How We Handle Mass Spectra NIST Mass Spectrometry Data Center - - PowerPoint PPT Presentation

How We Handle Mass Spectra

NIST Mass Spectrometry Data Center

Numbers of Spectra

20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000 '78 '80 '83 '86 '88 '90 '93 '98 '02 Replicates Compounds

Red Books EPA NIST

NIST/EPA/NIH Mass Spectral Library

Libraries Distributed/Year

500 1000 1500 2000 2500 3000 3500 4000 4500 '88 '89 '90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04

The Data

Cl

11 17 10 7 5 9 8

NH

20 6 15 12 14 4 19 13 1 16 2 3

H H H H H N NH NH N NH2 O

Connection Table

Cl

1 2 3 4

S S S S S D S D

4 3 2 1 1 2 3 4

From Structure to Spectrum: A Mass “Fragmentogram”

P F O CH3 O C H C H3 C H3 P F O CH3 O C H C H3 C H3

+ +

+ e- 2e- mass = 140 u P F OH CH3 O H P F O CH3 O CH C H3 +

+

mass = 99 u + mass = 125 u

+

CH3 CH2 CH C H2

Molecular Fingerprints

VX HD GB

I will discuss

• Library Searching

– Full and Partial Spectra

• Spectrum Purification
• Chemical Structure Representation
• Peptide Spectra Libraries

50 100 150 200 250 300 200 400 600 800 1000

Instrument ‘Noise Signature’

250 Hexachlorobenzene Spectra same instrument, calibration mix Bars show quartiles

Instrument Effects

Library Search

unknown

MF=93 MF=68 sarin

Spectral Similarity

• M = f(Abundance) Peak in Measured Spectrum
• R = f(Abundance) Peak in Reference Spectrum
• Sum over all peaks
• f(Abundance)

– Abundance – Abundance * m/z – Certainty

• R

M MR

Top Hit Top 2 Hits Top 3 Hits Correlation – Weighted 74.9 86.9 91.7 Correlation 72.9 85.9 90.8 Euclidean Distance 71.9 83.9 88.9 Absolute Distance 67.9 80.3 85.5 PBM - Published 64.7 78.4 84.8 Hites/Hertz/Biemann 64.4 77.2 83.2

Algorithm Performance

12,592 Replicate Spectra against NIST Library

Percent Correct Model

20 40 60 80 100 0.0 0.2 0.4 0.6 0.8 1.0

False Positives (108,000 compounds) False Negatives (21,000 replicate spectra) Match Factor Threshold Fraction Recovered

FP/FP Above Given Match Factor for NIST Library Spectra

80 85 90 95 100 0.0 0.2 0.4 0.6 0.8

m/z weighting no weighting Match Factor Fraction Recovered

FP/FN Expanded View

FN FP x 10,000

20 40 60 80 100 50 100 150 200

decane decalin TMB HCB malathion DMPB sarin

HCB = hexachlorobenzene DMPB = dimethylpenobarbital TMB = 1,2,3-trimethylbenzene

FP Depends on Spectrum Uniqueness

Match Factor

FP

Multiple Ion Monitoring

• What is is?

– Use 2-5 Major Peaks in Spectrum of Target

• 10 – 100 more sensitive
• What’s the problem?

– Can match major Target peaks with Minor Sample Peaks

• What we have done:

– Examine risk using library as source of potential false positive IDs

False Positive Risk vs Number of Peaks Used

Figure 1. Median FPP vs. NP

0.00001 0.0001 0.001 0.01 0.1 1 1 2 3 4 5 Number of Peaks 1 1/2 1/4 1/8 1/16 1/32 1/64 1/128

BMA

Figure 1. Median FPP vs. NP

0.00001 0.0001 0.001 0.01 0.1 1 1 2 3 4 5 Number of Peaks 1 1/2 1/2 1/4 1/4 1/8 1/8 1/16 1/16 1/32 1/32 1/64 1/64 1/128 1/128

BMA

Number of Peaks FP/ spectrum

(median)

Abundance Ratio: Biggest Search Peak/ Matching Peak in FP

10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100 m/z Difference Relative Probabilities

s

10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100 m/z Difference Relative Probabilities

s

Mass Spectral Peak Occurrences are Correlated

Difference in Peak Position (m/z)

Joint Occurrence Prob.

Big Peaks Small Peaks Medium Peaks

FP Observed and Computed

(from individual peak probabilities)

0.1 1 10 100 1000 10000 1 2 3 4 5 6 7 8 9 10 Observed FPP Percentile 0.1 1 10 100 1000 10000 1 2 3 4 5 6 7 8 9 10 Observed FPP Percentile

FP Percentile/10

FP

Actual No Peak Correlation

Search Results Depend on Search Spectrum Quality

AMDIS: http://chemdata.nist.gov

Real Data

Total ion chromatogram A mass spectrum (scan)

Chromatogram with single ion

AMDIS Analysis of Data

AMDIS Match = 81

O P O F

Order of Analysis

• Noise Analysis – find ‘Noise Factor’
• Find and quantify maximizing ions
• Combine to create ‘Model Peak’
• Use Model Peak shape (intensity vs time) to

purify spectra

• Find best matching library spectrum

Intensity K Noise

noise

=

Intensity

Noise

Derive Noise Factor

Finding Possible Peaks for Each m/z

Maximum rate

Scan number n

Find Possible Compounds: Do Ions Maximize at Same Time?

10 36

.0 .1 .7 .6 .5 .4 .2 .3 .9 .8

1 2

Separate the Components

10 36 14 7 111 41 16 85 11 42 2 103 8 18 4

508

.3

yes

.2 82 751 75 16 15 13 305 14 19 22 82 37 147 6 .0 .1 .7 .6 .5 .4 .2 .3 .9 .8

1 2

264

.3

yes

.2

NO

.6

57 81 23 96 7

A ‘Model Peak’ Provides Shape

10 36 14 7 111 41 16 85 11 42 2 103 8 18 4

508

.3

yes

.2 82 751 75 16 15 13 305 14 19 22 82 37 147 6 .0 .1 .7 .6 .5 .4 .2 .3 .9 .8

1 2

264

.3

yes

.2

NO

.6

57 81 23 96 7

The model shape is defined as the sum of all of the ion chromatograms that maximize within the range and have a sharpness value within 75% of the maximum.

AMDIS Testing – Closely Eluting Components

Representing Chemical Identity

• Visual: 2D Structure
• Text: IUPAC Name
• Digital: No Accepted, Open Method
• Solution:

The IUPAC/NIST Chemical Identifier

Connection Table

Cl

1 2 3 4

S S S S S D S D

4 3 2 1 1 2 3 4

Chemical Identity Problems

CH3 C H3 CH3 C H3

Registry Number possible for each exact form, mixture, unknown, unspecified Experts required Expensive, ambiguous and error prone

Requirements

• Different compounds have different identifiers

– Keep all distinguishing structural information IChI - 1 IChI - 2

= =

Requirements

• One compound has only one identifier

– Omit unnecessary information

N O O N O O N

+ O

O

N O O

Same INChI = = =

3 Steps to INChI

• Chemistry

– ‘Normalize’ Input Structure

• Implement chemical rules
• Math

– ‘Canonicalize’ (label the atoms)

• Equivalent atoms get the same label
• Format

– ‘Serialize’ Labeled Structure

• Output as character string (‘name’)

formula connectivity stereo isotope Chemical Substances

“Layers”

Nitrobenzene

CH

5

CH

3

CH

1

CH

2

CH

4

C

6

N

+

7

O

8

O

9

Canonical numbering

Description Layers

formula C6H5NO2 connectivity 8-7(9)6-4-2-1-3-5-6 H-atoms 1-5H charges

MSG

C

4

C

5

O

8

CH2

2

O

9

CH2

1

CH

3

O

10

O H

7

NH2

6

Na

+

1

Canonical numbering

Description Layers

formula C5H8NO4.Na connectivity 6-3(5(9)10)1-2-4(7)8; H-atoms 1-2H2,3H,6H2(H-,7,8,9,10); stereo sp3 3-; charges -1;+1

C5H9NO4.Na/c6-3(5(9)10)1-2-4(7)8;/h1- 2H2,3H,6H2,(H,7,8)(H,9,10);/q;+1/p-1/t3-;/m1./s1

INChI Test Version

Input/ Result Mobile H On/Off Include Org- Metal Bonds

Peptide Mass Spectra: Libraries for Organisms

• Proteins are linear sequences of amino acids

– characteristic of Genome (organism)

• Peptides are ‘digested’ fragments of proteins
• MS ‘sequences’ peptides to reveal source Protein
• Peptides fragmentation spectra are not quite

predictable

• Peptide fragmentation spectra for a ‘genome’ can

be contained in one Library.

Spectrum Prediction Programs

Peptide Spectra Reference Library

(multiple measurements each of 10,000 peptides)

HLQLAIR/2+

MS Mapped to the Genome From Eric Deutsch, ISB, 6/2004