1 / 15

Integrative modeling from single proteins to assemblies

Integrative modeling from single proteins to assemblies. Daniel Russel Šali Lab, UCSF, Google August, 2012. TRiC/CCC Sali, Frydman, Chiu. Actin Sali, Chiu. RyR channel Sali, Serysheva, Chiu. Ribosomes, Sali, Frank; Sali, Akey. Hsp90 landscape Sali, Agard. Nuclear Pore Complex,

brand
Download Presentation

Integrative modeling from single proteins to assemblies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Integrative modeling from single proteins to assemblies • Daniel Russel • Šali Lab, UCSF, Google • August, 2012

  2. TRiC/CCC Sali, Frydman, Chiu Actin Sali, Chiu RyR channel Sali, Serysheva, Chiu Ribosomes, Sali, Frank; Sali, Akey Hsp90 landscape Sali, Agard Nuclear Pore Complex, Sali, Rout, Chait Nup84 complex, Sali, Rout, Chait Nuclear Pore Complex transport, Sali, Rout, Chait, Chook, Liphardt Microtubule nucleation Sali, Agard 26 Proteasome Sali, Baumeister PCS9K-Fab complex Sali, Cheng, Agard, Pons Spindle Pole Body Sali, Davis, Muller Chromatin globin domain Marti-Renom Lymphoblastoid cell genome Alber, Chen Assembly Modeling

  3. Why we need data integration? 100Å 1Å assembly residues domains proteins atoms Protein structure prediction X-ray Crystallography Cryo-electron microscopy Phylogenetic profiling Gene/protein microarrays Ab initio Modeling NMR spectroscopy Cryo-electron tomography Hydrodynamics Experiments SAXS Immuno- Precipitation/MS Immuno-electron microscopy Yeast-two hybrid Chemical Cross-linking Computational Docking Bioinformatics Information retrieval Quantitativeimmunoblotting Site-directed mutagenesis FRET [D. Russel, K. Lasker, B. Webb, J. Velazquez-Muriel, E. Tjioe, D. Schneidman-Duhovny, B. Peterson, A. Sali. Putting the pieces together: integrative structure determination of macromolecular assemblies. PLoS Biol 2012]

  4. Integrative structure modeling

  5. Integrative structure modeling

  6. IMP C++/Python library multifit/restrainer Chimera tools/ web apps Domain-specific applications IMP Simplicity Expressiveness http://www.integrativemodeling.org Russel et al, PLoS Biology, 2012

  7. Connecting data and algorithms 2D EM Monte Carlo SAXS Rigid atomic models Molecular dynamics Proteomics Flexible homology models DOMINO Yeast two hybrid Brownian dynamics 3D EM Coarse grained structures PDB IMP base RMF

  8. File formats Protein structure prediction X-ray Crystallography Cryo-electron microscopy Phylogenetic profiling Gene/protein microarrays Ab initio Modeling NMR spectroscopy Cryo-electron tomography Hydrodynamics Experiments SAXS Immuno- Precipitation/MS Immuno-electron microscopy Yeast-two hybrid Chemical Cross-linking Computational Docking Bioinformatics Information retrieval Quantitativeimmunoblotting Site-directed mutagenesis

  9. Rich visualization

  10. Modeling transport varying concentrations variable numbers of binding sites physical properties of fg chains varying interaction strengths with Barak Raveh

  11. Dynamics models

  12. Reproducible modeling Gatheringexperimental data and other information Designing modelrepresentationand evaluation Samplinggood models Analyzing modelsand information

  13. Reproducible modeling Gatheringexperimental data and other information Designing modelrepresentationand evaluation The paper: experimental data experimental protocol pictures of structures analysis of structure sketch of modeling protocol Samplinggood models Analyzing modelsand information

  14. Reproducible modeling Gatheringexperimental data and other information Designing modelrepresentationand evaluation Samplinggood models The trash:all the needed details to practically reproduce the computational result Analyzing modelsand information

  15. Acknowledgments

More Related