Mass Spectrometry

in Life Science:

Technology and

Data-EvaluationH. Thiele

Bruker Daltonik, Germany

MALDI-TOF Mass SpectrometryProteomics

Functional Genomics

Proteome AnalysisInvestigation of

protein diversity

Genomics

SNP GenotypingSearch

for genetic variations

MALDI-TOF MSIdentification

No a priori knowledgeabout analyte

Screening

Analyte of known MW

Bridging Proteomics & Genomics

Mass Spectrometer Mass Spectrometer

for for

Biopolymer ResearchBiopolymer Research

The Technology

Laser

Sampleplate

Analytemolecules in matrix

Accelerationgrids

Drift region

Ion detector

Mass spectrum

Vacuum system

Vacuumlock

20 to 200 spectra have to be added;total duration 2 to 20 seconds

with 50 (200) Hertz Laser

Principle of MALDI-TOF-MS

Linear flight tube

Flight time m/z

• all ions with Ekin = 1/2mv2

space/energyuncertainty

HiRes mass spectrum

Iondetector

MALDI ionsource

Ion reflector

The reflector focuses ions of same mass but different Ekin (velocity)

on detector;

high resolution is obtained

Laser

High resolution TOF-MS with Reflector

Flight time m/z

0V + kV

MS/MS by PSDMS/MSMS/MS = fragment ion or = fragment ion or

tandem mass tandem mass spectromentryspectromentry

PSDPSD = Post Source Decay = Post Source Decay

Electr. potentialion energy

Metastable decay of molecular ions,

energy is reduced according to mass ratio

Source Reflector

PSD by Reflectron TOF (Scheme)

Adjustment ofvoltages

Segment 1

Segment 2

Segment 3

Segment 4

E = 1/2 mv2 v=const.

eg. if M+ = 1000, m = 500 has 4 keV

m = 100 has 0.8 keV

m =25 has 100 eV

Daughter ion mass spectrum

Iondetector

Parent ionselector

MALDI ionsource

Laser

The daughter ion spectrum can only be measured in segments

which have to be pasted together.10 - 15 segments are necessary.

Strong fieldWeaker fieldWeaker fieldWeak field

Adjustment of voltagesAdjustment of voltagesAdjustment of voltages

Ion reflector

4 3 2 1

Manual operation: 20 – 40 minutes;automatic operation: 5 – 10 minutes

per daughter ion spectrum(100 acquisitions in each segment)

TOF-MS/MS by PSD

In proteomics, many proteins have to be separated and analysed fast to avoid degradation

Regarding structure information,MALDI MS/MS appears to be optimal, but PSD is much too slow !

Consequence: Development of a fast MALDI MS/MS instrument !

MALDI TOF/TOFMALDI TOF/TOFwith post-with post-

accelerationaccelerationby potential LIFTby potential LIFT

Electr. potentialion energy

Decaying ions, energy reduced,

low speed

Source ReflectorLIFT

All fragment ions can beanalyzed simultaneously,no segmenting necessary

1. TOF 2. TOF

Even low mass ionshave high energy,good for detection

Potential is switched whenions are in LIFT

TOF/TOF with LIFT (Scheme)

Daughter ion mass spectrum

Iondetector

Ion reflector

Parent ionselector

MALDI ionsource

PotentialLIFT for post acceleration

MS/MS spectrum of daughter ionsis measured in a single acquisition;

no pasting of segments;low sample consumption,

high speed, high sensitivity

1 to 200 spectra needed;1 to 10 seconds only

with 20 Hertz laser

Laser

Parent ionsupressor

TOF -MS/MS with post-acceleration by LIFT

LIDLID

CollisionCell (CID)

Identification of Proteins (sequence of amino acids)

and Protein modifications

Data Evaluation

Goal :

– Fragmentation of proteins / peptides

resulting in PMF / PFF spectra

– Detection (annotation) of the masses of the fragments

– Identification by database searches

Method :

- Detection of peaks with low signal/noise ratio

- Identification (mass, area, intensity) of (overlapping) isotopic patterns

- Score the results

- Detection of multiple charges (TOF spectra z = 1,2)

average mass

monoisotopic mass

Problems to be solved by Bioinformatics

Detection ofprotonated molecular ion[M+H]+

Isotopicresolution

nominal mass

12C93 1H146 14N24 16O24 32S+ : monoisotopic

Isotopic pattern of peptides

12C93 1H145 2H 14N24 16O24 32S+ : 1.4%,

m=2094.0547

12C92 13C 1H146 14N24 16O24 32S+ : 88.9%,

m=2094.0517

12C93 1H146 14N23 15N 16O24 32S+ : 8.1%,

m=2094.045512C93 1H146 14N24 16O24 33S + : 0.7%, m=2094.0478

12C93 1H146 14N24 16O23 17O1 32S+ : 0.9%,

m=2094.0526

Deisotoping: Assigning monoisotopic masses

SNAP approach:• Peak selection

- Damping of chemical noise using FFT filtering- Baseline correction- noise calculation- peak search

• Iterative search for isotopic patterns– Analysing the largest peaks first– Alignment of patterns using peak list heuristic and FFT deconvolution– Nonlinear fit using asymmetric line shape– Subtraction of analysed patterns

• Reevaluation– Fit of intensities of overlapping patterns, optional addition of ICAT

masses– Calculation of Quality Factor

SNAP : Regularized FFT Deconvolution

Uncertainty of mean peptide isotopic distribution

SNAP : Nonlinear Fit

- 2

Exponentially modified gaussians for asymmetric line shapes:

Local optima for least square fit:

SNAP : Quality Factor

Idea: Get a value for the quality of a pattern which can be usedin favor of S/N or intensity for selecting the “best” peaks

Fuzzy Scoring

Basic Scoring

2 Area/Width Mean deviation

, for all patterns

Kind of Spectrum/

Instrument

Quality factor

From overlapping peak groups

SNAP : Use Case

To monoisotopic masses

Wavelet Methods for Denoising Proteomics Spectra

Denoising by Hard Thresholding

Scale - adaptive Thresholds

Preservation of Position, Shape and Amplitude of major Peaks

WaveletTransform

HardThresholding

InverseWaveletTransform

Denoising by Hard Thresholding

Further Developments

Baseline Correction

Deconvolution of Isotopic Patterns

Scale-Energy Parameters for enhanced Clustering

Charge Deconvolution : Without Isotopic Resolution

m/z

Different m/z peaks of Equine Apomyoglobin Protein

MW is calculated from m/z differences between adjacant

peaks by deconvolution software (result see inlet).

M16+

M17+

M14+

M15+

800 900 1000 1100 1200 1300 1400

0.5

1.0

1.5

2.0

2.5

998.1

943.0

893.7

849.1

1130.7

1211.5

1304.7

1413.6

16930 16950 16970

16950.584

M18+

M19+

M20+ M12+

Protein Z = 15-70Peptide Z = 1,2,3,4Small molecules Z = 1

Charge states for ESI

RelatedIonDeconvolution

Peak Pickingm/z ; intensity

Deconvolutionenvelope;distances

ResultZ + MW

[M+zH]z+/z

M

Charge Deconvolution: Isotopic Resolution

14341148

d (m/z) =0.25 ud (m/z) =0.2 u

(M+4H)4+(M+5H)5+

For isotopically resolved patterns the charge state and the

mass can be determined from a single pattern.

CalibrationCalibration

Get more Get more

accurate dataaccurate data

Problems to be solved by Bioinformatics

Automatic „Smart“ Calibration

• Automatic Control based on external and internal data

• Resulting Accuracy <10 ppm

• High Precision Correction improves stability & accuracy

Contaminants,self digestion

Mass distributionof peptides

External calibrationspots

Statistical References Internal Calibrants External Calibration

Automatic “Smart”

Calibration

Tof(m/z) = c0 +c1 (m/z)1/2 +c2 (m/z)

+ fixed high precision correction

Statistical Calibration for Proteomics

Peaklist

Statistical Reference Masses

Assign Masses (dM < dErr)

Calibrate

dErr := Max(50, 0.5*dErr)

dErr>=50 Stop

• Initial Error dErr<500 ppm

• Using modified Mann’s clustering

• Resulting Accuracy <20ppm

NoYes

Details of the Calibration Routine: Internal Multipoint Calibration – an Example

843.0081 591903.9288

1023.23561046.1874 5961062.1533 5561068.1865 5921077.9011 6531119.17841242.40391273.45721303.4928 5971317.45941431.63571476.6355 6001749.53261805.02271821.00561827.99841844.02841925.13001929.19181942.13872212.5501 6542226.5907 6612240.6103 6582274.5346 6572299.6929 2252385.5507 6702422.79732430.92282718.8983

842.4952 -181045.5582 -61061.5150 -451067.5447 -91077.2538 521302.7164 11475.7600 72211.2533 672225.2859 742239.2975 722273.2024 712298.3462 -3612384.1549 85

842.5338 281045.5679 41061.5225 -381067.5513 -31077.2590 571302.6896 -201475.7086 -282211.0974 -32225.1280 42239.1376 12273.0376 -22383.9745 9

Matching with contaminantsExclusion limit 800ppm

842.5469 441045.5792 141061.5336 -271067.5623 81302.6984 -131475.7158 -232211.0978 -32225.1283 42239.1377 12273.0374 -22383.9732 9

Final calibration

calibration,reject in-accuratemasses

average error: 13.4ppm

843.0081 591903.9288

1023.23561046.1874 5961062.1533 5561068.1865 5921077.9011 6531119.17841242.40391273.45721303.4928 5971317.45941431.63571476.6355 6001749.53261805.02271821.00561827.99841844.02841925.13001929.19181942.13872212.5501 6542226.5907 6612240.6103 6582274.5346 6572299.6929 2252385.5507 6702422.79732430.92282718.8983

calibration,reject un-matchedmasses

1.Calibration roundExclusion limit 150ppm

average error: 66.7 ppm

842.4952 -181045.5582 -61061.5150 -451067.5447 -91077.2538 521302.7164 11475.7600 72211.2533 672225.2859 742239.2975 722273.2024 712298.3462 -3612384.1549 85

2.Calibration roundExclusion limit 40ppm

calibration,reject in- accurate masses

average error: 16.3ppm

842.5338 281045.5679 41061.5225 -381067.5513 -31077.2590 571302.6896 -201475.7086 -282211.0974 -32225.1280 42239.1376 12273.0376 -22383.9745 9

error [ppm]

measured mass [Da]

Iterative Generation of internal calibrant list

Calibration

PMFSearch

Generation of an improved

calibrant list

Start of PMF identification with a default calibrant list

usually 2 repeats aresufficient

The default calibrant list usually consists of three typical trypsin peptides

Improved calibrant lists typically contain of 60-100 masses – averagely 10-20 of these can be found in a spectra

MS basedMS basedIdentity SearchIdentity Search

Search Search EnginesEngines

Problems to be solved by Bioinformatics

MS Protein Identification is Probability based

How closely is a given protein or peptide sequence matching to the measured masses ?

There are several strategies for a matching “ score“ :

For example:-Probability based MOWSE score (Mascot)-Bayesian probability (ProFound)-Cross correlation (MS-Fit)

Masses determined by MS are not unique

Identification is probability based

Problem of assigning true probabilitiesto a given identification

Part 2

Successively changing various search parameters to test their influence. Optimisation of search parameters.

Part 1

Comparison of the performance of the search engines using a typical set of search parameters.

Dataset:168 MALDI PMF spectra

About 10,000 searches have been performed to establish a statistical basis

the data was acquired in the environment of a typical proteome project

Evaluation of PMF and Search Engines

0.0 0.5 1.0 1.5 2.0 2.502468

101214161820

% o

f sea

rche

s

ProFound Z score

5% significance levelProFound

0 50 100 150 200 250 30002468

101214161820

% o

f sea

rche

s

Mascot score

5% significance level

Mascot

0 1 2 3 4 5 602468

101214161820

% o

f sea

rche

s

log (MS-Fit MOWSE Score)

MS-Fit Mascot MS-Fit ProFound

Correct identifications 89 (53%) 55 (32,7%) 90 (53,6%)

Correct identifications above the 5% significance level

63 (37,5%) - 49 (29,%)

Correct identifications above the highest score that has been obtained from an in-correct identification

54 (32,1%) 9 (5,6%) 69 (41,1%)

Comparision of PMF Search Engines – Score Distribution

randommatches

0.0 0.5 1.0 1.5 2.0 2.50

2

4

6

8

10

12

14

16

18

20

% o

f se

arc

he

s

ProFound Z score

5% significance level

range of uncertainty

correctidentifications

ProFound - scoring distribution

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3ProFound Z Score

Met

aSco

reIdea:

Integration of search results from

different engines could improve

significance and confidence!

An effective ranking of results can be assessed by

individual search score distributions

Converting the Scoring Distribution to a MetaScore

- Effective sorting of reported results of several search engines

- More correct Proteins are on rank number one

- Elimination of false positives

- drawback: MetaScore does not reflect true probabilities

Ranking of Search Results of different PMF algorithms by MetaScore

Automated Automated validationvalidation

of Search Resultsof Search Results

Search Search EnginesEngines

Problems to be solved by Bioinformatics

m/z

PMF

Result judgement

m/z

MS/MS

m/z

List of precursor masses

• Fuzzy Engine

• MetaScoring

MTP-Viewer

Result visualization

Identified ?

No

• Auto MS/MS definition• Search result driven

• Queries

Yes

From Automation to High Throughput

Fuzzy Engine for Protein Identifikation from PMF spectra

FLFL

Probability Score

Probability Score

Score Ratioto unrelated Sequence

Sequence Coverage

Correlation Coefficient

Peak Quality Factor

Identified

Undefined

Uncertain (unique)Uncertain (multiple)

Identified (multiple)

Bad data

AutomatedAutomatedMS/MS Precursor MS/MS Precursor

Ion SelectionIon Selection

Automation &Automation &

High ThroughputHigh Throughput

Problems to be solved by Bioinformatics

Strategies for automated MS/MS acquisition

Acknowledgement

Jens Decker , Michael Kuhn

Bruker Daltonik

Martin Blüggel , Daniel Chamrad

Peter MaaßKristian Bredies