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Bioinformatica 01-12-2011-t7-protein

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Bioinformatica 01-12-2011-t7-protein

  1. 2. FBW 1-12-2011 Wim Van Criekinge
  2. 3. Inhoud Lessen: Bioinformatica <ul><li>don 29-09-2011: 1* Bioinformatics (practicum 8.30-11.00) </li></ul><ul><li>don 06-10-2011: 2* Biological Databases (practicum 9.00-11.30) </li></ul><ul><li>don 20-10-2011: 3 Sequence Similarity (Scoring Matrices) </li></ul><ul><li>don 27-10-2011: 4 Sequence Alignments </li></ul><ul><li>don 10-11-2011: 5 Database Searching Fasta/Blast </li></ul><ul><li>don 17-11-2011: afgelast </li></ul><ul><li>don 24-11-2011: 6 Phylogenetics </li></ul><ul><li>don 01-12-2011: 7 Protein Structure </li></ul><ul><li>don 08-12-2011: 8 Gene Prediction, Gene Ontologies & HMM </li></ul><ul><li>don 15-12-2011: 9-10 Bio- & Cheminformatics in Drug Discovery (inhaalweek) </li></ul><ul><li>Opgelet: Geen les op don 13-10-2010 en don 3-11-2010 </li></ul>
  3. 4. Biobix: Applied Bioinformatics Research <ul><li>Thesisonderwerpen </li></ul><ul><ul><li>Lopend onderzoek </li></ul></ul><ul><ul><ul><li>Biomerker predictie / Methylatie </li></ul></ul></ul><ul><ul><ul><li>Metabonomics </li></ul></ul></ul><ul><ul><ul><li>Peptidomics </li></ul></ul></ul><ul><ul><ul><li>Translational biotechnology (text mining) </li></ul></ul></ul><ul><ul><ul><li>Structural Genomics </li></ul></ul></ul><ul><ul><ul><li>miRNA prediction / Target Prediction </li></ul></ul></ul><ul><ul><ul><li>Exploring genomic dark matter ( junk mining ) </li></ul></ul></ul><ul><ul><li>Samenwerking met diverse instituten </li></ul></ul><ul><ul><li>Ambities om te peer-reviewed te publiceren </li></ul></ul>
  4. 5. <ul><li>empirical finding: if two biological sequences are sufficiently similar, almost invariably they have similar biological functions and will be descended from a common ancestor. </li></ul><ul><li>(i) function is encoded into sequence , this means: the sequence provides the syntax and </li></ul><ul><li>(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. </li></ul><ul><ul><li>The reason for “bioinformatics” to exist ? </li></ul></ul>
  5. 6. Protein Structure <ul><li>Introduction </li></ul><ul><li>Why ? </li></ul><ul><li>How do proteins fold ? </li></ul><ul><li>Levels of protein structure </li></ul><ul><li>0,1,2,3,4 </li></ul><ul><li>X-ray / NMR </li></ul><ul><li>The Protein Database (PDB) </li></ul><ul><li>Protein Modeling </li></ul><ul><li>Bioinformatics & Proteomics </li></ul><ul><li>Weblems </li></ul>
  6. 7. <ul><li>Proteins perform a variety of cellular tasks in the living cells </li></ul><ul><li>Each protein adopts a particular folding that determines its function </li></ul><ul><li>The 3D structure of a protein can bring into close proximity residues that are far apart in the amino acid sequence </li></ul><ul><li>Catalytic site: Business End of the molecule </li></ul>Why protein structure ?
  7. 8. 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 homology rational mutagenesis biochemical analysis model studies Protein structure - three dimensional - complicated - mediates function
  8. 9. About the use of protein models (Peitch) <ul><li>Structure is preserved under evolution when sequence is not </li></ul><ul><ul><li>Interpreting the impact of mutations/SNPs and conserved residues on protein function. Potential link to disease </li></ul></ul><ul><ul><ul><li>Function ? </li></ul></ul></ul><ul><ul><ul><ul><li>Biochemical: the chemical interactions occerring in a protein </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Biological: role within the cell </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Phenotypic: the role in the organism </li></ul></ul></ul></ul><ul><ul><ul><li>Gene Ontology functional classification ! </li></ul></ul></ul><ul><ul><li>Priorisation of residues to mutate to determine protein function </li></ul></ul><ul><ul><li>Providing hints for protein function: Catalytic mechanisms of enzymes often require key residues to be close together in 3D space </li></ul></ul><ul><ul><li>(protein-ligand complexes, rational drug design, putative interaction interfaces) </li></ul></ul>
  9. 10. MIS-SENSE MUTATION e.g. Sickle Cell Anaemia Cause : defective haemoglobin due to mutation in β-globin gene Symptoms : severe anaemia and death in homozygote
  10. 11. <ul><li>Normal β-globin - 146 amino acids </li></ul><ul><li>val - his - leu - thr - pro - glu - glu - --------- </li></ul><ul><li>1 2 3 4 5 6 7 </li></ul><ul><li>Normal gene (aa 6) Mutant gene </li></ul><ul><li>DNA CTC C A C </li></ul><ul><li>mRNA GAG GUG </li></ul><ul><li>Product Glu Valine </li></ul><ul><li>Mutant β-globin </li></ul><ul><li>val - his - leu - thr - pro - val - glu - --------- </li></ul>
  11. 12. Protein Conformation <ul><li>Christian Anfinsen Studies on reversible denaturation “Sequence specifies conformation” </li></ul><ul><li>Chaperones and disulfide interchange enzymes: involved but not controlling final state, they provide environment to refold if misfolded </li></ul><ul><li>Structure implies function: The amino acid sequence encodes the protein’s structural information </li></ul>
  12. 13. <ul><li>by itself: </li></ul><ul><ul><li>Anfinsen had developed what he called his &quot;thermodynamic hypothesis&quot; 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. </li></ul></ul><ul><ul><li>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. </li></ul></ul><ul><ul><li>&quot;The native conformation is determined by the totality of interatomic interactions and hence by the amino acid sequence, in a given environment.&quot; </li></ul></ul>How does a protein fold ?
  13. 14. Protein Structure <ul><li>Introduction </li></ul><ul><li>Why ? </li></ul><ul><li>How do proteins fold ? </li></ul><ul><li>Levels of protein structure </li></ul><ul><li>0,1,2,3,4 </li></ul><ul><li>X-ray / NMR </li></ul><ul><li>The Protein Database (PDB) </li></ul><ul><li>Protein Modeling </li></ul><ul><li>Bioinformatics & Proteomics </li></ul><ul><li>Weblems </li></ul>
  14. 15. <ul><li>Proteins are linear heteropolymers: one or more polypeptide chains </li></ul><ul><li>Below about 40 residues the term peptide is frequently used. </li></ul><ul><li>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. </li></ul><ul><li>Protein sizes range from this lower limit to several hundred residues in multi-functional proteins. </li></ul><ul><li>Three-dimentional shapes (folds) adopted vary enormously </li></ul><ul><li>Experimental methods: </li></ul><ul><ul><li>X-ray crystallography </li></ul></ul><ul><ul><li>NMR (nuclear magnetic resonance) </li></ul></ul><ul><ul><li>Electron microscopy </li></ul></ul><ul><ul><li>Ab initio calculations … </li></ul></ul>The Basics
  15. 16. <ul><li>Zeroth: amino acid composition (proteomics, %cysteine, %glycine) </li></ul>Levels of protein structure
  16. 17. 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 &quot;CO-R-N&quot; in a clockwise direction.  <ul><ul><li>Amino Acid Residues </li></ul></ul>
  17. 19. <ul><ul><li>Amino Acid Residues </li></ul></ul>
  18. 20. <ul><ul><li>Amino Acid Residues </li></ul></ul>
  19. 21. <ul><ul><li>Amino Acid Residues </li></ul></ul>
  20. 22. <ul><ul><li>Amino Acid Residues </li></ul></ul>
  21. 23. <ul><li>Primary: This is simply the order of covalent linkages along the polypeptide chain, I.e. the sequence itself </li></ul>Levels of protein structure
  22. 24. Backbone Torsion Angles
  23. 25. Backbone Torsion Angles
  24. 26. <ul><li>Secondary </li></ul><ul><ul><li>Local organization of the protein backbone: alpha-helix, Beta-strand (which assemble into Beta-sheets) turn and interconnecting loop. </li></ul></ul>Levels of protein structure
  25. 27. Ramachandran / Phi-Psi Plot
  26. 28. The alpha-helix
  27. 29. <ul><li>Residues with hydrophobic properties conserved at i, i+2, i+4 separated by unconserved or hydrophilic residues suggest surface beta- strands. </li></ul><ul><li>A short run of hydrophobic amino acids (4 residues) suggests a buried beta-strand. </li></ul><ul><li>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. </li></ul><ul><ul><li>A Practical Approach: Interpretation </li></ul></ul>
  28. 30. Beta-sheets
  29. 31. Topologies of Beta-sheets
  30. 32. Secondary structure prediction ?
  31. 33. <ul><li>Chou, P.Y. and Fasman, G.D. (1974). </li></ul><ul><li>Conformational parameters for amino acids in helical,  -sheet, and random coil regions calculated from proteins. </li></ul><ul><li>Biochemistry 13 , 211-221. </li></ul><ul><li>Chou, P.Y. and Fasman, G.D. (1974). </li></ul><ul><li>Prediction of protein conformation. </li></ul><ul><li>Biochemistry 13 , 222-245. </li></ul>Secondary structure prediction:CHOU-FASMAN
  32. 34. <ul><li>Method </li></ul><ul><ul><li>Assigning a set of prediction values to a residue, based on statistic analysis of 15 proteins </li></ul></ul><ul><ul><li>Applying a simple algorithm to those numbers </li></ul></ul>Secondary structure prediction:CHOU-FASMAN
  33. 35. Calculation of preference parameters <ul><li>observed counts </li></ul><ul><li>P = Log --------------------- + 1.0 </li></ul><ul><li>expected counts </li></ul><ul><li>Preference parameter > 1.0  specific residue has a preference for the specific secondary structure. </li></ul><ul><li>Preference parameter = 1.0  specific residue does not have a preference for, nor dislikes the specific secondary structure. </li></ul><ul><li>Preference parameter < 1.0  specific residue dislikes the specific secondary structure. </li></ul>For each of the 20 residues and each secondary structure (  -helix,  -sheet and  -turn): Secondary structure prediction:CHOU-FASMAN
  34. 36. Preference parameters Secondary structure prediction:CHOU-FASMAN 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
  35. 37. Applying algorithm <ul><li>Assign parameters to residue. </li></ul><ul><li>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. </li></ul><ul><li>Repeat this procedure to locate all of the helical regions. </li></ul><ul><li>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. </li></ul><ul><li>Rest: P(a)>P(b)   -helix. P(b)>P(a)   -sheet. </li></ul><ul><li>To identify a bend at residue number i, calculate the following value: </li></ul><ul><li>p(t) = f(i)f(i+1)f(i+2)f(i+3) </li></ul><ul><li>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. </li></ul>Secondary structure prediction:CHOU-FASMAN
  36. 38. Successful method? <ul><li>19 proteins evaluated: </li></ul><ul><li>Successful in locating 88% of helical and 95% of  regions </li></ul><ul><li>Correctly predicting 80% of helical and 86% of  -sheet residues </li></ul><ul><li>Accuracy of predicting the three conformational states for all residues, helix, b, and coil, is 77% </li></ul><ul><li>Chou & Fasman:successful method </li></ul><ul><li>After 1974:improvement of preference parameters </li></ul>Secondary structure prediction:CHOU-FASMAN
  37. 40. Sander-Schneider: Evolution of overall structure <ul><li>Naturally occurring sequences with more than 20% sequence identity over 80 or more residues always adopt the same basic structure (Sander and Schneider 1991) </li></ul>
  38. 41. Sander-Schneider <ul><li>HSSP: homology derived secondary structure </li></ul>
  39. 42. <ul><li>SCOP: </li></ul><ul><ul><li>Structural Classification of Proteins </li></ul></ul><ul><li>FSSP: </li></ul><ul><ul><li>Family of Structurally Similar Proteins </li></ul></ul><ul><li>CATH: </li></ul><ul><ul><li>Class, Architecture, Topology, Homology </li></ul></ul>Structural Family Databases
  40. 43. Levels of protein structure <ul><li>Tertiary </li></ul><ul><ul><li>Packing of secondary structure elements into a compact spatial unit </li></ul></ul><ul><ul><li>Fold or domain – this is the level to which structure is currently possible </li></ul></ul>
  41. 44. Domains
  42. 45. Protein Architecture
  43. 46. <ul><li>Protein Dissection into domain </li></ul><ul><li>Conserved Domain Architecture Retrieval Tool (CDART) uses information in Pfam and SMART to assign domains along a sequence </li></ul><ul><li>(automatic when blasting) </li></ul>Domains
  44. 47. <ul><li>From the analysis of alignment of protein families </li></ul><ul><li>Conserved sequence features, usually associate with a specific function </li></ul><ul><li>PROSITE database for protein “signature” protein (large amount of FP & FN) </li></ul><ul><li>From aligment of homologous sequences (PRINTS/PRODOM) </li></ul><ul><li>From Hidden Markov Models (PFAM) </li></ul><ul><li>Meta approach: INTERPRO </li></ul>Domains
  45. 48. Protein Architecture
  46. 49. Levels of protein structure: Topology
  47. 50. Hydrophobicity Plot P53_HUMAN (P04637) human cellular tumor antigen p53 Kyte-Doolittle hydrophilicty, window=19
  48. 52. 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
  49. 54. <ul><li>Membrane-bound receptors </li></ul><ul><li>A very large number of different domains both to bind their ligand and to activate G proteins. </li></ul><ul><li>6 different families </li></ul><ul><li>Transducing messages as photons, organic odorants, nucleotides, nucleosides, peptides, lipids and proteins. </li></ul>GPCR Topology <ul><li>Pharmaceutically the most important class </li></ul><ul><li>Challenge: Methods to find novel GCPRs in human genome … </li></ul>
  50. 55. GPCR Topology
  51. 56. <ul><li>Seven transmembrane regions </li></ul>GPCR Structure <ul><li>Conserved residues and motifs (i.e. NPXXY) </li></ul><ul><li>Hydrophobic/ hydrophilic domains </li></ul>GPCR Topology
  52. 57. GPCR Topology Eg. Plot conserverd residues (or multiple alignement: MSA to SSA)
  53. 58. Levels of protein structure <ul><li>Difficult to predict </li></ul><ul><li>Functional units: Apoptosome, proteasome </li></ul>
  54. 59. Protein Structure <ul><li>Introduction </li></ul><ul><li>Why ? </li></ul><ul><li>How do proteins fold ? </li></ul><ul><li>Levels of protein structure </li></ul><ul><li>0,1,2,3,4 </li></ul><ul><li>X-ray / NMR </li></ul><ul><li>The Protein Database (PDB) </li></ul><ul><li>Protein Modeling </li></ul><ul><li>Bioinformatics & Proteomics </li></ul><ul><li>Weblems </li></ul>
  55. 60. <ul><li>X-ray crystallography is an experimental technique that exploits the fact that X-rays are diffracted by crystals. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>A model is then progressively built into the experimental electron density, refined against the data and the result is a quite accurate molecular structure. </li></ul>What is X-ray Crystallography
  56. 61. <ul><li>NMR uses protein in solution </li></ul><ul><ul><li>Can look at the dynamic properties of the protein structure </li></ul></ul><ul><ul><li>Can look at the interactions between the protein and ligands, substrates or other proteins </li></ul></ul><ul><ul><li>Can look at protein folding </li></ul></ul><ul><ul><li>Sample is not damaged in any way </li></ul></ul><ul><ul><li>The maximum size of a protein for NMR structure determination is ~30 kDa.This elliminates ~50% of all proteins </li></ul></ul><ul><ul><li>High solubility is a requirement </li></ul></ul><ul><li>X-ray crystallography uses protein crystals </li></ul><ul><ul><li>No size limit: As long as you can crystallise it </li></ul></ul><ul><ul><li>Solubility requirement is less stringent </li></ul></ul><ul><ul><li>Simple definition of resolution </li></ul></ul><ul><ul><li>Direct calculation from data to electron density and back again </li></ul></ul><ul><ul><li>Crystallisation is the process bottleneck, Binary (all or nothing) </li></ul></ul><ul><ul><li>Phase problem Relies on heavy atom soaks or SeMet incorporation </li></ul></ul><ul><li>Both techniques require large amounts of pure protein and require expensive equipment! </li></ul>NMR or Crystallography ?
  57. 62. Protein Structure <ul><li>Introduction </li></ul><ul><li>Why ? </li></ul><ul><li>How do proteins fold ? </li></ul><ul><li>Levels of protein structure </li></ul><ul><li>0,1,2,3,4 </li></ul><ul><li>X-ray / NMR </li></ul><ul><li>The Protein Database (PDB) </li></ul><ul><li>Protein Modeling </li></ul><ul><li>Bioinformatics & Proteomics </li></ul><ul><li>Weblems </li></ul>
  58. 63. PDB
  59. 64. PDB
  60. 65. PDB
  61. 66. PDB
  62. 67. Visualizing Structures Cn3D versie 4.0 (NCBI)
  63. 68. Ball: Van der Waals radius Stick: length joins center N, blue/O, red/S, yellow/C, gray (green) Visualizing Structures
  64. 69. From N to C Visualizing Structures
  65. 70. <ul><li>Demonstration of Protein explorer </li></ul><ul><li>PDB, install Chime </li></ul><ul><li>Search helicase (select structure where DNA is present) </li></ul><ul><li>Stop spinning, hide water molecules </li></ul><ul><li>Show basic residues, interact with negatively charged backbone </li></ul><ul><li>RASMOL / Cn3D </li></ul>Visualizing Structures
  66. 71. Protein Structure <ul><li>Introduction </li></ul><ul><li>Why ? </li></ul><ul><li>How do proteins fold ? </li></ul><ul><li>Levels of protein structure </li></ul><ul><li>0,1,2,3,4 </li></ul><ul><li>X-ray / NMR </li></ul><ul><li>The Protein Database (PDB) </li></ul><ul><li>Protein Modeling </li></ul><ul><li>Bioinformatics & Proteomics </li></ul><ul><li>Weblems </li></ul>
  67. 72. Modeling
  68. 73. <ul><li>Protein Stucture </li></ul><ul><li>Molecular Modeling: </li></ul><ul><li>building a 3D protein structure </li></ul><ul><li>from its sequence </li></ul>
  69. 74. <ul><li>Finding a structural homologue </li></ul><ul><li>Blast </li></ul><ul><ul><li>versus PDB database or PSI-blast (E<0.005) </li></ul></ul><ul><ul><li>Domain coverage at least 60% </li></ul></ul><ul><li>Avoid Gaps </li></ul><ul><ul><li>Choose for few gaps and reasonable similarity scores instead of lots of gaps and high similarity scores </li></ul></ul>Modeling
  70. 75. <ul><li>Extract “template” sequences and align with query </li></ul><ul><li>Whatch out for missing data (PDB file) and complement with additonal templates </li></ul><ul><li>Try to get as much information as possible, X/NMR </li></ul><ul><li>Sequence alignment from structure comparson of templates (SSA) can be different from a simple sequence aligment </li></ul><ul><li>>40% identity, any aligment method is OK </li></ul><ul><li><40%, checks are essential </li></ul><ul><ul><li>Residue conservation checks in functional regions (patterns/motifs) </li></ul></ul><ul><ul><li>Indels: combine gaps separted by few resides </li></ul></ul><ul><ul><li>Manual editing: Move gaps from secondary elements to loops </li></ul></ul><ul><ul><li>Within loops, move gaps to loop ends, i.e. turnaround point of backbone </li></ul></ul><ul><li>Align templates structurally, extract the corresponding SSA or QTA (Query/template alignment) </li></ul>Modeling
  71. 76. <ul><li>Input for model building </li></ul><ul><li>Query sequence (the one you want the 3D model for) </li></ul><ul><li>Template sequences and structures </li></ul><ul><li>Query/Template(s) (structure) sequence aligment </li></ul>Modeling
  72. 77. <ul><li>Methods (details on these see paper): </li></ul><ul><ul><li>WHATIF, </li></ul></ul><ul><ul><li>SWISS-MODEL, </li></ul></ul><ul><ul><li>MODELLER, </li></ul></ul><ul><ul><li>ICM, </li></ul></ul><ul><ul><li>3D-JIGSAW, </li></ul></ul><ul><ul><li>CPH-models, </li></ul></ul><ul><ul><li>SDC1 </li></ul></ul>Modeling
  73. 78. <ul><li>Model evaluation (How good is the prediction, how much can the algorithm rely/extract on the provided templates) </li></ul><ul><ul><li>PROCHECK </li></ul></ul><ul><ul><li>WHATIF </li></ul></ul><ul><ul><li>ERRAT </li></ul></ul><ul><li>CASP (Critical Assessment of Structure Prediction) </li></ul><ul><ul><li>Beste method is manual alignment editing ! </li></ul></ul>Modeling
  74. 79. **T112/dhso – 4.9 Å (348 residues; 24%) **T92/yeco – 5.6 Å (104 residues; 12%) **T128/sodm – 1.0 Å (198 residues; 50%) **T125/sp18 – 4.4 Å (137 residues; 24%) **T111/eno – 1.7 Å (430 residues; 51%) **T122/trpa – 2.9 Å (241 residues; 33%) Comparative modelling at CASP CASP4: overall model accuracy ranging from 1 Å to 6 Å for 50-10% sequence identity CASP2 fair ~ 75% ~ 1.0 Å ~ 3.0 Å CASP3 fair ~75% ~ 1.0 Å ~ 2.5 Å CASP4 fair ~75% ~ 1.0 Å ~ 2.0 Å CASP1 poor ~ 50% ~ 3.0 Å > 5.0 Å BC excellent ~ 80% 1.0 Å 2.0 Å alignment side chain short loops longer loops
  75. 81. Protein Engineering / Protein Design

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