Consensus Fold Recognition Methods

1. Consensus Fold Recognition Methods Dongbo Bu School of Computer Science University of Waterloo Joint work with S.C. Li, X. Gao, L. Yu, J. Xu, M. Li Nov. 2006

2. Outline • Background • Consensus Prediction Methods • ACE7: consensus method by identifying latent servers • Experimental Results • Future Work

3. Background

4. From sequence to structure • The Rate Gap • gene prediction is fast, • but experimental structure determination is slow • The First Principle • Sequence almost determine structure • CASP Competition • A fair and objective examination motivation Computational Methods possibility benchmark

5. Homologous Modeling --- sequence-sequence alignment

6. Threading---sequence-structure alignment

7. Ab initio--- database independent

8. Why Consensus? • Observation: • no single server can reliably predict the best models for all the targets. • a particular structure prediction server may perform well on some targets, but badly on others. • A natural idea to solve this issue: • to combine the strengths of different prediction methods to obtain better structural models.

9. What is Consensus Method?

10. Formal Description • Notations: • Target: the query protein sequence • Server: implementation of a prediction method • Model: a predicted structure

11. Classical Consensus Methods

12. Research History • Early exploration of consensus idea: • Consensus many methods in one server. • INBGU (SHGU) D. Fischer 2000 • 3D-PSSM (Phyre) L. Kelly 2000 • The first consensus server • CAFASP-CONSENS: D. Fischer 2001 • Successors: • Pcons/Pmodeller J. Lundstrom, A. Elofsson 2001 • 3D-Jury K. Ginalski, A. Elofsson 2003 • 3D-Shotgun D. Fischer 2003 • ACE L. Yu, J. Xu, M. Li 2004

13. Three-step Process • Step1: Model Comparison • determine model similarities • Step2: Feature Extraction • formal description of a model • Step3: Model Selection • select a model, or part of it. • Many machine learning techniques were introduced in the 3rd step.

14. 3D-Shotgun: Majority Voting • Basic Idea: • Reminiscent of “cooperative algorithms” • Five Input Servers: • GONP, GONPM, PRFSEQ, SEQPPRF, SEQPMPRF • Step 1. Model Comparison • For each initial model, to find models with LOCAL similarity.

15. 3D-Shotgun (cont) • Step 2. Feature Extraction • For each model M, superimpose similar models upon M, • Using the shared similarity to compute transformation • Build a multiple structure alignment A(M) as a result, • Feature: • the number of models share structural element with A(M).

16. 3D-Shotgun (cont) • Step 3. Selection • Majority Voting • Choose the structural element with the highest count. • The underlying rationale: • The recurring structural elements are most likely to be correct.

17. Confidence Assignment • For each assembled model M’, a confidence score S’ is given as follows: • Here, • k,l run over all the input models • S_{k,l} is the confidence score given by the individual server • Sim() adopts MaxSub.

18. Performance of 3D-Shotgun

19. CAFASP-Consensus and Pcons: Neutral Network • Step 1. Model Comparison • CAFASP-Consensus: check SCOP id, or run MaxSub • Pcons: LGScore2 to detect similarity • Step 2: Feature Extraction • CAFASP-Consensus: number of similar models • Pcons: ratio of the similar models weighted f1 ratio of the similar 1st model

20. CAFASP-Consensus and Pcons: (cont) • Step 3. Model Selection • Formulated into a machine learning problem • Attribute: • Log(LGScore2), significantly better than LGScore2.

21. Pmodeller = Pcons + ProQ • ProQ: • a neutral network package to measure the quality of a structure • Pmodeller has an advantage over Pcons because a number of high-score but false-positive models are eliminated.

22. Performance of Pcons/Pmod

23. ACE: SVM Regression • Step 1. Model Comparison • MaxSub • Step 2. Feature Extraction • f1: the normalized similarity with all the other models • f2: the normalized similarity with the most similar one • f3: for each target, to measure the divergence of server predictions.

24. ACE (cont) • Step 3: Selection • SVM Regression: to predict the model quality • Attribute: • MaxSub with the native structure

25. Performance of ACE • In CASP6, ACE was ranked 2nd among 87 automatic servers. • On LiveBench test set:

26. Other techniques • 3D-Jury: • Rationale: the average of lower energy conformation is similar to the native structure. • Basic Idea: Mimic the average step by the following scoring function:

27. Other techniques (cont) • Robetta: • For each fragment, choose a local structure from a set, and assemble them to minimize an energy funtion. • BPROMPT: • Bayesian Belief Network • JPred: • Decision Tree

28. CASP7 Performance

29. ACE7: A Consensus Method by Identifying Latent Servers

30. Motivation • Server Correlation: • Although consensus servers assume that each individual server is independent of others, it is observed from CASP6 results that correlation exists between different servers to some degree. • Negative Effect: • this kind of correlation sometimes makes a native-like model receive less support than the incorrect models.

31. Examination of ACE on CASP6 Dataset • Observation: • If a native-like model receives support from only 1or 2 server, it is difficult to select it.

32. Source of Server Correlation • Server Correlation: • some servers tend to generate similar results, • Reason: • Roughly speaking, the correlations arose from the fact that these servers adopt similar techniques, including sequence alignment tools, secondary structure prediction methods, and scoring functions,etc. • Latent Servers: • Here, we use independent latent servers to represent the common features shared by these implicit servers.

33. ACE7: to reduce the server correlation • Step 1. Adopting Maximum Likelihood to estimate the server correlation. • Step 2. Employing Principle Component Analysis technique to derive the latent servers. • Step 3. Using an ILP model to weigh the latent servers.

34. Two Assumptions of ACE7 • Assumption 1: • Here, we approximate Ci,m by: • Assumption 2:

35. Maximum Likelihood Estimation of Server Correlation Here,

36. Server Correlation • Observation: • The server correlation is significant with respect to the fact that there are thousands of candidate models. • some servers are correlated more tightly than others. • mGenThreader and RAPTOR (0.383) vs. FUGUE3 and Prospect (0.182). • Implication: • These individual server may be clustered into cliques according to correlations; • the servers in a small clique may be underestimated according to the simple “majority voting” rule.

37. Uncovering the Latent Server

38. Uncovering the Latent Servers (cont) • Using the PCA technique, the latent severs can be estimated as:

39. Explanation of Latent Servers • Observation: • H1: represents MGTH and RAPT • H2: SPKS • H3: FUG3 • H4: ST02 • H5: PROS • H6: no preference

40. Construct a More Accurate Server • Since latent servers are mutually independent, it is reasonable to assume: • Key Point: • How to set the weight of each latent server? • An ILP model: • To maximize the gap between the scores of the native-like models and incorrect models.

41. ILP Model (soft-margin idea)

42. Experiment on CASP7 Dataset • Observation: • For T0363, ACE7 succeeds even only one server votes the native-like model.

43. Sensitivity of ACE7 • Observation: • ACE7 has a higher sensitivity than any individual server.

44. Future Work

45. Conclusion • Though consensus methods rely on structure clustering property, the server correlation also bring negative effect.

46. Future Work • To find a better approximation of Ci,m. • Using MaxSub instead of GDT. • RAPTOR has a good performance in choosing the top 5 models, but always be puzzled to choose the top 1 model. • We try to help to choose the best from the top 5 models remains an open problem.

47. Thanks.