1. Modeling the Structures of Proteins and Macromolecular Assemblies
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.
Andrej Šali
Depts. of Biopharmaceutical Sciences and Pharmaceutical Chemistry
California Institute for Quantitative Biomedical Research
University of California at San Francisco U C S F
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2. Topics Large-scale comparative protein structure modeling
Target selection for NYSGRC
Questions 2, 7, 5
Comments
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3. MODPIPE: Large-Scale Comparative Modeling
START
MODELLER
Get profile for sequence (SP/TrEMBL)
Align sequence profile with multiple structure profile using local dynamic programming
Select templates using permissive E-value cutoff
For each target sequence
Build models for target segment by satisfaction of spatial restraints
Evaluate models
MODELLER
R. Sánchez & A. Šali, Proc. Natl. Acad. Sci. USA 95, 13597, 1998.
Eswar et al. Nucl. Acids Res. 31, 3375–3380, 2003.
Pieper et al., Nucl. Acids Res. 32, 2004.
N. Eswar, M. Marti-Renom, M.S. Madhusudhan, B. John, A. Fiser, R. Sánchez, F. Melo, N. Mirkovic, B. Webb, M.-Y. Shen, A. Šali.
PSI-BLAST run as optimized by C. Chothia. Just mention the permissive E-value filter for now. Point out we are using sequence-structure alignment (new). Mention MODELLER for model building (old method). Point out we are using optimized model assessment (new). Point out the permissivness in E-value – to find nugets in the mud. Point out similarity/difference with threading.
For each template profile
END
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4. 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
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5. Statistics from ModBase
Modeling coverage of SP/TR
Distribution of sequence identities
Distribution of model length
Coverage of SCOP domains
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6. http://salilab.org/modbase Pieper et al., Nucl. Acids Res. 32, 2004.
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7. Major bidirectional resources involving ModBase
Daniel Greenblatt, Conrad C. Huang,
Thomas E. Ferrin
UCSC Human Gene Family Browser
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8. MODBASE and associated resources http://salilab.org/
Eswar et al.
Nucl. Acids Res. 31,
3375–3380, 2003.
Pieper et al.,
Nucl. Acids Res. 32, 2004.
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9. Synergy of crystallography and comparative modeling in structural genomics
Pieper et al., Nucl. Acids Res. 32, 2004.
NYSGXRC X-ray Structure
MODBASE Models
PDB
Code
Database Accession Number
Annotation
Total Sequences
Fold
&
Model
1b54
P38197
Hypothetical UPF0001 protein YBL036C
151
132 2 17
1f89
P49954
Hypothetical 32.5 kDa protein YLR351C
553
488 55 10
1njr
Q04299
Hypothetical 32.1 kDa protein in ADH3-RCA1 intergenic region 4 1 3
1nkq
P53889
Hypothetical 28.8 kDa protein in PSD1-SKO1 intergenic region
379
207
172
1jzt
P40165
Hypothetical 27.5 kDa protein in SPX19-GCR2 intergenic region
1058 39
1006 13
1jr7
P76621
Hypothetical protein ygaT 11
1ku9
YF63_METJA hypothetical protein MJ1563
598
131
214
253
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10. Two important cutoffs: Fold assignment. 30% sequence identity.
For estimating the scope, not necessarily applied to actual target selection.
It is convenient to divide comparative models into three classes, based on their predicted overall accuracy. Applications depend on accuracy. Applications may or may not succeed. Accuracy of a model needs to be predicted and considered before the model is used. Even low resolution models can be used for some questions. And even the highest resolution models, even x-ray structures, are not accurate enough for some questions (eg, catalysis).
D. Baker & A. Sali.
Science 294, 93, 2001.
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11. 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.
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12. Errors in the context of structural genomics
Currently, errors in fold assignment and alignment are the most frequent sources of errors, resulting into largest errors.
However, if the 30% sequence identity cutoff in PSI is satisfied, fold assignment and alignment errors will eventually be infrequent.
The most relevant problems will then be the modeling of loops, rigid body shifts/distortions, and sidechains.
But, …
The individual errors integrate into overall errors. It is good to be able to assess the overall accuracy of a model. It is not so bad if there are errors in a model, as long as one knows that and takes them into account when the model is used. A useful indicator of the overall structural error is a measure of sequence similarity between the modeled protein sequence and the sequence of the known template structure. The reason is … A simple sequence similarity measure is sequence identity. This plot shows … It was obtained by calculating automatically approx 10,000 models for proteins of known structure and by comparing these models with the actual structures. Describe the lower curve. Describe the upper curve. Mention the criticism of CM that it does not improve the template – this is what it refers to. But it is not a fair criticism because we do not know the correct target-template alignment in the absence of the target structure, and in any case even a model with a worse RMSD than the template can be more useful than the template to learn about the function of a protein, as I will demonstrate later.
R. Sánchez & A. Šali, Proc. Natl. Acad. Sci. USA 95, 13597, 1998.
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13. Benefits of improved comparative modeling for PSI
Reduce the effort, keep accuracy:
Example 1: If we could afford to model on 25% instead of 30% sequence identity, PSI would need many fewer of the expensive X-ray/NMR structures.
Example 2: If average alignment accuracy increases by 10%, ~100,000 more domain sequences will have their folds assigned correctly (John and Sali, Nucl. Acids Res. 31, 3982, 2003).
Keep the effort, increase accuracy:
More accurate models, for the same templates.
FREQUENCY
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14. Target selection for NYSGXRC
Define a set of proteins of biological interest.
Determine which sequences can be modeled (ModBase: >30% sequence identity, >100 residues).
Characterize and prioritize the remaining sequences (secondary structure, membrane, size, domains, Met content, …)
Establish redundancy/clusters of sequences (PsiBlast).
Display the sequences in IceDB for relatively efficient visual inspection and final selection.
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15. Target selections for NYSGXRC
Focus on groups of proteins of biological interest, while
conforming to the general requirements of structural genomics.
S. cerevesiae
Cancer-related
Cholesterol biosynthesis pathway
Enzymes
C. elegans
Extracellular proteins
Anti-bacterial drug targets
H. influenzae
D. radiodurans
Nuclear pore proteins
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16. A bottleneck
An efficient, accurate and automated method for defining families of appropriate “segments” of sequences in SP/TrEMBL.
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17. Scope of structural genomics (target selection)
Cluster PfamA.
Find out how many clusters can be modeled, the remaining ones are targets.
Extrapolate to whole SP/TrEMBL.
Extrapolate to all conceivable protein sequences.
There are ~16,000 30% seq id families that contain 90% of all sequences (Vitkup et al. Nat. Struct. Biol. 8, 559, 2001).
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18. Structure coverage of residues in SP/TrEMBL (1,288,388 sequences, 411,917,387 residues)
Start with unique sequences.
Low-complexity regions were identified using SEG (Wootton & Federhen).
Coiled-coil regions were identified using COILS (Lupas et.al.).
TM segments were identified using PHD (Rost et.al.).
Models (E-value ≤ 10-4 or GA341 ≥ 0.7) were extracted from ModBase (Pieper et.al.).
Segments shorter than 30aa were eliminated after each filter.
In this pie chart: good to have another cut for residues in models >80aa AND >30% seq id.
Number of residues in <30aa segments (but not low-complexity, coiled-coil) in another cut in the red piece.
Indicate transmembrane residues in the remaining part also, as well as total # of residues in TM.
Modeled segments were not split into models only, fold assignments only etc since including models with >30% seqid and >80aa would have led to too many combinations.
The >30% seqid and >80aa cut in the pie chart is not included since it would not be accurate due to the manner in which the filters have been applied.
The fold only, model only cuts were also removed since it does not actually matter in this case.
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19. Simple clustering of sequences within each PfamA family
PfamA (Version 10) contains 733,829 sequences (61% of SP/TrEMBL)
Sequences are sorted by length.
Longest sequence becomes the first representative.
All sequences are compared with the representative and those with sequence identities higher than threshold are included into the cluster and removed from the original stack.
The longest of the remaining sequences becomes the next representative, and so on.
Number of clusters of the PfamA sequences as a function of the threshold
threshold
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20. For the representatives of the “30%” PfamA clusters
Total number of clusters: 71,422
Non-modeled representatives longer than 80 aa: 25,101
Representatives longer than 80aa without models based on at least 30% seq.id: ~35,000
Less than 80 non-modeled aa.
Good Model: GA341 ≥ 0.7
Good Fold: E-value ≤ 10-4
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21. Summary But, there are a number of assumptions/approximations!
Question 2: How many structures will be needed to cover a high fraction of eukaryotic protein families and the combined prokaryotic and eukaryotic protein families?
~4,000 new structures to cover all 6,190 PfamA families at any level of similarity.
25,101 structures to cover all structurally unknown PfamA families longer than 80aa at 30% sequence identity.
~35,000 structures to cover all PfamA families longer than 80aa at 30% sequence identity.
~8,000 structures to cover 80% of sequences longer than 80aa in largest PfamA families at 30% sequence identity.
~13,000 structures to cover 90% of sequences longer than 80aa in largest PfamA families at 30% sequence identity.
~11,000 structures to cover 80% of sequences longer than 80aa in largest families in SP/TR at 30% sequence identity.
But, there are a number of assumptions/approximations!
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22. Question 7: Does sampling of multiple family members from the same or different organisms adequately improve success in obtaining a representative structure?
Attempting to determine structures of multiple members of a family to increase per family success rate is a key to the success of structural genomics. The approach is probably just fine (or at least it is not refuted by the present data):
Single proteins have ~17% chance to crystallize (once in the pipeline).
A family with an average of 2 members has ~35% chance that at least one of them will crystallize (once in the pipeline).
Thus, the probabilities to get crystals for different members of the same family are roughly independent from each other (binomial statistics …).
NYSGXRC data
Andras Fiser, Guiping Xu AECOM, analysis of NYSGXRC data
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23. Question 5: Should we designate a certain percentage of targets to membrane proteins and/or protein complexes?
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24. From domains to assemblies
Sali. NIH Workshop on Structural Proteomics of Biological Complexes. Structure 11, 1043, 2003.
Organized by W. Chiu and S. Almo.
domains
proteins
assemblies
After positive notes, qualify structural genomics. Docking needed: Because of the high-throughput nature of structural genomics (concentrating on domains, rather than whole proteins) and because of the modular nature of protein.
~2.5 domains in a protein
a few domain partners per domain
Sali, Earnest, Glaeser, Baumeister. From words to literature in
structural proteomics. Nature 422, , 2003.
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25. Target selection schemes for structural proteomics
the proteins organized into core biological processes (D. Drubin);
100 complex structures of some importance (M. Gerstein);
select subcomplexes, such as those determined by large-scale affinity purification experiments, (S. Almo);
the same complex from a number of species to facilitate discovery of evolutionary relationships and functional mechanisms (S. Almo);
a comprehensive set of representative binary domain interfaces that may allow us to model higher order complexes with useful accuracy and efficiency (A. Sali).
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26. Comments Biological and medical criteria:
A sensitive function of time and people, in addition to Nature;
Structural GENOMICS implies comprehensive uniform coverage.
True believers in structural biology might argue that determining the structures of uncharacterized proteins is a good way to start characterizing their biology.
Therefore, uniform coverage may be optimal for maximizing biological and medical impact (in the long run) – there may not be a frustration between uniform coverage and medical relevance after all.
Bottom line: A massive investment in structural biology is a good thing, details notwithstanding.
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27. A proposal to all of us: Goal:
To provide the best possible estimate of the scope of structural genomics, by comprehensively considering and contrasting a variety of target selection strategies.
Format:
Write a definitive exhaustive review contributed by all people with some results.
Perhaps associate it with separate in-depth papers by individuals, published in the same journal issue.
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28. Acknowledgments Comparative Modeling Narayanan Eswar Ursula Pieper
Comparative Modeling
Narayanan Eswar
Ursula Pieper
Ben Webb
Marc A. Martí-Renom
Roberto Sánchez (MSSM)
Bino John
András Fiser (AECOM)
Francisco Melo (CU, Chile)
M. S. Madhusudhan
Ash Stuart
Nebojša Mirkovic
Valentin Ilyin (NE)
Eric Feyfant (GI)
Min-Yi Shen
Structural Genomics
Stephen Burley (SGX)
Andras Fiser (AECOM)
Guiping Xu (AECOM)
NY-SGXRC
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
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