
![Page 10: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/10.jpg)
Example Mass Spectrum
H20
![Page 11: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/11.jpg)
Ionization Methods
• Matrix-assisted laser desorption/ionization (MALDI)
• Electrospray Ionization (ESI)
![Page 12: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/12.jpg)
MALDI• Creates ions by excitation with a laser of a sample that is
mixed with an excess amount of a matrix component• Singly charged ions are guided to mass analyzer and
detector• Used with time-of-flight (TOF) analyzer
![Page 13: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/13.jpg)
Protein Identification by MALDI-TOF Mass Spectrometry
![Page 14: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/14.jpg)
![Page 15: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/15.jpg)
![Page 16: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/16.jpg)
ESI• Creates gas-phase ions by applying a potential to a
flowing liquid that contains the analyte and solvent molecules.
• Yields multiply charged ions• Used with quadrupole or ion-trap mass analyzers in
tandem mass spectrometry
![Page 17: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/17.jpg)
![Page 18: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/18.jpg)
Sample Preparation for Mass Spectrometry
Enzymatic Digestand
Fractionation
Trypsin cleaves at Arg & Lys
![Page 19: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/19.jpg)
Single Stage MS
MS
![Page 20: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/20.jpg)
Tandem Mass Spectrometry(MS/MS)
MS/MS
![Page 21: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/21.jpg)
Peptide Fragmentation
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
Peptides consist of amino-acids arranged in a linear backbone.
![Page 22: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/22.jpg)
Peptide Fragmentation
![Page 23: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/23.jpg)
Peptide Fragmentation
-HN-CH-CO-NH-CH-CO-NH-
Ri CH-R’
bi
yn-iyn-i-1
bi+1
R”
i+1
i+1
![Page 24: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/24.jpg)
Peptide FragmentationPeptide: S-G-F-L-E-E-D-E-L-K
y1
y2
y3
y4
y5
y6
y7
y8
y9
ion
1020907778663534405292145
88MW
762SGFL EEDELKb4
389SGFLEED ELKb7
MWion
633SGFLE EDELKb5
1080S GFLEEDELKb11022SG FLEEDELKb2875SGF LEEDELKb3
504SGFLEE DELKb6
260SGFLEEDE LKb8147SGFLEEDEL Kb9
![Page 25: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/25.jpg)
Peptide Fragmentation
100
0250 500 750 1000 m/z
% In
tens
ity
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
147260389504633762875102210801166 y ions
![Page 26: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/26.jpg)
Peptide Fragmentation
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
100
0250 500 750 1000 m/z
% In
tens
ity
147260389504633762875102210801166 y ionsy6
y7
y2 y3 y4
y5
y8 y9
![Page 27: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/27.jpg)
Peptide Fragmentation
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
100
0250 500 750 1000 m/z
% In
tens
ity
147260389504633762875102210801166 y ionsy6
y7
y2 y3 y4
y5
y8 y9
b3
b5 b6 b7b8 b9
b4
![Page 28: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/28.jpg)
Peptide Identification
Given:• The mass of the parent ion, and• The MS/MS spectrumOutput:• The amino-acid sequence of the peptide
![Page 29: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/29.jpg)
Peptide Identification
Two methods:• De novo interpretation• Sequence database search
![Page 30: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/30.jpg)
De Novo Interpretation
100
0250 500 750 1000 m/z
% In
tens
ity
![Page 31: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/31.jpg)
De Novo Interpretation
100
0250 500 750 1000 m/z
% In
tens
ity
E L
![Page 32: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/32.jpg)
De Novo Interpretation
100
0250 500 750 1000 m/z
% In
tens
ity
E L F
KL
SGF G
E DE
L E
E D E L
![Page 33: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/33.jpg)
De Novo InterpretationResidual MWAmino-AcidResidual MWAmino-Acid
163.06333 Tyrosine Y113.08407 Leucine L186.07932 TryptophanW128.09497 LysineK99.06842 Valine V113.08407 Isoleucine I
101.04768 Threonine T137.05891 Histidine H
87.03203 Serine S57.02147 Glycine G156.10112 ArginineR147.06842 Phenylalanine F128.05858 Glutamine Q129.04260 Glutamic acid E97.05277 Proline P115.02695 Aspartic acid D
114.04293 Asparagine N103.00919 Cysteine C131.04049 Methionine M71.03712 AlanineA
![Page 34: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/34.jpg)
De Novo Interpretation
…from Lu and Chen (2003), JCB 10:1
![Page 35: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/35.jpg)
De Novo Interpretation
![Page 36: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/36.jpg)
De Novo Interpretation
…from Lu and Chen (2003), JCB 10:1
![Page 37: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/37.jpg)
De Novo Interpretation• Find good paths in spectrum graph• Can’t use same peak twice
– Forbidden pairs– “Nested” forbidden pairs
• Simple peptide fragmentation model• Usually many apparently good solutions• Needs better fragmentation model• Needs better path scoring
![Page 38: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/38.jpg)
De Novo Interpretation• Amino-acids have duplicate masses!• Incomplete ladders create ambiguity.• Noise peaks and unmodeled fragments
create ambiguity• “Best” de novo interpretation may have
no biological relevance• Current algorithms cannot model many
aspects of peptide fragmentation• Identifies relatively few peptides in
high-throughput workflows
![Page 39: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/39.jpg)
Sequence Database Search
• Compares peptides from a protein sequence database with spectra
• Filter peptide candidates by– Parent mass– Digest motif
• Score each peptide against spectrum– Generate all possible peptide fragments– Match putative fragments with peaks– Score and rank
![Page 40: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/40.jpg)
Sequence Database Search
100
0250 500 750 1000 m/z
% In
tens
ity
KLEDEELFGS
![Page 41: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/41.jpg)
Sequence Database Search
100
0250 500 750 1000 m/z
% In
tens
ity
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
147260389504633762875102210801166 y ions
![Page 42: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/42.jpg)
Sequence Database Search
K1166
L1020
E907
D778
E663
E534
L405
F292
G145
S88 b ions
100
0250 500 750 1000 m/z
% In
tens
ity
147260389504633762875102210801166 y ionsy6
y7
y2 y3 y4
y5
y8 y9
b3
b5 b6 b7b8 b9
b4
![Page 43: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/43.jpg)
Sequence Database Search• No need for complete ladders• Possible to model all known peptide
fragments• Sequence permutations eliminated• All candidates have some biological
relevance• Practical for high-throughput peptide
identification• Correct peptide might be missing
from database!
![Page 44: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/44.jpg)
Peptide Candidate Filtering>ALBU_HUMAN
MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
No missed cleavage sitesMKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
![Page 45: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/45.jpg)
Peptide Candidate Filtering>ALBU_HUMAN
MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
One missed cleavage site
MKWVTFISLLFLFSSAYSRWVTFISLLFLFSSAYSRGVFRGVFRRRDAHKDAHKSEVAHRSEVAHRFKFKDLGEENFKDLGEENFKALVLIAFAQYLQQCPFEDHVKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…
![Page 46: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/46.jpg)
Peptide Candidate FilteringPeptide molecular weight• Only have m/z value
– Need to determine charge state• Ion selection tolerance• Mass for each amino-acid symbol?
– Monoisotopic vs. Average– “Default” residual mass– Depends on sample preparation
protocol– Cysteine almost always modified
![Page 47: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/47.jpg)
Peptide Scoring
• Peptide fragments vary based on– The instrument– The peptide’s amino-acid sequence– The peptide’s charge state– Etc…
• Search engines model peptide fragmentation to various degrees. – Speed vs. sensitivity tradeoff– y-ions & b-ions occur most frequently
![Page 48: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/48.jpg)
Sequence Database SearchTraps and Pitfalls
Search options may eliminate the correct peptide
• Parent mass tolerance too small• Fragment m/z tolerance too small• Incorrect parent ion charge state• Non-tryptic or semi-tryptic peptide• Incorrect or unexpected modification• Sequence database too conservative
![Page 49: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/49.jpg)
Sequence Database SearchTraps and Pitfalls
Search options can cause infinite search times
• Variable modifications increase search times exponentially
• Non-tryptic search increases search time by two orders of magnitude
• Large sequence databases contain many irrelevant peptide candidates
![Page 50: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/50.jpg)
Sequence Database SearchTraps and Pitfalls
Best available peptide isn’t necessarily correct!
• Score statistics are essential– What is the chance a peptide could score
this well by chance alone?• The wrong peptide can look correct if
the right peptide is missing!• Need scores that are invariant to
spectrum quality and peptide properties
![Page 51: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/51.jpg)
Sequence Database SearchTraps and Pitfalls
Search engines often make incorrect assumptions about sample prep
• Proteins with lots of identified peptides are not more likely to be present
• Peptide identifications do not represent independent observations
• All proteins are not equally interesting to report
![Page 52: Proteomics - University Of Marylandnsw/enpm808b/proteomics.pdfno biological relevance • Current algorithms cannot model many aspects of peptide fragmentation • Identifies relatively](https://reader033.fdocuments.in/reader033/viewer/2022051723/5abe37837f8b9a5d718cb67f/html5/thumbnails/52.jpg)
Sequence Database SearchTraps and Pitfalls
Good spectral processing can make a big difference
• Poorly calibrated spectra require large m/z tolerances
• Poorly baselined spectra make small peaks hard to believe
• Poorly de-isotoped spectra have extra peaks and misleading charge state assignments