New Approaches for High-Throughput Identification and Characterization of Protein Complexes

1. 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

2. 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/

3. 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

4. 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

5. 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

6. 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

7. 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

8. I Choose Identify genes of interest Cell Types Clone & Tag genes Cells Modified Cells In vitro translation II Grow cells under specific conditions III Disrupt & fractionate cells Make scFv Cell prep Cross - link Use bait Use as bait Isolate Isolate Isolate V Experiments IV Analyze Analyze Data structure (Gels) (LCMS, MS/MS) Bioinformatics 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

9. Mass Spec Analysis Bottom-Up Analysis Top-Down Analysis Native Expression Choose Gene and Growth Conditions Data Analysis Peptide Spectra Pull-down Protein Complex Engineer Tagged Protein Whole Protein Spectra Transfected Cells Grow Cells Under Specific Conditions Fractionate Cells

10. Select Gene Clone gene Make scFv Analyze (gel) Pull down Heterologous Expression Antigen with scFv Express & Purify Protein complex Antigen MS Analysis

11. “Bottom-Up” “Top-Down” Protein ID Peptide Mass Map (molecular weights) Partial aa sequence MS for Protein Identification Protein(s) (gel spot, or complex, or mixture, …) FTMS Intact Molecular Weight DB digestion DB MS Peptide mixture DB LC-MS-MS LC-(FT)MS AMT’s DB DB=database search

12. (-) high voltage emulsifier waste cells lysis + injection separation channel (+) high voltage Microfluidic Devices J.M. Ramsey, et al Note: arrows depict direction of flow.

13. 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, bindingforces

14. Other analytical techniques • Neutron scattering • X-ray scattering • Data from high resolution structural techniques • others

15. protein sample preparations Data from Center, other labs, etc. protein complex data depository MS, imaging, other analytical tools 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 data storage, management, analysis and transmission sample tracking system library information management system

16. dynamics, biophysical validation archival data mining crosslinking single cell Molec. Tools Sample Prep Analysis Data & Models Test System Resource For High Throughput Complex ID improved affinity reagents dynamic range, sensitivity automation, fluidics interactions, protein networks

17. Identification and Characterization of Protein Machines • New approaches needed for large-scale studies, both analytical and computational • Multiple tools required for full characterization • Requires multidisciplinary teams—biologists, chemists, computational scientists

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