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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 Center for Molecular & Cellular Systems 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