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New Approaches for High-Throughput
Identification and Characterization of
Protein Complexes
Michelle V. Buchanan
Oak 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/
Goal 1 includes three main steps
• Identify complement of protein complexes and their
components
• 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
which lay the foundation for GTL
Center for Molecular & Cellular Systems
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
Center for Molecular & Cellular Systems
Identification and Characterization
of Protein Machines
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
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
Center for Molecular & Cellular Systems
Center for Molecular and Cellular Systems
Deputy Directors
Steve Wiley (PNNL), Frank Larimer (ORNL)
Core
Steven Kennel, Thomas Squire
High Throughput Complex Processing
Mike Ramsey, Karin Rodland
Mass Spectrometry
Greg Hurst, Richard Smith
Molecular and Cellular Imaging
Mitch Doktycz, Steve Colson
Bioinformatics and Computing
Ying Xu, David Dixon
Ray Gesteland (U. Utah) mass spectrometry
Carol Giometti (ANL) gel electrophoresis
Mike Giddings (U. North Carolina) MS, compututation
Malin Young (SNL) cross-linking
An Approach for High Throughput Identification of
Protein Complexes
I
Choose
Cell Types
Combine complex isolation,
mass spectrometry and data
analysis
Clone & Tag genes
Cells Modified Cells
II









Bioinformatics
Controlled cell growth
Cloning, tagging
Affinity isolation
scFv
Cross-linking
Separation
Mass spectrometry
Data analysis, archival
Identify genes of interest
III
In vitro translation
Grow cells under
specific conditions
Disrupt & fractionate cells
Make scFv
Cell prep
Cross-link
Use bait
Use as bait
Isolate
IV
Isolate Isolate
V
Analyze
Analyze
(Gels) (LCMS, MS/MS)
Center for Molecular & Cellular Systems
Experiments
Data structure
Bioinformatics
Native Expression
Choose Gene
and Growth
Conditions
Mass Spec
Analysis
Engineer
Tagged
Protein
Pull-down
Protein
Complex
TopDown
Analysis
Transfected
Cells
Grow Cells
Under Specific
Conditions
BottomUp
Analysis
Fractionate
Cells
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Peptide
Spectra
Whole
Protein
Spectra
Data
Analysis
Heterologous Expression
Select
Gene
Make scFv
Clone gene
Express &
Purify
Antigen with scFv
Pull down
Analyze
(gel)
Protein complex
Antigen
MS
Analysis
Center for Molecular & Cellular Systems
MS for Protein Identification
“Bottom-Up”
“Top-Down”
Protein(s)
(gel spot, or complex,
or mixture, …)
FTMS Intact Molecular Weight
DB
digestion
Peptide mixture
LC-(FT)MS
AMT’s
MS
LC-MS-MS
Peptide Mass Map
(molecular weights)
Partial aa
sequence
DB
DB
Protein
ID
DB
DB=database search
Center for Molecular & Cellular Systems
Microfluidic Devices
(-) high voltage
emulsifier
waste
cells
lysis + injection
separation
channel
(+) high voltage
Note: arrows depict direction of flow.
Center for Molecular & Cellular Systems
J.M. Ramsey, et al
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
Center for Molecular & Cellular Systems
Other analytical techniques
 Neutron scattering
 X-ray scattering
 Data from high resolution structural
techniques
 others
Center for Molecular & Cellular Systems
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.
sample tracking
system
protein sample
preparations
library information
management system
MS, imaging, other
analytical tools
Community
support
data storage, management,
analysis and transmission
Data from Center,
other labs, etc.
Center for Molecular & Cellular Systems
protein complex
data depository
crosslinking
Test
System
improved
affinity
reagents
single cell
Molec.
Tools
dynamics,
biophysical
Sample
Prep
automation,
fluidics
validation
archival
data mining
Analysis
dynamic
range,
sensitivity
Data &
Models
interactions,
protein
networks
Center for Molecular & Cellular Systems
Resource
For High
Throughput
Complex
ID
Identification and Characterization
of Protein Machines
 New approaches needed for largescale studies, both analytical and
computational
 Multiple tools required for full
characterization
 Requires multidisciplinary teams—
biologists, chemists, computational
scientists
Center for Molecular & Cellular Systems
Acknowledgements
Research sponsored by Office of Biological
and Environmental Research, U.S.
Department of Energy.
Center for Molecular & Cellular Systems