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Assignment and prediction. Protein Secondary Structures. Helix. Bend. Turn. Secondary Structure Elements. ß-strand. *H = alpha helix *G = 3 10 - helix *I = 5 helix (pi helix) *E = extended strand, participates in beta ladder *B = residue in isolated beta-bridge

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Protein Secondary Structures

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Assignment and prediction

Protein Secondary Structures


Helix

Bend

Turn

Secondary Structure Elements

ß-strand


*H = alpha helix

*G = 310 - helix

*I = 5 helix (pi helix)

*E = extended strand, participates in beta ladder

*B = residue in isolated beta-bridge

*T = hydrogen bonded turn

*S = bend

*C = coil

Secondary Structure Type Descriptions


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)

    • DSSPcont (Andersen et al., 2002)


Automatic assignment programs

  • DSSP ( http://www.cmbi.kun.nl/gv/dssp/ )

  • STRIDE ( http://www.hgmp.mrc.ac.uk/Registered/Option/stride.html )

  • DSSPcont ( http://cubic.bioc.columbia.edu/services/DSSPcont/ )

# 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

DSSP


Prediction of protein secondary structure

  • What to predict?

  • How to predict?

  • How good are the best?


H

E

C

Secondary Structure Prediction

  • What to predict?

    • All 8 types or pool types into groups

DSSP

*H = alpha helix

*G = 310 -helix

*I = 5 helix (pi helix)

*E = extended strand

*B = beta-bridge

*T = hydrogen bonded turn

*S = bend

*C = coil


H

E

C

Secondary Structure Prediction

  • What to predict?

    • All 8 types or pool types into groups

Straight HEC

*H = alpha helix

*E = extended strand

*T = hydrogen bonded turn

*S = bend

*C = coil

*G = 310-helix

*I = 5 helix (pi helix)

*B = beta-bridge


Secondary Structure Prediction

  • Simple alignments

    • Align to a close homolog for which the structure has been experimentally solved.

  • Heuristic Methods (e.g., Chou-Fasman, 1974)

    • Apply scores for each amino acid an sum up over a window.

  • 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).


  • HoMo

    1D

    ….the art of

    being humble

    FoRc

    The pessimistic point of viewPrediction by alignment


    Secondary structure predictions of 1. and 2. generation

    • single residues (1. generation)

      • Chou-Fasman, GOR1957-70/8050-55% accuracy

    • segments (2. generation)

      • GORIII1986-9255-60% accuracy

    • problems

      • < 100% they said: 65% max

      • < 40% they said: strand non-local

      • short segments


    1974 Chou & Fasman~50-53%

    1978 Garnier63%

    1987 Zvelebil66%

    1988 Quian & Sejnowski64.3%

    1993 Rost & Sander70.8-72.0%

    1997 Frishman & Argos<75%

    1999 Cuff & Barton72.9%

    1999 Jones76.5%

    2000 Petersen et al.77.9%

    Improvement of accuracy


    Simple Alignments

    • Solved structure of a homolog to query is needed

    • Homologous proteins have ~88% identical (3 state) secondary structure

    • If no close homologue can be identified alignments will give almost random results


    Amino acid preferences in a-Helix


    Amino acid preferences in b-Strand


    Amino acid preferences in coil


    NameP(a)P(b)P(turn)f(i)f(i+1)f(i+2)f(i+3)

    Ala 14283660.060.0760.0350.058

    Arg 9893950.0700.1060.0990.085

    Asp 101541460.1470.1100.1790.081

    Asn 67891560.1610.0830.1910.091

    Cys 701191190.1490.0500.1170.128

    Glu 15137740.0560.0600.0770.064

    Gln 111110980.0740.0980.0370.098

    Gly 57751560.1020.0850.1900.152

    His 10087950.1400.0470.0930.054

    Ile 108160470.0430.0340.0130.056

    Leu 121130590.0610.0250.0360.070

    Lys 114741010.0550.1150.0720.095

    Met 145105600.0680.0820.0140.055

    Phe 113138600.0590.0410.0650.065

    Pro 57551520.1020.3010.0340.068

    Ser 77751430.1200.1390.1250.106

    Thr 83119960.0860.1080.0650.079

    Trp 108137960.0770.0130.0640.167

    Tyr 691471140.0820.0650.1140.125

    Val 106170500.0620.0480.0280.053

    Chou-Fasman


    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.


    Chou-Fasman

    • General applicable

    • Works for sequences with no solved homologs

    • But the accuracy is low!

      • 50%


    1974 Chou & Fasman~50-53%

    1978 Garnier63%

    1987 Zvelebil66%

    1988 Quian & Sejnowski64.3%

    1993 Rost & Sander70.8-72.0%

    1997 Frishman & Argos<75%

    1999 Cuff & Barton72.9%

    1999 Jones76.5%

    2000 Petersen et al.77.9%

    Improvement of accuracy


    PHD method (Rost and Sander)

    • Combine neural networks with sequence profiles

      • 6-8 Percentage points increase in prediction accuracy over standard neural networks (63% -> 71%)

    • Use second layer “Structure to structure” network to filter predictions

    • Jury of predictors

    • Set up as mail server


    Neural Networks

    • Benefits

      • General applicable

      • Can capture higher order correlations

      • Inputs other than sequence information

    • Drawbacks

      • Needs many data (different solved structures).

        • However, these does exist today (nearly 2500 solved structures with low sequence identity/high resolution.)

      • Complex method with several pitfalls


    How is it done

    • One network (SEQ2STR) takes sequence (profiles) as input and predicts secondary structure

      • Cannot deal with SS elements i.e. helices are normally formed by at least 5 consecutive aminoacids

    • Second network (STR2STR) takes predictions of first network and predicts secondary structure

      • Can correct for errors in SS elements, i.e remove single helix prediction, mixture of strand and helix predictions


    Weights

    Input Layer

    I

    K

    H

    E

    Output Layer

    E

    E

    C

    H

    V

    I

    I

    Q

    A

    E

    Hidden Layer

    Window

    IKEEHVIIQAEFYLNPDQSGEF…..

    Architecture


    Weights

    Input Layer

    H

    E

    H

    C

    Output Layer

    E

    H

    C

    E

    C

    H

    E

    C

    Window

    Hidden Layer

    IKEEHVIIQAEFYLNPDQSGEF…..

    Secondary networks(Structure-to-Structure)


    Example

    PITKEVEVEYLLRRLEE (Sequence)

    HHHHHHHHHHHHTGGG. (DSSP)

    ECCCHEEHHHHHHHCCC (SEQ2STR)

    CCCCHHHHHHHHHHCCC (STR2STR)


    Sequence profiles


    Slide courtesy by B. Rost 2004


    Slide courtesy by B. Rost 2004


    Slide courtesy by B. Rost 2004


    Prediction accuracy PHD

    Slide courtesy by B. Rost 2004


    Stronger predictions more accurate!


    PSI-Pred (Jones)

    • Use alignments from iterative sequence searches (PSI-Blast) as input to a neural network (Just like PHDsec)

    • Better predictions due to better sequence profiles

    • Available as stand alone program and via the web


    Petersen et al. 2000

    • SEQ2STR (>70 networks)

      • Not one single network architecture is best for all sequences

    • STR2STR (>70 network)

    • => 4900 network predictions,

      • (wisdom of the crowd!!!)

      • Others have 1


    Why so many networks?


    Why not select the best?


    Prediction accuracy (Q3=81.2%). 2006. (Petersen et al. 2000)


    Spectrin homology domain (SH3)

    CEEEEEEECCCCCCCCCCCCCCCCEEEEEECCCCCEEEEEECCCEEEECCCCCEECC

    .EEEEESS.B...STTB..B.TT.EEEEEE..SSSEEEEEETTEEEEEEGGGEEE..

    Petersen

    93%


    False prediction for engineered proteins!


    Benchmarking secondary structure predictions

    • CASP

      • Critical Assessment of Structure Predictions

      • Sequences from about-to-be-deposited-structures are given to groups who submit their predictions before the structure is published

      • Every 2. year

    • EVA

      • Newly solved structures are send to prediction servers.

      • Every week


    EVA results (Rost et al., 2001)

    • PROFphd77.0%

    • PSIPRED76.8%

    • SAM-T99sec76.1%

    • SSpro76.0%

    • Jpred275.5%

    • PHD71.7%

      • Cubic.columbia.edu/eva


    Petersen et al. Proteins 2000

    EVA: secondary structure

    76%


    Prediction of protein secondary structure

    • 1980: 55%simple

    • 1990: 60%less simple

    • 1993: 70%evolution

    • 2000: 76%more evolution

    • 2006: 80%more evolution

    • what is the limit?

      • 88% for proteins of similar structure

      • 80% for 1/5th of proteins with families > 100

      • missing through: better definition of secondary structureincluding long-range interactions

      • structural switches

      • chameleon / folding


    Links to servers

    • Database of links

      http://mmtsb.scripps.edu/cgi

      bin/renderrelres?protmodel

    • ProfPHD

      http://www.predictprotein.org/

    • PSIPRED

      http://bioinf.cs.ucl.ac.uk/psipred/

    • JPred

      http://www.compbio.dundee.ac.uk/~www-jpred/


    Conclusions

    • The big break through in SS prediction came due to sequence profiles

      • Rost et al.

    • Prediction of secondary structure has not changed in the last 5 years

      • More protein sequences => higher prediction accuracy

      • No new theoretical break through

    • Accuracy is close to 80% for globular proteins

    • If you need a secondary structure prediction use one of profile based:

      • ProfPHD, PSIPRED, and JPred

    • And not one of the older ones such as :

      • Chou-Fasman

      • Garnier


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