aims

Structure prediction tries to build models of 3D structures of proteins that could be useful for understanding structure-function relationships.

Possible scenarios

1. No homology1D predictions. Sequence motifs. Limited functional prediction. Ab-

initio prediction

2. Homology exist but cannot be recognized easily (psi-blast, threading)

Low resolution fold predictions are possible. No functional information.

3. Homology can be recognized using sequence comparison tools or protein family databases (blast, clustal, pfam,...).

Structural and functional predictions are feasible

1D prediction

Prediction is based on averaging aminoacid properties

AGGCFHIKLAAGIHLLVILVVKLGFSTRDEEASS

Average over a window

1D prediction. Properties

Secondary structure propensitites Hydrophobicity Accesibility ...

Aminoacido P() P() P(turn)Ala 1.29 0.9 0.78Cys 1.11 0.74 0.8Leu 1.3 1.02 0.59Met 1.47 0.97 0.39Glu 1.44 0.75 1Gln 1.27 0.8 0.97His 1.22 1.08 0.69Lys 1.23 0.77 0.96

Val 0.91 1.49 0.47Ile 0.97 1.45 0.51Phe 1.07 1.32 0.58Tyr 0.72 1.25 1.05Trp 0.99 1.14 0.75Thr 0.82 1.21 1.03

Gly 0.56 0.92 1.64Ser 0.82 0.95 1.33Asp 1.04 0.72 1.41Asn 0.9 0.76 1.23Pro 0.52 0.64 1.91

Arg 0.96 0.99 0.88

Propensities Chou-FasmanBiochemistry 17, 4277 1978

turn

Some programs (www.expasy.org)

BCM PSSP - Baylor College of Medicine Prof - Cascaded Multiple Classifiers for Secondary Structure Prediction GOR I (Garnier et al, 1978) [At PBIL or at SBDS] GOR II (Gibrat et al, 1987) GOR IV (Garnier et al, 1996) HNN - Hierarchical Neural Network method (Guermeur, 1997) Jpred - A consensus method for protein secondary structure prediction at

University of Dundee nnPredict - University of California at San Francisco (UCSF) PredictProtein - PHDsec, PHDacc, PHDhtm, PHDtopology, PHDthreader,

MaxHom, EvalSec from Columbia University PSA - BioMolecular Engineering Research Center (BMERC) / Boston PSIpred - Various protein structure prediction methods at Brunel University SOPM (Geourjon and Deléage, 1994) SOPMA (Geourjon and Deléage, 1995) AGADIR - An algorithm to predict the helical content of peptides

1D Prediction

Original methods: 1 sequence and uniform parameters (25-30%)

Original improvements: Parameters specific from protein classes

Present methods use sequence profiles obtained from multiple alignments and neural networks to extract parameters (70-75%, 98% for transmembrane helix)

PredictProtein (PHD)

1. Building of a multiple alignment using Swissprot, prosite, and domain databases

2. 1D prediction from the generated profile using neural networks

3. Fold recognition

4. Confidence evaluation

PredictProteinAvailable information

Multiple alignments MaxHom PROSITE motifs SEG Composition-bias Threading TOPITS Secondary structure PHDSec PROFsec Transmembrane helices PHDhtm, PHDtop Globularity GLOBE Coiled-coil COILS Disulfide bridges CYSPRED

Result

PredictProteinAvailable information

Signal peptides SignalP O-glycosilation NetOglyc Chloroplast import signal CloroP Consensus secondary struc. JPRED Transmembrane TMHMM, TOPPRED SwissModel

Methods for remote homology

Homology can be recognized using PSI-Blast

Fold prediction is possible using threading methods

Acurate 3D prediction is not possible: No structure-function relationship can be inferred from models

Threading

Unknown sequence is “folded” in a number of known structures

Scoring functions evaluate the fitting between sequence and structure according to statistical functions and sequence comparison

ATTWV....PRKSCTATTWV....PRKSCT

..........

10.510.5 5.2>> ..........

SELECTED HITSELECTED HIT

ATTWV....PRKSCTATTWV....PRKSCT SequenceSequenceHHHHH....CCBBBBHHHHH....CCBBBB Pred. Sec. Struc.Pred. Sec. Struc.eeebb....eeebebeeebb....eeebeb Pred. accesibilityPred. accesibility

..........

SequenceSequence GGTV....ATTW ........... ATTVL....FFRKGGTV....ATTW ........... ATTVL....FFRKObs SS Obs SS BBBB....CCHH ........... HHHB.....CBCB BBBB....CCHH ........... HHHB.....CBCB Obs Acc. Obs Acc. EEBE.....BBEB ........... BBEBB....EBBEEEBE.....BBEB ........... BBEBB....EBBE

Technical aspectsTechnical aspects

Alignment:Alignment: Dynamic programming (Needleman & Dynamic programming (Needleman & Wunsch, 1970)Wunsch, 1970)

Scoring FunctionScoring Function::

wwseqseq.P.Pseqseq + w + wstrstr . (P . (PSSSS + P + PACAC))

PPseqseq: Dayhoff matrix, P: Dayhoff matrix, PSSSS y P y PACAC: probability model on : probability model on

pred. SS and ACpred. SS and AC

Threading accurancyThreading accurancy

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

% ACIERTOS

5 10 15 20 25

% IDENTIDAD SECUENCIAS

Comparative modelling

Good for homology >30%

Accurancy is very high for homology > 60%

Remainder

The model must be USEFUL Only the “interesting” regions of the protein need

to be modelled

Expected accurancy

Strongly dependent on the quality of the sequence alignment

Strongly dependent on the identity with “template” structures. Very good structures if identity > 60-70%.

Quality of the model is better in the backbone than side chains

Quality of the model is better in conserved regions

Steps

1. Choose templates: Proteins with experimental 3D structure with significant homology (BLAST, PFAM, PDB)

2. Building multiple alignment of templates. Alignment quality is critical for accurancy.

Always use structure-based alignment. Reduce redundancies

Steps

1. Alignment of template structures

2. Alignment of unknown sequence against template alignment

• Structural alignment may not concide with evolution-based alignment.

• Gaps must be chosen to minimize structure distortion

Steps

1. Alignment of template structures

2. Alignment of unknown sequence against template alignment

3. Build structure of conserved regions (SCR)• Coordinates come from either a single structure or

averages.• Side chains are adapted to the original or placed in

standard conformations

Etapas

1. Alignment of template structures

2. Alignment of unknown sequence against template alignment

3. Build structure of conserved regions (SCR)

4. Build of unconserved regions (“loops” usually)

Ab initio PDB

“loops”

“loops”Chosen manually or energy-based

Optimization

1. Optimize side chain conformation1. Energy minimization restricted to standard conformers

and VdW energy

2. Optimize everything• Global energy minimization with restrains• Molecular dynamics

Quality test

No energy differences between a correct or wrong model

The structure must by “chemically correct” to use it in quantitative predictions

Analysis software

PROCHECK WHATCHECK Suite Biotech PROSA

Sources of information

300 best structures in PDB

Molecular geometry from CSD database

Theoretical data (Ramachandran, etc.)

Procheck

Covalent geometry Planarity Dihedral angels Quirality Non-bonded interactions Satisfied/unsatisfies Hydrogen-bonds Disulfide bonds

Whatcheck

Prediction software

SwissModel (automatic) http://www.expasy.org/swissmod/

SwissModel Repository http://swissmodel.expasy.org/repository/

3D-JIGSAW (M.Stenberg) http://www.bmm.icnet.uk/servers/3djigsaw/

Modeller (A.Sali) http://salilab.org/modeller/modeller.html

MODBASE (A. Sali) http://alto.compbio.ucsf.edu/modbase-cgi/index.cgi

Resultspdbv

Final test

The model must justify experimental data (i.e. differences between unknown sequence and templates) and be useful to understand function.