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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/

UCSF

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D. Baker & A. Sali. Science 294, 93, 2001.

Protein structure models can be useful, despite errors

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Determining the Structures of Proteins and AssembliesMaximize efficiency, accuracy, resolution, and completeness of the structural coverage of proteins and their assemblies.

Use structural information from anysource: 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

STATISTICSEXPERIMENT

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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.

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Scoring Function

There is nothing but points and restraints on them.

distance

p

P (R / I) = ∏pi (ri / Ii )

i

R … all degrees of freedom

I … all information

ri … ith restrained feature (eg, distance, angle, proximity, surface, density)

Ii … information about ith restrained feature

http://salilab.org/modeller/ Sali, Blundell.  J. Mol. Biol. 234, 779, 1993. Alber, Kim, Sali. Structure 13, 435, 2005.

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Hybrid modeling of the proteasome

(Frank Alber)

Best scoring model(out out 3,000 models)

Starting structureRandom configuration

Native

drms ~1.3 nmSimulated from the known structure:•some subunit comparative models,•3 crosslinks for some pairs,•14 pullouts, •assembly shape (EM).

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Spatial restraints on protein binary interactions from

physics and bioinformatics

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Identification of Binary Interactions by Structural Similarity

Davis and Sali. Bioinformatics, 2005.

Davis, Braberg, Shen, Pieper, Sali, Madhusudhan. Nucl. Acids Res., 2006.

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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

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Structural characterization of PDZ3-SH3-GK fragment in rat PSD-95

PDZ3SH3-GK

Results: predicted two alternate conformations

(1)

• PDZ3 interacts with proline-rich binding site of SH3;

• PXXP motif of PDZ3 might contribute to the interface

• C-terminus binding cleft of PDZ3 is accessible

(2)

• C-terminus binding cleft of PDZ3 and GMP binding site of GK become inaccessible upon binding

PDZ1

PDZ2

PDZ3

SH3

GKtarget subunits

PSD-95 domain architecture

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Experimental support of predictions1.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.

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Comparative patch analysis(Dmitry Korkin)

Ta

rge

t do

ma

ins

Pre

dict

ed

co

mp

lex

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The cycle of assembly structure characterization

Data generationCollection of

experimental data

ModelingModeling by satisfaction

of spatial restraints

Data interpretationTranslation into spatial restraints

Model interpretationEnsemble precision, accuracy

3D structure embedding

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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” …).

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Challenges

• Robust, user friendly implementation.

• More applications.

• Expressing experimental information as restraints.

• Combining restraints together.

• Optimization.

• Modeling of dynamic processes.

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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.