1. Modeling the Structures of Macromolecular Assemblies
Frank Alber
Maya Topf
Andrej Šali
Get the 3 Aims slide. That is the direction/spirit for the talk.
Do I quote lots of our papers????!!!! Ask people.
Improve "overall model accuracy" with better backbone plots; list error types on it.
Get the latest Chothia & Lesk plot from Eashwar.
Get the latest numbers for the TrEMBM run, pie chart, density plot.
Latest ModBase slide - perhaps from the ModView paper.
Something better on LigBase - more info; on the slide.
Have 3 slides on the BRCA1 BRCT domain.
Get the NPC slides from Mike & Frank!
Write the bullet points for each slide. Think globally.
Maybe have a text-based slide with steps in ModPipe, with references, indicating a lot of our methods work.
Depts. of Biopharmaceutical Sciences and Pharmaceutical Chemistry
California Institute for Quantitative Biomedical Research
University of California at San Francisco U C S F

2. Contents Modeling of assemblies by optimization (5’, Andrej)
Representation for modeling, illustrated by NPC (15’, Frank)
Comparative modeling (10’, Andrej)
Combined comparative modeling and density fitting (10’, Maya)

3. Acknowledgments http://salilab.org Our group/assemblies Frank Alber
Maya Topf
Fred Davis
Dmitry Korkin
M.S. Madhusudhan
Damien Devos
Marc A. Martí-Renom
Mike Kim
Bino John
Narayanan Eswar
Ursula Pieper
Min-yi Shen EM
Wah Chiu
Matt Baker
Wen Jiang
Chandrajit Bajaj
E. coli ribosome
H.Gao
J.Sengupta
M.Valle
A.Korostelev
S.Stagg
P.Van Roey
R.Agrawal
S.Harvey
M.Chapman
J.Frank
Yeast NPC
Tari Suprapto
Julia Kipper
Wenzhu Zhang
Liesbeth Veenhoff
Sveta Dokudovskaya
Mike Rout
Brian Chait
NIH
NSF
Sinsheimer Foundation
A. P. Sloan Foundation
Burroughs-Wellcome Fund
Merck Genome Res. Inst.
Mathers Foundation
I.T. Hirschl Foundation
The Sandler Family Foundation
SUN
IBM
Intel
Structural Genomix
Structural Genomics
Stephen Burley (SGX)
John Kuriyan (UCB)
NY-SGRC

4. Protein and assembly structure by experiment and computation
Sali, Earnest, Glaeser, Baumeister. From words to literature in structural proteomics. Nature 422, , 2003.

5. Determining the structures of proteins and 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.

6. Modeling proteins and macromolecular assemblies
by satisfaction of spatial restraints
Representation of a system.
Scoring function (spatial restraints).
Optimization.
There is nothing but points and
restraints on them.
Sali, Earnest, Glaeser, Baumeister. Nature 422, , 2003.

7. Assembly representation for modeling (visualization)
Multi-scale representation:
Resolution of subunit structures can vary largely.
Hierarchical representation:
Spatial information about subunit-subunit interactions may be available at different resolutions.
Flexibility of subunits:
Subunit structures may be subject to conformational changes upon assembly formation.
Points and restraints between them (distance, angle dihedral restraints).

8. Software implementation
Assembler, extension of MODELLER
Provides a framework for assembly modelling:
multi-scale hierarchical representations,
integration of experimental input data,
calculates models that are consistent with input data.

9. Resolution of restraints
300Å 1Å
Cryo-EM/
tomography
Site-directed
mutagenesis
Cross-linking
FRET
Binding site
predictions
Computational
docking
Correlated
mutations
Site-directed
mutagenesis
Cross-linking
FRET
Binding site
predictions
Computational
docking
Cryo-EM/
tomography
Tandem affinity
purification
FRET
Site-directed
mutagenesis
Binding site
predictions
Computational
docking
Cryo-EM/
tomography
Cross-linking
Immuno-EM
Yeast-two-hybrid
Tandem affinity
purification
FRET
Site-directed
mutagenesis
Binding site
predictions
Computational
docking
Cryo-EM/
tomography
Cross-linking
Cross-linking
FRET
Site-directed
mutagenesis
Computational
docking
Binding site
predictions
Correlated
mutations

10. Resolution of restraints
300Å 1Å
Cross-linking
Cross-linking
Cross-linking
Cross-linking
Cross-linking
Cryo-EM/
tomography
FRET
FRET
FRET
FRET
FRET
Site-directed
mutagenesis
Site-directed
mutagenesis
Site-directed
mutagenesis
Site-directed
mutagenesis
Site-directed
mutagenesis
Computational
docking
Cryo-EM/
tomography
Cryo-EM/
tomography
Computational
docking
Computational
docking
Binding site
predictions
Binding site
predictions
Binding site
predictions
Cryo-EM/
tomography
Cryo-EM/
tomography
Cryo-EM/
tomography
Tandem affinity
purification
Tandem affinity
purification
Immuno-EM
Yeast-two-hybrid

11. Properties of points Interaction centers:
Binding patches
Define interaction centers at each
structural level.
Interaction centers transmit interactions
between assembly subunits.

12. Properties of points Interaction centers: Subunit flexibility: q
Allow or restrict conformational degrees
of freedom within assembly subunits.
Subunit flexibility: q
Define interaction centers at each
structural level.
Interaction centers transmit interactions
between assembly subunits.
Design alternative internal restraints sets
for different conformational states.

13. Modeling the Yeast Nuclear Pore complex by satisfaction of spatial restraints
Andrej Sali
Frank Alber, Damien Devos
Mike Rout,
T. Suprapto, J. Kipper, L. Veenhoff,
S. Dokudovskaya
Brian Chait,
W. Zhang
Depts. Of Biopharmaceutical Sciences and Pharmaceutical Chemistry
California Institute for Quantitative Biomedical Research
University of California at San Francisco
The Rockefeller University,
1230 York Avenue, New York

14. Integrate spatial information
NUP
Stoichiometry
NUP
Localization
NUP- NUP Interactions
Symmetry
Global shape
NUP Shape 2) 3) 1)

15. Successful optimization

16. Analysis of models Assessing the well scoring models:
How similar are the models to each other?
Do the models make sense given other data?
Search for conservation of :
Protein-protein contacts
Structural features
Visualization of the results:
Probability distribution of subunit positions (density maps)

17. Comparative modeling

18. Steps in Comparative Protein Structure Modeling
Template Search
TEMPLATE
START
TARGET
ASILPKRLFGNCEQTSDEGLKIERTPLVPHISAQNVCLKIDDVPERLIPERASFQWMNDK No
Target – Template
Alignment
MSVIPKRLYGNCEQTSEEAIRIEDSPIV---TADLVCLKIDEIPERLVGE
ASILPKRLFGNCEQTSDEGLKIERTPLVPHISAQNVCLKIDDVPERLIPE
Model Building
OK?
Model Evaluation
END
Yes
Main steps in comparative modeling, all approaches: 1) Threading can be used, others, in step 1.
A. Šali, Curr. Opin. Biotech. 6, 437, 1995.
R. Sánchez & A. Šali, Curr. Opin. Str. Biol. 7, 206, 1997.
M. Marti et al. Ann. Rev. Biophys. Biomolec. Struct., 29, 291,

19. Comparative modeling by satisfaction of spatial restraints MODELLER
3D GKITFYERGFQGHCYESDC-NLQP…
SEQ GKITFYERG---RCYESDCPNLQP…
1. Extract spatial restraints
F(R) = P pi (fi /I) i
2. Satisfy spatial restraints
Comparative Modeling by Satisfaction of Spatial Restraints. Two points: Similar to NMR structure refinement formally. We have invented a formalism that allows us to combine physical and evolutionary information about a given protein sequence in a flexible and relatively accurate manner.
A. Šali & T. Blundell. J. Mol. Biol. 234, 779, 1993.
J.P. Overington & A. Šali. Prot. Sci. 3, 1582, 1994.
A. Fiser, R. Do & A. Šali, Prot. Sci., 9, 1753, 2000.

20. Comparative modeling of the TrEMBL database
Unique sequences processed: 1,182,126
Sequences with fold assignments or models: 659,495 (56%)
70% of models based on <30% sequence identity to template.
On average, only a domain per protein is modeled
(an “average” protein has 2.5 domains of 175 aa).
9/15/ ~4 weeks on 660 Pentium CPUs

21.
Pieper et al., Nucl. Acids Res

22. Structural Genomics
Sali. Nat. Struct. Biol. 5, 1029, 1998.
Sali et al. Nat. Struct. Biol., 7, 986, 2000.
Sali. Nat. Struct. Biol. 7, 484, 2001.
Baker & Sali. Science 294, 93, 2001.
Characterize most protein sequences based on related known structures.
Characterize most protein sequences based on related known structures.
The number of “families” is much smaller than the number of proteins.
Any one of the members of a family is fine.
CM is an important technique, partly because it is an intrinsic part of structural genomics. Organize the universe into groups, get members of the groups by experiment. Model the rest. And most proteins, even after structural genomics, will be models, not experimentally determined structures.
There are ~16,000 30% seq id families (90%)
(Vitkup et al. Nat. Struct. Biol. 8, 559, 2001).

23. Comparative modeling and EM density fitting

24. E. coli ribosome
H.Gao, J.Sengupta, M.Valle, A. Korostelev, N.Eswar, S.Stagg, P.Van Roey, R.Agrawal, S.Harvey, A.Sali, M.Chapman, and J.Frank. Cell 113, , 2003.
Cryo-EM density map of the 70S ribosome of E. coli was determined at ~12Å.
Calculate comparative models of the proteins and rRNA based on T. thermophilus, H. marismortui, and D. radiodurans ribosome structures.
For each protein, select the best model among several alternatives based on model assessment by statistical potentials, model compactness, target-template sequence identity, quality of template structure, and fit into the EM map.
Obtain an assembly model by multi-rigid body real-space refinement of the fit into the EM map, by RSRef (M. Chapman).
Repeat 3 and 4 iteratively to get an optimized model.

25. E. coli ribosome
Fitting of comparative models into 12Å cryo-electron density map.
All 29 proteins in 30S and 29 of 36 proteins in the 50S subunit could be modeled on 22-73% seq.id. to a known structure.
The models generally cover whole folds, but tails.
H.Gao, J.Sengupta, M.Valle, A. Korostelev, N.Eswar, S.Stagg, P.Van Roey, R.Agrawal, S.Harvey, A.Sali, M.Chapman, and J.Frank. Cell 113, , 2003.

26. Errors in comparative models vs. resolution
Incorrect template
Rigid body
movement
Misalignment
Region without
a template
Distortion/shifts in
aligned regions
Sidechain packing
20Å
10Å 2Å
Application of comparative modeling to proteins of known structure identifies the following five types of errors in comparative models. Template selection (<25% sequence identity). Misalignments (<35% sequence identity). Loop modeling, shifts, sidechain modeling (whole range).
Maps courtesy of Wah Chiu.

27. Moulding: iterative alignment, model building, model assessment
B. John, A. Sali. Nucl. Acids Res. 31, 3982, 2003.
Alignments
Models per alignment
Comparative modeling
Threading
Moulding 1
104
1030
105
model building
alignment
model assessment
Idea: Start with any alignments, and then build models based on them, assess models, improve alignments in the next iteration based on assessment, and so on. Based on assessement of 3D models as opposed to alignment score.
Another perspective, comparison with threading and CM. Both of these methods limit the phase space explored in the search for the best model, but both are extremes. They existed for many years, but nobody tried to generalize and explore in-between, a better balance between exploring the conformational and alignment degrees of freedom in the search for the best model. Practical considerations suggest on the order of 10,000 alignments.

28. Moulding
A more detailed scheme. GA for alignment evolution. MODELLER for model building, and a variety of criteria for model assessment.
alignment
model building
model assessment

29. Application to a difficult modeling case 1BOV-1LTS (4
Application to a difficult modeling case 1BOV-1LTS (4.4% sequence identity)
initial
Ca RMSD 3.6Å
final
Here, illustrated visually. Starting model is useless. The panel on the right will show the evolution, resulting in a much better model.
Ca RMSD 10.1Å
1lts structure
1lts model

30. Given a sequence-structure pair and an EM map: Exploring alignment (CM) and rigid body (EM) degrees of freedom
1. Generate a set of comparative models by a standard procedure.
2. Select the model that fits the EM map best.
More generally:
Use the quality of the best fit into the EM map as one of the model fitness criteria in MOULDER.
With Wah Chiu et al.

31. helixhunter, sheehunter
Moulding protocol S
fold assignment
helixhunter, sheehunter
PROF, PSIPRED
Initial alignment
MODELLER
alignment
MODELLER
model building
When modeling proteins that are related at less than 30% sequence identity, the alignment errors become frequent. In addition, it is
often difficult to assign a fold to the target sequence using sequence-based search methods.
Moulding: iterative alignment, model building, model fitting, model assessment. Idea: Start with any alignments, and then build models based on them, assess models,
improve alignments in the next iteration based on assessment, and so on. Based on assessment of 3D models as opposed to alignment score.
Fold assignment and initial alignments
for MOULDER will be obtained by using the secondary structure assignments from cryo-EM maps, correlated with the secondary
structure predictions based on sequence alone.
foldhunter,
EM docking algorithm
model fitting
model building
alignment
model assessment
GA341 score
PROSA score
EM fitting score
model assessment E

32. Application: Rice Dwarf Virus (6.8Å EM)
Fold assignment: HELIXHUNTER and DEJAVU - 2 templates:
Upper domain (beta sheet) - bluetongue virus VP7 (1bvp)
Lower domain (helical) - rotavirus VP6 (1qhd)
Generate initial alignments : HELIXHUNTER and secondary structure prediction methods
MOULDING:
Iterative alignment - restrained by EM data
Model building with MODELLER, loop optimization
Model assessment (GA341 score and PROSA score)
Model fitting (FOLDHUNTER)
With Wah Chiu et al.

33. Upper domain - good fit
loop optimization:
EM-derived restraints +
MM forcefield

34. MODELLER-derived restraints
Lower domain - to improve fit
best fitted structure
model fitting
EM-derived restraints

35. END

36. Acknowledgments http://salilab.org Comparative Modeling Bino John
Narayanan Eswar
Ursula Pieper
Marc A. Martí-Renom
Ribosomes
H.Gao
J.Sengupta
M.Valle
A.Korostelev
S.Stagg
P.Van Roey
R.Agrawal
S.Harvey
M.Chapman
J.Frank
Structural Genomics
Stephen Burley (SGX)
John Kuriyan (UCB)
NY-SGRC
NIH
NSF
Sinsheimer Foundation
A. P. Sloan Foundation
Burroughs-Wellcome Fund
Merck Genome Res. Inst.
Mathers Foundation
I.T. Hirschl Foundation
The Sandler Family Foundation
SUN
IBM
Intel
Structural Genomix
LSD
Patsy Babbitt, Fred Cohen,
Ken Dill, Tom Ferrin, John Irwin, Matt Jacobson, Tanja Kortemme, Tack Kuntz, Brian Shoichet,
Chris Voigt

37. Analysis Assessing the well scoring models
How similar are the models to each other?
Do the models make sense given other data?
Using “toy” models as benchmarks.
Score

38. Analysis Search conservation of : Protein-protein contacts
Structural features
frequency
NUP type

39. E. coli ribosome
H.Gao, J.Sengupta, M.Valle, A. Korostelev, N.Eswar, S.Stagg, P.Van Roey, R.Agrawal, S.Harvey, A.Sali, M.Chapman, and J.Frank. Cell 113, , 2003. …
Calculate comparative models of the proteins and rRNA based on T. thermophilus, H. marismortui, and D. radiodurans ribosome structures.
For each protein, select the best model among several alternatives based on model assessment by statistical potentials, model compactness, target-template sequence identity, quality of template structure, and fit into the EM map.
Obtain the final assembly model by rigid body real-space refinement of the fit into the EM map, by RSRef (M. Chapman).
Repeat 3 and 4.

40. Combining Modeller and Docking of Atomic Models into EM Density Maps (ModEM?)

41. Improving resolution and accuracy of molecular models determined by cryo-EM and comparative protein structure prediction
Map courtesy of Wah Chiu.

42. Typical errors in comparative models
Incorrect template
MODEL
X-RAY
TEMPLATE
Misalignment
Region without a
template
Distortion/shifts in
aligned regions
Sidechain packing
Application of comparative modeling to proteins of known structure identifies the following five types of errors in comparative models. Template selection (<25% sequence identity). Misalignments (<35% sequence identity). Loop modeling, shifts, sidechain modeling (whole range).
Marti-Renom et al. Annu.Rev.Biophys.Biomol.Struct. 29, , 2000.

43. Properties of points Points: “Interaction centers”
Binding patches
Define “interaction centers” at each structural level.
Interaction centers transmit “interactions” between assembly subunits.