Bioinformatics t7-protein structure-v2013_wim_vancriekinge

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Protein Structure

Protein Structure

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  • 1. FBW 19-11-2013 Wim Van Criekinge
  • 2. The reason for “bioinformatics” to exist ? • empirical finding: if two biological sequences are sufficiently similar, almost invariably they have similar biological functions and will be descended from a common ancestor. • (i) function is encoded into sequence, this means: the sequence provides the syntax and • (ii) there is a redundancy in the encoding, many positions in the sequence may be changed without perceptible changes in the function, thus the semantics of the encoding is robust.
  • 3. Protein Structure Introduction Why ? How do proteins fold ? Levels of protein structure 0,1,2,3,4 X-ray / NMR The Protein Database (PDB) Protein Modeling Bioinformatics & Proteomics Weblems
  • 4. Why protein structure ? • Proteins perform a variety of cellular tasks in the living cells • Each protein adopts a particular folding that determines its function • The 3D structure of a protein can bring into close proximity residues that are far apart in the amino acid sequence • Catalytic site: Business End of the molecule
  • 5. Rationale for understanding protein structure and function Protein sequence -large numbers of sequences, including whole genomes ? Protein function - rational drug design and treatment of disease - protein and genetic engineering - build networks to model cellular pathways - study organismal function and evolution structure determination structure prediction Protein structure - three dimensional - complicated - mediates function homology rational mutagenesis biochemical analysis model studies
  • 6. About the use of protein models (Peitch) • Structure is preserved under evolution when sequence is not – Interpreting the impact of mutations/SNPs and conserved residues on protein function. Potential link to disease • Function ? – Biochemical: the chemical interactions occerring in a protein – Biological: role within the cell – Phenotypic: the role in the organism • Gene Ontology functional classification ! – Priorisation of residues to mutate to determine protein function – Providing hints for protein function:Catalytic mechanisms of enzymes often require key residues to be close together in 3D space – (protein-ligand complexes, rational drug design, putative interaction interfaces)
  • 7. MIS-SENSE MUTATION e.g. Sickle Cell Anaemia Cause: defective haemoglobin due to mutation in βglobin gene Symptoms: severe anaemia and death in homozygote
  • 8. Normal β-globin - 146 amino acids val - his - leu - thr - pro - glu - glu - --------1 2 3 4 Normal gene (aa 6) DNA CTC mRNA GAG Product Glu 5 6 7 Mutant gene CAC GUG Valine Mutant β-globin val - his - leu - thr - pro - val - glu - ---------
  • 9. Protein Conformation • Christian Anfinsen Studies on reversible denaturation “Sequence specifies conformation” • Chaperones and disulfide interchange enzymes: involved but not controlling final state, they provide environment to refold if misfolded • Structure implies function: The amino acid sequence encodes the protein’s structural information
  • 10. How does a protein fold ? • by itself: – Anfinsen had developed what he called his "thermodynamic hypothesis" of protein folding to explain the native conformation of amino acid structures. He theorized that the native or natural conformation occurs because this particular shape is thermodynamically the most stable in the intracellular environment. That is, it takes this shape as a result of the constraints of the peptide bonds as modified by the other chemical and physical properties of the amino acids. – To test this hypothesis, Anfinsen unfolded the RNase enzyme under extreme chemical conditions and observed that the enzyme's amino acid structure refolded spontaneously back into its original form when he returned the chemical environment to natural cellular conditions. – "The native conformation is determined by the totality of interatomic interactions and hence by the amino acid sequence, in a given environment."
  • 11. Protein Structure Introduction Why ? How do proteins fold ? Levels of protein structure 0,1,2,3,4 X-ray / NMR The Protein Database (PDB) Protein Modeling Bioinformatics & Proteomics Weblems
  • 12. The Basics • Proteins are linear heteropolymers: one or more polypeptide chains • Below about 40 residues the term peptide is frequently used. • A certain number of residues is necessary to perform a particular biochemical function, and around 40-50 residues appears to be the lower limit for a functional domain size. • Protein sizes range from this lower limit to several hundred residues in multi-functional proteins. • Three-dimentional shapes (folds) adopted vary enormously • Experimental methods: – – – – X-ray crystallography NMR (nuclear magnetic resonance) Electron microscopy Ab initio calculations …
  • 13. Levels of protein structure • Zeroth: amino acid composition (proteomics, %cysteine, %glycine)
  • 14. Amino Acid Residues The basic structure of an a-amino acid is quite simple. R denotes any one of the 20 possible side chains (see table below). We notice that the Ca-atom has 4 different ligands (the H is omitted in the drawing) and is thus chiral. An easy trick to remember the correct L-form is the CORN-rule: when the Ca-atom is viewed with the H in front, the residues read "CO-R-N" in a clockwise direction.
  • 15. Amino Acid Residues
  • 16. Amino Acid Residues
  • 17. Amino Acid Residues
  • 18. Amino Acid Residues
  • 19. Levels of protein structure • Primary: This is simply the order of covalent linkages along the polypeptide chain, I.e. the sequence itself
  • 20. Backbone Torsion Angles
  • 21. Backbone Torsion Angles
  • 22. Levels of protein structure • Secondary – Local organization of the protein backbone: alphahelix, Beta-strand (which assemble into Betasheets) turn and interconnecting loop.
  • 23. Ramachandran / Phi-Psi Plot
  • 24. The alpha-helix
  • 25. A Practical Approach: Interpretation • Residues with hydrophobic properties conserved at i, i+2, i+4 separated by unconserved or hydrophilic residues suggest surface beta- strands. A short run of hydrophobic amino acids (4 residues) suggests a buried betastrand. Pairs of conserved hydrophobic amino acids separated by pairs of unconserved, or hydrophilic residues suggests an alfa-helix with one face packing in the protein core. Likewise, an i, i+3, i+4, i+7 pattern of conserved hydrophobic residues.
  • 26. Beta-sheets
  • 27. Topologies of Beta-sheets
  • 28. Secondary structure prediction ?
  • 29. Secondary structure prediction:CHOU-FASMAN • Chou, P.Y. and Fasman, G.D. (1974). Conformational parameters for amino acids in helical, sheet, and random coil regions calculated from proteins. Biochemistry 13, 211-221. • Chou, P.Y. and Fasman, G.D. (1974). Prediction of protein conformation. Biochemistry 13, 222-245.
  • 30. Secondary structure prediction:CHOU-FASMAN •Method •Assigning a set of prediction values to a residue, based on statistic analysis of 15 proteins • Applying a simple algorithm to those numbers
  • 31. Secondary structure prediction:CHOU-FASMAN Calculation of preference parameters For each of the 20 residues and each secondary structure ( helix, -sheet and -turn): observed counts • P = Log --------------------- + 1.0 expected counts • Preference parameter > 1.0  specific residue has a preference for the specific secondary structure. • Preference parameter = 1.0  specific residue does not have a preference for, nor dislikes the specific secondary structure. • Preference parameter < 1.0  specific residue dislikes the specific secondary structure.
  • 32. Secondary structure prediction:CHOU-FASMAN Preference parameters Residue P(a) P(b) P(t) f(i) f(i+1) f(i+2) f(i+3) Ala 1.45 0.97 0.57 0.049 0.049 0.034 0.029 Arg 0.79 0.90 1.00 0.051 0.127 0.025 0.101 Asn 0.73 0.65 1.68 0.101 0.086 0.216 0.065 Asp 0.98 0.80 1.26 0.137 0.088 0.069 0.059 Cys 0.77 1.30 1.17 0.089 0.022 0.111 0.089 Gln 1.17 1.23 0.56 0.050 0.089 0.030 0.089 Glu 1.53 0.26 0.44 0.011 0.032 0.053 0.021 Gly 0.53 0.81 1.68 0.104 0.090 0.158 0.113 His 1.24 0.71 0.69 0.083 0.050 0.033 0.033 Ile 1.00 1.60 0.58 0.068 0.034 0.017 0.051 Leu 1.34 1.22 0.53 0.038 0.019 0.032 0.051 Lys 1.07 0.74 1.01 0.060 0.080 0.067 0.073 Met 1.20 1.67 0.67 0.070 0.070 0.036 0.070 Phe 1.12 1.28 0.71 0.031 0.047 0.063 0.063 Pro 0.59 0.62 1.54 0.074 0.272 0.012 0.062 Ser 0.79 0.72 1.56 0.100 0.095 0.095 0.104 Thr 0.82 1.20 1.00 0.062 0.093 0.056 0.068 Trp 1.14 1.19 1.11 0.045 0.000 0.045 0.205 Tyr 0.61 1.29 1.25 0.136 0.025 0.110 0.102 Val 1.14 1.65 0.30 0.023 0.029 0.011 0.029
  • 33. Secondary structure prediction:CHOU-FASMAN Applying algorithm 1. 2. 3. 4. 5. 6. Assign parameters to residue. Identify regions where 4 out of 6 residues have P(a)>100: -helix. Extend helix in both directions until four contiguous residues have an average P(a)<100: end of -helix. If segment is longer than 5 residues and P(a)>P(b): -helix. Repeat this procedure to locate all of the helical regions. Identify regions where 3 out of 5 residues have P(b)>100: -sheet. Extend sheet in both directions until four contiguous residues have an average P(b)<100: end of -sheet. If P(b)>105 and P(b)>P(a): -helix. Rest: P(a)>P(b)  -helix. P(b)>P(a)  -sheet. To identify a bend at residue number i, calculate the following value: p(t) = f(i)f(i+1)f(i+2)f(i+3) If: (1) p(t) > 0.000075; (2) average P(t)>1.00 in the tetrapeptide; and (3) averages for tetrapeptide obey P(a)<P(t)>P(b): -turn.
  • 34. Secondary structure prediction:CHOU-FASMAN Successful method? 19 proteins evaluated: • Successful in locating 88% of helical and 95% of regions • Correctly predicting 80% of helical and 86% of sheet residues • Accuracy of predicting the three conformational states for all residues, helix, b, and coil, is 77% Chou & Fasman:successful method After 1974:improvement of preference parameters
  • 35. Sander-Schneider: Evolution of overall structure • Naturally occurring sequences with more than 20% sequence identity over 80 or more residues always adopt the same basic structure (Sander and Schneider 1991)
  • 36. Sander-Schneider • HSSP: homology derived secondary structure
  • 37. Structural Family Databases • SCOP: – Structural Classification of Proteins • FSSP: – Family of Structurally Similar Proteins • CATH: – Class, Architecture, Topology, Homology
  • 38. Levels of protein structure • Tertiary – Packing of secondary structure elements into a compact spatial unit – Fold or domain – this is the level to which structure is currently possible
  • 39. Domains
  • 40. Protein Architecture
  • 41. Domains • Protein Dissection into domain • Conserved Domain Architecture Retrieval Tool (CDART) uses information in Pfam and SMART to assign domains along a sequence • (automatic when blasting)
  • 42. Domains • From the analysis of alignment of protein families • Conserved sequence features, usually associate with a specific function • PROSITE database for protein “signature” protein (large amount of FP & FN) • From aligment of homologous sequences (PRINTS/PRODOM) • From Hidden Markov Models (PFAM) • Meta approach: INTERPRO
  • 43. Protein Architecture
  • 44. Levels of protein structure: Topology
  • 45. Hydrophobicity Plot P53_HUMAN (P04637) human cellular tumor antigen p53 Kyte-Doolittle hydrophilicty, window=19
  • 46. The ‘positive inside’ rule (EMBO J. 5:3021; EJB 174:671,205:1207; FEBS lett. 282:41) Bacterial IM In: 16% KR out: 4% KR Eukaryotic PM In: 17% KR out: 7% KR Thylakoid membrane In: 13% KR out: 5% KR Mitochondrial IM In: 10% KR out: 3% KR
  • 47. GPCR Topology • Membrane-bound receptors • Transducing messages as photons, organic odorants, nucleotides, nucleosides, peptides, lipids and proteins. • 6 different families • A very large number of different domains both to bind their ligand and to activate G proteins. • Pharmaceutically the most important class • Challenge: Methods to find novel GCPRs in human genome …
  • 48. GPCR Topology
  • 49. GPCR Topology GPCR Structure • Seven transmembrane regions • Hydrophobic/ hydrophilic domains • Conserved residues and motifs (i.e. NPXXY)
  • 50. GPCR Topology Eg. Plot conserverd residues (or multiple alignement: MSA to SSA)
  • 51. Levels of protein structure • Difficult to predict • Functional units: Apoptosome, proteasome
  • 52. Protein Structure Introduction Why ? How do proteins fold ? Levels of protein structure 0,1,2,3,4 X-ray / NMR The Protein Database (PDB) Protein Modeling Bioinformatics & Proteomics Weblems
  • 53. What is X-ray Crystallography • X-ray crystallography is an experimental technique that exploits the fact that X-rays are diffracted by crystals. • X-rays have the proper wavelength (in the Ångström range, ~10-8 cm) to be scattered by the electron cloud of an atom of comparable size. • Based on the diffraction pattern obtained from X-ray scattering off the periodic assembly of molecules or atoms in the crystal, the electron density can be reconstructed. • A model is then progressively built into the experimental electron density, refined against the data and the result is a quite accurate molecular structure.
  • 54. NMR or Crystallography ? • NMR uses protein in solution – Can look at the dynamic properties of the protein structure – Can look at the interactions between the protein and ligands, substrates or other proteins – Can look at protein folding – Sample is not damaged in any way – The maximum size of a protein for NMR structure determination is ~30 kDa.This elliminates ~50% of all proteins – High solubility is a requirement • X-ray crystallography uses protein crystals – – – – – – No size limit: As long as you can crystallise it Solubility requirement is less stringent Simple definition of resolution Direct calculation from data to electron density and back again Crystallisation is the process bottleneck, Binary (all or nothing) Phase problem Relies on heavy atom soaks or SeMet incorporation • Both techniques require large amounts of pure protein and require expensive equipment!
  • 55. Protein Structure Introduction Why ? How do proteins fold ? Levels of protein structure 0,1,2,3,4 X-ray / NMR The Protein Database (PDB) Protein Modeling Bioinformatics & Proteomics Weblems
  • 56. PDB
  • 57. PDB
  • 58. PDB
  • 59. PDB
  • 60. Visualizing Structures Cn3D versie 4.0 (NCBI)
  • 61. Visualizing Structures Ball: Van der Waals radius Stick: length joins center N, blue/O, red/S, yellow/C, gray (green)
  • 62. Visualizing Structures From N to C
  • 63. Visualizing Structures • Demonstration of Protein explorer • PDB, install Chime • Search helicase (select structure where DNA is present) • Stop spinning, hide water molecules • Show basic residues, interact with negatively charged backbone • RASMOL / Cn3D
  • 64. Protein Structure Introduction Why ? How do proteins fold ? Levels of protein structure 0,1,2,3,4 X-ray / NMR The Protein Database (PDB) Protein Modeling Bioinformatics & Proteomics Weblems
  • 65. Modeling
  • 66. Protein Stucture Molecular Modeling: building a 3D protein structure from its sequence
  • 67. Modeling • Finding a structural homologue • Blast –versus PDB database or PSIblast (E<0.005) –Domain coverage at least 60% • Avoid Gaps –Choose for few gaps and reasonable similarity scores instead of lots of gaps and high similarity scores
  • 68. Modeling • Extract “template” sequences and align with query • • Whatch out for missing data (PDB file) and complement with additonal templates Try to get as much information as possible, X/NMR • Sequence alignment from structure comparson of templates (SSA) can be different from a simple sequence aligment • • >40% identity, any aligment method is OK <40%, checks are essential – – – – • Residue conservation checks in functional regions (patterns/motifs) Indels: combine gaps separted by few resides Manual editing: Move gaps from secondary elements to loops Within loops, move gaps to loop ends, i.e. turnaround point of backbone Align templates structurally, extract the corresponding SSA or QTA (Query/template alignment)
  • 69. Modeling Input for model building • Query sequence (the one you want the 3D model for) • Template sequences and structures • Query/Template(s) (structure) sequence aligment
  • 70. Modeling • Methods (details on these see paper): – WHATIF, – SWISS-MODEL, – MODELLER, – ICM, – 3D-JIGSAW, – CPH-models, – SDC1
  • 71. Modeling • Model evaluation (How good is the prediction, how much can the algorithm rely/extract on the provided templates) – PROCHECK – WHATIF – ERRAT • CASP (Critical Assessment of Structure Prediction) – Beste method is manual alignment editing !
  • 72. Comparative modelling at CASP BC alignment side chain short loops longer loops CASP1 CASP2 CASP3 CASP4 excellent ~ 80% 1.0 Å 2.0 Å poor ~ 50% ~ 3.0 Å > 5.0 Å fair ~ 75% ~ 1.0 Å ~ 3.0 Å fair ~75% ~ 1.0 Å ~ 2.5 Å fair ~75% ~ 1.0 Å ~ 2.0 Å CASP4: overall model accuracy ranging from 1 Å to 6 Å for 50-10% sequence identity **T128/sodm – 1.0 Å (198 residues; 50%) **T111/eno – 1.7 Å (430 residues; 51%) **T122/trpa – 2.9 Å (241 residues; 33%) **T125/sp18 – 4.4 Å (137 residues; 24%) **T112/dhso – 4.9 Å (348 residues; 24%) **T92/yeco – 5.6 Å (104 residues; 12%)
  • 73. Protein Engineering / Protein Design