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Journal of Bioinformatics and Computational Biology Vol. 4, No. 5 (2006) 1033­1056 c Imperial College Press

RECOGNITION OF TRANSMEMBRANE SEGMENTS IN PROTEINS: REVIEW AND CONSISTENCY-BASED BENCHMARKING OF INTERNET SERVERS

NATALIYA S. SADOVSKAYA Institute for Information Transmission Problems, Russian Academy of Science Bolshoi Karetny per. 19, Moscow 127994, Russia and State Scientific Center "GosNIIGenetika" 1st Dorozhny proezd 1, Moscow 113545, Russia nathalie@ippi.ru ROMAN A. SUTORMIN State Scientific Center "GosNIIGenetika" 1st Dorozhny proezd 1, Moscow 113545, Russia and Department of Bioengineering and Bioinformatics Moscow State University, Vorobievy Gory 1-73, Moscow, Russia sutor ra@mail.ru MIKHAIL S. GELFAND Institute for Information Transmission Problems, Russian Academy of Science Bolshoi Karetny per. 19, Moscow 127994, Russia and Department of Bioengineering and Bioinformatics Moscow State University, Vorobievy Gory 1-73, Moscow, Russia gelfand@iitp.ru

Received 28 February 2006 Revised 21 June 2006 Accepted 22 June 2006 Membrane proteins perform a number of crucial functions as transporters, receptors, and components of enzyme complexes. Identification of membrane proteins and prediction of their topology is thus an important part of genome annotation. We present here an overview of transmembrane segments in protein sequences, summarize data from largescale genome studies, and report results of benchmarking of several popular internet servers. Keywords : Transmembrane proteins; transmembrane helix prediction; benchmarking.


Corresponding author. 1033


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1. Intro duction Membrane proteins constitute 15­30% of a typical organism's proteome1­3 and include important functional classes such as transporters,4 ion channels,5­7 receptors,8 components of respiration chains,9 etc. They can be roughly divided into three classes: globular proteins anchored to the membrane via a single hydrophobic alpha-helix, proteins lo cated in the membrane and containing several membranespanning alpha-helices, and beta-structural membrane proteins. The latter are relatively rare, although they also include numerous important representatives, e.g. outer membrane porins.4 Much less is known about the structure of transmembrane (TM) domains than that of globular domains. The MPtopo database (last updated May 17, 2005) contains a total of 167 proteins with 1028 TM segments.10 The entire TM section of PDB, PDB TM, contains 482 proteins (as of April 2005), which is less than 2% of all PDB entries.11­13 This is mainly due to technical difficulties: membrane proteins are usually present at low levels in biological membranes, and most membrane proteins cannot be readily obtained in sufficient amounts from their native environments. Furthermore, they are difficult to overexpress14 and to crystallize.15­17 Properties of membrane proteins are quite different from those of globular proteins,17 and generic algorithms for prediction of protein secondary structure do not work well when applied to membrane proteins.18 Although recently a ma jor international pro ject has been launched that aims at large-scale determination of TM-protein structures (http://www.utoronto.ca/AlEdwardsLab/membrane proteomics index.html), it still has not produced sufficient amount of data to cover all structural classes of membrane proteins. Moreover, in several cases, there is discrepancy in experimental evidence about the secondary structure of TM-protein, in particular, the number of TM-segments.19­23

2. Statistical Prop erties of TM-Segments The need to identify and characterize TM-proteins yielded numerous algorithms for identification of alpha-helical TM-segments. They differ in features of TM-segments taken into account and in algorithmic techniques. The most important distinguishing feature of TM-helices is the amino acid composition. As these segments span the lipid bilayer, they predominantly contain hydrophobic residues. Most TM-helices have length between 12 and 35 residues,3 with the average being 21 residues.24 However, these numbers should be considered with due caution.25 Indeed, boundaries of alpha-helices are difficult to define rigorously,26 and no experimental metho ds can exactly map the boundary between membrane-embedded and external residues in a TM-helix. The ends of TM-helices, the so-called caps, can be recognized by specific amino acid composition.27,28 Due to the special mechanism of membrane translo cation of TM-proteins, the amino acid composition of inner and outer lo ops


Recognition of Transmembrane Segments in Proteins

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is different: short non translo cated (cytoplasmic side) lo ops contain many positively charged residues (Arg, His, Lys, Glu, and Asp), whereas translo cated lo ops contain few such residues.28­36 Inside and outside caps of TM-helices also differ in the amino acid composition;27,28 specifically, these regions are abundant in polar and aromatic residues (Lys, Arg, Trp, His, Glu, Gln, and Tyr).37,38 Globular regions and lo ops between TM-helices are usually shorter than 60 residues.1 Finally, topological constraints put natural restrictions on the order of TM-helices and inside and outside loops that can also be taken into account.39,40 There are also differences in the amino acid composition of inside/outside domains and TM-caps between single-TM-helix and multiple-TM-helix proteins.27,28 An additional complication in the analysis of single-TM-helix membraneanchored proteins is to distinguish them from secreted proteins containing an N terminal signal peptide. There are specific programs for recognition of signal peptides that take into account not only amino acid composition of the signal peptide, but also the positional residue frequencies around the cleavage sites.41,42 These programs are, in particular: SignalP41,43 (http://www.cbs.dtu.dk/services/SignalP/), PrediSi44 (http://www.predisi.de/), SIGSEQ45 (ftp://ftp.ebi.ac.uk/pub/software/unix/sigseq.tar.Z), SPEPlip46 (http://gpcr.biocomp.unibo.it/predictors/). On the other hand, there exist specific metho ds for prediction of single TMhelices.47 A special case is that of TM-proteins that have the antiparallel beta-barrel fold, such as bacterial porins.48 The amino acid composition of TM-beta-strands is different from both TM-alpha-helices35,49 and beta-strands of globular proteins.50,51 Although there exist several methods for prediction of transmembrane betastrands,52­68 only 12 of them are available over the internet (Table 1). We do not consider them here.

Table 1. Servers for the prediction of transmembrane beta strands. Server Omp topo predict B2TMPRED TRAMPLE HMM-B2TMR, B2TMR PRED-TMBB ConBBPRED PROFtmb TBBpred TMBETA-NET TMBETA-SVM References 61 66 59 66, 69 64 68 65 62 60 67 U RL http://turn18.biologie.uni-konstanz.de/test/ om topo predict2b.html http://gpcr.biocomp.unibo.it/cgi/predictors/ outer/pred outercgi.cgi http://gpcr.biocomp.unibo.it/biodec/ http://gpcr.biocomp.unibo.it/predictors/ http://bioinformatics.biol.uoa.gr/PRED-TMBB http://bioinformatics.biol.uoa.gr/ConBBPRED/ http://www.rostlab.org/services/PROFtmb/ http://www.imtech.res.in/raghava/tbbpred http://psfs.cbrc.jp/tmbeta-net/ http://tmbeta-svm.cbrc.jp


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3. Algorithmic Approaches to the Identification of TM-Segments The simplest technique for identification of TM-segments is the computation of a hydrophobicity score in a sliding window. Many different hydrophobicity scales have been suggested based on physical characteristics of amino acid residues.38,70­78 Alternatively, one can use statistical approaches, comparing residue frequencies in TM-segments, inner and outer lo ops, and calculating residue propensity toward these regions.27,79,80 For that, one score may be computed in a sliding window;70 or a combined score may be computed by merging several physio chemical indices characterizing TM-segments, caps of TM-segments, and lo ops.37,79,81 A similar technique is to predict conformation/lo cation of each residue with subsequent averaging at adjacent positions.80 Instead of simple statistical procedures, windows can be classified into TMsegments and nonmembrane ones using more complicated pattern recognition techniques such as logical rules,36 k -nearest neighbor analysis,82 classification,83 and neural networks.84,85 However, isolated prediction of TM-segments and their orientation is less reliable than an analysis of the entire protein. One approach to that problem is to use greedy algorithms,86 dynamic programming,27 or other algorithms87 to reconcile individually characterized TM-segments with additional constraints. A more consistent approach is to use Hidden Markov Mo dels (HMMs) with states corresponding to TM-segments, inner and outer caps, inner and outer lo ops, and globular domains.88,89 Residue frequencies in different regions and segment lengths (symbol probabilities and duration of states, respectively, in the HMM terminology) are learned from training samples, and then each new sequence is decomposed into the most probable sequence of hidden states, satisfying natural topology constraints. Thus, although some algorithms predict only TM-segments,25,37,38,79,84,90 most current methods predict the entire protein topology30,40,61,88,89,91­93 (Table 2). Some metho ds allow the user to constrain the prediction by available experimental or comparative data, so that particular regions of a protein are set into a userdefined state.94 In addition to prediction of topology of TM-proteins with multiple TM-helices, especially transporters, it is important to perform rotational positioning of these helices. Indeed, substrates may interact with sites within a TM-domain; and these sites may define the specificity and kinetics of transport.108 This analysis is based on the assumption that the side of a TM-helix that contacts the membrane is hydrophobic, whereas the side facing the channel may contain hydrophilic residues. Indeed, TM-segments in transporters demonstrate perio dicity in positions of hydrophilic residues.109 In densely packed proteins, there are differences in the composition of lipid-exposed and buried helix sides.110­112 However, this statistical trend is to o weak to analyze relatively short TM-segments; and such an analysis requires additional data.80 One possible complication obscuring the simple channel side versus membrane side picture above comes from helix­helix interactions. Specific indices were developed for the analysis of such interactions.113­115


Table 2. Servers for prediction of TM-segments and topology of TM-proteins. Boldface: ten servers used in benchmarking. Algorithm Slidng window Hydrophobicity, amphiphilicity at caps, charge, length Hydrophobicity, length, charge in inner/outer loops, amino acid composition of long loops Hydrophobicity, length, TM and cap propensities Inner/outer loop propensities Hydrophobicity and other scales, hydrophobic moment, conformational propensity, charge motifs TM and inner loop propensitiy, correlations between adjacent residues, conservation TM propensities (indirectly) Hydrophobicity, charge difference, signal motifs Parameters U RL http://sosui.proteome.bio.tuat. ac.jp/sosuiframe0.html http://bioweb.pasteur.fr/ seqanal/interfaces/ toppred.html http://o2.biol.uoa.gr/PREDTMR/ http://o2.biol. uoa.gr/orienTM/ http://split.pmfst.hr/split/ http://split.pmfst.hr/split/4/

Server

References

What is Predicted

SOSUI

37, 38

TM-segments

TopPred II

95

TM-segments, orientation Sliding window Topology Residue analysis with averaging

Multiple alignment, sliding window, topology

PRED-TMR

79

TM-segments

OrienTM

40

Orientation

SPLIT

80, 93

TM-segments, orientation

PHDhtm

84, 86

TM-segments, orientation

Multiple alignment, sliding window, neural network, dynamic programming Averaging of dot-plots k -nearest neighbour method

http://npsa-pbil.ibcp.fr/cgibin/npsa automat.pl? page=/NPSA /npsa htm.html http://www.sbc.su.se/ miklos/DAS/ http://www.psort.org/

DAS

25

TM-segments

Recognition of Transmembrane Segments in Proteins

PSORT

56, 82, 96­99

TM-segments, orientation

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Table 2. (Continued) Algorithm HMM http://www.cbs.dtu.dk/ services/TMHMM-2.0/ http://www.enzim.hu/ hmmtop/index.html http://www.sacs.ucsf.edu/ secure/cgi-bin/memsat.pl http://o2.biol.uoa.gr/ CoPreTHi/ http://bioinfo.si.hirosaki-u. ac.jp/ConPred2/ Amino acid composition and lengths of TM-segments, caps and loops Amino acid compositions and lengths of TM-segments, caps and loops PSI-BLAST profiles for topological models 3 out of DAS, ISREC-SAPS, PHD, SOSUI, TMpred, TopPred II, PRED-TMR Average of KKD, TMpred, TopPred II, DAS, TMAP, MEMSAT 1.8, SOSUI, TMHMM 2.0 and HMMTOP 2.0. Combination of HMMTop 2, DAS, SOSUI, TMpred, TopPred II Amino acid composition, length Hydrophobicity Amino acid composition, length Parameters U RL

Server

References

What is Predicted

TMHMM 2.0

89, 100

TM-segments, orientation HMM

HMMTOP 2.0

88, 94

TM-segments, orientation Dynamic programming Sequence profile

MEMSAT 2

101

TM-segments, orientation

CoPreTHi

102

TM-segments

N. S. Sadovskaya, R. A. Sutormin & M. S. Gelfand

ConPred II

103

TM-segments, orientation

Sliding window

BPROMPT

104

TM-segments, orientation Not described

Bayesian Belief Network

http://www.jenner.ac.uk/ BPROMPT http://www.ch.embnet.org/ software/TMPRED form.html http://www.isrec.isb-sib.ch/ software/SAPS form.html http://www.mbb.ki.se/ tmap/single.html

TMpred

105

TM-segments, orientation Statistics Multiple alignment, sliding window, topology

SAPS

83

TM-segments

TMAP

106, 107

TM-segments, orientation


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An important feature of many methods is that they do not make isolated predictions, but place them in context. There are three different approaches to that. One approach is to predict TM-segments in aligned homologous proteins. The basic assumption here is that the number and positions of TM-segments in homologous proteins are the same; and taking that into account decreases the noise. This technique was used in Refs. 24, 84, 86, 91, 95, and 106. However, alignment of TMproteins is not trivial, as they consist of segments with clearly different statistical properties, and different amino acid substitution matrices may be needed in these segments, while boundaries are unknown.116 MEMSAT uses host-slave-type multiple alignments generated by PSI-BLAST,117 and averages the predictions across the alignment.101 The use of homology increased the fraction of correctly predicted topologies from 78% to 93%. An alignment algorithm based on averaging of dotplots is more robust as regards the level of sequence identity;77 and the DAS algorithm for prediction of TM-segments is based on matching of the query protein to all TM-proteins in the database.25 This is an indirect way of computing residue propensities to TM-helical regions. Another approach is to consider all possible structural assignments for a protein at once. This is especially important for proteins with one TM-helix and for the analysis of N -terminal TM-helices. Indeed, one of the problems is to distinguish between TM-segments and predicted signal peptides;89 and some methods even require prior prediction and removal of signal peptides.93 To avoid this, PSORT predicts TM-segments and various signals for cellular lo calization of proteins.82 Finally, several servers map TM-segments by averaging predictions of other servers. For example, a simple voting can be done separately for each residue102 or for sliding windows,118 or a Bayesian decision pro cedure can be implemented.104 Overall success rate for the predictions of TM-protein topology was claimed to be 95% for bacterial and 83% for eukaryotic proteins by TopPred II,95 86% by PHDhtm,86 85% by HMMTOP,88 93% by MEMSAT 2,101 87% to 97% for prokaryotic and 94% to 97% for eukaryotic proteins by OrienTM,40 78% by TMHMM,89 and 75% by SPLIT.93 It was been shown that the use of taxonomy-specific parameters may improve the performance on bacterial,34 eukaryotic,30,33 mitochondrial,119 and chloroplast120 proteins. Some structural families may present specific problems, e.g. G-coupled receptors containing seven TM-helices.24

4. Neural Networks and Hidden Markov Mo dels for Identification of TM-Segments We describe in this section two of the most successful algorithmic techniques implemented in three programs. The most successful application of the neural network metho dology to the TMsegment prediction is PHDhtm. Initially created as an algorithm for the prediction


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of the protein secondary structure,18 it was then specifically re-designed for the identification and analysis of TM-proteins.84,86 PHDthm consists of two successive neural networks and a number of prepro cessing and post-pro cessing filters. The first, a sequence-to-structure network, pro cesses overlapping sliding windows of length 13 and estimates the probability of TM lo calization for the residue in the middle of the window taking into account the identity of all 13 residues and also global parameters such as the protein length, the window distance to the N - and C -termini, and the protein amino acid composition. The network consists of an input layer, a hidden layer with three units, and an output layer that pro duces measures of TM-propensity and non-TM-propensity, that is, the preferences of the central residue to be in a TM helix or in a non-TM lo op. The feed-forward network with a complete set of connections and a sigmoid trigger function is trained using the gradient descent pro cedure. The technical details are described in Ref. 18. Then one more neural network is applied. This second network, a structureto-structure network, takes into account the context of predictions, e.g. to filter out TM-segments that are to o short. Its input is the output of the first network in 21-residue windows supplemented with global information, and its hidden layer contains 15 units. At the next step, a decision is made on whether the considered protein is a TMprotein. This is done by computing the total TM-propensity of sliding windows of length 18: if no such window passes a threshold, the protein is predicted to be a non-TM one. For a predicted TM-protein, a dynamic programming-based pro cedure is used to reconcile conflicting predictions and to determine the optimal TM-segment boundaries.86 A set of possible TM-segments is compiled by considering all windows of length 18 through 25. The average TM-propensity is calculated for each window. An optimal path through all windows is constructed using a dynamic programming procedure, with a restriction that the TM-segments should be separated by lo ops of the minimal length 4. This path represents the optimal decomposition of the protein into TM-segments and non-TM-loops. Finally, for a given set of TM-segments, the topology (that is, the protein orientation relative the membrane) is predicted.86 This is done by computing the overall charge of each non-TM-segment, and then taking the difference between the total charge of o dd and even non-TM-regions. The set with the more positive charge should reside on the cytoplasmic side, based on the so-called "positive-inside rule". Although quite powerful, to perform well, any neural network-based approach needs to involve a considerable number of ad hoc filters and pro cedures (cf. the evolution of post-processing filtering/reconciliation steps in the three papers describing PHDthm18,84,86 ), and thus lacks conceptual clarity. A uniform language for the description of symbol sequences containing segments of different statistical properties is the HMMs.121


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A HMM is defined by a set of transition probabilities Q(S T ) between states (here, the probability to move into state T if the current state is S ) and a set of emission probabilities R(A|S ) for each state (here, the probability to observe an outcome, that is, residue A if the current state is S ). Each realization (A1 ,... ,An ; S1 ,...,Sn ) of a HMM is a set of observable outcomes and hidden states, and its probability is defined as P (A1 ,...,An ; S1 ,... ,Sn )= q (S1 ) · r(A1 |S1 ) · k =2,...,n Q(Sk-1 Sk )R(Ak |Sk ) where q (S ) and r(A|S ) are the vectors of initial probabilities to be in the state S and generate in this state the outcome A, respectively. This simplest definition can be easily generalized for Markov chains of higher order for states. It is also simple to require that an outcome depends not only on the state, but also on the previous outcome, thus intro ducing the Markovian dependence to the sequence of outcomes. These general definitions are applied to the recognition of TM-segments as follows. The protein is assumed to represent a sequence of outcomes, and the aim is to reconstruct the most likely sequence of hidden states, so that P (A1 ,... ,An ; S1 ,...,Sn ) is maximized over all state sequences S1 ,...,Sn . The hidden states may be TM-segment, external lo op, internal lo op, external cap (region of transition between the membrane and non membrane surroundings), cytoplasmic cap, N -terminus, C -terminus, external globular domain, internal globular domain etc., dependent on the architecture of the HMM. The natural restrictions (e.g. an external loop should follow an external cap, etc.) are easily taken into account by allowing only certain transitions (Fig. 1(a)); to take into account the length distribution in TM-segments, loops and caps, these states are multiplied (Fig. 1(b)) and the transition probabilities are set according to the distribution. The transition and emission probabilities are learned at the training step. The reconstruction of the most likely sequence of hidden states is done via a dynamic programming-like technique called the Baum-Welch (or forwardbackward) algorithm. This approach was implemented, in particular, in HMMTOP88,94 and TMHMM.89,100 These two algorithms differ in minor details of their HMM architecture, cf. Fig. 3 of Ref. 88 and Fig. 1 of Ref. 89. Finally, it should be noted that both neural network and HMM algorithms can be easily mo dified to accept as input not single sequences, but multiple alignments.84,121 5. Applications to Genomic Analysis Large-scale analyses of ORFs in complete genomes by various algorithms, in particular JTT2,122 TopPred,1 SOSUI,2 MEMSAT,28 PHDhtm,3,84,86 TMHMM,89 produced a consistent estimate of about 25% and seemed to show no difference in the TM-protein fraction in bacterial, archaeal, and eukaryotic genomes, although an increase of the TM-proteins fraction with the genome complexity was reported.1


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Internal globular domain External side External cap

Internal globular domain External cap

Helix

Membrane

Helix

Internal cap Cytoplasmic side Internal loop
(a)

Internal cap

Internal globular domain

Previous state

1 ... min. length ... max. length

Subsequent state
(b) Fig. 1. (a) Architecture of a Hidden Markov Model for identification of TM-segments. (b) Module with multiplied steps, allowing for exact modeling of length distribution.

In individual genomes, this fraction ranges from 19% through 30% (Table 3). The difference between individual predictors was only a few percent points (Table 3). However, there exists a controversy about the existence of preferred TM-protein topologies. Several studies observed simple monotonic decrease in the fraction of TM-proteins as the number of TM-segments increased,123­125 whereas overrepresentation of proteins with seven TM-segments was observed in multicellular eukaryotes, and over-representation of proteins with 6 and 12 TM-segments, in prokaryotes.3 Membrane proteins seem to come in two basic varieties: those with many TM-segments and short connecting loops, and those with few TM-segments


Table 3. Estimated fraction of TM-proteins in selected complete genomes.
a

Algorithm 1 37% n/a 41% 34% 34% 26% 27% n/a n/a n/a 29% 25% 23% n/a n/a n/a n/a n/a 35% n/a n/a n/a 26% n/a n/a 20% 26% n/a 30% 23% 24% 18% 19% n/a n/a n/a 23% 18% 16% n/a n/a n/a n/a n/a 24% n/a n/a n/a 19% n/a n/a 14% n/a n/a n/a 25% 26% 23% 25% n/a n/a n/a 30% 25% 26% 23% 29% n/a 26% 25% 27% n/a n/a n/a 28% n/a n/a 21% 27% n/a 25% 18% 26% 23% n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 14­18% 18% 30% 22­25% 25% 19% 21% 24% 28% 19% 19% 19% 18% 21% 27% 27% 22% 18% n/a 17% 20% 18% 18% 21% 23% 18% n/a 20% 30% 21% 21% 19% 19% 21% 26% 18% 24% 20% 18% 24% 29% 28% 25% 21% 26% 19% 24% 22% 20% 23% 26% n/a 1 2 28 3, 84, 86 89

TopPred

TopPredb SOSUI MEMSAT PHDhtm TM H M M

TopPredc

References

1

H. sapiens D. melanogaster C. elegans S. cerevisiae E. coli H. influenzae H. pylori C. jejuni R. prowazekii M. tuberculosis B. subtilis M. genitalium M. pneumoniae T. pal lidum B. burgdorferi C. pneumoniae C. trachomatis A. aeolicus Synechocystis sp. D. radiodurans T. maritima M. thermoautotrophicum A. fulgidus P. abyssi P. horikoshii M. jannaschii

43% n/a 46% 40% 40% 30% 32% n/a n/a n/a 33% 29% 29% n/a n/a n/a n/a n/a 41% n/a n/a n/a 30% n/a n/a 23%

Recognition of Transmembrane Segments in Proteins

a

b

c

TopPred: set 1: at least two "putative" or "certain" TM; TopPred: set 2: at least one "certain" and one "putative" or "certain" TM; TopPred: set 3: at least two "certain" TM.

1043


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and large extra-membrane domains, whereas proteins with multiple TM-segments and large extra-membrane domains are relatively rare.1 Further, if the orientation relative to the membrane is taken into account, all organisms excluding C. elegans (but including some other multicellular eukaryotes) prefer the topology with an even number of TM-segments and the N -terminus in the cytoplasmic side, whereas C. elegans has more proteins with an odd number of TM-segments, external N terminus and internal C -terminus.89 A more detailed analysis in Ref. 28 showed that bacterial TM-proteins with an even number of TM-segments (4, 6, 10, 12, 14) tend to have the topology with the N -terminus in the internal side of the membrane (nontranslocated), whereas the proteins with 5 TM-segments tend to have external N -termini. In yeast, the preferred topologies are almost the same (even with N -termini inside), whereas in multicellular eukaryotes the preferred topologies are 4-TM and 12-TM with the internal N -terminus, and 7-TM with the external N -terminus. The latter are likely due to the large family of G protein-coupled receptors,126 whereas 12-TM proteins are mainly permeases and TM-components of ABC-transporters.4

6. Benchmarking Benchmarking of algorithms for prediction of TM-segments is complicated by a relatively small number of resolved TM-protein structures and the low rate of newly arising structures, leading to difficulty in finding examples new for each particular metho d. The same datasets have been used over and over again, e.g. the 64 TM-protein dataset in Refs. 27, 79, 84, and 90. Several hydrophobicity scales were compared in Ref. 75, and benchmarking of several popular metho ds (TMHMM 1.0, 2.089,100 and a retrained version of 2.0,127 MEMSAT,27 Eisenberg,128 Kyte/Doolittle,70 TMAP,107 DAS,25 HMMTOP,88 SOSUI,37 PHD,129 TMpred,105 KKD,29 ALOM236 and Topred II95 ) was done in Ref. 127 using a newly compiled database of about 188 TM-protein topologies obtained by low-resolution experiments such as C -terminal fusions with indicator proteins and antibody binding.130 A commonly used database of TM-protein structures is MP-topo.10 A combined database of structures and topologies supplemented by proteins whose structural similarity could be inferred from sequence comparison131 was used for comprehensive benchmarking in Refs. 132 and 133. This database also serves as an automated benchmark server for evaluation of new algorithms (http://cubic.bioc.columbia.edu/services/tmh benchmark/). However, despite specific attempts to perform benchmarking on new data samples or to apply bo otstrapping approaches, it has been repeatedly noted that it is difficult to compile a clearly independent sample. Thus, the performance of all metho ds tends to be over-estimated.132,134 The results of these benchmarks are discussed below. Here we tried to benchmark TM-segment prediction algorithms using a comparative approach. It is based on the assumption that homologous proteins have the same number of TM-helices, and these TM-helices can be aligned. Thus instead of


Recognition of Transmembrane Segments in Proteins

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matching predictions to experimental data, we measure the server's reliability using the consistency of its predictions on a group of aligned homologs. This approach is complementary to the experimental data-based benchmarking. It is clear that this approach has limitations. Firstly, a consistently wrong algorithm would get a high score. However, we feel that such possibility is unlikely, and the results presented below seem to confirm this assumption. Secondly, algorithms using multiple alignments as input cannot be considered, as they use the same type of data and thus the consistency approach would be based on a circular argument. Thirdly, protein families having an intermediate level of similarity should be analyzed. Indeed, the proteins should be sufficiently similar to each other to satisfy the assumption of the conserved topology and, moreover, to allow for reliable alignment. On the other hand, it is useless to consider proteins that are to o similar, as they would provide no independent data.

7. Data and Metho ds Initially, all representatives of bacterial transporters from class TC.2A were considered, downloaded from TCDB databases http://www-biology.ucsd.edu/msaier /transport/4,135 and TransportDB http://www.membranetransport.org/.136 This sample consisted of 1312 proteins from 101 families. The ma jority of sequences were from four families from the MFS superfamily (438 proteins), five families from the APC family (103 sequences), and four families from the RND family (114 proteins). The CPA3 and NFE families, consisting of subunits of multicomponent systems, were not considered. Then each sequence from the initial sample was used to scan the ERGO database (http://ergo.integratedgenomics.com/ERGO/)137 using BLAST.138 Only relatively complete genomes were considered, such that at least 500 genes were sequenced as not more than 10 contigs. It produced 860 additional proteins homologous to proteins from the initial sample. The obtained sample was divided into clusters using the single linkage pro cedure, with the BLAST identity value serving as the similarity measure. Clusters with identity 40% to 49% and 50% to 59% were considered. Clusters larger than 50 proteins were further subdivided by increasing the identity threshold. Each cluster was aligned using CLUSTALW.139 The comparisons were done for all pairs of proteins belonging to one cluster with pairwise alignment induced by the multiple alignment. Overall, 2356 pairs of proteins from the first group (40% to 49%) and 909 pairs of proteins from the second group (50% to 59%) were considered. For each pair, TM-segments were predicted independently, and the consistency indices between the predictions by one metho d were computed as follows. Overlapping or immediately adjacent (with zero size lo op) TM-segments were merged and considered as one TM-segment. The overlap index Q compared predictions at the residue level. For each pair it was equal to the size of the intersection divided by the size of the union of the segment pro jections. More formally, let S be the


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number of aligned residue pairs where both members were predicted to belong to TM-segments, and let U be the number of aligned residue pairs where at least one member is predicted to belong to a TM-segment. Then Q = S/U. (1) Another measure, the segment consistency index C , measured the fraction of common TM-segments in two proteins. Let n1 and n2 be the numbers of TMsegments in the first and second protein, respectively. Let i =1,... ,n1 ,j =1,... ,n2 be TM-segments in the first and second proteins, respectively, and consider all pairs ij of TM-segments whose pro jections intersect by at least one residue pair. Define the indicator value, the lo cal overlap Vij , for segment i with respect to segment j . By definition,Vij = 1, if at least half of segment i intersects with segment j , and Vij = 0 otherwise (more exactly, intersection of pro jections is considered). Formally, let Li be the size of segment i, let Mj be the size of segment j , and let Kij be the size of intersection of (pro jections of ) segments i and j . Then, Vij = 1, Vij = 0, Wji = 1, Wji = 0, if Kij /Li 0.5, if Kij /Lj < 0.5, if Kij /Mj 0.5, if Kij /Mj < 0.5. (3) (2)

and, symmetrically, the local overlap of segment j with respect to segment i is

The total segment consistency C is the sum of lo cal overlaps for all pairs of TM-segments whose pro jections intersect: C = ij (Vij + Wji )/(n1 + n2 ). (4) For similar predictions, both Q and C are close to 1. If the same numbers of TM-segments are predicted in two aligned proteins and these segments roughly correspond to each other (have more than half of each segment in common), but positions of the TM-segment termini differ, then Q < 1, but still C = 1. On the other hand, if a single TM-segment in one protein often corresponds to two segments in the other protein (broken segments), Q 1 and C < 1. Thus, these two indices capture two different aspects of the prediction consistency. The following ten servers were considered: DAS, HMMTOP, PHDhtm, PREDTMR, PSORT, SOSUI, TMAP, TMHMM 2.0, TMpred, and TopPred II. This choice depended on server's availability and popularity, possibility to make multiple queries, and possibility to make predictions for single proteins (not only multiple alignments). 8. Results and Discussion Here we have analyzed ten advanced methods for the analysis of single sequences. The URLs of the considered servers are listed in Table 2. The average values of the consistency indices Q and C and standard deviations ( ) are listed in Table 4.


Recognition of Transmembrane Segments in Proteins Table 4. The Q and C consistency indices and standard deviation () in different ID intervals. Proteins are divided into several groups according to their identity level. ID PHDhtm HMMTOP 2.0 TMHMM 2.0 TMpred TopPred II PRED-TMR SOSUI TMAP DAS PSORT 40­49% C 0.88 0.73 0.72 0.70 0.71 0.69 0.69 0.64 0.64 0.63 ± ± ± ± ± ± ± ± ± ± ± 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 12 12 13 10 12 13 11 10 11 14 40­49% 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. Q 94 93 92 91 89 89 88 85 83 84 ± ± ± ± ± ± ± ± ± ± ± 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 11 12 12 08 10 12 13 11 10 14 50­59% 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. C 89 76 76 74 76 73 72 67 69 69 ± ± 0.12 ± 0.13 ± 0.11 ± 0.10 ± 0.13 ±0.13 ± 0.11 ±0.11 ± 0.10 ± 0.14 50­59% Q 0.94 0.94 0.93 0.93 0.91 0.90 0.89 0.87 0.87 0.86 ± ± ± ± ± ± ± ± ± ± ± 0.11 0.13 0.12 0.08 0.09 012 0.13 0.10 0.09 0.14

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The most consistent predictions were made by PHDhtm, closely followed by HMMTOP and TMHMM. It is instructive to compare these results with benchmarks that used test sets. Among six hydrophobicity scales and eight advanced methods considered in Ref. 127, the best results were demonstrated by TMHMM; the predictions by PHDhtm and HMMTOP were considerably worse. Eight internet servers and 19 hydrophobicity scales were compared in Ref. 132. The results depend on the details of the pro cedure used to evaluate the predictions. HMMTOP and PHDhtm usually were among the leaders, whereas TMHMM pro duced average results. TopPred and TMpred consistently o ccupy the middle of the list. This was observed in our study as well as Ref. 127. The last group of servers is PSORT, DAS, and TMAP. DAS demonstrated average performance in Refs. 127 and 132; TMAP was in the middle in Ref. 127; the remaining servers were not considered in the cited papers. The four metho ds shown to be consistently performing well in Ref. 140 are SPLIT4, TMHMM, HMMTOP, and TMAP. Thus, we can see that the results obtained in several benchmark studies are largely similar, despite the use of different criteria. Most successful metho ds use the HMM technique. That seems to be the best technique for capturing various statistical properties of membrane-spanning proteins. An alternative, the use of the sliding windows approach, suffers from various problems ranging from insufficient specificity127,134 to inexact definition of TM-segment boundaries and spurious merging of adjacent segments, requiring introduction of ad hoc fine-tuning pro cedures.84 One specific problem, not addressed here, is the initial identification of membrane-spanning proteins. Indeed, no single metho d but DAS could identify all such protein in Ref. 132; but it was rather non specific and predicted TM-segments in globular proteins. Two more technical but important points, whose detailed analysis is beyond the scope of this study, are the fact that advantages and disadvantages of different metho ds balance each other, and thus averaging over metho ds improves the


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reliability of predictions.134 Indeed, this observation has been exploited by a number of successful programs (Table 2).102,104,118,141 However, these programs require reliable alignments, and the next generation of metho ds probably will combine multiple alignment and TM-segment identification using the HMM language common to these two problems.142 Finally, as signal peptides are often taken for TM-segments by the TMrecognition software, identification of TM-segments should be combined with the analysis of signal peptides either manually or algorithmically.134,143 Overall, if one single server is to be recommended, it is probably TMHMM followed by PHDhtm and HMMTOP. However, in important cases, it is clearly advisable to submit a query to several servers; and also to analyze not one protein, but an entire family of homologs. Acknowledgments We are Mironov This Institute Cellular grateful to Alexandra Rakhmaninova, Vsevolo d Makeev, and Andrei for numerous useful discussions. study was partially supported by grants from the Howard Hughes Medical (55005610), the Russian Academy of Sciences (programs "Molecular and biology"), The Russian Fund of Basic Research (04-04-49361), INTAS.

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Natalya S. Sadovskaya is a junior fellow researcher in the Laboratory of Bioinformatics, State Scientific Center GosNIIGenetika, Moscow, Russia. She obtained her M.Sc. degree from the Department of Molecular and Biology Physics, Moscow Institute of Physics and Technology (State University) in 2000. Her research interests are the evolution of metabolic pathways and secondary structure of membrane proteins. Roman A. Sutormin graduated as M.Sc. from the Department of Mechanics and Mathematics, Moscow State University, Russia. He is currently a Ph.D. student at the State Scientific Center of Genetics and Selection and programming teacher at the Department of Bioengineering and Bioinformatics, Moscow State University. His research interests include statistical properties of amino acid sequences of membrane proteins and their evolution, prediction of the secondary structure of TM-proteins both from single sequence and using multiple alignment information, development of the alignment techniques in case of membrane proteins. Mikhail S. Gelfand is the Head of the Research and Training Center in Bioinformatics of the Institute for Information Transmission Problems, RAS in Moscow, Russia and a Professor at the Department of Bio engineering and Bioinformatics of the Moscow State University. He graduated from the Department of Mathematics of the Moscow State University, received his Ph.D. (Math.) degree from the Institute of Theoretical and Experimental Biophysics, RAS (Pushchino), and the Doctor of Sciences degree from the State Research Institute for Genetics and Selection of Industrial Micro organisms (Moscow). He is a member of editorial boards of


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several journals, in particular, "PLoS Biology", "Bioinformatics", "BMC Bioinformatics", "Journal of Bioinformatics and Computational Biology," and "Journal of Computational Biology". He received the Baev prize (1999) from the Russian State "Human Genome" Council, and Best Scientist of the Russian Academy of Sciences" award (2004). His research interests include comparative genomics, metabolic reconstruction and mo deling, evolution of metabolic pathways and regulatory systems, function and evolution alternative splicing, functional annotation of genes and regulatory signals.