New Approaches for High-Throughput Identification and Characterization of

Protein Complexes

Michelle V. BuchananOak Ridge National Laboratory

NIH Workshop on Structural Proteomics of Biological Complexes

April 8, 2003

Identification and Characterization of Protein Complexes is one of Four Goals of the GTL Program

Goal 1: Identify the molecular machines of life

Goal 2: Characterize gene regulatory networks

Goal 3: Characterize the functional repertoire of natural microbial communities

Goal 4: Develop computational capabilities to advance understanding of complex biological systems and predict their behavior

http://DOEGenomesToLife.org/

Center for Molecular & Cellular Systems

Goal 1 includes three main steps

• Identify complement of protein complexes and their components

which lay the foundation for GTL

• Elucidate function and dynamics of complexes— intermediates, nature of interactions, cellular location, kinetics

• Establish how changes arising from environmental stress, development, etc., affect complex formation and function

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Impact of Goal 1

Molecular level understanding of protein complexes and, ultimately, networks

Predict/change behavior of organism and community

Predict function, biological pathways by homology

Discover new functions

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New approaches needed for large-scale studies

No single analytical tool will provide all required information

Integrated computational tools

Analyze, compare, predict, share data Quality assessment Guide experimental design and data collection

Develop integrated approach to correlate identified complexes with data from gene expression, protein expression, imaging, and other methods

Identification and Characterization of Protein Machines

Center for Molecular & Cellular Systems

Strategy to Achieve Goal 1

Initiate protein complex identification using affinity separation combined with mass spectrometry and computational tools

Evaluate new approaches for high-throughput identification

Incorporate additional tools, data to characterize complexes

• Multiple, controlled sample growth conditions

• Define conditions for quality assurance

Deputy DirectorsSteve Wiley (PNNL), Frank Larimer (ORNL)

CoreSteven Kennel, Thomas Squire

High Throughput Complex ProcessingMike Ramsey, Karin Rodland

Mass SpectrometryGreg Hurst, Richard Smith

Molecular and Cellular ImagingMitch Doktycz, Steve Colson

Bioinformatics and ComputingYing Xu, David Dixon

Ray Gesteland (U. Utah) mass spectrometryCarol Giometti (ANL) gel electrophoresisMike Giddings (U. North Carolina) MS, compututationMalin Young (SNL) cross-linking

Center for Molecular and Cellular Systems

Center for Molecular & Cellular Systems

An Approach for High Throughput Identification of Protein Complexes

Combine complex isolation, mass spectrometry and data analysis

Bioinformatics Controlled cell growth Cloning, tagging Affinity isolation scFv Cross-linking Separation Mass spectrometry Data analysis, archival

Identify genes of interest Choose I

Make scFv

Experiments

Bioinformatics

Cells Grow cells under specific conditions

Disrupt & fractionate cells Cell prep

Cross-link

Isolate

Use bait

Analyze (Gels)

Analyze (LCMS, MS/MS)

Isolate Isolate

III

IV

Clone & Tag genes In vitro translation

Use as bait

Data structure

Modified Cells

Cell Types

V

II

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Choose Gene and Growth Conditions

Engineer Tagged Protein

Grow Cells Under Specific

Conditions

Fractionate Cells

Pull-down Protein

Complex

Mass Spec Analysis

Bottom-Up

Analysis

Top-Down

Analysis

Transfected Cells

Data Analysis

Whole Protein Spectra

Peptide Spectra

Native Expression

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Heterologous Expression

Express & Purify

Antigen

Select Gene

Clone geneMake scFv

MS

Analysis

Pull down Analyze (gel)

Antigen with scFv

Protein complex

Center for Molecular & Cellular Systems

MS for Protein Identification“Top-Down”“Bottom-Up”

Protein(s) (gel spot, or complex, or mixture, …)

FTMS Intact Molecular Weight

digestion

Peptide mixturePeptide Mass Map(molecular weights)

MS

LC-(FT)MS LC-MS-MS

AMT’s

Partial aasequence

ProteinID

DB=database search

DB

DB

DB

DB

Center for Molecular & Cellular Systems

Microfluidic Devices

J.M. Ramsey, et al

cells

emulsifier

waste

separationchannel

(-) high voltage

(+) high voltage

lysis + injection

Note: arrows depict direction of flow.

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Molecular and Cellular Imaging

Validate the composition of protein complexes Characterize protein complexes in isolation, within

cells, and on cell surfaces/interfaces Employ multimodality approaches to molecular

imaging—optical probes, molecular recognition force

microscopy, afm/optical, (optical)n

Determine the location of specific complexes at cellular/subcellular

locations Characterize dynamics, binding forces

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Other analytical techniques

Neutron scattering X-ray scattering Data from high resolution structural

techniques others

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Computational Tools Support All Aspects of Center

sample tracking, work flow monitoring library information management data processing, storage, management,

transmission data communication and technical support tools for predicting and validating members of

protein complexes, structures, function, etc.Community

support

sample tracking system

library informationmanagement system

MS, imaging, otheranalytical tools

protein samplepreparations

Data from Center, other labs, etc.

protein complex data depository

data storage, management,analysis and transmission

Center for Molecular & Cellular Systems

Molec.Tools

Sample Prep

Data &Models

AnalysisTest

System

ResourceFor HighThroughputComplexID

improvedaffinity reagents

automation,fluidics

dynamic range, sensitivity

crosslinking single cell dynamics,biophysical

validationarchivaldata mining

interactions,protein networks

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New approaches needed for large-scale studies, both analytical and computational

Multiple tools required for full characterization

Requires multidisciplinary teams—biologists, chemists, computational scientists

Identification and Characterization of Protein Machines

Center for Molecular & Cellular Systems

Acknowledgements

Research sponsored by Office of Biological and Environmental Research, U.S. Department of Energy.