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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.
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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
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) mTP bound to Tom20 cTP
output layer input layer A simple artificial neural network (ANN) 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 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 www.cbs.dtu.dk Membrane proteins
Helix bundle Only two basic structures(Quart.Rev.Biophys. 32:285) ß-barrel Lipid/prot interactions
The basic model(courtesy Bill Skach) 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) 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 out in prediction
number of genomes amino acid The positive-inside rule applies to all organisms(Nilsson, Persson & von Heijne, submitted)
0+ 0+ 4+ Topology can be manipulated(Nature 341:456) PK 2+ 2+ 10+ Lep constructs expressed in E. coli
Topology prediction - a classical problem in bioinformatics 4 characteristics
Trp, Tyr Three important characteristics ~20 hydrophobic residues ’Positive inside’ rule predictors
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
- construct all possible topologies - rank based on D+ TopPred(JMB 225:487) E. coli LacY http://bioweb.pasteur.fr/ seqanal/interfaces/ toppred.html TMHMM
TMHMM(Sonnhammer et al., ISMB 6:175, Krogh et al., JMB 305:567) A hidden Markov model-based method www.cbs.dtu.dk www.sbc.su.se h & l models
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 5 prediction methods used 46% of 764 predicted E. coli IM proteins are in the 5/0 or 4/1 classes fraction correct/coverage majority level Partial consensus
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 TMHMM reliability scores(Melén et al. JMB 327:735) TMHMM output: 1. Mean probability pmean 2. Minimum probability pmin(label) 3. PbestPath/PallPaths S3 results
TMHMM (score 3) Prediction accuracy vs. coverage 92 bacterial proteins percent correct ~45% ~70% coverage Test set bias
percent 0-0.25 0.25-0.5 0.5-0.75 0.75-1 score interval ”Experimentally known topologies” is a biased sample Estimate true performance
Correlation between accuracy and TMHMM S3 score percent correct mean score genomes
Expected TMHMM performance on proteomes test set percent correct C. elegans E. coli S. cerevisiae coverage 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
Experimental information helps(JMB 327:735) 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