Refinement: A Crucial Step to Approach Accurate Predictions

1. Refinement: A Crucial Step to Approach Accurate Predictions Xin Gao PhD student 2006.11.6

2. Outline • Traditional Protein Structure Prediction • Introduction • Methods Review • Experimental Results • Refinement • Motivation • Methods Review • Proposed Research Plan

3. Outline • Traditional Protein Structure Prediction • Introduction • Methods Review • Experimental Results • Refinement • Motivation • Methods Review • Proposed Research Plan

4. Traditional Protein Structure Prediction — Introduction • WHY do we study protein structure prediction problem? • WHAT determines protein structures? • HOW can we know protein structures?

5. Traditional Protein Structure Prediction — Introduction • WHY? • One of the most significant “grand challenges” in Science. • Key problem in Proteomics, the next step in understanding life processes after the Human Genome Project are successfully completed. • Necessary step in studying protein functions. Improve, or even revolutionize human medicine and health care.

6. Traditional Protein Structure Prediction — Introduction • WHAT? • Inference of Structure from Sequence Observation: Structure of a protein is uniquely determined by its amino acid sequence according to both energy and kinematics. (exceptions exist)

7. Traditional Protein Structure Prediction — Introduction • Inference of Function from Structure Observation: 1) Proteins perform functions through their structures. 2) Proteins in the same fold usually have similar functions. 3) Proteins with novel, not yet observed, folds are rarely discovered recently.

8. Traditional Protein Structure Prediction — Introduction • HOW? • Experimental Methods X-ray Crystallography Nuclear Magnetic Resonance Spectroscopy (NMR) Shortage: Costly and time consuming. • Computational Methods Have been studied for 3 decades. Great process has been made.

9. Outline • Traditional Protein Structure Prediction • Introduction • Methods Review • Experimental Results • Refinement • Motivation • Methods Review • Proposed Research Plan

10. Traditional Protein Structure Prediction — Methods Review • Basic hypothesis • Anfinsen’s (1973) thermodynamic hypothesis: Proteins are not assembled into their native structures by a biological process, but folding is a purely physical process that depends only on the specific amino acid sequence of the protein. • Anfinsen’s hypothesis implies that in principle protein structure can be predicted if a model of the free energy is available, and if the global minimum of this function can be identified.

11. Traditional Protein Structure Prediction — Methods Review • Computational Methods • Ab Initio Methods • Comparative Modeling Methods • Fold-recognition Methods • Consensus-based Methods • Other Methods

12. Traditional Protein Structure Prediction — Methods Review

13. Traditional Protein Structure Prediction — Methods Review • Ab Initio Methods (Template-free Modeling) 1) Basic Idea: According to Anfinsen’s (1973) thermodynamic hypothesis, such methods attempt to identify the structure with the minimum free energy by solely using the first principles: energy and kinematics.

14. Traditional Protein Structure Prediction — Methods Review • Ab Initio Methods (Template-free Modeling) 2) Major Steps: • Choose a first principle based energy function. • Apply an algorithm to generates all possible conformations. • Use a search strategy to search for the conformation that minimizes the energy function.

15. Traditional Protein Structure Prediction — Methods Review • Ab Initio Methods (Template-free Modeling) 3) Advantages: • Do not depend on any template databases. • Can be used when other methods fail. • Can be used as a complementary approach for others, e.g., loop modeling. 4) Limitations: Computationally demanding.

16. Traditional Protein Structure Prediction — Methods Review • Ab Initio Methods (Template-free Modeling) 5) Famous Servers: • Folding@Home (A distributed computing project-people from through out the world download and run software to band together). 6) Current Development: Becoming more and more important to deal with hard targets or hard parts of targets; hybrid servers with other methods are preferred.

17. Traditional Protein Structure Prediction — Methods Review • Comparative Modeling Methods 1) Basic Idea: Aim to predict the structures of a target protein, when a clear evolutionary relationship between the target and a protein of known structure can be easily detected from the sequence. Based on the observation that when two proteins have more than 30% sequence identity, the structures of them are very similar.

18. Traditional Protein Structure Prediction — Methods Review • Comparative Modeling Methods 2) Major Steps: • Choose a template database and a scoring matrix or profile. • Do sequence-sequence alignment on each template in the database, and select the one best aligned. • Refine side chains and regions of low sequence identity.

19. Traditional Protein Structure Prediction — Methods Review • Comparative Modeling Methods 3) Advantages: • If there is indeed a homologous template in the database, the prediction result can be very accurate, usually with rmsd<4A. • Can do prediction very fast. 4) Limitations: • Database dependent. • Can only generate good predictions for easy targets, which have homologous templates in the database.

20. Traditional Protein Structure Prediction — Methods Review • Comparative Modeling Methods 5) Famous Servers: SAM-T02, FFAS03. 6) Current Development: Structure dependent score and gap penalty, and profile-profile alignment techniques are being used to deal with targets with distant homology from templates.

21. Traditional Protein Structure Prediction — Methods Review • Fold Recognition Methods 1) Basic Idea: Aim to predict the structure of a target protein even if no sequence similarity can be detected. Based on the notion that structure is evolutionary more conserved than sequence.

22. Traditional Protein Structure Prediction — Methods Review • Fold Recognition Methods 2) Major Steps: • Choose a reasonable structure database and an energy function. • Do sequence-structure alignment on each template in the database, and select the one best aligned. • Refine side chains and non-aligned regions.

23. Traditional Protein Structure Prediction — Methods Review • Fold Recognition Methods 3) Advantages: • Can detect distant homology. • Can predict protein structures even if they have no sequence similarity or they are evolutionarily unrelated. 4) Limitations: • Database dependent. • The predictions generated are usually medium resolution.

24. Traditional Protein Structure Prediction — Methods Review • Fold Recognition Methods 5) Famous Servers: RAPTOR, SPARK, PROSPECTOR, FUGUE. 6) Current Development: Different profile extracting methods are being tested. Fragment assembly and mini-threading techniques are used to improve the accuracy.

25. Traditional Protein Structure Prediction — Methods Review • Consensus Based Methods 1) Basic Idea: Based on the observation that different servers usually generate good predictions for different targets. Why not combine their strength together?

26. Traditional Protein Structure Prediction — Methods Review • Consensus Based Methods 2) Major Kinds: • Selection-only consensus methods: Try to choose the best predictions from the input prediction set. Can not do better on a target than the best input server. • Hybrid consensus methods: Try to combine different regions extracted from different input predictions to construct a new and hopefully better prediction.

27. Traditional Protein Structure Prediction — Methods Review • Consensus Based Methods 3) Famous Servers: ACE, Pcons, Pmodeller, 3D-SHOTGUN. 4) Current Development: Bad quality predictions are sometimes supported by many servers, and are then selected. New techniques are being used to eliminate the input server correlation to overcome this problem.

28. Traditional Protein Structure Prediction — Methods Review • Other Methods Combine different methods together. Fragment assembly is usually used. Famous servers including ROSETTA, TOUCHSTONE.

29. Outline • Traditional Protein Structure Prediction • Introduction • Methods Review • Experimental Results • Refinement • Motivation • Methods Review • Proposed Research Plan

30. Traditional Protein Structure Prediction — Experimental Results • Critical Assessment of Protein Structure Prediction (CASP) • Began in 1994 (CASP1) • Held every two years • The most objective assessment in the field. • In CASP7 (May-Aug, 2006), 98 automated servers and 204 human expert servers are registered.

32. Traditional Protein Structure Prediction — Experimental Results • Best servers: TASSER, ROSETTA, RAPTOR-ACE, RAPTOR, PModeller, SPARK. • Observation: 1) Consensus servers usually outperform individual servers. 2) There are more and more hybrid servers. 3) Most servers can generate good predictions or at least good regions for many targets. Thus, refinement is urgently needed.

33. Outline • Traditional Protein Structure Prediction • Introduction • Methods Review • Experimental Results • Refinement • Motivation • Methods Review • Proposed Research Plan

34. Refinement — Motivation • What is refinement? • Goal: To make predictions to be more accurate. • No formal definition. • My definition: Given a set of reasonably good predictions, construct a prediction that is more close to the native structure.

35. Refinement — Motivation • Reasonably good: The whole structure is close to native, or there are good regions in the structure that are close to those regions in native, • Close to native: One of the most controversial problems in the field. No measure is considered to be perfect. Here, rmsd or GDT score is better than some thresholds.

36. Refinement — Motivation • Why is refinement possible? Data are taken from SBC evaluation, on 2006.10.30, of 86 targets. http://www.pdc.kth.se/~bjornw/casp7/targets/results/

37. Refinement — Motivation • Quick notes about GDT: 1) Zemla et al, Global Distance Test 2) Defined as the average coverage of the target sequence of the substructures with the four different distance thresholds (1, 2, 4, and 8A). 3) Weakness: Since the GDT score focuses only on the size of the substructures, the detailed match information of models and native structures is partially missed.

38. Refinement — Motivation • Some Instances: For T0198 of CASP6, RAPTOR predicted two good regions, but the orientation of them is wrong, which got a low score. T0198 by RAPTOR T0198 Native

39. Refinement — Motivation Taken from Zhang Yang’s online evaluation server. http://zhang.bioinformatics.ku.edu/TM-score/

40. Outline • Traditional Protein Structure Prediction • Introduction • Methods Review • Experimental Results • Refinement • Motivation • Methods Review • Proposed Research Plan

41. Refinement — Methods Review • Two Major Categories of Methods • Partial Structure Refinement • Whole Structure Refinement • Ab Initio Methods • Template-Based Methods • Consensus-Based Methods

42. Refinement — Methods Review • Partial Structure Refinement • Based on the assumption that backbone structures of core regions are good. Aim to refine other regions. • Loop modeling methods: LOOPY (Honig Lab, ab initio method to generate initial conformations, random tweak method to close conformations) • Side chain packing methods: SCWRL (Dunbrack Lab, graph theory) SCATD (Jinbo Xu, tree decomposition)

43. Refinement — Methods Review • Whole Structure Refinement • Ab Initio Methods: Basic Idea: Assume the structure is roughly good, just need to “shake” a little bit to achieve a conformation with lower energy. Server: RAPTORESS (Xin Gao et al., integer linear programming based backbone refinement)

44. Refinement — Methods Review • Template-Based Methods: Basic Idea: Extract information from a set of particularly chosen templates, and refine the structure according to such information Server: MODELLER (Andrej Sali, try to optimize probability density function for each of the restraint features of the model); SEGMOD (Michael Levitt, a segment match modeling using a database of known protein X-ray structures).

45. Refinement — Methods Review • Consensus-Based Methods: Basic Idea: Suppose we can get an input prediction set, each structure of which contains some close to native regions, try to combine them together and get a hybrid but closer to native structure. Server: TASSER (Zhang Yang, hyperbolic Monte Carlo sampling method to assemble continuous template fragments); POPULUS (Marc Offman et al., “move-set” based genetic algorithm to reshuffle and repack structural components).

46. Refinement — Methods Review • TASSER (Threading/ASSEmbly/Refinement) Steps: 1) Thread the sequence through a representative template library (35% pairwise sequence identity cutoff) by PROSPECTOR. 2) Split target sequence into threading template aligned and unaligned regions, parallel hyperbolic Monte Carlo sampling is exploited to assemble full-length protein models by rearranging the continuous aligned fragments (building blocks) excised from threading templates. During assembly, building blocks are kept rigid and off-lattice to retain their geometric accuracy, unaligned regions are modeled on a cubic lattice by an ab initio procedure. Performance: Ranked number one in CASP7, much better than any other servers, even including consensus servers.

47. Refinement — Methods Review • TASSER (Threading/ASSEmbly/Refinement)

48. Refinement — Methods Review • POPULUS Move set: X = single crossover XX = double crossover C = coil mutation H = helix mutation CCD = Cyclic Coordinate Descent Algorithm

49. Refinement — Methods Review Move set: Protein Mutation

50. Refinement — Methods Review Flowchart: CASP6 submitted models Ratio: 2:1:1:1 Energy based scoring scheme, top 25 D(Ave, Best) < 0.0001, Sum(Cur)=Sum(Previous), D(Si, Sj) < 0.04, N(rounds) > 20 Top 20 structures returned