Prediction of protein localization and membrane protein topology

Gunnar von Heijne

Department of Biochemistry and Biophysics

Stockholm Bioinformatics Center

Stockholm University

Stockholm Bioinformatics Center

www.sbc.su.se

sorting

Protein localization

Protein sorting in a eukaryotic cell

SP

The ’canonical’ signal peptide

n h c

-3 -1

n-region: positively charged

h-region: hydrophobic

c-region: more polar, small residues in -1, -3

mTP

mTPs are rich in R & K and can form amphiphilic helices

(Abe et al., Cell 100:551)

cTP

mTP bound to Tom20

Typical chloroplast transit peptide

IV X A A

mature

MA-

no G,P,K,R

no D,E

high S,T

no D,E

high S,T

high R

no D,E

high S,T

ANN

A simple artificial neural network (ANN)

A C G T A C G T A C G T

A A G AC

1 0 0 0 1 0 0 0 0 0 1 0

ACGnot

ACG output layer

input layer

Inside ANN

Artificial neural networks:a summary

- a high-quality dataset (positive and negative examples)

- an ANN architecture (can be optimized)

- all internal parameters in the ANN are systematically optimized during a training session

- evaluate the predictive performance using cross- validation

ChloroP

ChloroP(Prot.Sci. 8:978)

0

10

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

MEME score

residue

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

network score

-30

-20

-10

TargetP

TargetP - a four-state SP/mTP/cTP/other predictor

(JMB 300:1105)

performance

TargetP sensitivity/specificity

sens spec

SP .91 .96

mTP .82 .90

cTP .85 .69

other .85 .78

sens = tp/(tp+fn) spec = tp/(tp+fp)

Other predictors

Other ways to predict localization

- amino acid composition

- sequence homology

- domain structure

- phylogenetic profiles

- expression profiles

Membrane proteins

Popular prediction programs

SignalP (NN, HMM)

ChloroP

TargetP

LipoP

-------

MitoProt

PSORT

Membrane proteins

www.cbs.dtu.dk

Membrane protein topology

A simulated lipid bilayer(Grubmüller et al.)

QuickTime™ and aYUV420 codec decompressorare needed to see this picture.

Only two basic structures(Quart.Rev.Biophys. 32:285)

Helix bundle ß-barrel

Lipid/prot interactions

Most MPs are synthesized at the ER

SP

The basic model(courtesy Bill Skach)

prediction

Topology prediction

TM helix lengths are typically 20-30 residues

(Bowie, JMB 272:780)

Trp, Tyr

Trp & Tyr are enriched in the region near the lipid headgroups

(Prot.Sci. 6:808; 7:2026)

Loop lengths

Loops tend to be short(Tusnady & Simon, JMB 283:489)

PI rule

The ’positive inside’ rule(EMBO J. 5:3021; EJB 174:671, 205:1207; FEBS Lett. 282:41)

N

C

+ + +

Bacterial IMin: 16% KR out: 4% KR

Eukaryotic PMin: 17% KR out: 7% KR

Thylakoid membranein: 13% KR out: 5% KR

Mitochondrial IMIn: 10% KR out: 3% KR

in

out

prediction

The positive-inside rule applies to all organisms

(Nilsson, Persson & von Heijne, submitted)

0

10

20

30

40

50

60

70

80

90

100

110

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

(D+E) (K+R) (W+Y)

num

ber

of g

enom

es

amino acid

Topology can be manipulated(Nature 341:456)

Lep constructs expressed in E. coli

f-Met-Ala-Asn-Met-Phe-

H1 H2

P1

P2

+

+

- -

QSLNASASE

H1 H2

P1

P2

++

+

+ +

+

++

+

+

- -

---

f-Met-Ala-Asn-Met-Phe-

Ala-Asn-Met-(Lys) -Phe-

H1H2

P1

P2

+

+

- -

QSLNASASE

4-

-

Lep wt Lep' Lep'-inv

periplasm

cytoplasm10+

2+

2+

4+

0+0+

PK

Topology prediction - a classical problem in bioinformatics

MDSQRNLLVIALLFVSFMIWQAWE....

4 characteristics

Three important characteristics

~20 hydrophobic residues

predictors

’Positive inside’ rule

Trp, Tyr

Popular topology predictors

TMHMM (HMM)HMMTOP (HMM)TopPred (h-plot + PI-rule)MEMSAT (dynamic programming)TMAP (h-plot, mult. alignment)PHD (NN, mult. alignment)

toppred

TopPred(JMB 225:487)

0 100 200 300 400-3

-2

-1

0

1

2

3

position

<H>

http://bioweb.pasteur.fr/seqanal/interfaces/toppred.html 2 3 5 4 2 2

1 0 0 1 1 0

2

∆+ = 17

2

1

3

0

5

0

4

1

2

3

0

2

∆+ = 9

- construct all possible topologies

- rank based on +

E. coli LacY

TMHMM

TMHMM(Sonnhammer et al., ISMB 6:175, Krogh et al., JMB

305:567)

h & l models

www.cbs.dtu.dkwww.sbc.su.se

A hidden Markov model-based method

HMMTOP(Tusnady & Simon, JMB 283:489)

performance

Helix & loop models in TMHMM

HMMTOP

TMHMM performance(Krogh et al., JMB 305:567; Melén et al. JMB 327:735)

Discrimination globular/membrane:sens & spec > 98%

Correct topology: 55-60%

Single TM identification:sensitivity: 96%specificity: 98%

Training set:160 membrane proteins650 globular proteins

# of TM proteins

Can performance be improved?

Consensus predictions

Multiple alignments

Experimental constraints

# of TM proteins

’Consensus’ predictions indicate reliability

(FEBS Lett. 486:267)

0

0,2

0,4

0,6

0,8

1

5/0 4/1 3/2 & 3/1/1 2/1/1/1

60 E. coli proteins

majority level

frac

tion

corr

ect/

cove

rage

5 prediction methods used

46% of 764 predicted E. coli IM proteins are in the 5/0 or 4/1 classes

Partial consensus

TMHMM reliability scores(Melén et al. JMB 327:735)

TMHMM output:

1. Mean probability pmean

2. Minimum probability pmin(label)

3. PbestPath/PallPaths

Sequence: M C Y G K C I p(i): 0.78 0.78 0.78 0.76 0.76 0.08 0.03 p(h): 0.00 0.00 0.02 0.02 0.15 0.85 0.93 p(o): 0.22 0.22 0.20 0.20 0.08 0.07 0.04 Label: i i i i i h h

S3 results

TMHMM (score 3)Prediction accuracy vs. coverage

Test set bias

60

70

80

90

100

0 20 40 60 80 100

perc

ent

corr

ect

coverage

~70%~45%

92 bacterial proteins

”Experimentally known topologies” is a biased sample

0

10

20

30

40

test set

C. elegans

S.cerevisiae

E.coli

perc

ent

0-0.

25

0.25

-0.5

0.5-

0.75

0.75

-1

score interval

Estimate true performance

Correlation between accuracy and TMHMM S3 score

02040608010000.20.40.60.81

mean score

perc

ent

corr

ect

genomes

Expected TMHMM performance on proteomes

E. coli

S. cerevisiae

test set

C. elegans

40

50

60

70

80

90

100

0 25 50 75 100

coverage

perc

ent

corr

ect

Add C-term.

Original TMHMM prediction, one TM helix missing

TMHMM prediction with C-terminus fixed to inside

Experimental information helps(JMB 327:735)

improvement

When the location of the C-terminus is

known, the correct topology is predicted for

an estimated ~70% of all membrane proteins

(~ 55% when not known)

Reporter fusions

Experimental information helps(JMB 327:735)