Plant proteomics in a nutshell
Dr. Juan Antonio Vizcaíno
PRIDE Group Coordinator
Proteomics Services Team
EMBL-EBI
Hinxton, Cambridge, UK
Overview
• Short intro to proteomics and mass spectrometry
• Things to consider in the bioinformatics analysis
• Current existing MS proteomics approaches
• Protein sequence databases (with some specifics
for plants)
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
From the genome to the proteome
Genomics
Transcriptomics
Proteomics
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Key technologies in modern biology
Genomics
DNA sequencing is a central
technology for studying DNA
Transcriptomics
Microarrays and RNA-seq are a
central technology for studying
RNA
Proteomics
Mass spectrometry is a central
technology for studying the
proteome.
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Definitions to start
Proteomics is the large-scale study of proteins, particularly
their structures and functions
The proteome is the entire complement of proteins
including the modifications made to a particular set of
proteins, produced by an organism or system. This will vary
with time and distinct requirements, or stresses, that a cell
or organism undergoes
proteome = ‘protein’ + ‘genome’ (M. Wilkins, 1994)
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Genome vs. proteome
•Genome
•Proteome
• Essentially static over time
• Non location specific
• Human genome mapped
(2000)
• ~20,000 genes
• PCR is available to amplify
DNA
Juan A. Vizcaíno
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• Dynamic over time
• Location specific
• Human proteome nonmapped:
• How many???
• No equivalent of PCR for
proteins
Agricultural-Omics Course
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Mass spectrometry (MS)
MS is an analytical technique that measures the mass-to-charge (m/z)
ratio of charged particles. It is used for determining masses of particles,
for the determination of the elemental composition of a sample or
molecule, and for elucidating the chemical structures of molecules, such as
peptides and other chemical compounds.
Many applications…
one of them is proteomics
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Exclusive information available through MS proteomics
• Sometimes there is not much correlation between
gene expression and protein expression…
• Biomarkers: easy access to human fluids (plasma,
urine, …)
• Post-Translational Modifications (PTMs).
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
MAIN MS PROTEOMICS
WORKFLOWS
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Mass Spectrometry (MS)-based proteomics
• Many different workflows.
• Discovery mode:
• Bottom-up proteomics
• Top down proteomics
• Targeted mode:
• SRM
(Selected
Monitoring)
Juan A. Vizcaíno
[email protected]
Reaction
Agricultural-Omics Course
Hinxton, 20 February 2014
10
Mass Spectrometry (MS)-based proteomics
• Many different workflows.
• Discovery mode:
• Bottom-up proteomics
• Top down proteomics
• Targeted mode:
• SRM
(Selected
Monitoring)
Juan A. Vizcaíno
[email protected]
Reaction
Agricultural-Omics Course
Hinxton, 20 February 2014
11
MS proteomics: tandem MS (bottom-up)
MS/MS matching identifies
peptides, not proteins.
Proteins are inferred from the
peptide sequences.
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
From protein centric to peptide centric
The rapid
development of
genomics has allowed
the development of
proteomics
MS
Shot-gun
proteomics:
Method of
identifying proteins
in complex mixture
HPLC
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0
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
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MS proteomics: tandem MS (bottom-up)
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
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Start with a protein
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[email protected]
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Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
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Cut with a protease (trypsin)
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Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
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Select a peptide
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Juan A. Vizcaíno
P
Agricultural-Omics Course
Hinxton, 20 February 2014
Digestion with trypsin
546 aa 60 kDa; 57 461 Da
pI = 4.75
>RBME00320 Contig0311_1089618_1091255 EC-mopA 60 KDa chaperonin GroEL
MAAKDVKFGR TAREKMLRGV DILADAVKVT LGPKGRNVVI EKSFGAPRIT KDGVSVAKEV
ELEDKFENMG AQMLREVASK TNDTAGDGTT TATVLGQAIV QEGAKAVAAG MNPMDLKRGI
DLAVNEVVAE LLKKAKKINT SEEVAQVGTI SANGEAEIGK MIAEAMQKVG NEGVITVEEA
KTAETELEVV EGMQFDRGYL SPYFVTNPEK MVADLEDAYI LLHEKKLSNL QALLPVLEAV
VQTSKPLLII AEDVEGEALA TLVVNKLRGG LKIAAVKAPG FGDCRKAMLE DIAILTGGQV
ISEDLGIKLE SVTLDMLGRA KKVSISKENT TIVDGAGQKA EIDARVGQIK QQIEETTSDY
DREKLQERLA KLAGGVAVIR VGGATEVEVK EKKDRVDDAL NATRAAVEEG IVAGGGTALL
RASTKITAKG VNADQEAGIN IVRRAIQAPA RQITTNAGEE ASVIVGKILE NTSETFGYNT
ANGEYGDLIS LGIVDPVKVV RTALQNAASV AGLLITTEAM IAELPKKDAA PAGMPGGMGG
MGGMDF
The sequence of the generated peptides is known
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Digestion with trypsin
MAAK
DVK
FGR
TAR
EK
MLR
GVDILADAVK
VTLGPK
GR
NVVI EK
SFGAPR
ITK
DGVSVAK
EVELEDK
FENMGAQMLR
VQTSKPLLIIAEDVEGEALATLVVNK
EVASK
TNDTAGDGTT TATVLGQAIVQEGAK
AVAAG MNPMDLK
GI DLAVNEVVAELLK
KA
INT SEEVAQVGTI SANGEAEIGK
MIAEAMQK
VG NEGVITVEEA
KTAETELEVVEGMQFDR
GYLSPYFVTNPEK
MVADLEDAYILLHEK
LSNLQALLPVLEAVLR
Juan A. Vizcaíno
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GGLK
IAAVK
APGFGDCR
AMLEDIAILTGGQV ISEDLGIK
LESVTLDMLGR
AK
VSISK
ENTTIVDGAGQK
AEIDAR
VGQIK
QQIEETTSDYDR
EK
LQER
LAK
LAGGVAVIR
VGGATEVEVK
DR
VDDALNATR
AAVEEGIVAGGGTALL R
ASTK
ITAK
GVNADQEAGIN IVR
AIQAPAR
QITTNAGEEASVIVGK
ILENTSETFGYNTANGEYGDLISLGIVDPVK
VVR
TALQNAASVAGLLITTEAMIAELPK
DAAPAGMPGGMGGMGGMDF
Agricultural-Omics Course
Hinxton, 20 February 2014
MS proteomics: tandem MS (bottom-up)
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Mass Spec Principles
Sample
+
_
Ionization Source
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Mass Analyzer/s
Agricultural-Omics Course
Hinxton, 20 February 2014
Detector
Schematic view of a generalized mass spec
sample
ion source
mass analyzer(s)
detector
digitizer
Generalized mass spectrometer
- All mass analyzers operate on gas-phase ions using electromagnetic fields. The latter
can be in absolute or relative measurements.
- The ion source therefore makes sure that (part of) the sample molecules are ionized
and brought into the gas phase.
- The detector is responsible for actually recording the
presence of ions. Time-of-flight analyzers also require a digitizer (ADC).
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
MS/MS
MS analysis
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Fingerprinting
(PMF)
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Fragmentation
Peptide sequence
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information
(on top of Mass and Charge)
MS/MS analysis
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Juan A. Vizcaíno
[email protected]
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Agricultural-Omics Course
Hinxton, 20 February 2014
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Why tandem-MS?
peptide structure
x3
y3
R1
NH2
C
H
a1
CO
b1
N
H
c1
z3
y2
x2
z2
R2
R3
CH2
CH2
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CO
a2
b2
N
H
c2
y1
x1
C
H
R4
CO
a3
z1
b3
N
H
C
H
COOH
c3
There are several other ion types that can be annotated, as well as
‘internal fragments’. The latter are fragments that no longer contain an intact
terminus. These are harder to use for ‘ladder sequencing’, but can still be interpreted.
This nomenclature was coined by Roepstorff and Fohlmann (Biomed. Mass Spec., 1984) and Klaus Biemann (Biomed.
Environ. Mass Spec., 1988) and is commonly referred to as ‘Biemann nomenclature’. Note the link with the Roman alphabet.
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Comparison between the instruments
From: Domon & Aebersold, Science, 2006
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
MS proteomics: tandem MS (bottom-up)
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
MS/MS IDENTIFICATION
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Three types of MS/MS identification
Protein database based comparison
database
sequence
theoretical
spectrum
compare
experimental
spectrum
Sequential comparison: de novo approaches
database
sequence
compare
de novo
sequence
experimental
spectrum
Spectral comparison
Spectral
library
experimental
spectrum
compare
experimental
spectrum
Modified From: Eidhammer, Flikka, Martens, Mikalsen – Wiley 2007
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
MS proteomics: peptide IDs and protein IDs
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proteins
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
MS proteomics: peptide IDs and protein IDs
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proteins
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
MS proteomics: peptide IDs and protein IDs
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peptides
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Search
engine
TDMDNQIVVSDYAQ
MDR
LFDQAFGLPR
AKPLMELIER
DESTNVDMSLAQR
DIVVQETMEDIDK
NGMFFSTYDR
GTAGNALMDGASQL
proteins
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Search engines
Proteins
Peptides
UniProt
IPI
RefSeq
sequence
database
TDMDNQIVVSDYAQMDR
LFDQAFGLPR
AKPLMELIER
DESTNVDMSLAQR
DIVVQETMEDIDK
NGMFFSTYDR
GTAGNALMDGASQL
VDMSLAQR
DIVVQETMEDIDK
…
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Juan A. Vizcaíno
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Sequence database matching
Agricultural-Omics Course
Hinxton, 20 February 2014
Theoretical
Spectra
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Search engines
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Spectra
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Theoretical
Spectra
How good is the correlation?
-Scores are generated by search engines
-Usually the best match is kept
[email protected]
1600
m/z
m/z
Juan A. Vizcaíno
1200
Agricultural-Omics Course
Hinxton, 20 February 2014
2400
Search engines
Taken from Nesvizhskii, J Proteomics, 2010
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Agricultural-Omics Course
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Search engines
Taken from Nesvizhskii, J Proteomics, 2010
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
The most popular algorithms
•
MASCOT (Matrix Science)
http://www.matrixscience.com
•
SEQUEST (Scripps, Thermo Fisher Scientific)
http://fields.scripps.edu/sequest
•
X!Tandem (The Global Proteome Machine Organization)
http://www.thegpm.org/TANDEM
•
OMSSA (NCBI)
http://pubchem.ncbi.nlm.nih.gov/omssa/
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Overall concept of scores and cut-offs
Incorrect identifications
Threshold score
Correct
identifications
False negatives
False positives
Adapted from: www.proteomesoftware.com – Wiki pages
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Playing with probabilistic cut-off scores
higher stringency
6%
100%
90%
5%
80%
identifications
4%
70%
60%
3%
50%
false positives
40%
2%
30%
20%
1%
10%
0%
0%
p=0.05
Juan A. Vizcaíno
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p=0.01
p=0.005
p=0.0005
PROTEIN INFERENCE
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Agricultural-Omics Course
Hinxton, 20 February 2014
The protein inference problem
Slide from J. Cottrell, Matrix Science
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Protein inference
A
B
C
D
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Protein inference
A
B
C
D
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Protein inference
A
B
C
D
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Protein inference
A
B
C
D
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Protein inference
A
B
C
D
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Protein inference
A
B
C
D
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Protein inference
A
B
C
D
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Protein inference
A
B
C
D
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Protein inference
A
B
C
D
Unambiguous peptide
Juan A. Vizcaíno
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PROTEIN SEQUENCE DATABASES
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Agricultural-Omics Course
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What is needed from a protein database
1. Comprehensive (whatever is not in the DB will not be
included in your results).
2. Not too redundant at the protein sequence level
- Protein inference gets easier
- It is not very good if the database is too big.
3. Quality of annotation
4. Stability of identifiers
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Main databases used
a) UniProt Knowledgebase
curated)/ TrEMBL.
(UniProtKB):
SWISS-PROT
(manually
b) NCBI non-redundant database: It compiles all protein sequences
available from the following databases: ‘GenBank’ translations, the
Protein Data Bank (PDB), UniProtKB/Swiss-Prot, PIR and PRF.
c) Ensembl: Genomics centric resource. Integration of the information with
genomics is easy.
d) IPI (International Protein Index): It has been discontinued (09/2011).
Different builds for different species (Human, Mouse, Cow, Rat,
Zebrafish, Dog, Arabidopsis).
a) Model organisms DBs (for instance, TAIR for Arabidopsis).
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Databases for non-model organisms
- This is the case for many plants!
- If the species is not well represented in the protein databases, there is a
much stronger need to search ESTs or genomic databases.
-The search engine will translate the 6 possible ORFs for each nucleotide
sequence.
- ESTs are not suitable for PMF approaches (incomplete proteins).
- The alternative is to filter comprehensive databases like UniProt by
species or genus, or to use a protein DB from a close organism.
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
Importance of choosing the right DB
-Since each database has a different focus, the
databases can vary in terms of completeness, degree of
redundancy, and quality of annotations.
-More inclusive bigger protein databases will take longer
to search
- For the bigger resources, it may also result on more
false-positive identifications and reduced statistical
significance (the probability of random match is higher).
Juan A. Vizcaíno
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Agricultural-Omics Course
Hinxton, 20 February 2014
OTHER MS PROTEOMICS APPROACHES
Juan A. Vizcaíno
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Agricultural-Omics Course
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Mass Spectrometry (MS)-based proteomics
• Many different workflows.
• Discovery mode:
• Bottom-up proteomics
• Top down proteomics
• Targeted mode:
• SRM
(Selected
Monitoring)
Juan A. Vizcaíno
[email protected]
Reaction
Agricultural-Omics Course
Hinxton, 20 February 2014
56
MS-based proteomics: Discovery mode
Compton & Kelleher, Nat. Methods, 2012
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Mass Spectrometry (MS)-based proteomics
• Many different workflows.
• Discovery mode:
• Bottom-up proteomics
• Top down proteomics
• Targeted mode:
• SRM (Selected Reaction Monitoring)
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
58
Targeted proteomics
Selected Reaction Monitoring: the objective is to be able to detect one
particular protein in the sample.
- Obvious implications for diagnosis (biomarkers).
Image from http://demo.shimadzu.com/
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Multiple/Selected Reaction Monitoring (MRM/SRM)
collision
cell
mass filter 1
peptide
mixture
selected
peptides
mass filter 2
fragments
of both
peptides
selected
fragment
MRM/SRM removes noise, yielding better signal-to-noise ratio
MRM/SRM removes ‘contaminating’ peaks, aiding targeted identification
MRM/SRM works well with proteotypic peptides
MRM/SRM can be performed with Q-Q-Q, Q-LIT and IT instruments
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Overview
• Short intro to proteomics and mass spectrometry
• MS/MS proteomics
• Search engines and protein inference
• Protein sequence databases
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
No time for quantitation
Not only identify, but also quantify the
amount of each protein in the sample
The current methods rely mainly on MS:
Vaudel et al., Proteomics 2010 Feb;10(4):650-670
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Recommended reading
Mallick P, Kuster B.
Nat Biotechnol. 2010 Jul;28(7):695-709.
Juan A. Vizcaíno
[email protected]
Nesvizhskii, J Proteomics, 2010
Oct 10;73(11):2092-123.
Agricultural-Omics Course
Hinxton, 20 February 2014
The analysis process should not be a black box!
From: Lilley et al., Proteomics, 2011
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014
Questions?
Juan A. Vizcaíno
[email protected]
Agricultural-Omics Course
Hinxton, 20 February 2014