Visualisation/prediction 3D structures. Recognition ability is the basis of biological function 3D...
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Transcript of Visualisation/prediction 3D structures. Recognition ability is the basis of biological function 3D...
Objectives
Visualize / understand 3D structures and their interactions Derive structure-function relationships
Predict 3D structure
aim
Structure prediction tries to build models of 3D structures of proteins that could be useful for understanding structure-function relationships.
The protein folding problem
The information for 3D structures is coded in the protein sequence
Proteins fold in their native structure in seconds
Native structures are both thermodynamically stables and kinetically available
Ab-initio prediction
“In theory”, we should be able to build native structures from first principles using sequence information and molecular dynamics simulations: “Ab-initio prediction of structure”
Simulaciones de 1 s de “folding” de una proteína modelo (Duan-Kollman: Science, 277, 1793, 1998).
Simulaciones de folding reversible de péptidos (20-200 ns) (Daura et al., Angew. Chem., 38, 236, 1999).
Simulaciones distribuidas de folding de Villin (36-residues) (Zagrovic et al., JMB, 323, 927, 2002).
... the bad news ...
It is not possible to span simulations to the “seconds” range
Simulations are limited to small systems and fast folding/unfolding events in known structures steered dynamics biased molecular dynamics
Simplified systems
Some protein from ESome protein from E.coli.coli predicted at 7.6 Åpredicted at 7.6 Å
(CASP3, H.Scheraga)(CASP3, H.Scheraga)
Results from ab-initio
Average error 5 Average error 5 Å - 10 ÅÅ - 10 Å
Function cannot Function cannot be predictedbe predicted
Long simulationsLong simulations
comparative modelling
The most efficient way to predict protein structure is to compare with known 3D structures
Basic concept
In a given protein 3D structure is a more conserved characteristic than sequence Some aminoacids are “equivalent” to each
other Evolutionary pressure allows only
aminoacids substitutions that keep 3D structure largely unaltered
Two proteins of “similar” sequences must have the “same” 3D structure
Possible scenarios
1. Homology can be recognized using sequence comparison tools or protein family databases (blast, clustal, pfam,...).
Structural and functional predictions are feasible
2. Homology exist but cannot be recognized easily (psi-blast, threading)
Low resolution fold predictions are possible. No functional information.
3. No homology
1D predictions. Sequence motifs. Limited functional prediction. Ab-initio prediction
1D prediction
Prediction is based on averaging aminoacid properties
AGGCFHIKLAAGIHLLVILVVKLGFSTRDEEASS
Average over a window
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 recognition4. Confidence evaluation
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 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 Dynamic programming (Needleman & Wunsch, 1970)(Needleman & 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 : probability
model on pred. SS and ACmodel on pred. 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
3D-PSSM Steps
Building of 1D/superfamily profile Building of 3D/superfamily profile Determine/predict secondary
structure and accesibility Best score from
1. Structure vs. query PSSM2. Query vs. 1D-PSSM structures3. Query vs. 3D-PSSM structures
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. Alignment of template structures2. Alignment of unknown sequence
against template alignment3. Build structure of conserved
regions (SCR)4. Build of unconserved regions
(“loops” usually)
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
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
Final test
The model must justify experimental data (i.e. differences between unknown sequence and templates) and be useful to understand function.