1-D Predictions. Prediction of local features: Secondary structure & surface exposure

Transcription

1 Programme Last week s quiz results Prediction of secondary structure & surface exposure Protein disorder prediction Break get computers upstairs Ex.: Secondary structure prediction Break Summary & discussion Quiz 1

2 Feedback Persons 2

3 Programme Last week s quiz results Prediction of secondary structure & surface exposure Protein disorder prediction Break Ex.: Secondary structure prediction Break Summary & discussion Quiz 3

4 1-D Predictions Prediction of local features: Secondary structure & surface exposure 4

5 Learning Objectives After today s session you should be able to: Explain the meaning and usage of the following local feature terms: Secondary structure Surface accessibility/exposure Transmembrane helix Signal peptide Protein disorder Use different 1-D prediction servers and interpret the results (the exercise). 5

6 Residue Patterns Helices Helix capping Amphiphilic residue patterns C N Sheets Amphiphilic residue patterns Residue preferences at edges vs. middle Special residues Proline Helix breaker Glycine In turns/loops/bends 6

7 1-D predictions Local Structures " Secondary Structure " Trans Membrane Helix Features " Surface Accessibility " Signal Peptides 7

8 Secondary Structure Elements α-helix = H helix = G π-helix = I Extended (ß)-Strand = E Isolated ß-bridge = B Turn = T Bend = S Rest (Coil) = C/. 8

9 Assignment from Structure DSSP ( ) STRIDE ( ) DSSPcont ( ) 9

10 Helices 10

11 Three-State Prediction of Classes Α-helix = H helix = G π-helix = I Extended (ß)-Strand = E Isolated ß-bridge = B Turn = T Bend = S The Rest (Coil) =./C H E C 11

12 Prediction Servers PSIPRED ( PHDProf Jpred 12

13 PSIPRED PSIPRED PREDICTION RESULTS!! Key!! Conf: Confidence (0=low, 9=high)! Pred: Predicted secondary structure (H=helix, E=strand, C=coil)! AA: Target sequence!!! # PSIPRED HFORMAT (PSIPRED V2.3 by David Jones)!! Conf: ! Pred: CCCHHHHHHHHHHHCCCCCCCHHHHHHHHHHHCCCCCCHHHHHHHHHCCCCCCHHHHHHH! AA: MSLLTEVETYVLSIIPSGPLKAEIAQRLEDVFAGKNTDLEVLMEWLKTRPILSPLTKGIL! !! Conf: ! Pred: HHHHHHCCCCHHHHHHHHHHHCCCCCCCCCHHHHHHHHHHHHHHHHCCHHHHHHHHHCCC! AA: GFVFTLTVPSERGLQRRRFVQNALNGNGDPNNMDKAVKLYRKLKREITFHGAKEISLSYS! !! Conf: ! Pred: HHHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCHHHHHHHHH! AA: AGALASCMGLIYNRMGAVTTEVAFGLVCATCEQIADSQHRSHRQMVTTTNPLIRHENRMV! !! 13!

14 PSIPRED 14

15 Trans-Membrane Helices 15

16 Transmembrane Helix Predictors TMHMM HMMTOP DAS 16

17 Signal Peptide SignalP Phobius Philius 17

18 Prediction Methods Exemplified by Secondary Structure Predictions 18

19 Amino Acid Statistics VKEFLAKAKEDFLKKWETPSQNTAQLDQFDRIKTLGTGSFGRVMLVKHKESGNHYAMKILDKQKVVKLKQIEHTLNEKRI!.HHHHHHHHHHHHHHHHS...GGGEEEEEEEEE.SS.EEEEEEETTTTEEEEEEEEEHHHHHHTT.HHHHHHHHHH! Helix VKEFLAKAK! KEFLAKAKE! EFLAKAKED!!.!.!.!.!.! Strand QLDQFDRIK! LDQFDRIKT! DQFDRIKTL!!.!.!.!.!.! Coil KKWETPSQN! KWETPSQNT! WETPSQNTA!!.!.!.!.!.! 19

20 Propensities Helix 20

21 BLOSUM Substitution A R N D C Q E G H I L K M F P S T W Y V B Z X * A R N D C Q E G H I L K M F P S T W Y V

22 Position Specific Substitution Matrices (PSSM) 22

23 PSSM A R N D C Q E G H I L K M F P S T W Y V 1 I K E E H V I I Q A E F Y L N P D

24 Neural Networks Benefits Generally applicable Can capture higher order correlations Inputs other than sequence information Drawbacks Needs a lot of data (different solved structures with low sequence identity). Complex methods with several pitfalls. 24

25 Neural Networks Input Layer Weights I K E E H V I I Q A E Window IKEEHVIIQAEFYLNPDQSGEF.. H E C Hidden Layer Output Layer 25

26 NetSurfP Prediction of Real Value Solvent Accessibility By Bent Petersen 26

27 Objective Predict residues as being either buried or exposed (25 % threshold) Two states/classes, Buried/Exposed Predict the Relative Solvent Accessibility Real Value 27

28 Why predict RSA? Residues exposed on surface can be: Involved in PTM s Potential antigenic regions Involved in Protein-Protein interactions Prediction of Disease-SNP s 28

29 What is ASA? Accessible Solvent Area, Å 2 Surface area accessible to a rolling water molecule 29

30 RSA RSA = Relative Solvent Accessibility ACC = Accessible area in protein structure ASA = Accessible Surface Area in Gly-X-Gly or Ala-X-Ala Classification Networks Real value Networks Classification: Buried = RSA < 25 %, Exposed = RSA > 25 %" Real Value: values 0-1, RSA > 1 set to 1" 30

31 Learning / Training dataset Training set: Cull_1764: Max. Seq. ID: 25 % Resolution: 2.0 Å R-Factor: 0.2 Seq. Length AA Excluding non-x-ray entries 31

32 Learning / Training dataset Homology reduced against evaluation set CB513 (302 sequences removed) Final Training set: 1764 sequences amino acids Buried: % ( amino acids) Exposed: % ( amino acids) 32

33 Neural Network - Input Position Specific Scoring Matrices, PSSM A R N D C Q E G H I L K M F P S T W Y V B H 2BEM.A A G 2BEM.A A Y 2BEM.A A V 2BEM.A B E 2BEM.A time iterativ psi-blast against nr70 Secondary Structure predictions B H 2BEM.A A G 2BEM.A A Y 2BEM.A A V 2BEM.A B E 2BEM.A " (sec predictor by Pernille Andersen) 33

34 Method 34

35 Results - Real Value Prediction Training / Evaluation Train Evaluated Method Ahmad et al. (2003) Not Published 0.48 ANN Yuan and Huang (2004) Not Published 0.52 SVR Nguyen and Rajapakse(2006) Not Published 0.66 Two-Stage SVR Dor and Zhou (2007) Not Published ANN NetSurfP ANN 35

36 NetSurfP /usr/cbs/bio/src/netsurfp/netsurfp -h 36

37 NetSurfP Output 37

38 Programme Last week s quiz results Prediction of secondary structure & surface exposure Protein disorder prediction Break Ex.: Secondary structure prediction Break Summary & discussion Quiz 38

39 Protein D iso r d e r Introduction to DisEMBL, IUPred & FoldUnfold 39

40 Protein Folding Initially formed structure is in molten globule state (ensemble). E T Transition state(s), one or more narrow ensembles Molten globule condenses to native fold via transition state. U Unfolded state, ensemble ΔG F Native fold, one structure 40

41 Degrees of Structure 41

42 Structures of Unstructured Regions Estimate: 20% of all proteins contain unstructured regions. 1% of structures in PDB contain unstructured regions. Structural genomics Special structural genomics projects Selection and modification of targets Prediction of crystallisable domains Protein disorder publications in PubMed Iakoucheva & Dunker Structure

43 What s the Fuss About? Properties of Disordered Regions Flexible, i.e. adaptable Accessible Contain Extended Linear Motifs (ELM) Different behaviour in interaction interfaces Very adaptable Many hydrophobic interactions (close packing) No fixed structure without interaction partner Folding upon binding 43

44 DisEMBL Basic notion No consensus on protein disorder definition. Defines three types of disorder The method ANN-based Disorder definitions Loop/Coil (DSSP-assigned residues: T, S, B, I) Hot loops (high B-factor) Missing residues (in X-ray structures, Remark 465 ) 44 Linding et al. Structure 2003

45 DisEMBL Derived propensity scale (implicit) 45

46 DisEMBL Output Ero1-Lα 46

47 IUPred Basic notion: Globular proteins need to make a large number of inter-residue interactions to overcome the loss of entropy upon folding. The method 20 x 20 energy predictor matrix (pairwise interactions). Derived from globular proteins. Quadratic expression in amino acid composition. Definitions Binary definition: Order/disorder Two ranges: long ~ regions/domains Short ~ loops Domain prediction (inverse of long range predictions). 47 Dosztanáyi et al. Bioinformatics 2005

48 IUPred Output Ero1-Lα Position Residue Disorder Tendency 1 E E Q P P

49 FoldUnfold Basic notion Globular proteins need to establish a high number of interactions to compensate for the loss of entropy during the folding process. The method Mean packing density Derived from globular proteins. ANN-based. Definitions Binary definition: Order/disorder Two ranges: Long ~ regions/domains Short ~ loops 49 Galzitskaya et al. Bioinformatics 2006 & Protein Science 2000

50 FoldUnfold Output Ero1-Lα disordered: disordered: disordered: disordered: disordered:

51 Comparison DisEMBL IUPred FoldUnfold Disordered residues:

52 Ero1 example 52

53 Links DisEMBL: IUPred: FoldUnfold 53

54 Programme Last week s quiz results Prediction of secondary structure & surface exposure Protein disorder prediction Break Ex.: Secondary structure prediction Break Summary & discussion Quiz 54

55 Exercise Step