1. Structure prediction: Homology modeling
Lecture 8
Structural Bioinformatics
Dr. Avraham Samson
81-871

2. Homology Modeling Presentation Fold recognition Model building
Loop building
Sidechain modeling
Refinement
Testing methods: the CASP experiment

3. Homology Modeling Presentation Fold recognition Model building
Loop building
Sidechain modeling
Refinement
Testing methods: the CASP experiment

4. Why do we need homology modeling ?
To be compared with:

5. Structural Genomics project
Aim to solve the structure of all proteins: this is too much work experimentally!
Solve enough structures so that the remaining structures can be inferred from those experimental structures
The number of experimental structures needed depend on our abilities to generate a model.

6. Structural Genomics
Proteins
with
known
structures
Unknown proteins

7. Homology Modeling: why it works
High sequence identity
High structure similarity

8. Homology Modeling: How it works
Find template
Align target sequence
with template
Generate model:
- add loops
- add sidechains
Refine model

9. Homology Modeling Presentation Fold recognition Model building
Loop building
Sidechain modeling
Refinement
Testing methods: the CASP experiment

10. Fold Recognition
Homology modeling refers to the easy case when the template structure can be
identified using BLAST alone.
What to do when BLAST fails to identify a template?
Use more sophisticated sequence methods
Profile-based BLAST: PSIBLAST
Hidden Markov Models (HMM)
Use secondary structure prediction to guide the selection of a template,
or to validate a template
Use threading programs: sequence-structure alignments
Use all of these methods! Meta-servers:

11. Fold Recognition Blast for PDB search Full homology modeling packages
Profile based approach
HMM
Structure-derived profiles
Fold recognition and
Secondary structure prediction

12. Homology Modeling Presentation Fold recognition Model building
Loop building
Sidechain modeling
Refinement
Testing methods: the CASP experiment

13. Very short loops: Analytic Approach
Wedemeyer,
Scheraga
J. Comput. Chem.
20,
(1999)

14. Medium loops: A database approach
Scan database and search protein fragments with correct number of residues
and correct end-to-end distances

15. Medium loops: A database approach
cRMS (Ǻ)
Method breaks
down for loops
larger than 9
Loop length

16. data clustered library Long loops: A fragment-based approach
1) Clustering Protein Fragments to Extract a Small Set of Representatives (a Library)
data
clustered
library

17. Generating Loops
Fragment library

18. Generating Loops
Fragment library

19. Generating Loops
Fragment library

20. Generating Loops
Fragment library

21. Generating Loops
Fragment library
Threading

22. Long loops: A fragment-based approach
Test cases: 20 loops for each loop length
Methods: database search, and fragment building, with fragment libraries of size L
<cRMS (Ǻ)
Loop length

23. Loop building: Other methods
Heuristic sampling (Monte Carlo, simulated annealing)
Inverse kinematics
Relaxation techniques
Systematic sampling

24. Homology Modeling Presentation Fold recognition Model building
Loop building
Sidechain modeling
Refinement
Testing methods: the CASP experiment

25. Self-Consistent Mean-Field Sampling
P(J,2)
P(J,1)
P(J,3)

26. Self-Consistent Mean-Field Sampling
P(i,2)
P(i,1)+P(i,2)+P(i,3)=1
P(i,1)
P(i,3)

27. Self-Consistent Mean-Field Sampling
Multicopy Protein
i,1
i,2
i,3
j,1
j,3
j,2

28. Self-Consistent Mean-Field Sampling
Multicopy Protein
Mean-Field Energy
E(i,k) = U(i,k) + U(i,k,Backbone)
i,1
i,2
i,3
j,1
j,3
j,2

29. Self-Consistent Mean-Field Sampling
Multicopy Protein
Mean-Field Energy
E(i,k) = U(i,k) + U(i,k,Backbone)
i,1
i,2
i,3
j,1
j,3
j,2
Update Cycle
(Koehl and Delarue, J. Mol. Biol., 239: (1994))

30. Self-Consistent Mean-Field Sampling
Another way is simply aligning the side chains as well

31. Dead End Elimination (DEE) Theorem
There is a global minimum energy conformation (GMEC) for which there is a unique
rotamer for each residue
The energy of the system must be pairwise.
Each residue i has a set of possible rotamers.
The notation ir means residue i has the conformation described by rotamer r.
The energy of any conformation C of the protein is given by:
Note that:

32. Dead End Elimination (DEE) Theorem
Consider two rotamers, ir and it, at residue i and the set of all other
rotamer conformations {S} at all residues excluding i.
If the pairwise energy between ir and js is higher than the pairwise energy
between it and js, for all js in {S}, then ir cannot exist in the GMEC and
is eliminated. Mathematically: If
then
ir does not belong to the GMEC

33. Dead End Elimination (DEE) Theorem
This is impractical as it requires {S}. It can be simplified to: If
then
ir does not belong to the GMEC
Iteratively eliminate high energy rotamers: proved to converge to GMEC
Desmet, J, De Maeyer, M, Hazes, B, Lasters, I. Nature, 356: (1992)

34. Other methods for side-chain modeling
Heuristics (Monte Carlo, Simulated Annealing)
SCWRL (Dunbrack)
Pruning techniques
Mean field methods

35. Loop building + Sidechain Modeling: generalized SCMF
Template
Add multi-copies
of candidate “loops”
Add multi-copies
of candidate side-chains
Final model
Koehl and Delarue.
Nature Struct. Bio.
2, (1995)

36. Homology Modeling Presentation Fold recognition Model building
Loop building
Sidechain modeling
Refinement
Testing methods: the CASP experiment

37. Refinement ? CASP6 assessors, homology modeling category:
“We are forced to draw the disappointing conclusion that, similarly
to what observed in previous editions of the experiment, no model
resulted to be closer to the target structure than the template to
any significant extent.”
The consensus is not to refine the model, as refinement usually pulls the
model away from the native structure!!

38. Homology Modeling: Practical guide
Approach 2: Submit target sequence to automatic servers
- Fully automatic:
- 3D-Jigsaw :
- EsyPred3D:
- SwissModel:
- Fold recognition:
- 3D-PSSM:
- Useful sites:
- Meta server:
- PredictProtein:

39. Comparative Modeling Server & Program
COMPOSER
MODELER
InsightII
SYBYL