2005_lecture_01.ppt
- 1. 1 Protein Structure, Structure Classification and Prediction Bioinformatics X3 January 2005 P. Johansson, D. Madsen Dept.of Cell & Molecular Biology, Uppsala University
- 2. 2 Overview • Introduction to proteins, structure & classification • Protein Folding • Experimental techniques for structure determination • Structure prediction
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- 4. 4 Proteins • Proteins play a crucial role in virtually all biological processes with a broad range of functions. • The activity of an enzyme or the function of a protein is governed by the three-dimensional structure
- 5. 5 20 amino acids - the building blocks
- 7. 7 Hydrophilic or hydrophobic..? • Virtually all soluble proteins feature a hydrophobic core surrounded by a hydrophilic surface • But, peptide backbone is inherently polar ? • Solution ; neutralize potential H-donors & acceptors using ordered secondary structure
- 9. 9 • 3.6 residues / turn • Axial dipole moment • Not Proline & Glycine • Protein surfaces Secondary Structure: a-helix
- 11. 11 Secondary Structure: b-sheets • Parallel or antiparallel • Alternating side-chains • No mixing • Loops often have polar amino acids
- 12. 12 Structural classification • Databases – SCOP, ’Structural Classification of Proteins’, manual classification – CATH, ’Class Architecture Topology Homology’, based on the SSAP algorithm – FSSP, ’Family of Structurally Similar Proteins’, based on the DALI algorithm – PClass, ’Protein Classification’ based on the LOCK and 3Dsearch algorithms
- 13. 13 Structural classification, CATH • Class, four types : – Mainly a – a / b structures – Mainly b – No secondary structure • Arhitecture (fold) • Topology (superfamily) • Homology (family)
- 15. 15 Structural classification.. • Two types of algorithms – Inter-Molecular, 3D, Rigid Body ; structural alignment in a common coordinate system (hard) e.g. VAST, LOCK.. alg. – Intra-Molecular, 2D, Internal Geometry ; structural alignment using internal distances and angles e.g. DALI, STRUCTURAL, SSAP.. alg.
- 16. 16 Structural classification, SSAP • SSAP, ‘Sequential Structure Alignment Program’ Basic idea ; The similarity between residue i in molecule A and residue k in molecule B is characterised in terms of their structural surroundings This similarity can be quantified into a score, Sik Based on this similarity score and some specified gap penalty, dynamic programming is used to find the optimal structural alignment
- 17. 17 Structural classification, SSAP The structural neighborhood of residue i in A compared to residue k in B i k
- 18. 18 Structural classification, SSAP.. Distance between residue i & j in molecule A ; dA i,j Similarity for two pairs of residues, i j in A & k l in B ; , , b d d a s B kl A ij kl ij a,b constants Similarity between residue i in A and residue k in B ; n n m B m k k A m i i k i b d d a S , , , Idea ; Si,k is big if the distances from residue i in A to the 2n nearest neighbours are similar to the corresponding distances around k in B
- 19. 19 Structural classification, SSAP.. This works well for small structures and local structural alignments - however, insertions and deletions cause problems unrelated distances HSERAHVFIM.. GQ-VMAC-NW.. i=5 k=4 A : B : - The real algorithm uses Dynamic programming on two levels, first to find which distances to compare Sik, then to align the structures using these scores
- 20. 20 Experimental techniques for structure determination • X-ray Crystallography • Nuclear Magnetic Resonance spectroscopy (NMR) • Electron Microscopy/Diffraction • Free electron lasers ?
- 22. 22 X-ray Crystallography.. • From small molecules to viruses • Information about the positions of individual atoms • Limited information about dynamics • Requires crystals
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- 24. 24 NMR • Limited to molecules up to ~50kDa (good quality up to 30 kDa) • Distances between pairs of hydrogen atoms • Lots of information about dynamics • Requires soluble, non-aggregating material • Assignment problem
- 25. 25 Electron Microscopy/ Diffraction • Low to medium resolution • Limited information about dynamics • Can use very small crystals (nm range) • Can be used for very large molecules and complexes
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- 28. 28 Protein Folding • Different sequence Different structure • Free energy difference small due to large entropy decrease, DG = DH - TDS
- 29. 29 Structure Prediction Why is structure prediction and especially ab initio calculations hard..? • Many degrees of freedom / residue • Remote noncovalent interactions • Nature does not go through all conformations • Folding assisted by enzymes & chaperones
- 30. 30 Ab initio calculations used for smaller problems ; • Calculation of affinity • Enzymatic pathways Molecular dynamics
- 31. 31 Sequence Classification rev. • Class : Secondary structure content • Fold : Major structural similarity. • Superfamily : Probable common evolutionary origin. • Family : Clear evolutionary relationship.
- 32. 32 • Search sequence data banks for homologs • Search methods e.g. BLAST, PSIBLAST, FASTA… • Homologue in PDB..? Structure Prediction IVTY…PGGG HYW…QHG
- 33. 33 Multiple sequence / structure alignment • Contains more information than a single sequence for applications like homology modeling and secondary structure prediction • Gives location of conserved parts and residues likely to be buried in the protein core or exposed to solvent Structure Prediction
- 34. 34 HFD fingerprint Multiple alignment example
- 35. 35 • Statistical Analysis (old fashioned): – For each amino acid type assign it’s ‘propensity’ to be in a helix, sheet, or coil. • Limited accuracy ~55-60%. • Random prediction ~38%. MTLLALGINHKTAP... CCEEEEEECCCCCC... Secondary Structure Prediction
- 36. 36 • Each residue is classified as: – Ha/Hb, strong helix / strand former. – ha/hb, weak helix / strand former. – I, indifferent. – ba/bb, weak helix/strand breaker. – Ba/Bb, strong helix / strand breaker. The Chou & Fasman Method
- 37. 37 The Chou & Fasman Method.. • Score each residue: – Ha/ha=1, Ia=0 or ½, Ba/ba=-1. – Hb/hb=1, Ib=0 or ½, Bb/bb=-1. • Helix nucleation: – Score > 4 in a “window” of 6 residues. • Strand nucleation: – Score > 3 in a “window” of 5 residues. • Propagate until score < 1 in a 4 residue “window”.
- 38. 38 GPSRYIVTLANGK Helix: Strand -1 -1 0 0 -1 1 1 0 1 1 -1 -1 1 -1 -1 -1 .5 1 1 1 1 1 0 0 -1 -1 -2 0 1 2 3 3 1 No nucl. -1.5 .5 2.5 4.5 5 4 3 1 -1 -2.5 -.5 1.5 … 3 1 -1 Nucleation Propagate GPSRYIVTLANGK Result The Chou & Fasman Method..
- 39. 39 • Neural networks (e.g. the PHD server): – Input: a number of protein sequences + secondary structure. – Output: a trained network that predicts secondary structure elements with ~70% accuracy. • Use many different methods and compare (e.g. the JPred server)! Modern methods
- 40. 40 Summary • The function of a protein is governed by its structure • Different sequence Different structure • PDB, protein data bank • Secondary structure prediction is hard, tertiary structure prediction is even harder • Use homologs whenever possible or different methods to assess quality
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