Introduction to Bioinformaticsfor Medical Research

Gideon Greenspangdg@cs.technion.ac.il

Lecture 10Protein Tertiary (3D) Structure

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Protein Tertiary Structure

• Defining Structure• Determining experimentally

– PDB• Predicting Structure

– TOPITS– GenTHREADER

• Structural classification– SCOP

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Defining Structure

1 N MET A 1 -14.830 -2.121 10.034 2 CA MET A 1 -14.608 -1.535 8.679 3 C MET A 1 -15.821 -1.799 7.781 4 O MET A 1 -15.713 -2.464 6.770 5 CB MET A 1 -13.372 -2.254 8.135 6 CG MET A 1 -13.531 -3.764 8.330 7 SD MET A 1 -12.739 -4.636 6.956 8 CE MET A 1 -13.839 -6.072 6.937 9 1H MET A 1 -15.554 -2.865 9.976 10 2H MET A 1 -13.942 -2.531 10.386

Hydrogen number

Residue numberRemotenessAtomic symbol

3D co-ordsChainResidue

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X-ray Crystallography

• Create repetitive crystal of molecule– Often difficult, especially hydrophobic portions

• X-rays generate diffraction pattern– Pattern represents electron density

• Generate comparison patterns– Add ions or change wavelength

• Obtain electron density map– Fit protein sequence to map

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Nuclear Magnetic Resonance

• Dissolve molecules in water– Allows free tumbling and vibration

• Detect activity of atoms with quantum spin– 1Hydrogen (natural), 13Carbon, 15Nitrogen

• Defines set of atomicdistance constraints– Ensemble of models

• Can detect motion

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PDB

• Database of molecular structures– Obtained by crystallography or NMR– Carefully curated and validated

• Founded in 1971– 19375 proteins, 2117 other structures

• Additional protein information– Secondary structure– References, external links

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PDB: Summary Information

Chains in molecule

Experimentalmethod

Molecule in PDB entry

Link to SCOP

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PDB: 3D Structure

• Still images at fixed orientation– Generate at any size

• Interactive molecule explorer– Requires Java or Chime plug-in

• Download structure file– Display in RasMol,

Swiss-PDBViewer, etc…• Demonstration

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Predicting 3D Structure

• Outstanding difficult problem• Based only on protein sequence

– Comparative modeling (homology)– Ab-initio modeling

• Based on secondary structure– Fold recognition– Protein threading

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Comparative Modeling

• Similar sequence suggests similar structure– Amino acid characteristics determine folding

• Similarity particularly high in core– Alpha helices and beta sheets preserved– But even near-identical sequences vary in loops

• Effectiveness depends on protein length– Longer fi less sequence similarity required

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Ab Initio Modeling

• Compute molecular structure from laws ofphysics and chemistry alone– Ideal solution (theoretically)

• Simulate process of protein folding– Apply minimum energy considerations

• Practically nearly impossible– Exceptionally complex calculations– Biophysics understanding incomplete

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Protein Folds

• A combination of secondary structural units– Forms basic level of classification

• Each protein family belongs to a fold– Estimated 700–1500 different folds

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Fold Recognition / Threading

• Compare sequence against known structures– Try to ‘thread’ sequence along chain

• Score suitability of the threading– Can adjacent amino acids bond?– Are amino acids close to or far from water?– Are secondary structures similar?

• Examine list of most threadable structures– Correct answer is often in top 10 or so

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Threading Example

Knownstructure

Querysequence

Gaps inthreading

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TOPITS Output (1)

Alignmentscore

Alignmentlength

Lengthof indels

Numberof indels

Length ofsequence

Alignmentsignificance

Matchedsequence

% sequenceidentity

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TOPITS Output (2)

Querysequence

Predictedstructure

Buried /Outside

Databasesequence

Amino acidmatches

Database knownsecondary structure

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GenTHREADER Output

Predictionconfidence

Expectederrors

Score fromneural network

Sequence alignmentscore and length

Energymeasurements

Length ofsequence

Structurefrom PDB

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Prediction Flowchart

PSI-BLAST

Ab initiomethods

TOPITS,GenTHREADER

PHDsec,PSIpred

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Structure Classification

• Class– All alpha, all beta, alpha/beta, alpha+beta

• Fold– Strong structural similarity

• Superfamily– Probably common evolutionary origin

• Family– Evolutionary relationship, sequence similarity

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SCOP

• Structural Classification of Proteins– Based on known protein structures– Manually created by visual inspection

• Hierarchical database structure– Class, fold, superfamily, family– Proteins/domains, species instances

• Founded in 1995– 765 folds, 1232 superfamilies, 2164 families

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SCOP: Navigation

Nodename

Nodedescription

Path fromroot to node

Childrenof node

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Other Resources

• CATH (classification of protein domains)– http://www.biochem.ucl.ac.uk/bsm/cath/

• SWISS-MODEL (comparative modeling)– http://www.expasy.ch/swissmod/

• CASP (structure prediction competition)– http://predictioncenter.llnl.gov/

• GTSP (guide to structure prediction)– http://speedy.embl-heidelberg.de/gtsp/