top down mass spectrometry on a proteomic scale? - the protein … · 2013. 7. 9. · summary and...

Top Down Mass Spectrometry on a Proteomic Scale?

Neil L. Kelleher

Department of Chemistry and

The Center for Top Down Proteomics,

University of Illinois at Urbana-Champaign

November 18, 2004

The Top Down

Approach

100% “sequence coverage” at the Intact Protein Level

Gas Phase Fragment Ions (contain a terminus)

N C

Top Down vs. Bottom Up Approaches to DNA-Predicted Protein Sequence Analysis

Automating Top Down: Methods

1. “Front end” Separations and Protein Processing

2. Automation of a Quadrupole-FTMS Hybrid

3. “Back end” data processing and informatics: ProSight PTM

Development of A General Measurement Platform for Intact Proteins and Their Post-translational

Modifications

A Platform for Top Down Proteomics: Separations, MS Engine, and Informatics

Beckman Coulter2D Proteome Fractionation

PF 2D

AdvionBioSciencesNanoMate 100

Quadrupole (Ion trap) / FT Hybrids

MS/MS Engine

ProSight PTMProtein

Characterization& Bioinformatics

The Measurement Challenge for Proteomics of Mammalian Cells

XPi

polymorphisms

CN

N-terminal processing(in vivo halflife)

UnexpectedModifications

KnownModifications

Intron/exonBoundaries (?)

Ac Me

Taylor, Kim, Forbes, Meng, McCarthy, and Kelleher, Anal. Chem., 2003, 75, 4081-4086.

Populating ProSight PTM with known and predicted

biological information

Shotgun Annotation of a Diverse Protein

Correct Gene Family

Correct Gene

Correct Protein Form

FTMS for Top Down

•100% Sequence coverage

•High Resolution

•Low ppm mass acuracy

•Whatever else neil wants to talk about

Automating Top Down: A Custom 8.5 T Q-FTMS

Dana Robinson

Nanospray Robot

Capillary/ Source

Quadrupole/ Collision Octopole

Transfer Octopoles

Automated Isolation of a 12 kDa Yeast Protein

Automated Fragmentation of a 12 kDa Yeast Protein

Probability Score = 4 x 10-10

The PF 2D System

Separate Proteins based on pI, followed by Hydrophobicity

Studies Underway in the Kelleher Laboratory

• Methanosarcinaacetivorans– CO vs. CH3OH

metabolism• Yeast

– aerobic vs. anaerobic growth (O2 vs. N2)

• Human HeLa cells– Nuclei from

synchronized cells, different stages of cell cycle

Methanosarcina Acetivorans

- 5,751,492 base pairs - ~4500 open reading frames, - Average coding region ~936

-76% genome for coding-49% predicted protein regions

- 20% predicted hypothetical sequences- 31% no known similarities to other species

-Genetically, physiological and environmentally diverse – large gene families adapt in different

environments (generalist strategy)J. E. Galagan, et. al., Genome Research, 2002, 12, 532-542.

PF2DpH 8.5

pH 4.0

RPLC Fraction 2

RPLC Fraction 2RPLC 15MA1083aMA1083bMA1259MA1088

RPLC 16MA1081

MA0646?MA4113?

RPLC 17MA1079MA1350

MA0308?MA1369

MA0483?

RPLC 18MA1071MA0828

RPLC 19MA1110MA3195MA4099MA1077 RPLC 48

MA3737

RPLC 20MA3580?MA1296

RPLC 21MA4547

HPCF Chromatogram on Yeast Lysate

HPCF Fraction #3 pH>8.7

RPLC #7 from HPCF Fraction #3

Ribosomal Protein 60S L19

Ribosomal Protein 60S L13A

Ribosomal Protein 60S L2

ESI FTMS of PF2D Sample

Recent HPCF Chromatogramof Human Nuclei (HeLa cells)

HPCF-1314-RPLC-01-06

Automated MS/MS of Low Abundance Species

18+ charge state theo 13955.85-0exp 13997.81-0 ∆m=42.01 ±0.02 Da

SWIFT

ECD

Broadband

Simultaneous Characterization of a PTM, cSNP, and Gene Family Member

Mix

Auto-ECD …of a 8% component

Automated Data Analysis:Finding Monoisotopic Masses of Fragment Ions

ProSight PTM

Click Here

ThresholdMS/MS

(b/y)

Electron-basedMS/MS(c/z•)

Integration of Threshold and Electron Fragmentation Approaches for Intact Protein Characterization

Top Down Fragmentation Data

Absolute fragmention masses

(e.g., 2369.65 Da, 4567.56 Da,

8763.23 Da, ...)

Protein Identification

Protein Characterization

“Sequence Tags” (e.g., ...EVPDG...)

Explanation of ∆m’s

From One Gene, Many Protein Forms: A Major Theme in Contemporary Proteomics

DNA

Gene Family,coding SNPs

Alternativesplicing

mRNA Protein

CovalentModification

“Shotgun Annotation” of Biological Variation: Over-Population of “Post-Translational Space”

Summary and Outlook• Throughput for Top Down Analysis of Proteins and Protein Mixtures is increasing with the advent of more intelligent automation tools and strategies

• Top Down Protein Analysis is capable of simultaneousGene-Family, SNP, and PTM determination “Shotgun Annotation” for human proteins

• Top Down is extendable to non-mass spectrometristsowing to high specificity and full characterization

• IEF and Chromatofocusing is perhaps the best current answer to the Protein Processing Problem for Top Down MS

Acknowledgements

Leah MillerYi Du

Shaun McLoughlinJim Pesavento

Lihua JiangLeslie Hicks

Jon FergusonMichael BoyneMichael Roth

Dana RobinsonPaul Thomas

Kelleher Group

Fiscal SupportPackard, Burroughs Wellcome, Searle, Sloan, Res. Corp. Cottrell

NIH (GM 067193), NSF CAREER

Seyoung SohnTom Junysk

Comp. Sci. (Undergrad.)

Dr. Eric ThomasDr. Pieter Dorrestein

Post. Doc.

Ben CargileJeff Johnson

Dr. Jay CharleboisDr. Fanyu MengDr. Steven Patrie

Dr. Andrew ForbesDr. Karen Topp

Dr. Matthew MazurGreg Taylor Lee Bynum

Joe SolaAndrew Birck

Comp. Sci. (Grad.)

Yong-bin KimRich LeducIan Brooks

Former

Nicole FrielJosh Norris

Undergrad.

DatabasesGeneva Belford

https://prosightptm.scs.uiuc.edu