Membrane structure prediction 1 cb1 tmh1 - ROSTLAB.ORG · Borenstein DB, Ringel P, Basler M, Wingreen NS (2015) Established Microbial Colonies Can Survive Type VI Secretion Assault

/77© Burkhard Rost

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title: Membrane structure prediction 1short title: cb1_tmh1

lecture: Computational Biology 1 - Protein structure (for Informatics) - TUM summer semester

/77© Burkhard Rost

Videos: YouTube / www.rostlab.orgTHANKS :. EXERCISES: Special lectures: • 07/xx Predrag Radivojac - Indiana Univ. • 06/xx Yana Bromberg - Rutgers Univ. No lecture: • 05/09 no lecture • 05/23 Student assembly (SVV) • 05/25 Ascension day • 06/06 Whitsun holiday • 06/15 Corpus Christi LAST lecture: bef: Jul 11 after: Jul 28 Examen: WEDNESDAY(!!) July 12: 18:00-19:30 TBA • Makeup: TBC: Oct 17 & 19, 2017 - lecture time

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CONTACT: Lothar Richter [email protected]

Dmitrij Nechaev

Lothar Richter

Christian Dallago

http://www.rostlab.org

mailto:[email protected]?subject=

© Burkhard Rost

Recap: 1D secondary

structure prediction

3

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Goal of structure prediction

Epstein & Anfinsen, 1961:sequence uniquely determines structure

• INPUT: sequence

3D structureand function

• OUTPUT:

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5

Zones

Day

light

Zon

e

Twili

ght Z

one

Mid

nigh

t Zon

eprofile - profile

sequence - profilesequence - sequence

sequ

ence

sim

ilar

->

stru

ctur

e sim

ilar

B Rost (1997) Fold Des 2:S19-24B Rost (1999) Protein Eng 12:85-94

/78© Burkhard Rost

6structure (PDB id 4lpk): JM Ostrem et al. & KM Shokat (2013) Nature 503:548-51

Comparative modeling predicts 3D structure in silico

pretein seqwence

priteen peqwinse

Query

PDB

predicts 3D structure for short regions in 50% of all

known protein s

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

50-55% accuracy

segments (2. generation) • GORIII 1986-92

55-60% accuracy

problems • < 100% may be: 65% max • < 40% may be: strand non-local • short segments

7

Secondary structure prediction: 1.+2. Generation

B Rost and C Sander (2000) Methods in Molecular Biology 143: 71-95

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8B Rost (1996) Methods in Enzymology 266: 525-39

ACDEFGHIKLMNPQRSTVWY.

H

E

L

D (L)

R (E)

Q (E)

G (E)

F (E)

V (E)

P (E)

A (H)

A (H)

Y (H)

V (E)

K (E)

K (E)

Neural Network for secondary structure

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50-55% accuracy


55-60% accuracy

problems • < 100% they said: 65% max • < 40% they said: strand non-local • short segments

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Secondary structure predictions of 1. and 2. generation

/78© Burkhard Rost

H

E

L

V (E)

P (E)

A (H)

PHDsec:

structure-to-structure

10

PHDsec: structure-to-structure network

B Rost (1996) Methods Enzymol 266:525-39

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50-55% accuracy


55-60% accuracy

problems • < 100% they said: 65% max • < 40% problem was NOT locality but distribution • short segments

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Secondary structure predictions of 1. and 2. generation

© Burkhard Rost /77

STILL ONLY 60+ε% accuracy.

How to improve beyond that?

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/78© Burkhard Rost

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

13

PHD: Neural network & evolutionary information

B Rost & C Sander (1993) PNAS 90:7558-62B Rost (1996) Methods Enzymol 266:525-39

/78© Burkhard Rost

P H D s e c

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 proteinlength 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

localalign-ment13

adjacentresidues

:::

AAA

AA.

LLL

LII

AAG

CCS

GVV

:::

globalstatist.wholeprotein

%AALength∆ 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

14

PHDsec: more details



Does global information help?

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With/without global information

Q3

Only sliding window, i.e. only local 72 %

Local & global 72 %

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Protein structure comparisons

1bww3sdh 1xne

All-alpha All-beta AlphaBeta

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P H D s e c

H

L

E

4+1""""""

20444

outputlayer

inputlayer

hiddenlayer

20444

21+3""""""

H

L

E

0.5

0.1




localalign-ment13

adjacentresidues

:::

AAA

AA.

LLL

LII

AAG

CCS

GVV

:::




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


second levelstructure-to- structure18

Predict protein class


in out

All-alphaAll-beta

AlphaBeta

P H D s e c

H

L

E

4+1""""""

20444

outputlayer

inputlayer

hiddenlayer

20444

21+3""""""

H

L

E

0.5

0.1




localalign-ment13

adjacentresidues

:::

AAA

AA.

LLL

LII

AAG

CCS

GVV

:::




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



/78© Burkhard Rost

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With/without global information

Q3 (per-

residue)

Q4 (per-protein)

Only sliding window, i.e. only local 72 % 70 %

Local & global 72 % 75 %

© Burkhard Rost

1D: TM

transmembrane helix

prediction20

© Burkhard Rost

Intro: membranes

21


What to put around a cell?

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Roshni Nelson: Cell Membranes

© Roshni Nelson UT Southwestern Dallas http://www.roshninelson.com

http://utsouthwestern.edu/STARS - vimeo.com/31412291

http://www.roshninelson.com

http://gregs-educational.info

/78© Burkhard Rost

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Roshni Nelson: Phospholipids

© Roshni Nelson UT Southwestern Dallas http://www.roshninelson.com

http://utsouthwestern.edu/STARS - vimeo.com/31412291

http://www.roshninelson.com

http://gregs-educational.info

/78© Burkhard Rost K Rogers (2011) Britannica

Prokaryotic Cell Eukaryotic Cell(bacillus type)

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Cellular compartments

© Tatyana Goldberg (TUM Munich)

/78© Burkhard Rost

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How 2 separate outside/inside?

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Lipid bilayer

Wikipedia © http://en.wikipedia.org/wiki/Lipid_bilayer

https://www.uclaaccess.ucla.edu/uploads/image/faculty/134.jpg

/78© Burkhard Rost

-+-

+ +++

-

-

--

----

- ++

++

+

+

HHHHH

H HH

HHH H H

H-

+

solvent

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Hydrophobic core of a protein

/78© Burkhard Rost

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Lipid bilayer: hydrophobic in inside

© Wikipedia http://en.wikipedia.org/wiki/Lipid_bilayer

/78© Burkhard Rost

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Lipid bilayer: hydrophobic in insideeasy to pull aroundhorizontally


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Lipid bilayer: hydrophobic in insidehard to enter


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Bacterial injection needles

Model of type VI secretion system (TSS6) in gram-negative bacteria

Marek Basler Biozentrum Basel

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33Borenstein DB, Ringel P, Basler M, Wingreen NS (2015) Established Microbial Colonies Can Survive Type VI Secretion Assault. PLoS Comput Biol 11(10): e1004520. doi:10.1371/

Shot through two membranes

Marek Basler

Biozentrum Basel

/78© Burkhard Rost

34Marek Basler, BT Ho, JJ Mekalanos (2013) Cell 152:884-894

Tit-for-tat: type 6 secretion system counter-attack

Marek Basler

Biozentrum Basel

/78© Burkhard Rost

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Localization for drug targets

TMBakheetandAJDoig(2008)Bioinforma)cs

Membrane57 %

Cytoplasm13 %

Extra-cellular13 %

ER7 %

Nucleus3 %

Mito2 %

Other2 %

Microsome2 %

Pero1 %

Drug targets tend to be found in membranes, cytoplasm or are

extra-cellular!© Tatyana Goldberg (TUM Munich)

© Burkhard Rost

TMH (Transmembrane

helix) background

36

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1JB0Cyanobacterial Photosystem I

Jordan P, Krauss N

1E7PFumarate ReductaseLancaster CD, Michel

H

periplasm

cytoplasmCytoplasm (stromal side)

?

/78© Burkhard Rost

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Membrane prediction

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TM prediction wait for db growth ...

1993

1999

1996

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Topology for membrane helical proteins.

exex tratra -cy-cy toto pp ll aa smsm ii cc

ii nn tt rr aa -- cc yy tt oo pp ll aa ss mm ii ccin

protein Aprotein C

C-term

out

in

protein B

C-term

C-term

lipid membranebilayer

inside cytoplasm

outside cytoplasm

© Burkhard Rost

TMH prediction

42

/78© Burkhard Rost

P H D s e c

H

L

E

4+1""""""

20444

outputlayer

inputlayer

hiddenlayer

20444

21+3""""""

H

L

E

0.5

0.1




localalign-ment13

adjacentresidues

:::

AAA

AA.

LLL

LII

AAG

CCS

GVV

:::




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



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Membrane helices are helices, right?


/78© Burkhard Rost

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PHDsec “success” on Poly-Valine

HEADER LIPOPROTEIN(SURFACE FILM)COMPND PULMONARY SURFACTANT-ASSOCIATED POLYPEPTIDE C(SP-C)SOURCE PIG (SUS SCROFA)AUTHOR J.JOHANSSON,T.SZYPERSKI,T.CURSTEDT,K.WUTHRICH

AA LRIPCCPVNLKRLLVVVVVVVLVVVVTVGALLMGLOBS sec HHHHHHHHHHHHHHHHHHHHHHHHHPHD sec EEEEEEEEEEEEEEEEEEEEEEE

/78© Burkhard Rost

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Goes wrong because swap: outside/inside

Protein

Membrane

H=hydrophobic

LIPID

H

HHHH

H

HH H H

H

HProtein

non-membrane(globular water-soluble)

H=hydrophobicL= hydrophilic

Water

H

LL

HL L

HH

HL L L

L

LL

/78© Burkhard Rost

-+-

+ +++

-

-

--

----

- ++

++

+

+

HHHHH

H HH

HHH H H

H-

+

solvent

46

Hydrophobic core of a protein

/78© Burkhard Rost

47




protein Aprotein C

C-term

out

in

protein B

C-term

C-term


inside cytoplasm

outside cytoplasm

/78© Burkhard Rost

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Hydrophobic side chains

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Eisenberg hydrophobicity indexAA-3 AA-1 Eisenberg

Ile I 1.38Phe F 1.19Val V 1.08Leu L 1.06Trp W 0.81Met M 0.64Ala A 0.62Gly G 0.48Cys C 0.29Tyr Y 0.26Pro P 0.12Thr T -0.05Ser S -0.18His H -0.4Glu E -0.74Asn N -0.78Gln Q -0.85Asp D -0.9Lys K -1.5Arg R -2.53

David Eisenberg, UCLA © https://www.uclaaccess.ucla.edu/

uploads/image/faculty/134.jpg

D Eisenberg et al. (1984) J Mol Biol 179:125-42



/78© Burkhard Rost

50

Pure hydrophobicity scaleshydrophobicity scales

-6.75

-4.50

-2.25

0.00

2.25

4.50

6.75

9.00

A R N D C Q E G H I L K M F P S T W Y VGES EISEN KYDO

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5 Hydrophobicity/tm/occupancy scaleshydrophobicity scales

0.00

0.25

0.50

0.75

1.00

A R N D C Q E G H I L K M F P S T W Y VGES EISEN KYDO OOI HEIJNE

/78© Burkhard Rost

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Many indices exist

K Tomii and M Kanehisa (1996) Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins. Protein Eng 9:27-36: Fig. 2 (402 indices)

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PHDsec success on Poly-Valine



NLKRLLVVVVVVVLVVVVTVGALL h hhhhhhhhhhhhhh h hhhh: hydrophobic

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Identify hydrophobic regions

G von Heijne (1992) Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. J Mol Biol 225: 487-94: Fig. 4

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55




protein Aprotein C

C-term

out

in

protein B

C-term

C-term


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G von Heijne (1986) The distribution of positively charged residues in bacterial inner membrane proteins correlates with the trans-membrane topology. EMBO J 5:3021-7 Fig. 2

56

Positive-inside rule

cytosolic loops

periplasmic loops

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protein Aprotein C

C-term

out

in

protein B

C-term

C-term


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Heijne rule: positive inside out

0.920.95

0.93

0.91 0.900.92

0.870.89

N-term C-term

5 30 6 5

outout

Eight bestHTM's

µ=0: 0 HTM

µ=2: 2 HTMµ=3: 3 HTM

µ=1: 1 HTM

Loop lengths

Charge:Number of R+Kin loops 1-4

final prediction:∆ =(5+1) - (2+3)>0=> first loop out lipid membrane bilayer

extra-cytoplasmic

intra-cytoplasmic

R+KΣ=2

R+KΣ =5

R+KΣ =3

R+KΣ=1

/77© Burkhard Rost

1. predict <H> 2. assign positive inside-out 3. choose threshold to optimize inside-out difference

59

G von Heijne (1992) Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. J Mol Biol 225: 487-94: Fig. 4

Identify hydrophobic regions

/77© Burkhard Rost

S Jayasinghe, K Hristova, SH White (2001) Energetics, stability, and prediction of transmembrane helices. J Mol Biol 12:927-34idea: optimize hydrophobicity scale for prediction

60

Hydrophobicity-based

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PHDsec success on Poly-Valine



/77© Burkhard Rost

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HTM

nonHTM

outputlayer

inputlayer

hiddenlayer

20444

21+3""""""

percentage of each amino acid in proteinlength of protein (≤60, ≤120, ≤240, >240)distance: centre, N-term (≤40,≤30,≤20,≤10)distance: centre, C-term (≤40,≤30,≤20,≤10)



localalign-ment13

adjacentresidues

:::

AAA

AA.

LLL

LII

AAG

CCS

GVV

:::




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

HTM

nonHTM

3+1""""""

20444



P H D ht m

/78© Burkhard Rost

63

Dynamic programming on NN ‘energy’

1

01

0residue number

T

N

/78© Burkhard Rost

64

PHDhtm

refine

0.920.95

0.93

0.91 0.900.92

0.870.89

N-term C-term

5 30 6 5

outout

Eight bestHTM's

µ=0: 0 HTM

µ=2: 2 HTMµ=3: 3 HTM

µ=1: 1 HTM

Loop lengths

Charge:Number of R+Kin loops 1-4

final prediction:∆ =(5+1) - (2+3)>0=> first loop out lipid membrane bilayer

extra-cytoplasmic

intra-cytoplasmic

R+KΣ=2

R+KΣ =5

R+KΣ =3

R+KΣ=1

/78© Burkhard Rost

65

PHDhtm on Poly-Valine


AA LRIPCCPVNLKRLLVVVVVVVLVVVVTVGALLMGLOBS htm TTTTTTTTTTTTTTTTTTTTTTTTTPHD htm TTTTTTTTTTTTTTTTTTTTTTTT

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66

Membrane helix prediction: TMHMM

TMHMM: sketch

details: inside/outside loop

details: TM core

A Krogh, B Larsson, G von Heijne, EL Sonnhammer (2001) 305:567-80, Fig. 1

/77© Burkhard Rost

Gabor E Tusnady & Istvan Simon (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics 17:849-850.

67

Membrane helix prediction: HMMTOP

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TMHs (helices) correctly predicted?

C-P Chen, A Kernytsky & B Rost 2002 Protein Science 11, 2774-91

Observed Helix 1 (O1) O2 O3

Predicted Helix 1 (P1) P2 P3

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TMHs (helices) correctly predicted: if at most ±5 residues overlap

C-P Chen, A Kernytsky & B Rost 2002 Protein Science 11, 2774-91

Observed Helix 1 (O1) O2 O3

Predicted Helix 1 (P1) P2 P3

here=0

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70

Prediction of membrane helicesQo

k: %

of p

rote

in w

ith al

l TM

H rig

ht

J Reeb, E Kloppmann, M Bernhofer & B Rost (2015) Proteins 83:473-84

© Burkhard Rost

Other problems unravelled by recent

structures

71

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72

Kingdoms similar in length

J Liu. & B Rost (2001) Prot. Sci. 10, 1970-1979.B Rost (2002) Curr Op Struct Biol, 12, 409-416

eukaryotesbacteriaarchaea

/78© Burkhard Rost

73

Kingdoms similar in amino acids usage

J Liu. & B Rost (2001) Prot Sci 10, 1970-1979.B Rost (2002) Curr Op Struct Biol, 12, 409-416

eukaryotes

bacteria

archaea

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74

Inventory of life: membrane proteins

0 5 10 15 20 25 30

A pernixA fulgidus

M jannaschiiM thermoautotrophicu

P abyssiP horikoshii

A aeolicusB subtilis

B burgdorferiC jejuni

C pneumoniaeC trachomatisD radiodurans

E coliH influenzae

H pyloriM genitalium

M pneumoniaeM tuberculosisN meningitidis

R prowazekiiS PCC6803T maritimaT pallidum

U urealyticum

S cerevisiaeC elegans

D melanogasterH sapiens (SP/TrEmbl

H sapiens(chr 22)

%mem

eukaryotes

bacteria

archaea

J Liu. & B Rost (2001) Prot. Sci. 10, 1970-1979.

2013 note: some issues with data (incomplete sequences?)e.g. human has more than 18%

/78© Burkhard Rost

75

Inventory of life: coiled-coil proteins

0 5 10 15 20 25 30

A pernixA fulgidus

M jannaschiiM thermoautotrophicu

P abyssiP horikoshii

A aeolicusB subtilis

B burgdorferiC jejuni

C pneumoniaeC trachomatisD radiodurans

E coliH influenzae

H pyloriM genitalium

M pneumoniaeM tuberculosisN meningitidis

R prowazekiiS PCC6803T maritimaT pallidum

U urealyticumS cerevisiae

C elegansD melanogaster

H sapiens (SP/TrEmblH sapiens(chr 22)

%mem

0 2 4 6 8 10 12

%coils

J Liu. & B Rost (2001) Prot. Sci. 10, 1970-1979.

eukaryotes

bacteria

archaea

/77© Burkhard Rost

statistics for PDB in June 2010:67,086 structures in PDB (June 2010) 1,197 transmembrane 1,014 alpha helical 179 beta barrel

-> < 2% BUT: >20% of all proteins!

76

TMH proteins: reminders

GE Tusnady, ZS Dosztanyi & I Simon (2005) Bioinformatics 21:1276-7

/77© Burkhard Rost

statistics for PDB in June 2010:67,086 structures in PDB (June 2010) 246 unique* transmembrane

-> < way less than 2% BUT: >20% of all proteins!

• * unique=non-identical sequence (can have PIDE>99.5%!)

77


S Jayasinghe, K Hristova, SH White (2001) Protein Sci 10:455-8

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Edda Kloppmann & Marco Punta: 1,035 PDB unique TM structures (Jan 2012)-> 107 Pfam families

78


E Kloppmann, M Punta & B Rost (2012) Curr Op Struct Biol 22:326-32

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79

Thanks to Arne Elofsson

Following slides taken from Arne Elofsson, Stockholm Univ

/77© Burkhard Rost

78 interface helices ~50% of chains contain interface helix Average length ~ 9 aa Longest is 19 aa Most frequent in photosynthetic reaction center

E Granseth, G von Heijne & A Elofsson (2005) J Mol Biol 346:377-85

© Arne Elofsson (Stockholm Univ) 80

Interface helices (Granseth, JMB 2005)

/77© Burkhard Rost

36 reentrant helices • 20 in new classification 24% contain reentry 72% on the outside Length 3-32 residues Loops 11-117 residues

81

Re-entry regions

H Viklund, E Granseth & A Elofsson (2006) J Mol Biol 361:591-603

© Arne Elofsson (Stockholm Univ)

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82

36 reentry regions in 3 classes

Helix-coil/Coil-helix

Helix-coil-helix Coil

H Viklund, E Granseth & A Elofsson (2006) J Mol Biol 361:591-603© Arne Elofsson (Stockholm Univ)

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83

Predict re-entry regions

H Viklund, E Granseth & A Elofsson (2006) J Mol Biol 361:591-603: Fig. 5

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84

Re-entry predicted in entire genomes



0.280.720.24079Observed in dataset

0.520.480.167773E. coli0.400.600.10757S. cerevisiae

0.540.460.154181H. sapiens

Reentrants in

Reentrants out

Reentrant fraction

ProteinsGenome

0.310.220.110.07FractionChannels

Active transporters

Electron transporters

Signal receptors

/77© Burkhard Rost

Membrane protein structures are complex • TM-helices ends at different locations • Different angles • Neighboring helices often interact • Interface helices • reentrant regions No sheets close to the membrane

85

The not so simple TM proteins



/78© Burkhard Rost

86

More complex structures need new prediction methodsNout

Cin

C

N

cytoplasm

periplasm

Membrane



/78© Burkhard Rost

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The Z-coordinate

Z-coordinate: distance residue 2 membrane center

Z

0

15

-15

Periplasm

Cytoplasm

H Viklund, E Granseth & A Elofsson (2006) J Mol Biol 361:591-603 © Arne Elofsson (Stockholm Univ)

/00© Burkhard Rost

01: 04/25 Tue: no lecture 02: 04/27 Thu: no lecture 03: 05/02 Tue: Intro 1: organization of lecture: intro into cells & biology 04: 05/04 Thu: Intro 2: amino acids, protein structure (comparison), domains 05: 05/09 Tue: No lecture 06: 05/11 Thu: Alignment 1 07: 05/16 Tue: Alignment 2 08: 05/18 Thu: Comparative modeling & exp structure determination & secondary structure assignment 09: 05/23 Tue: SKIP: student assembly (SVV) 10: 05/25 Thu: SKIP: Ascension Day 11: 05/30 Tue: 1D: Secondary structure prediction 1 12: 06/01 Thu: 1D: Secondary structure prediction 2 13: 06/06 Tue: SKIP: Whitsun holiday (06/03-06) 14: 06/08 Thu: 1D: Secondary structure prediction 3 15: 06/13 Tue: 1D: Transmembrane structure prediction 1 16: 06/15 Thu: SKIP: Corpus Christi 17: 06/20 Tue: 1D: Transmembrane structure prediction 2 / Solvent accessibility prediction 18: 06/22 Thu: 1D: Disorder prediction 19: 06/27 Tue: 2D prediction 20: 06/29 Thu: 3D prediction / Nobel prize symposium 21: 07/04 Tue: TBA 22: 07/06 Thu: recap 1 23: 07/11 Tue: recap 2 24: 07/12 Thu: examen 25: 07/13 Tue: TBA 26: 07/18 Thu: TBA 27: 07/20 Tue: TBA 28: 07/22 Thu: TBA 29: 07/25 Thu: TBA

88

Lecture plan (CB1 structure)

today

Documents

Membrane structure prediction 1 cb1 tmh1 - ROSTLAB.ORG · Borenstein DB, Ringel P, Basler M, Wingreen NS (2015) Established Microbial Colonies Can Survive Type VI Secretion Assault