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Programme 8.00-8.30 Last week’s quiz results 8.30-9.00 Prediction of secondary structure &

surface exposure 9.00-9.20 Protein disorder prediction 9.20-9.30 Break 9.30-11.00 Ex.: Secondary structure prediction 11.00-11.10 Break 11.10-11.40 Summary & discussion 11.40-12.00 Quiz

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Feedback Persons

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Programme 8.00-8.30 Last week’s quiz results 8.30-9.00 Prediction of secondary structure &

surface exposure 9.00-9.20 Protein disorder prediction 9.20-9.30 Break 9.30-11.00 Ex.: Secondary structure prediction 11.00-11.10 Break 11.10-11.40 Summary & discussion 11.40-12.00 Quiz

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1-D Predictions

Prediction of local features: Secondary structure

& surface exposure

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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).

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Residue Patterns §  Helices

–  Helix capping –  Amphiphilic residue

patterns

§  Sheets –  Amphiphilic residue

patterns –  Residue preferences at

edges vs. middle

§  Special residues –  Proline

•  Helix breaker

–  Glycine •  In turns/loops/bends

N

C

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1-D predictions § Local Structures "  Secondary Structure "  Trans Membrane Helix

§ Features "  Surface Accessibility "  Signal Peptides

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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/.

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Assignment from Structure

• DSSP ( http://www.cmbi.kun.nl/gv/dssp/ )• STRIDE ( http://www.hgmp.mrc.ac.uk/Registered/Option/stride.html )• DSSPcont ( http://cubic.bioc.columbia.edu/services/DSSPcont/ )

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Helices

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§  Α-helix = H §  310 - helix = G §  π-helix = I §  Extended (ß)-Strand = E §  Isolated ß-bridge = B §  Turn = T §  Bend = S §  The Rest (Coil) = ./C

Three-State Prediction of Classes

H

E

C

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Prediction Servers

§ PSIPRED (http://bioinf.cs.ucl.ac.uk/psipred/)

§ PHDProf § Jpred

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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: 962265677776523477650688877787645776578999999733875215678887!Pred: CCCHHHHHHHHHHHCCCCCCCHHHHHHHHHHHCCCCCCHHHHHHHHHCCCCCCHHHHHHH! AA: MSLLTEVETYVLSIIPSGPLKAEIAQRLEDVFAGKNTDLEVLMEWLKTRPILSPLTKGIL! 10 20 30 40 50 60!!Conf: 754642045401245555330224688880246788999999865213001344431012!Pred: HHHHHHCCCCHHHHHHHHHHHCCCCCCCCCHHHHHHHHHHHHHHHHCCHHHHHHHHHCCC! AA: GFVFTLTVPSERGLQRRRFVQNALNGNGDPNNMDKAVKLYRKLKREITFHGAKEISLSYS! 70 80 90 100 110 120!!Conf: 113899999987067751045678889988888742346778777764042033332466!Pred: HHHHHHHHHHHHHCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCHHHHHHHHH! AA: AGALASCMGLIYNRMGAVTTEVAFGLVCATCEQIADSQHRSHRQMVTTTNPLIRHENRMV! 130 140 150 160 170 180!!!Conf: 554368888741366024789999999999999999862489875310478899999998!Pred: HHHHHHHHHHHHCCCCHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCHHHHHHHHHHHHH! AA: LASTTAKAMEQMAGSSEQAAEAMEVASQARQMVQAMRTIGTHPSSSAGLKNDLLENLQAY! 190 200 210 220 230 240!!!Conf: 886363002159!Pred: HHHHCCHHHCCC! AA: QKRMGVQMQRFK! 250!!!Calculate PostScript, PDF and JPEG graphical output for this result using: !http://bioinf2.cs.ucl.ac.uk/cgi-bin/psipred/gra/nph-view2.cgi?

id=3644f256afcf5ec3.psi!!

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PSIPRED

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Trans-Membrane Helices

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Transmembrane Helix Predictors

§ TMHMM § HMMTOP § DAS

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Signal Peptide

SignalP Phobius Philius

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Prediction Methods

Exemplified by Secondary Structure Predictions

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Amino Acid Statistics

VKEFLAKAKEDFLKKWETPSQNTAQLDQFDRIKTLGTGSFGRVMLVKHKESGNHYAMKILDKQKVVKLKQIEHTLNEKRI!.HHHHHHHHHHHHHHHHS.......GGGEEEEEEEEE.SS.EEEEEEETTTTEEEEEEEEEHHHHHHTT.HHHHHHHHHH!

VKEFLAKAK!

KEFLAKAKE!

EFLAKAKED!!.!.!.!.!.!

Helix QLDQFDRIK!

LDQFDRIKT!

DQFDRIKTL!!.!.!.!.!.!

Strand KKWETPSQN!

KWETPSQNT!

WETPSQNTA!!.!.!.!.!.!

Coil

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Propensities

Helix

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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 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 -4 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 -1 -4 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 -1 -4 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 -4 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -4 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 -1 -4 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 -1 -4 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 -4 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 -1 -1 -4 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 -4 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 -2 -4 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 -4 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 -4 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 -2 -4 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 -1 -4 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -4

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Position Specific Substitution Matrices (PSSM)

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PSSM

A R N D C Q E G H I L K M F P S T W Y V 1 I -2 -4 -5 -5 -2 -4 -4 -5 -5 6 0 -4 0 -2 -4 -4 -2 -4 -3 4 2 K -1 -1 -2 -2 -3 -1 3 -3 -2 -2 -3 4 -2 -4 -3 1 1 -4 -3 2 3 E 5 -3 -3 -3 -3 3 1 -2 -3 -3 -3 -2 -2 -4 -3 -1 -2 -4 -3 1 4 E -4 -3 2 5 -6 1 5 -4 -3 -6 -6 -2 -5 -6 -4 -2 -3 -6 -5 -5 5 H -4 2 1 1 -5 1 -2 -4 9 -5 -2 -3 -4 -4 -5 -3 -4 -5 1 -5 6 V -3 0 -4 -5 -4 -4 -2 -3 -5 1 -2 1 0 1 -4 -3 3 -5 -3 5 7 I 0 -2 -4 1 -4 -2 -4 -4 -5 1 0 -2 0 2 -5 1 -1 -5 -3 4 8 I -3 0 -5 -5 -4 -2 -5 -6 1 2 4 -4 -1 0 -5 -2 0 -3 5 -1 9 Q -2 -3 -2 -3 -5 4 -1 3 5 -5 -3 -3 -4 -2 -4 2 -1 -4 2 -2 10 A 2 -4 -4 -3 2 -3 -1 -4 -2 1 -1 -4 -3 -4 1 2 3 -5 -1 1 11 E -1 3 1 1 -1 0 1 -4 -3 -1 -3 0 3 -5 4 -1 -3 -6 -3 -1 12 F -3 -5 -5 -5 -4 -4 -4 -1 -1 1 1 -5 2 5 -1 -4 -4 -3 5 2 13 Y 3 -5 -5 -6 3 -4 -5 -2 -1 0 -4 -5 -3 3 -5 -2 -2 -2 7 1 14 L -1 -3 -4 -2 1 5 1 -1 -1 -1 1 -3 -3 1 -5 -1 -1 -2 3 -2 15 N -1 -4 4 1 5 -3 -4 2 -4 -4 -4 -3 -2 -4 -5 2 0 -5 0 0 16 P -2 4 -4 -4 -5 0 -3 3 2 -5 -4 0 -4 -3 0 1 -2 -1 5 -3 17 D -3 -2 1 5 -6 -2 2 2 -1 -2 -2 -3 -5 -4 -5 -1 2 -6 -3 -4

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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.

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Neural Networks

I K E E H V I I Q A E

H E C

IKEEHVIIQAEFYLNPDQSGEF….. Window

Input Layer

Hidden Layer

Output Layer

Weights

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NetSurfP

Prediction of Real Value Solvent Accessibility

By Bent Petersen

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Objective §  Predict residues as being either buried or

exposed (25 % threshold) – Two states/classes, Buried/Exposed

§  Predict the Relative Solvent Accessibility –  “Real” Value

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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

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What is ASA?

§  Accessible Solvent Area, Å2

§  Surface area accessible to a rolling water molecule

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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"

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Learning / Training dataset

§  Training set: Cull_1764: – Max. Seq. ID: 25 % – Resolution: ≤ 2.0 Å – R-Factor: ≤ 0.2 – Seq. Length 30-3000 AA – Excluding non-X-ray entries

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Learning / Training dataset §  Homology reduced against evaluation set

CB513 (302 sequences removed)

§  Final Training set: – 1764 sequences – 417.978 amino acids

•  Buried: 55.80 % (233.221 amino acids) •  Exposed: 44.20 % (184.757 amino acids)

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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 1 -4 -3 -2 -4 -6 -2 -3 -5 11 -6 -5 -3 -4 -4 -5 -3 -4 -5 -1 -6 A G 2BEM.A 2 -2 -5 -3 -4 -5 -4 -5 7 -5 -7 -6 -4 -5 -6 -5 -3 -4 -5 -6 -6

A Y 2BEM.A 3 -1 1 -4 -3 -5 -4 -4 -4 1 -4 -1 -4 -1 2 -5 0 -1 4 7 -2

A V 2BEM.A 4 -1 -5 -5 -6 -4 -4 -5 -5 -5 4 1 -5 6 -3 -2 -2 0 -5 -4 4

B E 2BEM.A 5 -2 -4 -3 0 -4 -1 3 -2 -4 0 -3 -2 1 -2 -3 3 3 -5 -4 0

4 time iterativ psi-blast against nr70

Secondary Structure predictions B H 2BEM.A 1 0.003 0.003 0.966

A G 2BEM.A 2 0.018 0.086 0.868

A Y 2BEM.A 3 0.020 0.199 0.752

A V 2BEM.A 4 0.021 0.271 0.679

B E 2BEM.A 5 0.020 0.199 0.752 (sec predictor by Pernille Andersen)

"

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Method

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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) 0.738 Not Published ANN

NetSurfP 0.722 0.70 ANN

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NetSurfP

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

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NetSurfP Output

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Programme 8.00-8.30 Last week’s quiz results 8.30-9.00 Prediction of secondary structure &

surface exposure 9.00-9.20 Protein disorder prediction 9.20-9.30 Break 9.30-11.00 Ex.: Secondary structure prediction 11.00-11.10 Break 11.10-11.40 Summary & discussion 11.40-12.00 Quiz

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Introduction to

DisEMBL, IUPred & FoldUnfold

Protein D i s o r d e r 39

Protein Folding §  Initially formed

structure is in molten globule state (ensemble).

§  Molten globule condenses to native fold via transition state.

E

U

F

T

ΔG

Unfolded state, ensemble

Native fold, one structure

Transition state(s), one or more narrow ensembles

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Degrees of Structure

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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 2003

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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

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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”)

Linding et al. Structure 2003 44

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DisEMBL §  Derived propensity scale (implicit)

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DisEMBL Output §  Ero1-Lα

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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).

Dosztanáyi et al. Bioinformatics 2005 47

IUPred Output §  Ero1-Lα

Position Residue Disorder Tendency 1 E 0.5055 2 E 0.3740 3 Q 0.1731 4 P 0.2164 5 P 0.1852

…

…

…

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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

Galzitskaya et al. Bioinformatics 2006

& Protein Science 2000 49

FoldUnfold Output

§  Ero1-Lα

disordered: 77 —— 99 disordered: 110 —— 126 disordered: 135 —— 152 disordered: 196 —— 207 disordered: 341 —— 351

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Comparison

Disordered residues: 77 —— 99 110 —— 126 135 —— 152 196 —— 207 341 —— 351

DisEMBL

IUPred

FoldUnfold

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Ero1 example

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Links

§  DisEMBL: – http://dis.embl.de/

§  IUPred: – http://iupred.enzim.hu/

§  FoldUnfold – http://skuld.protres.ru/~mlobanov/ogu/

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Programme 8.00-8.30 Last week’s quiz results 8.30-9.00 Prediction of secondary structure &

surface exposure 9.00-9.20 Protein disorder prediction 9.20-9.30 Break 9.30-11.00 Ex.: Secondary structure prediction 11.00-11.10 Break 11.10-11.40 Summary & discussion 11.40-12.00 Quiz

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Exercise

http://xray.bmc.uu.se/gerard/embo2001/predic/index.html Step 1-5

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