1. Thomas Blicher Center for Biological Sequence Analysis
Homology Modelling
Thomas Blicher
Center for Biological Sequence Analysis

2. Why Do We Need Homology Modelling?
Ab Initio protein folding (random sampling):
100 aa, 3 conf./residue gives approximately 1048 different overall conformations!
Random sampling is NOT feasible, even if conformations can be sampled at picosecond (10-12 sec) rates.
Levinthal’s paradox
Do homology modelling instead.

3. How Is It Possible?
The structure of a protein is uniquely determined by its amino acid sequence (but sequence is sometimes not enough):
prions
pH, ions, cofactors, chaperones
Structure is conserved much longer than sequence in evolution.
Structure > Function > Sequence

4. How Often Can We Do It?
There are currently ~30000 structures in the PDB (but only ~4000 if you include only ones that are not more than 30% identical and have a resolution better than 3.0 Å).
An estimated 25% of all sequences can be modeled and structural information can be obtained for ~50%.

5. Worldwide Structural Genomics
”Fold space coverage”
Complete genomes
Signaling proteins
Improving technology
Disease-causing organisms
Model organisms
Membrane proteins
Protein-ligand interactions

6. Structural Genomics in North America
10 year $600 million project initiated in 2000, funded largely by NIH.
AIM: structural information on unique proteins (now ), so far 1000 have been determined.
Improve current techniques to reduce time (from months to days) and cost (from $ to $20.000/structure).
9 research centers currently funded (2005), targets are from model and disease-causing organisms (a separate project on TB proteins).

7. Homology Modeling for Structural Genomics
Roberto Sánchez et al. Nature Structural Biology 7, 986 - 990 (2000)

8. How Well Can We Do It?
Sali, A. & Kuriyan, J. Trends Biochem. Sci. 22, M20–M24 (1999) 

9. How Is It Done? Identify template(s) – initial alignment
Improve alignment
Backbone generation
Loop modelling
Side chains
Refinement
Validation 

10. Template identification
Search with sequence
Blast
Psi-Blast
Fold recognition methods
Use biological information
Functional annotation in databases
Active site/motifs

11. Alignment

12.
PHE ASP ILE CYS ARG LEU PRO GLY SER ALA GLU ALA VAL CYS
PHE ASN VAL CYS ARG THR PRO GLU ALA ILE CYS
PHE ASN VAL CYS ARG THR PRO GLU ALA ILE CYS F D I C R L P G S A E V 6 -2 -3 2 -1 N 1 5 T 8

13.
PHE ASP ILE CYS ARG LEU PRO GLY SER ALA GLU ALA VAL CYS
PHE ASN VAL CYS ARG THR PRO GLU ALA ILE CYS
PHE ASN VAL CYS ARG THR PRO GLU ALA ILE CYS F D I C R L P G S A E V 6 -2 -3 2 -1 N 1 5 8 T

14. Improving the Alignment
PHE ASP ILE CYS ARG LEU PRO GLY SER ALA GLU ALA VAL CYS
PHE ASN VAL CYS ARG THR PRO GLU ALA ILE CYS
PHE ASN VAL CYS ARG THR PRO GLU ALA ILE CYS
From ”Professional Gambling” by Gert Vriend

15. Template Quality Selecting the best template is crucial!
The best template may not be the one with the highest % id (best p-value…)
Template 1: 93% id, 3.5 Å resolution 
Template 2: 90% id, 1.5 Å resolution 

16. The Importance of Resolution
high
low
4 Å
3 Å
2 Å
1 Å

17. Ramachandran Plot Allowed backbone torsion angles in proteins
Amino acid residue

18. Template Quality – Ramachandran Plot
NMR structure – low quality data…
X-ray structure – good data.

19. Backbone Generation
Generate the backbone coordinates from the template for the aligned regions.
Several programs can do this, most of the groups at CASP6 use Modeller:

20. Loop Modelling Knowledge based: Energy based: Combination
Searches PDB for fragments that match the sequence to be modelled (Levitt, Holm, Baker etc.).
Energy based:
Uses an energy function to evaluate the quality of the loop and minimizes this function by Monte Carlo (sampling) or molecular dynamics (MD) techniques.
Combination

21. Loops – the Rosetta Method
Find fragments (10 per amino acid) with the same sequence and secondary structure profile as the query sequence.
Combine them using a Monte Carlo scheme to build the loop.
David Baker et al.

22. Side Chains
Side chain rotamers are dependent on backbone conformation.
Most successful method in CASP6 was SCWRL by Dunbrack et al.:
Graph-theory knowledge based method to solve the combinatorial problem of side chain modelling.

23. Side Chains
Prediction accuracy is high for buried residues, but much lower for surface residues
Experimental reasons: side chains at the surface are more flexible.
Theoretical reasons: much easier to handle hydrophobic packing in the core than the electrostatic interactions, including H-bonds to waters.

24. Side Chains
If the seq. id is high, the networks of side chain contacts may be conserved, and keeping the side chain rotamers from the template may be better than predicting new ones.

25. Refinement Energy minimization Molecular dynamics
Big errors like atom clashes can be removed, but force fields are not perfect and small errors will also be introduced – keep minimization to a minimum or matters will only get worse.

26. Error recovery
If errors are introduced in the model, they normally can NOT be recovered at a later step
The alignment can not make up for a bad choice of template.
Loop modeling can not make up for a poor alignment.
If errors are discovered, the step where they were introduced should be redone.

27. Validation Most programs will get the bond lengths and angles right.
The Ramachandran plot of the model usually looks pretty much like the Ramachandran plot of the template (so select a high quality template).
Inside/outside distributions of polar and apolar residues can be useful.

28. Validation – ProQ Server
ProQ is a neural network based predictor that based on a number of structural features predicts the quality of a protein model.
ProQ is optimized to find correct models in contrast to other methods which are optimized to find native structures.
Arne Elofssons group:

29. Structure Validation ProCheck WhatIf server
WhatIf server

30. Homology Modelling Servers
Eva-CM performs continous and automated analysis of comparative protein structure modeling servers
A current list of the best performing servers can be found at:

31. CASP6 Results

32. The Top 4 Homology Modeling Groups in CASP6

33. Prediction Method Performance

34. The Hardest Target i CASP6
Only 8% sequence id between target and template.
Dunbrack, Wang & Jin (2004) CASP6 Fold Recognition Assessment

35. Summary Successful homology modelling depends on the following:
Template quality
Alignment (add biological information)
Modelling program/procedure (use more than one)
Always validate your final model!