1. Prediction of protein structure

2. aim
Structure prediction tries to build models of 3D structures of proteins that could be useful for understanding structure-function relationships.

3. Genbank/EMBL
Uniprot
PDB

5. The protein folding problem
The information for 3D structures is coded in the protein sequence
Proteins fold in their native structure in seconds
Native structures are both thermodynamically stables and kinetically available

6. ab-initio prediction Prediction from sequence using first principles
AVVTW...GTTWVR

7. Ab-initio prediction
“In theory”, we should be able to build native structures from first principles using sequence information and molecular dynamics simulations: “Ab-initio prediction of structure”
Simulaciones de 1 ms de “folding” de una proteína modelo (Duan-Kollman: Science, 277, 1793, 1998).
Simulaciones de folding reversible de péptidos ( ns) (Daura et al., Angew. Chem., 38, 236, 1999).
Simulaciones distribuidas de folding de Villin (36-residues) (Zagrovic et al., JMB, 323, 927, 2002).

8. ... the bad news ...
It is not possible to span simulations to the “seconds” range
Simulations are limited to small systems and fast folding/unfolding events in known structures
steered dynamics
biased molecular dynamics
Simplified systems

9. typical shortcuts Reduce conformational space
1,2 atoms per residue
fixed lattices
Statistic force-fields obtained from known structures
Average distances between residues
Interactions
Use building blocks: 3-9 residues from PDB structures

10. Results from ab-initio
Average error 5 Å - 10 Å
Function cannot be predicted
Long simulations
Some protein from E.coli predicted at 7.6 Å
(CASP3, H.Scheraga)

11. comparative modelling
The most efficient way to predict protein structure is to compare with known 3D structures

12. Protein folds

13. Basic concept
In a given protein 3D structure is a more conserved characteristic than sequence
Some aminoacids are “equivalent” to each other
Evolutionary pressure allows only aminoacids substitutions that keep 3D structure largely unaltered
Two proteins of “similar” sequences must have the “same” 3D structure

14. Possible scenarios
1. Homology can be recognized using sequence comparison tools or protein family databases (blast, clustal, pfam,...).
Structural and functional predictions are feasible
2. Homology exist but cannot be recognized easily (psi-blast, threading)
Low resolution fold predictions are possible. No functional information.
3. No homology
1D predictions. Sequence motifs. Limited functional prediction. Ab-initio prediction

15. fold prediction

16. 3D struc. prediction

17. 1D prediction Prediction is based on averaging aminoacid properties
AGGCFHIKLAAGIHLLVILVVKLGFSTRDEEASS
Average over a window

19. 1D prediction. Properties
Secondary structure propensitites
Hydrophobicity (transmembrane)
Accesibility
...

20. Propensities Chou-Fasman
Biochemistry 17, a b
turn

21. Some programs (www.expasy.org)
BCM PSSP - Baylor College of Medicine
Prof - Cascaded Multiple Classifiers for Secondary Structure Prediction
GOR I (Garnier et al, 1978) [At PBIL or at SBDS]
GOR II (Gibrat et al, 1987)
GOR IV (Garnier et al, 1996)
HNN - Hierarchical Neural Network method (Guermeur, 1997)
Jpred - A consensus method for protein secondary structure prediction at University of Dundee
nnPredict - University of California at San Francisco (UCSF)
PredictProtein - PHDsec, PHDacc, PHDhtm, PHDtopology, PHDthreader, MaxHom, EvalSec from Columbia University
PSA - BioMolecular Engineering Research Center (BMERC) / Boston
PSIpred - Various protein structure prediction methods at Brunel University
SOPM (Geourjon and Deléage, 1994)
SOPMA (Geourjon and Deléage, 1995)
AGADIR - An algorithm to predict the helical content of peptides

22. 1D Prediction
Original methods: 1 sequence and uniform parameters (25-30%)
Original improvements: Parameters specific from protein classes
Present methods use sequence profiles obtained from multiple alignments and neural networks to extract parameters (70-75%, 98% for transmembrane helix)

24. Methods for remote homology
Homology can be recognized using PSI-Blast
Fold prediction is possible using threading methods
Acurate 3D prediction is not possible: No structure-function relationship can be inferred from models

25. Threading Unknown sequence is “folded” in a number of known structures
Scoring functions evaluate the fitting between sequence and structure according to statistical functions and sequence comparison

26. ATTWV....PRKSCT
SELECTED HIT
10.5
>
5.2

27. ATTWV. PRKSCT. Sequence HHHHH. CCBBBB. Pred. Sec. Struc. eeebb. eeebeb
ATTWV....PRKSCT Sequence HHHHH....CCBBBB Pred. Sec. Struc. eeebb....eeebeb Pred. accesibility
Sequence GGTV....ATTW ATTVL....FFRK
Obs SS BBBB....CCHH HHHB.....CBCB
Obs Acc. EEBE.....BBEB BBEBB....EBBE

28. Threading accurancy

31. Comparative modelling
Good for homology >30%
Accurancy is very high for homology > 60%
Reminder
The model must be USEFUL
Only the “interesting” regions of the protein need to be modelled

32. Expected accurancy
Strongly dependent on the quality of the sequence alignment
Strongly dependent on the identity with “template” structures. Very good structures if identity > 60-70%.
Quality of the model is better in the backbone than side chains
Quality of the model is better in conserved regions

34. Quality test No energy differences between a correct or wrong model
The structure must by “chemically correct” to use it in quantitative predictions

35. Analysis software
PROCHECK
WHATCHECK
Suite Biotech
PROSA

39. Prediction software SwissModel (automatic) SwissModel Repository
SwissModel Repository
3D-JIGSAW (M.Stenberg)
Modeller (A.Sali)
MODBASE (A. Sali)

40. Final test
The model must justify experimental data (i.e. differences between unknown sequence and templates) and be useful to understand function.