Protein Secondary Structures

Assignment and prediction

Use of secondary structure

• Classification of protein structures• Definition of loops (active sites)• Use in fold recognition methods• Improvements of alignments• Definition of domain boundaries

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)

Dihedral Angles

phi - dihedral angle about the N-Calpha bondpsi - dihedral angle about the Calpha-C bondomega - dihedral angle about the C-N (peptide) bond

From http://www.imb-jena.de

Helices

phi(deg) psi(deg) H-bond pattern-----------------------------------------------------------------right-handed alpha-helix -57.8 -47.0 i+4pi-helix -57.1 -69.7 i+5310-helix -74.0 -4.0 i+3

(omega is 180 deg in all cases)-----------------------------------------------------------------From http://www.imb-jena.de

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/

Secondary Structure Elements

ß-strand

Helix

TurnBend

Helix formation is local

THYROID hormone receptor (2nll)

i

i+3

b-sheet formation is NOT local

Erabutoxin β (3ebx)

Secondary Structure Type Descriptions

* 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

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?

Secondary Structure Prediction

• What to predict?– All 8 types or pool types into groups

H

E

C

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

• What to predict?– All 8 types or pool types into groups

Straight HEC

Secondary Structure Prediction

H

E

C

* 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).

FoRc

HoMo

1D

….the art of being humble

The pessimistic point of viewPrediction by alignment

Secondary structure predictions of 1. and 2. generation

• single residues (1. generation)– Chou-Fasman, GOR 1957-70/80

50-55% accuracy

• segments (2. generation)– GORIII 1986-92

55-60% accuracy

• problems– < 100% they said: 65% max

– < 40% they said: strand non-local

– short segments

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%

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

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

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%

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%

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

Architecture

IKEEHVI IQAE

HEC

IKEEHVIIQAEFYLNPDQSGEF…..Window

Input Layer

Hidden Layer

Output Layer

Weights

Secondary networks(Structure-to-Structure)

HECHECHEC

HEC

IKEEHVIIQAEFYLNPDQSGEF…..

Window

Input Layer

Hidden Layer

Output Layer

Weights

Example

PITKEVEVEYLLRRLEE (Sequence)

HHHHHHHHHHHHTGGG. (DSSP)

ECCCHEEHHHHHHHCCC (SEQ2STR)

CCCCHHHHHHHHHHCCC (STR2STR)

1 50fyn_human VTLFVALYDY EARTEDDLSF HKGEKFQILN SSEGDWWEAR SLTTGETGYIyrk_chick VTLFIALYDY EARTEDDLSF QKGEKFHIIN NTEGDWWEAR SLSSGATGYIfgr_human VTLFIALYDY EARTEDDLTF TKGEKFHILN NTEGDWWEAR SLSSGKTGCIyes_chick VTVFVALYDY EARTTDDLSF KKGERFQIIN NTEGDWWEAR SIATGKTGYIsrc_avis2 VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIsrc_aviss VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIsrc_avisr VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIsrc_chick VTTFVALYDY ESRTETDLSF KKGERLQIVN NTEGDWWLAH SLTTGQTGYIstk_hydat VTIFVALYDY EARISEDLSF KKGERLQIIN TADGDWWYAR SLITNSEGYIsrc_rsvpa .......... ESRIETDLSF KKRERLQIVN NTEGTWWLAH SLTTGQTGYIhck_human ..IVVALYDY EAIHHEDLSF QKGDQMVVLE ES.GEWWKAR SLATRKEGYIblk_mouse ..FVVALFDY AAVNDRDLQV LKGEKLQVLR .STGDWWLAR SLVTGREGYVhck_mouse .TIVVALYDY EAIHREDLSF QKGDQMVVLE .EAGEWWKAR SLATKKEGYIlyn_human ..IVVALYPY DGIHPDDLSF KKGEKMKVLE .EHGEWWKAK SLLTKKEGFIlck_human ..LVIALHSY EPSHDGDLGF EKGEQLRILE QS.GEWWKAQ SLTTGQEGFIss81_yeast.....ALYPY DADDDdeISF EQNEILQVSD .IEGRWWKAR R.ANGETGIIabl_mouse ..LFVALYDF VASGDNTLSI TKGEKLRVLG YnnGEWCEAQ ..TKNGQGWVabl1_human..LFVALYDF VASGDNTLSI TKGEKLRVLG YnnGEWCEAQ ..TKNGQGWVsrc1_drome..VVVSLYDY KSRDESDLSF MKGDRMEVID DTESDWWRVV NLTTRQEGLImysd_dicdi.....ALYDF DAESSMELSF KEGDILTVLD QSSGDWWDAE L..KGRRGKVyfj4_yeast....VALYSF AGEESGDLPF RKGDVITILK ksQNDWWTGR V..NGREGIFabl2_human..LFVALYDF VASGDNTLSI TKGEKLRVLG YNQNGEWSEV RSKNG.QGWVtec_human .EIVVAMYDF QAAEGHDLRL ERGQEYLILE KNDVHWWRAR D.KYGNEGYIabl1_caeel..LFVALYDF HGVGEEQLSL RKGDQVRILG YNKNNEWCEA RlrLGEIGWVtxk_human .....ALYDF LPREPCNLAL RRAEEYLILE KYNPHWWKAR D.RLGNEGLIyha2_yeastVRRVRALYDL TTNEPDELSF RKGDVITVLE QVYRDWWKGA L..RGNMGIFabp1_sacex.....AEYDY EAGEDNELTF AENDKIINIE FVDDDWWLGE LETTGQKGLF

Sequence profiles

Η

Ε

L

>

>

>

pickmaximal

unit=>

currentprediction

J2

inputlayer

first orhidden layer

second oroutput layer

s0 s1 s2J1

:GYIY

DPAVGDPDNGVEP

GTEF:

:GYIY

DPEVGDPTQNIPP

GTKF:

:GYEY

DPAEGDPDNGVKP

GTSF:

:GYEY

DPAEGDPDNGVKP

GTAF:

Alignments

5 . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 5 . .. . . . . . . 2 . . . . . 3 . . . . . .. . . . . . . . . . . . . . . . . 5 . .

. . . . 5 . . . . . . . . . . . . . . .

. . . 5 . . . . . . . . . . . . . . . .

. . 3 . . . . 2 . . . . . . . . . . . .

. . . . 1 . . 2 . . . 2 . . . . . . . .5 . . . . . . . . . . . . . . . . . . .. . . . 5 . . . . . . . . . . . . . . .. . . 5 . . . . . . . . . . . . . . . .. . . . 4 . 1 . . . . . . . . . . . . .. . . . 1 3 . . . 1 . . . . . . . . . .4 . . . . 1 . . . . . . . . . . . . . .. . . . . . . . . . . 4 . 1 . . . . . .. . . 1 . 1 . 1 2 . . . . . . . . . . .. . . 5 . . . . . . . . . . . . . . . .

5 . . . . . . . . . . . . . . . . . . .. . . . . . 5 . . . . . . . . . . . . .. 1 1 . 1 . . 1 1 . . . . . . . . . . .. . . . . . . . . . . . . . . . . . 5 .

GSAPD NTEKQ CVHIR LMYFW

profile table

:GYIY

DPEDGDPDDGVNP

GTDF:

Protein

corresponds to the the 21*3 bits coding for the profile of one residue

Slide courtesy by B. Rost 2004

PHDsec

H

L

E

4+1""""""

20444

outputlayer

inputlayer

hiddenlayer

20444

21+3""""""

H

L

E

0.5

0.1

0.4percentage of each amino acid in protein

length of protein (≤60, ≤120, ≤240, >240)

distance: centre, N-term (≤40,≤30,≤20,≤10)

distance: centre, C-term (≤40,≤30,≤20,≤10)

input global in sequence

input local in sequence

local

align-

ment

13

adjacent

residues

:::

AAA

AA.

LLL

LII

AAG

CCS

GVV

:::

global

statist.

whole

protein

% AA

Length

∆ N-term

∆ C-term

A C L I G S V ins del cons

100 0 0 0 0 0 0 0 0 1.17

100 0 0 0 0 0 0 33 0 0.42

0 0 100 0 0 0 0 0 33 0.92

0 0 33 66 0 0 0 0 0 0.74

66 0 0 0 33 0 0 0 0 1.17

0 66 0 0 0 33 0 0 0 0.74

0 0 0 33 0 0 66 0 0 0.48

first levelsequence-to- structure

second levelstructure-to- structure

Slide courtesy by B. Rost 2004

Prediction accuracy PHD

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90 100

Number of protein chains

Per-residue accuracy (Q3)

<Q3>=72.3% ; sigma=10.5%

1spf 1bct1stu

3ifm1psm

Slide courtesy by B. Rost 2004

Stronger predictions more accurate!

.

0

20

40

60

80

100

0

20

40

60

80

100

3 4 5 6 7 8 9

Q per protein3fit: Q

3fit = 21 + 8.7 * Q

3

Reliability index averaged over protein

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90 100

Number of protein chains

Per-residue accuracy (Q3)

<Q3>=72.3% ; sigma=10.5%

1spf 1bct1stu

3ifm1psm

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)

HEADER CYTOSKELETONCOMPND ALPHA SPECTRIN (SH3 DOMAIN) SOURCE CHICKEN (GALLUS GALLUS) BRAINAUTHOR M.NOBLE,R.PAUPTIT,A.MUSACCHIO,M.SARASTE

Spectrin homology domain (SH3)

59%65%

72%

CEEEEEEECCCCCCCCCCCCCCCCEEEEEECCCCCEEEEEECCCEEEECCCCCEECC.EEEEESS.B...STTB..B.TT.EEEEEE..SSSEEEEEETTEEEEEEGGGEEE.. 93%

Petersen

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)

• PROFphd 77.0%• PSIPRED 76.8%• SAM-T99sec 76.1%• SSpro 76.0%• Jpred2 75.5%• PHD 71.7%

– Cubic.columbia.edu/eva

EVA: secondary structure

Method B Q3 C Q3 Claim D SOV E Info F CorrH G CorrE H CorrL I Class K BAD L

PROF 76.0 72 0.35 0.67 0.63 0.55 82 2.7PSIPRED 76.0 76.5-78.3 M 72 0.36 0.65 0.62 0.55 78 2.8SSpro 76.0 76 71 0.35 0.67 0.63 0.56 83 2.8

JPred2 75.0 76.4 69 0.34 0.65 0.60 0.54 76 2.6PHDpsi 75.0 71 0.33 0.65 0.60 0.54 81 3.0

PHD 71.4 71.6 68 0.28 0.59 0.58 0.49 77 4.3

Copenhagen 78 N 77.8

Wang/Yuan 53 O

76%

Petersen et al. Proteins 2000

Prediction of protein secondary structure

• 1980: 55% simple• 1990: 60% less simple• 1993: 70% evolution• 2000: 76% more evolution• 2006: 80% more evolution• 2008: >80% more evolution

Links to servers

• Database of linkshttp://mmtsb.scripps.edu/cgibin/renderrelres?protmodel

• ProfPHD http://www.predictprotein.org/

• PSIPREDhttp://bioinf.cs.ucl.ac.uk/psipred/

• JPredhttp://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