1. JM - http://folding.chmcc.org 1 Knowledge-based protocols for protein structure prediction: from protein threading to solvent accessibility prediction and back to protein structure prediction by threading Jarek Meller Jarek Meller Division of Biomedical Informatics, Children’s Hospital Research Foundation & Department of Biomedical Engineering, UC

2. JM - http://folding.chmcc.org2 Outline of the talk Protein structure and complexity of conformational search: from de novo structure prediction to similarity based methods Protein structure prediction by sequence-to-structure matching (threading and fold recognition) Secondary structure and solvent accessibility prediction Improving fold recognition and de novo simulations with accurate solvent accessibility prediction A story from our backyard: predicting interaction between pVHL and RNA Pol II

3. JM - http://folding.chmcc.org3 Polypeptide chains: backbone and side-chains C-ter N-ter

4. JM - http://folding.chmcc.org4 Distinct chemical nature of amino acid side-chains ARG PHE GLU VAL CYS C-ter N-ter

5. JM - http://folding.chmcc.org5 Hydrogen bonds and secondary structures  helix  strand

6. JM - http://folding.chmcc.org6 Tertiary structure and long range contacts: annexin

7. JM - http://folding.chmcc.org7 Domains, interactions, complexes: VHL

8. JM - http://folding.chmcc.org8 Multiple alignment and PSSM

9. JM - http://folding.chmcc.org9 Protein folding problem The protein folding problem consists of predicting three-dimensional structure of a protein from its amino acid sequence Hierarchical organization of protein structures helps to break the problem into secondary structure, tertiary structure and protein-protein interaction predictions Computational approaches for protein structure prediction: similarity based and de novo methods

10. JM - http://folding.chmcc.org10 Ab initio (or de novo) folding simulations Ab initio folding simulations consist of conformational search with an empirical scoring function (“force field”) to be maximized (minimized) Computational bottleneck: exponential search space and sampling problem (global optimization!) Fundamental problem: inaccuracy of empirical force fields and scoring functions (folding potentials) Importance of mixed protocols, such as Rosetta by D. Baker and colleagues (Monte Carlo fragment assembly)

11. JM - http://folding.chmcc.org11 Similarity based approaches to structure prediction: from sequence alignment to fold recognition High level of redundancy in biology: sequence similarity is often sufficient to use the “guilt by association” rule: if similar sequence then similar structure and function Multiple alignments and family profiles can detect evolutionary relatedness with much lower sequence similarity, hard to detect with pairwise sequence alignments: Psi-BLAST by S. Altschul et. al. Many structures are already known (see PDB) and one can match sequences directly with structures to enhance structure recognition: fold recognition (not for new folds!) For both, fold recognition and de novo simulation, prediction of intermediate attributes such secondary structure or solvent accessibility helps to achieve better sensitivity and specificity

12. JM - http://folding.chmcc.org12 Why “fold recognition”? Divergent (common ancestor) vs. convergent (no ancestor) evolution PDB: virtually all proteins with 30% seq. identity have similar structures, however most of the similar structures share only up to 10% of seq. identity !

13. JM - http://folding.chmcc.org13 Going beyond sequence similarity: threading and fold recognition When sequence similarity is not detectable use a library of known structures to match your query with target structures. One needs a scoring (“energy”) function that measures compatibility between sequences and structures.

14. JM - http://folding.chmcc.org14 Scoring alternative conformations with empirical (knowledge-based) folding potentials misfolded native E Ideally, each misfolded structure should have an energy higher than the native energy, i.e. : E misfolded - E native > 0

15. JM - http://folding.chmcc.org15 Simple contact model for protein structure prediction Each amino acid is represented by a point in 3D space and two amino acids are said to be in contact if their distance is smaller than a cutoff distance, e.g. 7 [Ang].

18. JM - http://folding.chmcc.org18 Sequence-to-structure matching with contact models Generalized string matching problem: aligning a string of amino acids against a string of “structural sites” characterized by other residues in contact Finding an optimal alignment with gaps using inter- residue pairwise models: E =  k< l  k l, is NP-hard because of the non-local character of scores at a given structural site (identity of the interaction partners may change depending on location of gaps in the alignment) R.H. Lathrop, Protein Eng. 7 (1994)

19. JM - http://folding.chmcc.org19 Hydrophobic contact model and sequence-to-structure alignment HPHPP - Solutions to this yet another instance of the global optimization problem: a)Heuristic (e.g. frozen environment approximation) b)“Profile” or local scoring functions (folding potentials)

20. JM - http://folding.chmcc.org20 Implementing threading protocols: LOOPP LOOPP in CAFASP4 About average for all fold recognition targets (missing some easy targets, recognized by PsiBlast) Third best server in the category of difficult targets Best predictions among the servers for 3 difficult targets Further improvements necessary to make the predictions more robust Joint work with Ron Elber

21. JM - http://folding.chmcc.org21 Using sequence similarity, predicted secondary structures and contact potentials: fold recognition protocols In practice fold recognition methods are often mixtures of sequence matching and threading, with compatibility between a sequence and a structure measured by: i)sequence alignment ii)contact potentials iii)predicted secondary structures (compared to the secondary structure of a template)

22. JM - http://folding.chmcc.org22 Predicting 1D protein profiles from sequences: secondary structures and solvent accessibility SABLE server http://sable.cchmc.org POLYVIEW server http://polyview.cchmc.org a) Multiple alignment and family profiles improve prediction of local structural propensities b) Use of advanced machine learning techniques, such as Neural Networks or Support Vector Machines improves results as well B. Rost and C. Sander were first to achieve more than 70% accuracy in three state (H, E, C) classification, applying a) and b).

23. JM - http://folding.chmcc.org23 Predicting 1D protein profiles from sequences: secondary structures and solvent accessibility PDB Sable PsiPred Prof Relative solvent accessibility prediction is typically cast as a classification problem

24. JM - http://folding.chmcc.org24 Variability in surface exposure for structurally equivalent residues does not support classification

25. JM - http://folding.chmcc.org25 Neural Network-based regression for relative solvent accessibility (RSA) prediction

26. JM - http://folding.chmcc.org26 Accuracy of predictions depends on the level of surface exposure: error measures and fine tuning

27. JM - http://folding.chmcc.org27 Overall accuracy of different regression models S163 cc / MAE / RMSE S156 cc / MAE / RMSE S135 cc / MAE / RMSE S149 cc / MAE / RMSE SABLE-a0.65 / 15.6 / 20.80.64 / 15.9 / 21.00.66 / 15.3 / 20.50.64 / 16.0 / 21.0 SABLE-wa0.66 / 15.5 / 21.20.64 / 15.7 / 21.30.67 / 15.3 / 20.90.65 / 15.8 / 21.4 LS0.63 / 16.3 / 21.00.62 / 16.5 / 21.10.65 / 15.9 / 20.50.62 / 16.5 / 21.2 SVR10.62 / 15.9 / 21.30.61 / 16.1 / 21.40.64 / 15.6 / 20.80.62 / 16.2 / 21.5 SVR20.62 / 16.6 / 22.80.61 / 16.7 / 22.70.64 / 16.4 / 22.50.61 / 16.9 / 23.0 Non-linear models: Rafal Adamczak; Linear models: Michael Wagner; Datasets and servers: Aleksey Porollo and Rafal Adamczak

28. JM - http://folding.chmcc.org28 Regression vs. two-class classification MethodS163S156S135S149 ACCpro server 25%70.4% / 0.4169.8% / 0.4170.6% / 0.4271.1% / 0.43 SABLE-wa BS6271.7% / 0.4371.1% / 0.4272.2% / 0.44 SABLE-wa binary71.4% / 0.4270.9% / 0.4171.9% / 0.4372.1% / 0.44 SABLE-2c 25%76.7% / 0.5375.8% / 0.5277.1% / 0.5476.4% / 0.53 SABLE-wa77.3% / 0.5476.5% / 0.5277.3% / 0.5476.6% / 0.53

29. JM - http://folding.chmcc.org29 Predicting transmembrane domains

30. JM - http://folding.chmcc.org30 Predicting transmembrane domains

31. JM - http://folding.chmcc.org31 Now back to threading and folding simulations Applications in filtering out incorrect models in both de novo simulations and fold recognition Domain structure prediction, protein-protein interactions Better sensitivity in finding correct matches in threading: one story as an example

32. JM - http://folding.chmcc.org32 Modeling the RNA Polymerase II Interaction with the von Hippel-Lindau Protein Modeling the RNA Polymerase II Interaction with the von Hippel-Lindau Protein: from experimental clues to structure prediction and back to experiment. Jarek Meller Children’s Hospital Research Foundation Joint work with M. Czyzyk-Krzeska and her group, College of Medicine, University of Cincinnati

33. JM - http://folding.chmcc.org33 A play of life (script and beyond): Stage: protein society or proteosome Rules of life: proteins are assembled and degraded: nursery (ribosome) vs. police and gillotine (ubiquitination and proteasome) Social order: one look at the equilibrium in the system: Holy scriptures (DNA) Army of scribers (middle class proteins) Temple priests (selected proteins) Transcription Translation “I think we need to adjust the interpretation of the script … “ (regulation of replication and transcription) Law and oppression

34. JM - http://folding.chmcc.org34 Hypoxia-induced stabilization of Hif-1a Graphics from R.K. Bruick and S.L.McKnight, Science 295

35. JM - http://folding.chmcc.org35 Experimental clues:  Observation: correlation between pVHL levels and transcript elongation of the tyrosine hydroxylase gene (M. Czyzyk-Krzeska)  Could pVHL influence the transcription by interaction with elongation complex co-factors ?  Where to start? Experiment without a model is usually not a very good idea. Could in silico study and bioinformatics help?

36. 36 Searching for pVHL interaction targets:  Hif-1a ODD interacts with pVHL – other pVHL targets should have domains structurally resembling that of Hif1-a ODD  Use the Hif-1a ODD sequence as a query in order to find other structures that are compatible with it Rpb1 Rpb6 Hif-1a ODD Pro-OH pVHL

37. JM - http://folding.chmcc.org37 RNA Polymerase II in the act of transcription, RNA Polymerase II in the act of transcription, Gnatt, Kornberg et. al., Science 292 (2001)

38. JM - http://folding.chmcc.org38 C-ter Rpb1 Rpb6 The C-terminal of Rpb1 and Rpb6 form a pocket on the surface of RNA Polymerase II complex. The C-terminal of Rpb1 and Rpb6 form a pocket on the surface of RNA Polymerase II complex. C-ter of Rpb1 and Rpb6 represented by cartoons.

39. JM - http://folding.chmcc.org39 Could the Hif ODD fragment resemble C-terminal fragment of RNA Polymerase II ?  A motif similar to that of ODD found, but that could occur by chance. We used sequence alignments and threading to measure similarity between these fragments.  Sequences about 25% identical for a short fragment of about 50 aa – not significant.  Predicted secondary structures similar.  Suggestive but still not significant similarity.  However, a weak match between the adjacent Rpb6 and the consecutive part of the Hif-1a sequence was observed in threading (3D-PSSM, Loopp).  Prediction: the ODD shares 3D structure with C-ter fragment of Rpb1 and Rpb6.  Implication: VHL is likely to interact with Rpb1/Rpb6!

40. JM - http://folding.chmcc.org40 Experimental results (MCK): RNA Pol II peptides suggested by computational analysis do bind to pVHL and this binding is controlled by hydroxylation of the critical PRO residue. Co-immunoprecipitations of hyper- phosphorylated RNA Pol II and pVHL observed: interaction confirmed. Ubiquitination of Rpb1 confirmed. Biological meaning?