Prediction of protein localization and membrane protein topology Gunnar von Heijne Department of Biochemistry and Biophysics Stockholm Bioinformatics Center.

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Presentation transcript:

Prediction of protein localization and membrane protein topology Gunnar von Heijne Department of Biochemistry and Biophysics Stockholm Bioinformatics Center Stockholm University

Stockholm Bioinformatics Center sorting

Protein localization

Protein sorting in a eukaryotic cell SP

The ’canonical’ signal peptide 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 ANN

A simple artificial neural network (ANN) 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) TargetP

TargetP - a four-state SP/mTP/cTP/other predictor (JMB 300:1105) performance

TargetP sensitivity/specificity sensspec SP mTP cTP other 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

Membrane protein topology

A simulated lipid bilayer (Grubmüller et al.)

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 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) Bacterial IM in: 16% KR out: 4% KR Eukaryotic PM in: 17% KR out: 7% KR Thylakoid membrane in: 13% KR out: 5% KR Mitochondrial IM In: 10% KR out: 3% KR in out prediction

The positive-inside rule applies to all organisms (Nilsson, Persson & von Heijne, submitted) number of genomes amino acid

Topology can be manipulated (Nature 341:456) Lep constructs expressed in E. coli PK

Topology prediction - a classical problem in bioinformatics 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) seqanal/interfaces/ toppred.html - 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 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 proteins 650 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) 60 E. coli proteins majority level fraction correct/coverage 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 p mean 2. Minimum probability p min (label) 3. P bestPath /P allPaths Sequence: M C Y G K C I p(i): p(h): p(o): Label: i i i i i h h S3 results

TMHMM (score 3) Prediction accuracy vs. coverage Test set bias percent correct coverage ~70%~45% 92 bacterial proteins

”Experimentally known topologies” is a biased sample percent score interval Estimate true performance

Correlation between accuracy and TMHMM S3 score mean score percent correct genomes

Expected TMHMM performance on proteomes E. coli S. cerevisiae test set C. elegans coverage percent correct 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)