1. 05/27/2006 Modeling and Determining the Structures of Proteins and Macromolecular Assemblies Depts. of Biopharmaceutical Sciences and Pharmaceutical Chemistry California Institute for Quantitative Biomedical Research University of California at San Francisco Andrej Sali http://salilab.org/ U C S F

2. 05/27/2006 D. Baker & A. Sali. Science 294, 93, 2001. Protein structure models can be useful, despite errors

3. 05/27/2006 Determining the Structures of Proteins and Assemblies Maximize efficiency, accuracy, resolution, and completeness of the structural coverage of proteins and their assemblies. Use structural information from any source: measurement, first principles, rules, resolution: low or high resolution to obtain the set of all models that are consistent with it. Sali, Earnest, Glaeser, Baumeister. From words to literature in structural proteomics. Nature 422, 216-225, 2003. PHYSICS STATISTICS EXPERIMENT S

4. 05/27/2006 Characterizing Structures by Satisfaction of Spatial Restraints 1)Representation of a system. 2)Scoring function (spatial restraints). 3)Optimization. There is nothing but points and restraints on them.

5. 05/27/2006 Scoring Function There is nothing but points and restraints on them. distance p P (R / I) = ∏  p i (r i / I i ) i R … all degrees of freedom I … all information r i … i th restrained feature (eg, distance, angle, proximity, surface, density) I i … information about i th restrained feature http://salilab.org/modeller/ Sali, Blundell. J. Mol. Biol. 234, 779, 1993. Alber, Kim, Sali. Structure 13, 435, 2005.

6. 05/31/2006 Hybrid modeling of the proteasome (Frank Alber) Best scoring model (out out 3,000 models) Starting structure Random configuration Native drms ~1.3 nm Simulated from the known structure: some subunit comparative models, 3 crosslinks for some pairs, 14 pullouts, assembly shape (EM).

7. 05/31/2006 Spatial restraints on protein binary interactions from physics and bioinformatics

8. 05/31/2006 Identification of Binary Interactions by Structural Similarity Davis and Sali. Bioinformatics, 2005. Davis, Braberg, Shen, Pieper, Sali, Madhusudhan. Nucl. Acids Res., 2006.

9. 05/31/2006 Restraining binary protein interactions by computation (1) Comparative modeling - based on structures of homologous complexes - high accuracy - low coverage (2) Protein docking - based on surface complementarity of target domains - high coverage - low accuracy (3) Comparative patch analysis - based on complementarity of target domains and binding sites of their homologs - higher accuracy than in docking and coverage than in comparative modeling

10. 05/31/2006 Structural characterization of PDZ3-SH3-GK fragment in rat PSD-95 PDZ 3 SH3-GK Results: predicted two alternate conformations (1) PDZ 3 interacts with proline-rich binding site of SH3; PXXP motif of PDZ 3 might contribute to the interface C-terminus binding cleft of PDZ 3 is accessible (2) C-terminus binding cleft of PDZ 3 and GMP binding site of GK become inaccessible upon binding PDZ 1 PDZ 2 PDZ 3 SH3 GK target subunits PSD-95 domain architecture

11. 05/31/2006 Experimental support of predictions 1.Limited proteolysis - Limited proteolysis of recombinant PSD-95 was carried out using Proteinase K. - A prominent ~48 kDa band at 30 minutes which corresponds to the PDZ3-SH3-GK fragment. - Further digestion leads to appearance of a stable ~34 kDa band corresponding to the SH3- GK fragment. 2. Suggested experiments - Site-directed mutagenesis of the interface residues in the first proposed state could be used together with pull-down assays to validate the predicted interaction interface. - The lack of accessibility of the GMP-binding site in the second state could be tested using nucleotide-binding assays. - We expect the experimentally obtained SAXS spectra to be helpful in distinguishing the two PSD-95 states, based on the difference in theoretically predicted SAXS spectra.

12. 05/31/2006 Comparative patch analysis (Dmitry Korkin) Target domains Predicted complex

13. 05/28/2006 The cycle of assembly structure characterization Data generation Collection of experimental data Modeling Modeling by satisfaction of spatial restraints Data interpretation Translation into spatial restraints Model interpretation Ensemble precision, accuracy 3D structure embedding

14. 05/27/2006 Why Integrated Hierarchical System for Structural Biology? 1.No single method will give “best” possible assembly structure. 2.Need to integrate information to benefit from synergy among restraints. 3.Additional “information” provided by embedding in 3D. 4.Need to find all models consistent with the data, not just one. 5.Need to assess the results. 6.Provide feedback to guide future experiments. 7.“What if” simulations for optimized future experiments. 8.A way of “thinking” (information, “points of light” …).

15. 05/31/2006 Challenges Robust, user friendly implementation. More applications. Expressing experimental information as restraints. Combining restraints together. Optimization. Modeling of dynamic processes.

16. 05/28/2006 In Conclusion The goal is a comprehensive description of the multitude of interactions between molecular entities, which in turn is a prerequisite for the discovery of general structural principles that underlie all cellular processes. This goal will be achieved by a tight integration of experimental, physics-based, and statistics/rule-based approaches, spanning all relevant size and time scales. Sali, Earnest, Glaeser, Baumeister. From words to literature in structural proteomics. Nature 422, 216-225, 2003.