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Jürgen Sühnel jsuehnel@fli-leibniz.de

Jürgen Sühnel jsuehnel@fli-leibniz.de

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Jürgen Sühnel jsuehnel@fli-leibniz.de

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  1. 3D Structures of Biological Macromolecules Part 5: Protein Structure Prediction Jürgen Sühnel jsuehnel@fli-leibniz.de Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena Centre for Bioinformatics Jena / Germany Supplementary Material: http://www.fli-leibniz.de/www_bioc/3D/

  2. PDB Content Growth 1993: 698 =1588 structures (~ 2 structures per day) 2003: 4187 = 23674 structures (~ 11 structures per day) 2005: 5421 = 34325 structures (~ 15 structures per day) 2008: 7069 = 55063 structures (~ 19 structures per day) (experimental structures only) Start

  3. PDB Content Statistics May 12, 2009

  4. SwissProt/TrEMBL: Growth Rate 15-Jan-2008

  5. Swiss-Prot/TrEMBL: Amino Acid Composition Swiss-Prot TrEMBL 15-Jan-2008

  6. Structural Genomics Structural genomics consists in the determination of the three dimensional structure of all proteins of a given organism, by experimental methods such as X-ray crystallography, NMR spectroscopy or computational approaches such as homology modelling. As opposed to traditional structural biology, the determination of a protein structure through a structural genomics effort often (but not always) comes before anything is known regarding the protein function. This raises new challenges in structural bioinformatics, i.e. determining protein function from its 3D structure. One of the important aspects of structural genomics is the emphasis on high throughput determination of protein structures. This is performed in dedicated centers of structural genomics. While most structural biologists pursue structures of individual proteins or protein groups, specialists in structural genomics pursue structures of proteins on a genome wide scale. This implies large scale cloning, expression and purification. One main advantage of this approach is economy of scale. On the other hand, the scientific value of some resultant structures is at times questioned. en.wikipedia.org/wiki/Structural_genomics

  7. Structural Genomics

  8. Protein Structure Prediction clickable map http://speedy.embl-heidelberg.de/gtsp/flowchart2.html

  9. Protein Structure Prediction

  10. A Good Protein Structure • Minimizes disallowed torsion angles • Maximizes number of hydrogen bonds • Minimizes interstitial cavities or spaces • Minimizes number of “bad” contacts • Minimizes number of buried charges

  11. Protein Structure Prediction – CAFASP Contest http://www.cs.bgu.ac.il/~dfischer/CAFASP5/

  12. Protein Structure Prediction – CASP Contest http://predictioncenter.gc.ucdavis.edu/

  13. Secondary structure • 3D structure • Modeling by homology (Comparative modeling) • Fold recognition (Threading) • Ab initio prediction • Rule-based approaches • Lattice models • Simulating the time dependence of folding • Refinement • Exploring the effect of single amino acid substitutions • Ligand effects on protein structure and dynamics (induced fit) Protein Structure Prediction

  14. Lysozyme

  15. Lysozyme – 5lyz

  16. Lysozyme – 5lyz

  17. Lysozyme – 5lyz: Information from the JenaLib Atlas Page

  18. Lysozyme – 5lyz: Information from the JenaLib Atlas Page

  19. Lysozyme – 5lyz: Information from the JenaLib Atlas Page

  20. Lysozyme – 5lyz: Information from the JenaLib Atlas Page

  21. Lysozyme – 5lyz: PROSITE Signature

  22. Lysozyme – 5lyz: PROSITE Signature

  23. PROMOTIF Secondary Structure Analysis – 5lyz . .

  24. Protein Backbone Torsion Angles D. W. Mount: Bioinformatics, Cold Spring Harbor Laboratory Press, 2001.

  25. Protein Backbone Torsion Angles

  26. PROMOTIF Secondary Structure Analysis – 5lyz

  27. PROMOTIF Secondary Structure Analysis – 5lyz

  28. PROMOTIF Secondary Structure Analysis – 5lyz

  29. Chou-Fasman Secondary Structure Prediction

  30. Amino Acid Propensities From a database of experimental 3D structures, calculate the propensity for a given amino acid to adopt a certain type of secondary structure • Example: N(Ala)=2.000; N(tot)=20.000; N(Ala, helix)=568;N(helix)=4,000. P(Ala,helix) = [N(Ala,helix)/N(helix)] / [N(Ala)/N(tot)] P(Ala,helix) = [568/4.000]/[2.000/20.000] = 1.42 Used in Chou-Fasman algorithm

  31. Chou-Fasman Secondary Structure Prediction • Assign all of the residues in the peptide the appropriate set of parameters. • Scan through the peptide and identify regions where 4 out of 6 contiguous residues have P(a-helix) > 100. • That region is declared an alpha-helix. Extend the helix in both directions until a set of four contiguous • residues that have an average P(a-helix) < 100 is reached. That is declared the end of the helix. • If the segment defined by this procedure is longer than 5 residues and the average • P(a-helix) > P(b-sheet) for that segment, the segment can be assigned as a helix. • Repeat this procedure to locate all of the helical regions in the sequence. • Scan through the peptide and identify a region where 3 out of 5 of the residues have a value of • P(b-sheet) > 100. That region is declared as a beta-sheet. Extend the sheet in both directions • until a set of four contiguous residues that have an average P(b-sheet) < 100 is reached. • That is declared the end of the beta-sheet. Any segment of the region located by this procedure • is assigned as a beta-sheet if the average P(b-sheet) > 105 and the average P(b-sheet) > P(a-helix) • for that region. • Any region containing overlapping alpha-helical and beta-sheet assignments are taken to be helical if the • average P(a-helix) > P(b-sheet) for that region. It is a beta sheet if the average • P(b-sheet) > P(a-helix) for that region. • To identify a bend at residue number j, calculate the following value • p(t) = f(j)f(j+1)f(j+2)f(j+3) • where the f(j+1) value for the j+1 residue is used, the f(j+2) value for the j+2 residue is used and • the f(j+3) value for the j+3 residue is used. If: (1) p(t) > 0.000075; (2) the average value for • P(turn) > 1.00 in the tetrapeptide; and (3) the averages for the tetrapeptide obey the inequality • P(a-helix) < P(turn) > P(b-sheet), then a beta-turn is predicted at that location.

  32. Lysozyme – 5lyz: Chou-Fasman Secondary Structure Prediction http://fasta.bioch.virginia.edu/fasta_www/chofas.htm

  33. Lysozyme – 5lyz: Chou-Fasman Secondary Structure Prediction GRCE (0.57|0.98|0.70|1.39) 0.91 RCEL (0.98|0.70|1.39|1.41)1.12 CELA (0.70|1.39|1.41|1.42)1.23 ELAA (1.39|1.41|1.42|1.42) 1.41 http://fasta.bioch.virginia.edu/fasta_www/chofas.htm

  34. Lysozyme – 5lyz: PhD/PROF Structure Prediction PROF_sec: PROF predicted secondary structure: H=helix, E=extended (sheet), blank=other (loop) PROF = PROF: Profile network prediction Heidelberg Rel_sec reliability index for PROF_sec prediction (0=low to 9=high) SUB_sec subset of the PROFsec prediction, for all residues with an expected average accuracy > 82% (tables in header) NOTE: for this subset the following symbols are used: L: is loop (for which above ' ' is used) .: means that no prediction is made for this residue, as the reliability is: Rel < 5 O3_acc observed relative solvent accessibility (acc) in 3 states: b = 0-9%, i = 9-36%, e = 36-100%. P3_acc PROF predicted relative solvent accessibility (acc) in 3 states: b = 0-9%, i = 9-36%, e = 36-100%. Rel_acc reliability index for PROFacc prediction (0=low to 9=high) SUB_acc subset of the PROFacc prediction, for all residues with an expected average correlation > 0.69 (tables in header) NOTE: for this subset the following symbols are used: I: is intermediate (for which above ' ' is used) .: means that no prediction is made for this residue, as the reliability is: Rel < 4 http://cubic.bioc.columbia.edu/predictprotein/submit_def.html#top

  35. Lysozyme – 5lyz: PhD/PROF Structure Prediction, BLAST http://cubic.bioc.columbia.edu/predictprotein/submit_def.html#top

  36. Lysozyme – 5lyz: PhD/PROF Structure Prediction, BLAST http://cubic.bioc.columbia.edu/predictprotein/submit_def.html#top

  37. Lysozyme – 5lyz: PhD/PROF Structure Prediction • Perform BLAST search to find local alignments • Remove alignments that are “too close” • Perform multiple alignments of sequences • Construct a profile (PSSM) of amino-acid frequencies at each residue • Use this profile as input to the neural network • A second network performs “smoothing” • The third level computes jury decision of several different instantiations of the first two levels. http://cubic.bioc.columbia.edu/predictprotein/submit_def.html#top

  38. Lysozyme – 5lyz: PsiPred Structure Prediction http://bioinf.cs.ucl.ac.uk/psipred/psiform.html

  39. PsiPred PSIPRED is a  simple and reliable secondary structure prediction method, incorporating two feed-forward neural networks which perform an analysis on output obtained from PSI-BLAST (Position Specific Iterated - BLAST). Version 2.0 of PSIPRED includes a new algorithm which averages the output from up to 4 separate neural networks in the prediction process to further increase prediction accuracy. Using a very stringent cross validation method to evaluate the method's performance, PSIPRED 2.0 is capable of achieving an average Q3 score of nearly 78%. Predictions produced by PSIPRED were also submitted to the CASP4 server and assessed during the CASP4 meeting, which took place in December 2000 at Asilomar. PSIPRED 2.0 achieved an average Q3 score of 80.6% across all 40 submitted target domains with no obvious sequence similarity to structures present in PDB, which placed PSIPRED in first place out of 20 evaluated methods (an earlier version of PSIPRED was also ranked first in CASP3 held in 1998). http://bioinf.cs.ucl.ac.uk/psipred/psiform.html

  40. PSI-BLAST Position specific iterative BLAST (PSI-BLAST) refers to a feature of BLAST 2.0 in which a profile (or position specific scoring matrix, PSSM) is constructed (automatically) from a multiple alignment of the highest scoring hits in an initial BLAST search. The PSSM is generated by calculating position-specific scores for each position in the alignment. Highly conserved positions receive high scores and weakly conserved positions receive scores near zero. The profile is used to perform a second (etc.) BLAST search and the results of each "iteration" are used to refine the profile. This iterative searching strategy results in increased sensitivity.

  41. Comparing Secondary Structure Prediction Results PsiPred Chou-Fasman Phd/PROF

  42. Comparing Secondary Structure Prediction Results

  43. Protein Secondary Structure Prediction - Summary • 1st Generation - 1970s • Chou & Fasman, Q3 = 50-55% • 2nd Generation -1980s • Qian & Sejnowski, Q3 = 60-65% • 3rd Generation - 1990s • PHD, PSI-PRED, Q3 = 70-80% • Features of the new methods: • Taking into account evolutionary information • Neural networks • Failures: • Nonlocal sequence interactions • Wrong prediction at the ends of H/E Q3 – Percentage of correctly assigned amino acids in a test set

  44. Protein Structure Prediction http://speedy.embl-heidelberg.de/gtsp/flowchart2.html

  45. Modeling by Homology (Comparative Modeling) http://salilab.org/modeller/

  46. Modeling by Homology (Comparative Modeling) http://modbase.compbio.ucsf.edu/modbase-cgi-new/search_form.cgi

  47. Modeling by Homology (Comparative Modeling) http://modbase.compbio.ucsf.edu/modbase-cgi-new/search_form.cgi

  48. Modeling by Homology (Comparative Modeling) http://modbase.compbio.ucsf.edu/modbase-cgi-new/search_form.cgi

  49. Modeling by Homology (Comparative Modeling) http://swissmodel.expasy.org/

  50. Modeling by Homology (Comparative Modeling) • Comparative modeling predicts the three-dimensional structure of a given • protein sequence (target) based primarily on its alignment to one or more proteins • of known structure (templates). • The prediction process consists of • fold assignment, • target template alignment, • model building, and • model evaluation and refinement. • The number of protein sequences that can be modeled and the accuracy of • the predictions are increasing steadily because of the growth in the number of • known protein structures and because of the improvements in the modeling • software. • Further advances are necessary in recognizing weak sequence structure • similarities, aligning sequences with structures, modeling of rigid body shifts, • distortions, loops and side chains, as well as detecting errors in a model. • Despite these problems, it is currently possible to model with useful accuracy • significant parts of approximately one third of all known protein sequences. http://salilab.org/modeller/