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Protein Secondary Structures

Protein Secondary Structures. Assignment and prediction. Use of secondary structure. Classification of protein structures Definition of loops/core Use in fold recognition methods Improvements of alignments Definition of domain boundaries. Secondary Structure Elements.

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Protein Secondary Structures

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  1. Protein Secondary Structures Assignment and prediction Claus Lundegaard

  2. Use of secondary structure • Classification of protein structures • Definition of loops/core • Use in fold recognition methods • Improvements of alignments • Definition of domain boundaries Claus Lundegaard

  3. Secondary Structure Elements Claus Lundegaard

  4. Classification of secondary structure • Defining features • Dihedral angles • Hydrogen bonds • Geometry • Assigned manually by crystallographers or • Automatic • DSSP (Kabsch & Sander,1983) • STRIDE (Frishman & Argos, 1995) • Continuum (Andersen et al.) Claus Lundegaard

  5. Dihedral Angles From http://www.imb-jena.de phi - dihedral angle about the N-Calpha bond psi - dihedral angle about the Calpha-C bond omega - dihedral angle about the C-N (peptide) bond Claus Lundegaard

  6. Alpha helices phi(deg) psi(deg) H-bond pattern ------------------------------------------------------------------ right-handed alpha-helix -57.8 -47.0 i+4 pi-helix -57.1 -69.7 i+5 3-10 helix -74.0 -4.0 i+3 (omega is 180 deg in all cases) ----------------------------------------------------------------- From http://www.imb-jena.de Claus Lundegaard

  7. Beta Strands phi(deg) psi(deg) omega (deg) ------------------------------------------------------------------ beta strand -120 120 180 ----------------------------------------------------------------- Hydrogen bond patterns in beta sheets. Here a four-stranded beta sheet is drawn schematically which contains three antiparallel and one parallel strand. Hydrogen bonds are indicated with red lines (antiparallel strands) and green lines (parallel strands) connecting the hydrogen and receptor oxygen. From http://broccoli.mfn.ki.se/pps_course_96/ Claus Lundegaard

  8. Secondary Structure Types * H = alpha helix * B = residue in isolated beta-bridge * E = extended strand, participates in beta ladder * G = 3-helix (3/10 helix) * I = 5 helix (pi helix) * T = hydrogen bonded turn * S = bend Claus Lundegaard

  9. Automatic assignment programs • DSSP ( http://www.cmbi.kun.nl/gv/dssp/ ) • STRIDE ( http://www.hgmp.mrc.ac.uk/Registered/Option/stride.html ) # RESIDUE AA STRUCTURE BP1 BP2 ACC N-H-->O O-->H-N N-H-->O O-->H-N TCO KAPPA ALPHA PHI PSI X-CA Y-CA Z-CA 1 4 A E 0 0 205 0, 0.0 2,-0.3 0, 0.0 0, 0.0 0.000 360.0 360.0 360.0 113.5 5.7 42.2 25.1 2 5 A H - 0 0 127 2, 0.0 2,-0.4 21, 0.0 21, 0.0 -0.987 360.0-152.8-149.1 154.0 9.4 41.3 24.7 3 6 A V - 0 0 66 -2,-0.3 21,-2.6 2, 0.0 2,-0.5 -0.995 4.6-170.2-134.3 126.3 11.5 38.4 23.5 4 7 A I E -A 23 0A 106 -2,-0.4 2,-0.4 19,-0.2 19,-0.2 -0.976 13.9-170.8-114.8 126.6 15.0 37.6 24.5 5 8 A I E -A 22 0A 74 17,-2.8 17,-2.8 -2,-0.5 2,-0.9 -0.972 20.8-158.4-125.4 129.1 16.6 34.9 22.4 6 9 A Q E -A 21 0A 86 -2,-0.4 2,-0.4 15,-0.2 15,-0.2 -0.910 29.5-170.4 -98.9 106.4 19.9 33.0 23.0 7 10 A A E +A 20 0A 18 13,-2.5 13,-2.5 -2,-0.9 2,-0.3 -0.852 11.5 172.8-108.1 141.7 20.7 31.8 19.5 8 11 A E E +A 19 0A 63 -2,-0.4 2,-0.3 11,-0.2 11,-0.2 -0.933 4.4 175.4-139.1 156.9 23.4 29.4 18.4 9 12 A F E -A 18 0A 31 9,-1.5 9,-1.8 -2,-0.3 2,-0.4 -0.967 13.3-160.9-160.6 151.3 24.4 27.6 15.3 10 13 A Y E -A 17 0A 36 -2,-0.3 2,-0.4 7,-0.2 7,-0.2 -0.994 16.5-156.0-136.8 132.1 27.2 25.3 14.1 11 14 A L E >> -A 16 0A 24 5,-3.2 4,-1.7 -2,-0.4 5,-1.3 -0.929 11.7-122.6-120.0 133.5 28.0 24.8 10.4 12 15 A N T 45S+ 0 0 54 -2,-0.4 -2, 0.0 2,-0.2 0, 0.0 -0.884 84.3 9.0-113.8 150.9 29.7 22.0 8.6 13 16 A P T 45S+ 0 0 114 0, 0.0 -1,-0.2 0, 0.0 -2, 0.0 -0.963 125.4 60.5 -86.5 8.5 32.0 21.6 6.8 14 17 A D T 45S- 0 0 66 2,-0.1 -2,-0.2 1,-0.1 3,-0.1 0.752 89.3-146.2 -64.6 -23.0 33.0 25.2 7.6 15 18 A Q T <5 + 0 0 132 -4,-1.7 2,-0.3 1,-0.2 -3,-0.2 0.936 51.1 134.1 52.9 50.0 33.3 24.2 11.2 16 19 A S E < +A 11 0A 44 -5,-1.3 -5,-3.2 2, 0.0 2,-0.3 -0.877 28.9 174.9-124.8 156.8 32.1 27.7 12.3 17 20 A G E -A 10 0A 28 -2,-0.3 2,-0.3 -7,-0.2 -7,-0.2 -0.893 15.9-146.5-151.0-178.9 29.6 28.7 14.8 18 21 A E E -A 9 0A 14 -9,-1.8 -9,-1.5 -2,-0.3 2,-0.4 -0.979 5.0-169.6-158.6 146.0 28.0 31.5 16.7 19 22 A F E +A 8 0A 3 12,-0.4 12,-2.3 -2,-0.3 2,-0.3 -0.982 27.8 149.2-139.1 120.3 26.5 32.2 20.1 20 23 A M E -AB 7 30A 0 -13,-2.5 -13,-2.5 -2,-0.4 2,-0.4 -0.983 39.7-127.8-152.1 161.6 24.5 35.4 20.6 21 24 A F E -AB 6 29A 45 8,-2.4 7,-2.9 -2,-0.3 8,-1.0 -0.934 23.9-164.1-112.5 137.7 21.7 37.0 22.6 22 25 A D E -AB 5 27A 6 -17,-2.8 -17,-2.8 -2,-0.4 2,-0.5 -0.948 6.9-165.0-123.7 138.3 18.9 38.9 20.8 23 26 A F E > S-AB 4 26A 76 3,-3.5 3,-2.1 -2,-0.4 -19,-0.2 -0.947 78.4 -27.2-127.3 111.5 16.4 41.3 22.3 24 27 A D T 3 S- 0 0 74 -21,-2.6 -20,-0.1 -2,-0.5 -1,-0.1 0.904 128.9 -46.6 50.4 45.0 13.4 42.1 20.2 25 28 A G T 3 S+ 0 0 20 -22,-0.3 2,-0.4 1,-0.2 -1,-0.3 0.291 118.8 109.3 84.7 -11.1 15.4 41.4 17.0 26 29 A D E < S-B 23 0A 114 -3,-2.1 -3,-3.5 109, 0.0 2,-0.3 -0.822 71.8-114.7-103.1 140.3 18.4 43.4 18.1 27 30 A E E -B 22 0A 8 -2,-0.4 -5,-0.3 -5,-0.2 3,-0.1 -0.525 24.9-177.7 -74.1 127.5 21.8 41.8 19.1 Claus Lundegaard

  10. Secondary Structure Prediction • What to predict? • All 8 types or pool types into groups Q3 H * H = a helix * B = residue in isolated b-bridge * E = extended strand, participates in b ladder * G = 3-helix (3/10 helix) E * I = 5 helix (p helix) * T = hydrogen bonded turn * S = bend * C/.= random coil C Straight HEC CASP Claus Lundegaard

  11. Secondary Structure Prediction • Simple alignments. • Heuristic Methods (e.g., Chou-Fasman, 1974) • Neural Networks (different inputs) • Raw Sequence (late 80’s) • Blosum matrix (e.g., PhD, early 90’s) • Position specific alignment profiles (e.g., PsiPred, late 90’s) • Multiple networks balloting, probability conversion, output expansion (Petersen et al., 2000). Claus Lundegaard

  12. Improvement of accuracy 1974 Chou & Fasman ~50-53% 1978 Garnier 63% 1987 Zvelebil 66% 1988 Quian & Sejnowski 64.3% 1993 Rost & Sander 70.8-72.0% 1997 Frishman & Argos <75% 1999 Cuff & Barton 72.9% 1999 Jones 76.5% 2000 Petersen et al. 77.9% Claus Lundegaard

  13. Simple Alignments • Solved structures homologous to query needed • Homologous proteins have ~88% identical (3 state) secondary structure • If no homologue can be identified alignment will give almost random results Claus Lundegaard

  14. Amino acid preferences in a-Helix Claus Lundegaard

  15. Amino acid preferences in b-Strand Claus Lundegaard

  16. Amino acid preferences in coil Claus Lundegaard

  17. Chou-Fasman Name P(a) P(b) P(turn) f(i) f(i+1) f(i+2) f(i+3) Ala 142 83 66 0.06 0.076 0.035 0.058 Arg 98 93 95 0.070 0.106 0.099 0.085 Asp 101 54 146 0.147 0.110 0.179 0.081 Asn 67 89 156 0.161 0.083 0.191 0.091 Cys 70 119 119 0.149 0.050 0.117 0.128 Glu 151 37 74 0.056 0.060 0.077 0.064 Gln 111 110 98 0.074 0.098 0.037 0.098 Gly 57 75 156 0.102 0.085 0.190 0.152 His 100 87 95 0.140 0.047 0.093 0.054 Ile 108 160 47 0.043 0.034 0.013 0.056 Leu 121 130 59 0.061 0.025 0.036 0.070 Lys 114 74 101 0.055 0.115 0.072 0.095 Met 145 105 60 0.068 0.082 0.014 0.055 Phe 113 138 60 0.059 0.041 0.065 0.065 Pro 57 55 152 0.102 0.301 0.034 0.068 Ser 77 75 143 0.120 0.139 0.125 0.106 Thr 83 119 96 0.086 0.108 0.065 0.079 Trp 108 137 96 0.077 0.013 0.064 0.167 Tyr 69 147 114 0.082 0.065 0.114 0.125 Val 106 170 50 0.062 0.048 0.028 0.053 Claus Lundegaard

  18. Chou-Fasman 1. Assign all of the residues in the peptide the appropriate set of parameters. 2. 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. 3. Repeat this procedure to locate all of the helical regions in the sequence. 4. 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. 5. 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. 6. 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 tetra-peptide; and (3) the averages for the tetra-peptide obey the inequality P(a-helix) < P(turn) > P(b-sheet), then a beta-turn is predicted at that location. Claus Lundegaard

  19. Chou-Fasman • General applicable • Works for sequences with no solved homologs • Low Accuracy Claus Lundegaard

  20. Neural Networks • Benefits • General applicable • Can capture higher order correlations • Inputs other than sequence information • Drawbacks • Needs many data (different solved structures) • Risk of overtraining Claus Lundegaard

  21. Architecture Weights Input Layer I K H Output Layer E E E C H V I I Q A E Hidden Layer Window IKEEHVIIQAEFYLNPDQSGEF….. Claus Lundegaard

  22. Sparse encoding Inp Neuron 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 AAcid A 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 R 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 N 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 D 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Q 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 E 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Claus Lundegaard

  23. Input Layer 0 0 0 0 I 0 K 0 E 1 E 0 H 0 V 0 I 0 I 0 Q 0 A 0 E 0 0 0 0 0 0 Claus Lundegaard

  24. BLOSUM 62 A R N D C Q E G H I L K M F P S T W Y V B Z X * A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 -4 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 -1 -4 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 -1 -4 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 -4 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -4 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 -1 -4 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 -1 -4 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 -4 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 -1 -1 -4 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 -4 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 -2 -4 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 -4 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 -4 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 -2 -4 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 -1 -4 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -4 Claus Lundegaard

  25. Input Layer -1 0 0 I 2 K -4 E 2 E 5 H -2 V 0 I -3 I -3 Q 1 A -2 E -3 -1 0 -1 -3 -2 -2 Claus Lundegaard

  26. Structure to Structure Weights Input Layer H E H Output Layer C E H C E C H E C Window Hidden Layer IKEEHVIIQAEFYLNPDQSGEF….. Claus Lundegaard

  27. PHD method (Rost and Sander) • Combine neural networks with sequence profiles • 6-8 Percentage points increase in prediction accuracy over standard neural networks • Use second layer “Structure to structure” network to filter predictions • Jury of predictors • Set up as mail server Claus Lundegaard

  28. Position specific scoring matrices (BLAST profiles) A R N D C Q E G H I L K M F P S T W Y V 1 I -2 -4 -5 -5 -2 -4 -4 -5 -5 6 0 -4 0 -2 -4 -4 -2 -4 -3 4 2 K -1 -1 -2 -2 -3 -1 3 -3 -2 -2 -3 4 -2 -4 -3 1 1 -4 -3 2 3 E 5 -3 -3 -3 -3 3 1 -2 -3 -3 -3 -2 -2 -4 -3 -1 -2 -4 -3 1 4 E -4 -3 2 5 -6 1 5 -4 -3 -6 -6 -2 -5 -6 -4 -2 -3 -6 -5 -5 5 H -4 2 1 1 -5 1 -2 -4 9 -5 -2 -3 -4 -4 -5 -3 -4 -5 1 -5 6 V -3 0 -4 -5 -4 -4 -2 -3 -5 1 -2 1 0 1 -4 -3 3 -5 -3 5 7 I 0 -2 -4 1 -4 -2 -4 -4 -5 1 0 -2 0 2 -5 1 -1 -5 -3 4 8 I -3 0 -5 -5 -4 -2 -5 -6 1 2 4 -4 -1 0 -5 -2 0 -3 5 -1 9 Q -2 -3 -2 -3 -5 4 -1 3 5 -5 -3 -3 -4 -2 -4 2 -1 -4 2 -2 10 A 2 -4 -4 -3 2 -3 -1 -4 -2 1 -1 -4 -3 -4 1 2 3 -5 -1 1 11 E -1 3 1 1 -1 0 1 -4 -3 -1 -3 0 3 -5 4 -1 -3 -6 -3 -1 12 F -3 -5 -5 -5 -4 -4 -4 -1 -1 1 1 -5 2 5 -1 -4 -4 -3 5 2 13 Y 3 -5 -5 -6 3 -4 -5 -2 -1 0 -4 -5 -3 3 -5 -2 -2 -2 7 1 14 L -1 -3 -4 -2 1 5 1 -1 -1 -1 1 -3 -3 1 -5 -1 -1 -2 3 -2 15 N -1 -4 4 1 5 -3 -4 2 -4 -4 -4 -3 -2 -4 -5 2 0 -5 0 0 16 P -2 4 -4 -4 -5 0 -3 3 2 -5 -4 0 -4 -3 0 1 -2 -1 5 -3 17 D -3 -2 1 5 -6 -2 2 2 -1 -2 -2 -3 -5 -4 -5 -1 2 -6 -3 -4 Claus Lundegaard

  29. PSI-Pred (Jones, DT) • Use alignments from iterative sequence searches (PSI-Blast) as input to a neural network • Better predictions due to better sequence profiles • Available as stand alone program and via the web Claus Lundegaard

  30. Benchmarking secondary structure predictions • CASP • Critical Assessment of Structure Predictions • Sequences from about-to-be-solved-structures are given to groups who submit their predictions before the structure is published • EVA • Newly solved structures are send to prediction servers. Claus Lundegaard

  31. EVA results (Rost et al., 2001) • PROFphd 77.0% • PSIPRED 76.8% • SAM-T99sec 76.1% • SSpro 76.0% • Jpred2 75.5% • PHD 71.7% • Cubic.columbia.edu/eva Claus Lundegaard

  32. Output expansion Weights Input Layer H E I C K H Output Layer E E E C H H V E I C I Q A E Hidden Layer Window IKEEHVIIQAEFYLNPDQSGEF….. Claus Lundegaard

  33. Several different architectures • Sequence-to-structure • Window sizes 15,17,19 and 21 • Hidden units 50 and 75 • 10-fold cross validation => 80 predictions • Structure-to-structure • Window size 17 • Hidden units 40 • 10-fold cross validation => 800 predictions Claus Lundegaard

  34. Balloting procedure • Confidence of a per residue prediction • P(Highest) – P(second highest) • H: 0.80 E: 0.05 C:0.15 => conf.=0.65 • Mean per chain confidence for all 800 predictions • Calculate Mean and Standard deviation • Averaging of per chain predictions with Z >=2 Claus Lundegaard

  35. Activities to probabilities Coil conversion Helix activities Strand activities Coil probabilities 0.05 0.1 0.15 … 1.0 0.05 0.99 0.10 0.15 0.9 0.83 0.75 . . . 1.0 Claus Lundegaard

  36. Petersen et al.,Proteins, 41: 17-20, 2000 • Sequence profiles as input • Neural network technology • Balloting of large number of Neural Network predictions (0.2%) • Output expansion (0.5%) • Probability transformation (1.2%) EVA (400 low homology proteins) Claus Lundegaard

  37. Links to servers • Database of links • http://mmtsb.scripps.edu/cgi-bin/renderrelres?protmodel • ProfPHD • http://cubic.bioc.columbia.edu/ • PSIPRED • http://bioinf.cs.ucl.ac.uk/psipred/ • JPred • www.compbio.dundee.ac.uk/Software/JPred/jpred.html Claus Lundegaard

  38. Practical Conclusion • If you need a secondary structure prediction use one of the newer ones such as • ProfPHD, • PSIPRED, and • JPred • And not one of the older ones such as • Chou-Fasman, and • Garnier Claus Lundegaard

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