PhDcoursehomology..

1. Homology Modeling

Anne Mølgaard, CBS, BioCentrum, DTU

2. Why can we do it?

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

3. 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 estimate says that ~50% of all sequences have a structurally characterized homolog

4. Worldwide Structural Genomics

”Fold space coverage” Complete genomes Signaling proteins Improving technology Disease-causing organisms Model organisms Membrane proteins Protein-ligand interactions

5. Structural Genomics in North America

10 year $600 million project initiated in 2000, funded largely by NIH AIM: structural information on 10000 unique proteins (now 4-6000), so far 1000 have been determined Improve current techniques to reduce time (from months to days) and cost (from $100.000 to $20.000/structure) 9 research centers currently funded (2005), targets are from model and disease-causing organisms (a separate project on TB proteins)

6. Homology modeling for structural genomics

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

7. How well can we do it?

9. How can we do it?

Identify template(s) – initial alignment Improve alignment Backbone generation Loop modeling Side chains Refinement Validation

11. 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 ?

13. Template quality – Ramachandran plot

14. Improving the alignment

1 2 3 4 5 6 7 8 9 10 11 12 13 14 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 http://www.cmbi.kun.nl/gv/articles/text/gambling.html

15. 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: http://salilab.org/modeller/modeller.html

16. Loop modeling

Knowledge based: searches PDB for fragments that match the sequence to be modeled (Levitt, Holm, Baker etc.) Energy based: uses an energy function to evaluate the quality of the loop and minimizes this function by Monte Carlo or MD techniques Combination

17. 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 Baker et al

18. Side chains

Side chain rotamers are dependent on backbone conformation Most successful method in CASP6 was SCWRL by Dunbrack et al: uses a graph-theory knowledge based method to solve the combinatorial problem of side chain modeling http://dunbrack.fccc.edu/SCWRL3.php

19. 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

20. 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

21. 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

22. Error recovery

If errors are introduced in the model, they can normally 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 when they were introduced should be redone

23. 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

24. 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: http://www.sbc.su.se/~bjorn/ProQ/

25. Structure validation

ProCheck http://www.biochem.ucl.ac.uk/~roman/procheck/procheck.html WhatIf server http://swift.cmbi.kun.nl/WIWWWI//

26. Homology modeling 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: http://cubic.bioc.columbia.edu/eva/doc/intro_cm.html

27. CASP6 results

28. The top 4 homology modeling groups in CASP6