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Prediction of protein structure
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aim Structure prediction tries to build models of 3D structures of proteins that could be useful for understanding structure-function relationships.
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Genbank/EMBL Uniprot PDB
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DNA sequence Protein sequence Molecular recognition 3D structure
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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
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ab-initio prediction Prediction from sequence using first principles
AVVTW...GTTWVR
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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 ms de “folding” de una proteína modelo (Duan-Kollman: Science, 277, 1793, 1998). Simulaciones de folding reversible de péptidos ( ns) (Daura et al., Angew. Chem., 38, 236, 1999). Simulaciones distribuidas de folding de Villin (36-residues) (Zagrovic et al., JMB, 323, 927, 2002).
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... 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
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typical shortcuts Reduce conformational space
1,2 atoms per residue fixed lattices Statistic force-fields obtained from known structures Average distances between residues Interactions Use building blocks: 3-9 residues from PDB structures
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“lattice” folding
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Example PROSA potential
Total Hydrophobic Cb-Cb Very stable Low stability
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Results from ab-initio
Average error 5 Å - 10 Å Function cannot be predicted Long simulations Some protein from E.coli predicted at 7.6 Å (CASP3, H.Scheraga)
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comparative modelling
The most efficient way to predict protein structure is to compare with known 3D structures
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Protein folds
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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
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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
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fold prediction
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3D struc. prediction
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1D prediction Prediction is based on averaging aminoacid properties
AGGCFHIKLAAGIHLLVILVVKLGFSTRDEEASS Average over a window
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1D prediction. Properties
Secondary structure propensitites Hydrophobicity (transmembrane) Accesibility ...
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Propensities Chou-Fasman
Biochemistry 17, a b turn
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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
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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)
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PredictProtein (PHD) Building of a multiple alignment using Swissprot, prosite, and domain databases 1D prediction from the generated profile using neural networks Fold recognition Confidence evaluation
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PredictProtein Available 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
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PredictProtein Available information
Signal peptides SignalP O-glycosilation NetOglyc Chloroplast import signal CloroP Consensus secondary struc. JPRED Transmembrane TMHMM, TOPPRED SwissModel
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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
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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
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ATTWV....PRKSCT SELECTED HIT 10.5 > 5.2
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ATTWV. PRKSCT. Sequence HHHHH. CCBBBB. Pred. Sec. Struc. eeebb. eeebeb
ATTWV....PRKSCT Sequence HHHHH....CCBBBB Pred. Sec. Struc. eeebb....eeebeb Pred. accesibility Sequence GGTV....ATTW ATTVL....FFRK Obs SS BBBB....CCHH HHHB.....CBCB Obs Acc. EEBE.....BBEB BBEBB....EBBE
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Threading accurancy
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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
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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
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Steps Choose templates: Proteins with experimental 3D structure with significant homology (BLAST, PFAM, PDB) Building multiple alignment of templates. Alignment quality is critical for accurancy. Always use structure-based alignment. Reduce redundancies
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Template alignment
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Steps Alignment of template structures
Alignment of unknown sequence against template alignment Structural alignment may not concide with evolution-based alignment. Gaps must be chosen to minimize structure distortion
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PHE ASP ILE CYS ARG LEU PRO GLY SER ALA GLU ALA VAL CYS (green)
PHE ASN VAL CYS ARG THR PRO GLU ALA ILE CYS (red) PHE ASN VAL CYS ARG THR PRO GLU ALA ILE CYS (blue)
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Steps Alignment of template structures
Alignment of unknown sequence against template alignment 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
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Steps Alignment of template structures
Alignment of unknown sequence against template alignment Build structure of conserved regions (SCR) Build of unconserved regions (“loops” usually)
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“loops” Ab initio PDB
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Chosen manually or energy-based
“loops” Chosen manually or energy-based
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Optimization Optimize side chain conformation Optimize everything
Energy minimization restricted to standard conformers and VdW energy Optimize everything Global energy minimization with restrains Molecular dynamics
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Quality test No energy differences between a correct or wrong model
The structure must by “chemically correct” to use it in quantitative predictions
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Alignment quality Global test: compare sequence with N residue exchanges (N=1000). Calculate Z-score If (alignments res): Z > Ideal 5 < Z <= % core residues core right Z <= Problems
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Analysis software PROCHECK WHATCHECK Suite Biotech PROSA
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Sources of information
300 best structures in PDB Molecular geometry from CSD database Theoretical data (Ramachandran, etc.)
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Procheck Covalent geometry Planarity Dihedral angels Quirality
Non-bonded interactions Satisfied/unsatisfies Hydrogen-bonds Disulfide bonds
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Whatcheck
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Prediction software SwissModel (automatic) SwissModel Repository
SwissModel Repository 3D-JIGSAW (M.Stenberg) Modeller (A.Sali) MODBASE (A. Sali)
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spdbv Result
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Final test The model must justify experimental data (i.e. differences between unknown sequence and templates) and be useful to understand function.
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