1. . Protein Structure Prediction [Based on Structural Bioinformatics, section VII]

2. Predicting protein 3d structure Goal: 3d structure from 1d sequence What kind of fold the given sequence may adopt? Fold recognition Comparative modeling ab-initio An existing fold A new fold

3. Measuring progress CASP – Critical Assessment of Structure Prediction CAFASP – Critical Assessment of Fully Automated Structure Prediction Targets: unpublished NMR or X-ray structures Goal: predict target 3d structure and submit it for independent and comparative review

4. What Forces Hold the Structure? Hydrogen Bonds

5. What Forces Hold the Structure? Charge-charge interactions Positive charged groups prefer to be situated against negatively charged groups Hydrophobic effect

6. What Forces Hold the Structure? u Disulfide bonds l S-S bonds between Cysteine residues

7. Homology modeling Based on the two major observations: 1. The structure of a protein is uniquely defined by its amino acid sequence. 2. Similar sequences adopt practically identical structures, distantly related sequences still fold into similar structures.

8. Growth of the Protein Data Bank

9. Fraction of New Folds

10. [Rost, Protein Eng. 1999] Two zones of sequence alignment

11. The 7 steps to homology modeling 1. Template recognition and initial alignment ― BLAST, FASTA 2. Alignment correction ― Better alignment, MSA

12. The 7 steps to homology modeling 3. Backbone generation ― Copy backbone atoms [and side-chains of conserved residues] 4. Loop modeling ― Knowledge based ― Energy based

13. The 7 steps to homology modeling 5. Side-chain modeling ― Rotamer: a low energy side-chain conformation ― Rotamer library [backbone independent, dependent] ― HUGE search space [~5 N ] High accuracy for residues in the hydrophobic core [90%], much lower for residues in the surface [50%]

14. The 7 steps to homology modeling 6. Model optimization ― Predict the side-chains, then the resulting shifts in the backbone, then the rotamers for the new backbone … 7. Model validation ― Calculating the model’s energy ― Determination of normality indices: ― bond lengths, bond and torsion angles ― Inside/outside distribution of polar residues ― Radial distribution function

15. Predicting protein 3d structure Goal: 3d structure from 1d sequence What kind of fold the given sequence may adopt? Fold recognition Comparative modeling ab-initio An existing fold A new fold

16. Fold recognition Which of the known folds is likely to be similar to the (unknown) fold of a new protein when only its amino-acid sequence is known?

17. Fraction of new folds (PDB new entries in 1998) Koppensteiner et al., 2000, JMB 296:1139-1152.

18. Unrelated proteins adopt similar folds Only 100 folds account for ~50% of all protein superfamilies Possible explanations: 1. Divergent evolution 2. Convergent evolution 3. Limited number of folds 4. Misguided analysis

19. Proteins as seen by a Biologist Does a new protein sequence belong to a given family of proteins (with a specific set of mutation rules)? Fold recognition is based on: Sequence alignment, multiple sequence alignment Profile HMM, PSI-BLAST

20. Proteins as seen by a Physicist “Thermodynamic hypothesis”: The native conformation of a protein corresponds to a global free energy minimum of the system (protein + solvent) Naïve approach: having a correct energy function, search for the native structure in the conformational space

21. Threading Threading: energy based fold recognition Define: 1. Protein model and interaction description 2. Alignment algorithm 3. Energy parameterization 1 2 345 6 7 10 8 9 A C CEC A D A A C E ab E ab A C D E ….. A -3 -1 0 0.. C -1 -4 1 2.. D 0 1 5 6.. E 0 2 6 7.......

22. MAHFPGFGQSLLFGYPVYVFGD... Potential fold... 1)... 56)... n)... -10... -123... 20.5 Find best fold for a protein sequence: Fold recognition (threading)

23. GenTHREADER (Jones, 1999, JMB 287:797-815) For each template provide MSA l align the query sequence with the MSA l assess the alignment by sequence alignment score l assess the alignment by pairwise potentials l assess the alignment by solvation function l record lengths of: alignment, query, template

24. Essentials of GenTHREADER

25. Predicting protein 3d structure Goal: 3d structure from 1d sequence What kind of fold the given sequence may adopt? Fold recognition Comparative modeling ab-initio An existing fold A new fold

26. Ab-initio folding Goal: Predict structure from “first principles” Requires: l A free energy function, sufficiently close to the “true potential” l A method for searching the conformational space Benefits: l Works for novel folds l Shows that we understand the process

27. Ab-initio folding – the challenge 1. Current potential functions have limited accuracy 2. The conformational space is HUGE Possible simplifications: l Reduced representation l Simplified potentials l Coarse search strategies

28. Representation Detailed representation – include all atoms of the protein and the surrounding solvent  computational expansive Implicit solvent models United atom representation Side-chain as centroid or c α Restricted side-chain configurations (rotamers) Restricted backbone torsion angles

29. Rosetta [Simons et al. 1997] “Structural” signatures are reoccurring within protein structures Use these as cues during structure search I-sites Library – a catalog of local sequence-structure correlations Serine hairpinType-I hairpin Frayed helix

30. Fragment insertion Monte Carlo Energy function change backbone angles Convert to 3D accept or reject Choose a fragment fragments backbone torsion angles Rosetta: a folding simulation program evaluate

31. Potential functions Molecular mechanics – models the forces that determines protein conformation Van der Waals: Lennard-Jones 12-6 Electrostatic: Coulomb’s law Scoring functions – empirically derived from solved structures Useful with reduced complexity models Useful in treating aspects of protein thermodynamics

32. Search methods Molecular dynamics – Simulates the motion of a molecule in a given potential Impractical … Coarse sampling of energy landscape: Simulated annealing, genetic algorithms, …