1. Protein structure
(Part 2 of 2)

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are from Bioinformatics and Functional Genomics
by Jonathan Pevsner (ISBN ).
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3. Many databases explore protein structures
SCOP
CATH
Dali Domain Dictionary
FSSP
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4. Structural Classification of Proteins (SCOP)
SCOP describes protein structures using a
hierarchical classification scheme:
Classes
Folds
Superfamilies (likely evolutionary relationship)
Families
Domains
Individual PDB entries
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5. Page 297

6. SCOP statistics (October, 2002)
Class # folds # superfamilies # families
All a
All b
a/b
a+b …
Total
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7. Class, Architecture, Topology, and
Homologous Superfamily (CATH) database
CATH clusters proteins at four levels:
C Class (a, b, a&b folds)
A Architecture (shape of domain, e.g. jelly roll)
T Topology (fold families; not necessarily homologous)
H Homologous superfamily
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8. Fig. 9.23
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9. Fig. 9.24
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10. Fig. 9.24
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11. Fig. 9.25
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12. Fig. 9.25
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13. Page 301

14. Fig. 9.27
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15. Fig. 9.28
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16. Dali Domain Dictionary
Dali contains a numerical taxonomy of all known
structures in PDB. Dali integrates additional data
for entries within a domain class, such as
secondary structure predictions and solvent
accessibility.
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17. Fig. 9.29
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18. Fig. 9.30
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19. Fig. 9.30
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20. Fig. 9.30
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21. Fold classification based on structure-structure
alignment of proteins (FSSP)
FSSP is based on a comprehensive comparison of
PDB proteins (greater than 30 amino acids in length).
Representative sets exclude sequence homologs
sharing > 25% amino acid identity.
The output includes a “fold tree.”
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22. Fig. 9.31
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23. FSSP: fold tree
Fig. 9.32
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24. Fig. 9.33
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25. Fig. 9.34
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26. Approaches to predicting protein structures
There are about >20,000 structures in PDB, and
about 1 million protein sequences in SwissProt/
TrEMBL. For most proteins, structural models
derive from computational biology approaches,
rather than experimental methods.
The most reliable method of modeling and evaluating
new structures is by comparison to previously
known structures. This is comparative modeling.
An alternative is ab initio modeling.
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27. Approaches to predicting protein structures
obtain sequence (target)
fold assignment
comparative
modeling
ab initio
modeling
build, assess model
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28. Comparative modeling of protein structures
[1] Perform fold assignment (e.g. BLAST, CATH, SCOP);
identify structurally conserved regions
[2] Align the target (unknown protein) with the template.
This is performed for >30% amino acid identity
over a sufficient length
[3] Build a model
[4] Evaluate the model
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29. Errors in comparative modeling
Errors may occur for many reasons
[1] Errors in side-chain packing
[2] Distortions within correctly aligned regions
[3] Errors in regions of target that do not match template
[4] errors in sequence alignment
[5] use of incorrect templates
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30. Comparative modeling
In general, accuracy of structure prediction depends
on the percent amino acid identity shared between
target and template.
For >50% identity, RMSD is often only 1 Å.
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31. Baker and Sali (2000)
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32. Comparative modeling
Many web servers offer comparative modeling services.
Examples are
SWISS-MODEL (ExPASy)
Predict Protein server (Columbia)
WHAT IF (CMBI, Netherlands)
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33. Ab initio protein structure prediction
Ab initio prediction can be performed when a protein
has no detectable homologs.
Protein folding is modeled based on global free-energy
minimum estimates.
The “Rosetta Stone” methods was applied to sequence
families lacking known structures. For 80 of 131
proteins, one of the top five ranked models successfully
predicted the structure within 6.0 Å RMSD (Bonneau
et al., 2002).
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34. Protein structure and human disease
In some cases, a single amino acid substitution
can induce a dramatic change in protein structure.
For example, the DF508 mutation of CFTR alters
the a helical content of the protein, and disrupts
intracellular trafficking.
Other changes are subtle. The E6V mutation in the
gene encoding hemoglobin beta causes sickle-
cell anemia. The substitution introduces a
hydrophobic patch on the protein surface,
leading to clumping of hemoglobin molecules.
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35. Protein structure and human disease
Disease Protein
Cystic fibrosis CFTR
Sickle-cell anemia hemoglobin beta
“mad cow” disease prion protein
Alzheimer disease amyloid precursor protein
Table 9.5
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