1. Construyendo modelos 3D de proteinas ‘fold recognition / threading’

2. Why make a structural model for your protein ? The structure can provide clues to the function through structural similarity with other proteins With a structure it is easier to guess the location of active sites We can apply docking algorithms to the structures (both with other proteins and with small molecules) With a structure we can plan more precise experiments in the lab ‰ÙÚÈÏÈÌ ·ÁÏ·ÂÔ With

3. Protein Modeling Methods Ab initio methods: solution of a protein folding problem search in conformational space Energy-based methods: energy minimization molecular simulation Knowledge-based methods: homology modeling fold recognition / threading

4. Why do we need Ab Initio Methods? data taken from PDB http://www.rcsb.org/pdb/holdings.html New folds and those sequences with very little sequence homology <15%

5. Protein Modeling Methods Ab initio methods: solution of a protein folding problem search in conformational space Energy-based methods: energy minimization molecular simulation Knowledge-based methods: homology modeling fold recogniion

6. Predicting Protein Structure: Threading / Fold Recognition Threading / Fold RecognitionBasis It is estimated there are only around 1000 to 10 000 stable folds in nature* Fold recognition is essentially finding the best fit of a sequence to a set of candidate folds * Select the best sequence-fold alignment using a fitness scoring function*

7. The Threading Problem Find the best way to “mount” the residue sequence of one protein on a known structure taken from another protein

8. Why is it called threading ? threading a specific sequence through all known folds for each fold estimate the probability that the sequence can have that fold

9. Threading: Basic Strategy Sequence Template Spatial Interactions dhgakdflsdfjaslfkjsdlfjsdfjasd Library of folds Query Scoring & selection

10. Protein Threading Conserved Core Segments Protein B J L K I Protein AConserved Core Segments

11. Two structurally similar proteins Spatial adjacencies (interactions) Possible threading with a sequence

12. Input/Output of Protein Threading Pairwise amino acid scoring function Amino acid sequence a[1..n] g(…) Core segments C[1..m] THREADINGTHREADING

13. Fold recognition (Threading) The sequence: + Known protein folds SLVAYGAAM structural model

14. Input: sequence H bond donor H bond acceptor Glycin Hydrophobic Library of folds of known proteins

15. S=20S=5 S=-2 Z=5 Z=1.5 Z= -1 H bond donor H bond acceptor Glycin Hydrophobic

16. 10100N :::::::: 10100167-9987-242 10100-80101-50101 G ext G op Y…DCA Amino acid type Position on sequence

17. Fold recognition/ Threading Disadvantages: threading methods seldom lead to the alignment quality that is needed for homology modeling. less than 30% of the predicted first hits are true remote homologues (PredictProtein).

18. Threading resources TOPITS Heuristic Threader, part of larger structure prediction system 3DPSSM Integrated system, does its own MSA and secondary structure predictions and then threading GenThreader Similar to 3DPSSM

19. In homology modelling, construction of the side chains is done using the template structures when there is high similarity between the built protein and the templates In spite of the huge size of the problem (because each side chain influences its neighbours) there are quite succesful algorithms to this problem. Side chain construction Without such similarity the construction can be done using rotamer libraries A compromise between the probability of the rotamer and its fitness in specific position determines the score. Comparing the scores of all the rotamer for a given amino acid determines the preferred rotamer.

22. Ab initio The sequence SLVAYGAAM structural model

23. Ab initio methods for modelling This field is of great theoretical interest but, so far, of very little practical applications. Here there is no use of sequence alignments and no direct use of known structures The basic idea is to build empirical function that simulates real physical forces and potentials of chemical contacts If we will have perfect function and we will be able to scan all the possible conformations, then we will be able to detect the correct fold

24. Predicting Protein Structure: Ab Initio Methods Ab Initio Methods Sequence Secondary structure Prediction Tertiary structure Low energy structures Predicted structure Energy Minimization Validation Mean field potentials

25. Ab initio Methods Simplified models simplified alphabet (HP) simplified representation (lattice) Build-up techniques Deterministic methods quantum mechanics diffusion equations Stochastic searches Monte Carlo genetic algorithms

26. Rosetta approach Rosetta (David Baker) consistently outstanding performer in last two CASPs Integrated method –I-Sites: much finer grained substructures than secondary structures. A library of all structures each AA 9mer is found in (taken from PDB) –Heuristic global energy function to estimate quality of folds –Monte Carlo search through assignments of I-Sites to minimize energy function. Also, HMMSTR, HMM-driven method for assigning I-Sites.

27. Rosetta prediction method Define global scoring function that estimates probability of a structure given a sequence Generate version of I-sites with fixed length subsequences (9 amino acids) –Calculate P(I-Site|sequence) for all sequences and I- sites Generate structures by Monte Carlo sampling of assignments of fixed size I-sites to subsequences End up with ensemble of plausible structures

28. Rosetta is way ahead CASP 4 results. CASP 5 similar, but not as dramatic.

29. Fully automated predictions CAFASP-2 Meta-servers work best –Integrate predictions from several other servers –Significantly better predictions than any individual approach Several public metaservers available: –http://bioinfo.pl/Meta/ is best all-around