1 Protein Structure, Structure Classification and Prediction Bioinformatics X3 January 2005 P. Johansson, D. Madsen Dept.of Cell & Molecular Biology, Uppsala University

2 Overview Introduction to proteins, structure & classification Protein Folding Experimental techniques for structure determination Structure prediction

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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 20 amino acids - the building blocks

6 The Amino Acids

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

8 Secondary Structure Secondary Structure:  -helix

9 3.6 residues / turn Axial dipole moment Not Proline & Glycine Protein surfaces Secondary Structure Secondary Structure:  -helix

10 Secondary Structure Secondary Structure:  -sheets

11 Secondary Structure Secondary Structure:  -sheets Parallel or antiparallel Alternating side-chains No mixing Loops often have polar amino acids

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 Structural classification, CATH Class, four types : –Mainly  –   structures –Mainly  –No secondary structure Arhitecture (fold) Topology (superfamily) Homology (family)

14 Structural classification..

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 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, S ik Based on this similarity score and some specified gap penalty, dynamic programming is used to find the optimal structural alignment

17 Structural classification, SSAP The structural neighborhood of residue i in A compared to residue k in B i k

18 Structural classification, SSAP.. Distance between residue i & j in molecule A ; d A i,j Similarity for two pairs of residues, i j in A & k l in B ; a,b constants Similarity between residue i in A and residue k in B ; Idea ; S i,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 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  S ik, then to align the structures using these scores

20 Experimental techniques for structure determination X-ray Crystallography Nuclear Magnetic Resonance spectroscopy (NMR) Electron Microscopy/Diffraction Free electron lasers ?

21 X-ray Crystallography

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 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 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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27 Structure Prediction GPSRYIV… ?

28 Protein Folding Different sequence  Different structure Free energy difference small due to large entropy decrease,   G =  H - T  S

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 Ab initio calculations used for smaller problems ; Calculation of affinity Enzymatic pathways Molecular dynamics

31 Sequence Classification rev. Class : Secondary structure content Fold : Major structural similarity. Superfamily : Probable common evolutionary origin. Family : Clear evolutionary relationship.

32 Search sequence data banks for homologs Search methods e.g. BLAST, PSIBLAST, FASTA … Homologue in PDB..? Structure Prediction IVTY…PGGG HYW…QHG

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 HFD fingerprint Multiple alignment example

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 Each residue is classified as: –H  /H , strong helix / strand former. –h  /h , weak helix / strand former. –I, indifferent. –b  /b , weak helix/strand breaker. –B  /B , strong helix / strand breaker. The Chou & Fasman Method

37 The Chou & Fasman Method.. Score each residue: –H  /h  =1, I  =0 or ½, B  /b  =-1. –H  /h  =1, I  =0 or ½, B  /b  =-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 GPSRYIVTLANGK Helix: Strand No nucl … Nucleation Propagate GPSRYIVTLANGK Result The Chou & Fasman Method..

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 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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