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

Transcription

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

2 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). 2

3 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 3

4 1-D predictions Local Structures " Secondary Structure " Trans Membrane Helix Features " Surface Accessibility " Signal Peptides 4

5 Secondary Structure Elements α-helix = H 310-helix = G π-helix = I Extended (ß)-Strand = E Isolated ß-bridge = B Turn = T Bend = S Rest (Coil) = C/. 5

6 Assignment from Structure DSSP ( ) STRIDE ( ) DSSPcont ( ) 6

7 Helices 7

8 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 8

9 Prediction Servers Psi-Pred ( PHDProf Jpred 9

10 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" " " " Conf: " Pred: HHHHHHHHHHHHCCCCHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCHHHHHHHHHHHHH" AA: LASTTAKAMEQMAGSSEQAAEAMEVASQARQMVQAMRTIGTHPSSSAGLKNDLLENLQAY" " " " Conf: " Pred: HHHHCCHHHCCC" AA: QKRMGVQMQRFK" 250" " " Calculate PostScript, PDF and JPEG graphical output for this result using: " 10

11 PsiPred 11

12 Trans-Membrane Helices 12

13 Transmembrane Helix Predictors TMHMM Phobius (Philius) HMMTOP DAS 13

14 Signal Peptide SignalP Phobius Philius 14

15 Prediction Methods Exemplified by Secondary Structure Predictions 15

16 Amino Acid Statistics VKEFLAKAKEDFLKKWETPSQNTAQLDQFDRIKTLGTGSFGRVMLVKHKESGNHYAMKILDKQKVVKLKQIEHTLNEKRI!.HHHHHHHHHHHHHHHHS...GGGEEEEEEEEE.SS.EEEEEEETTTTEEEEEEEEEHHHHHHTT.HHHHHHHHHH! Helix Strand Coil VKEFLAKAK! QLDQFDRIK! KKWETPSQN! KEFLAKAKE! LDQFDRIKT! KWETPSQNT! EFLAKAKED! DQFDRIKTL! WETPSQNTA!!.!.!.!.!.!!.!.!.!.!.! 16!.!.!.!.!.!

17 Propensities Helix 17

18 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

19 Position Specific Substitution Matrices (PSSM) 19

20 PSSM Sequence The 20 amino acid residues 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

21 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. 21

22 Neural Networks (Hidden Layer) Weights 22

23 NetSurfP Prediction of Real Value Solvent Accessibility By Bent Petersen 23

24 Objective Predict residues as being either buried or exposed (25 % threshold) Two states/classes, Buried/Exposed Predict the Relative Solvent Accessibility Real Value 24

25 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 25

26 What is ASA? Accessible Solvent Area, Å 2 Surface area accessible to a rolling water molecule 26

27 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" 27

28 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 28

29 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) 29

30 Method 30

31 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 31

32 NetSurfP /usr/cbs/bio/src/netsurfp/netsurfp -h 32

33 NetSurfP Output 33

34 Protein D iso r d e r Introduction to DisEMBL, IUPred & FoldUnfold 34

35 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 35

36 Degrees of Structure 36

37 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

38 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 38

39 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 ) 39 Linding et al. Structure 2003

40 DisEMBL Derived propensity scale (implicit) 40

41 DisEMBL Output Ero1-Lα 41

42 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). 42 Dosztanáyi et al. Bioinformatics 2005

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

44 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 44 Galzitskaya et al. Bioinformatics 2006 & Protein Science 2000

45 FoldUnfold Output Ero1-Lα disordered: disordered: disordered: disordered: disordered:

46 Comparison DisEMBL IUPred FoldUnfold Disordered residues:

47 Ero1 example 47

48 Summary: Sequence Analysis Predict properties from sequence Cellular localisation (signal peptides, TM helices, etc.) Secondary structure Identification of flexibillity/disorder/stability etc. Modification sites (phosphorylation, glycosylation, acetylation, etc.) Identification of catalytic residues EPipe An automated tool for sequence comparison Under development 48

49 EPipe Difference Plot AKT1 variants involved in cancer: E17K R25C Alignment seq1 seq2 seq M S D V A I V K E G W L H K R G E Y I K T W R P R Y F L L K N D G T F I G Y K E R P Q D V D Q R E A P L M S D V A I V K E G W L H K R G K Y I K T W R P R Y F L L K N D G T F I G Y K E R P Q D V D Q R E A P L M S D V A I V K E G W L H K R G E Y I K T W R P C Y F L L K N D G T F I G Y K E R P Q D V D Q R E A P L NetSurfP Pfam IUpred PSIPRED Blicher et al. Protein annotation in the era of personal genomics, Curr Opin Struct Biol (2010).

50 Links DisEMBL: IUPred: FoldUnfold 50

51 Exercise Step