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RESEARCH ARTICLE

Open Access

Dissecting protein loops with a statistical scalpel suggests a functional implication of some structural motifs Leslie Regad1,2, Juliette Martin3 and Anne-Claude Camproux1,2*

Abstract Background: One of the strategies for protein function annotation is to search particular structural motifs that are known to be shared by proteins with a given function. Results: Here, we present a systematic extraction of structural motifs of seven residues from protein loops and we explore their correspondence with functional sites. Our approach is based on the structural alphabet HMM-SA (Hidden Markov Model - Structural Alphabet), which allows simplification of protein structures into uni-dimensional sequences, and advanced pattern statistics adapted to short sequences. Structural motifs of interest are selected by looking for structural motifs significantly over-represented in SCOP superfamilies in protein loops. We discovered two types of structural motifs significantly over-represented in SCOP superfamilies: (i) ubiquitous motifs, shared by several superfamilies and (ii) superfamily-specific motifs, over-represented in few superfamilies. A comparison of ubiquitous words with known small structural motifs shows that they contain well-described motifs as turn, niche or nest motifs. A comparison between superfamily-specific motifs and biological annotations of Swiss-Prot reveals that some of them actually correspond to functional sites involved in the binding sites of small ligands, such as ATP/GTP, NAD(P) and SAH/SAM. Conclusions: Our findings show that statistical over-representation in SCOP superfamilies is linked to functional features. The detection of over-represented motifs within structures simplified by HMM-SA is therefore a promising approach for prediction of functional sites and annotation of uncharacterized proteins.

Background Protein structures can usually be broken down into their component secondary structures: a-helices, b-strands and loops. a-helices and b-strands are regular secondary structures recurrent in many proteins. Protein loops correspond to all residues not assigned to regular secondary structures. Unlike a-helices and b-strands, protein loops were initially seen as random coils because their sequences and structures are highly variable. But the ever-increasing availability of protein structures in the Protein Data Bank (PDB) allowed extensive analyzes of protein loops, which suggested a more complex view. For example, Panchenko et al. [1] analyzed the evolution of protein loops and identified a linear correlation between sequence similarity and mean levels of structural similarity between loops in * Correspondence: [email protected] Full list of author information is available at the end of the article

protein families. They suggested that loops evolve through a process of insertion/deletion and concluded that even longer loop regions cannot be defined as irregular conformations or random coils. Several classifications of short and medium loops have been developed [2-7], according to the type and structure of flanking secondary structures, and the length and geometry of loops. These classifications have revealed the existence of recurrent amino-acid dependent loop conformations. Loop regions play a role in protein function [8]. They may be involved in the active sites of enzymes [9] or in binding sites [10-13]. The classification of protein loops has then been used to investigate the link between protein loops and function. From the loop classification system ArchDB [3], Espadaler et al. [14], developed an approach to identify loop clusters associated with the protein functional sites provided by the PROSITE database [15] or Gene Ontology (GO) [16]. They showed that

© 2011 Regad et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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loops contain structural motifs involved in the functional sites of proteins. Using a similar approach, Tendulkar et al. [17] and Manikandan et al. [18] extracted octapeptide clusters involved in protein function. They first classified octapeptides using geometric invariants [17] or dihedral angles [18]. They then identified octapeptide clusters associated with protein functions provided by SCOP superfamilies [19] or GO terms. Tendulkar et al. found that functional clusters consisted mostly of octapeptides extracted from loop regions [17]. In a similar vein, Polacco et al. [20] developed the GASPS approach (Genetic Algorithm Search for Pattern in Structure) to extract the structural motifs most useful for identifying SCOP superfamilies. Ausiello et al. [21] developed an approach called FunClust to identify conserved residues of three-dimensional (3D) structural motifs through local structural comparisons between non homologous proteins. The common point between all these studies is that no prior information about the location of the functional sites is required, making it possible to discover new functional sites. Contrary to the methods cited above, other approaches start from known functional sites and look for structural motifs associated with them [22-26]. In all these approaches, structural motifs are learned through structural alignment [27], conservation of environment [26,28], or calculation of geometrical parameters [22-24]. The goal, here, is different than the one pursued by classification studies: since the focus is set on known functional sites, these approaches are dedicated to the prediction of these known functional sites, not to the discovery of new sites with functional implication. There is a third family of studies that we need to introduce before presenting our work: the identification of functional sequential motifs in DNA sequences using pattern statistics. The strategy consists in searching for nucleotide motifs with unusually high or low frequencies, i.e. over- or under-represented, with respect to a reference model (generally a homogeneous Markov model) [29,30]. The underlying idea is that the unusual frequency of a sequence motif in a genome reflects a selective pressure on this motif, suggesting a functional role. Such studies have led to the successful identification of functional motifs, such as restriction sites [31], crossover hotspot instigator sites [32] and polyadenylation signals [33]. In this paper, we propose an approach inspired by this last category of studies to identify structural motifs in loops involved in protein function. Our approach is based on two components. The first one is the structural alphabet HMM-SA described in [34-37]. It is a collection of 27 structural prototypes of four residues, called structural letters, connected by transition rules. HMM-SA allows simplifying protein 3D structures into one-dimensional

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(1D) sequences of structural letters. After this simplification step, the search for 3D structural motifs is reduced to the search for structural words in the 1D structural-letter sequences. We can then apply the second component of our approach: the SPatt software that allows computing exact statistics in short sequences [38], which we use to detect over-represented structural words. We specifically focus on structural motifs of seven residues in loops, following the protocol developed in [39]. In this previous publication, we have shown that this protocol allowed grouping together seven-residue fragments with very similar structures, extracted from both short and long loops [39]. An advantage of this method is that it does not require pairwise comparison of all seven-residue fragments. In this study, we further investigate the functional implication of over-represented structural motifs. We consider the SCOP classification at the superfamily level, which groups protein with similar functions. For every structural word, we compute the over-representation separately in each SCOP superfamily. Based on the statistical over-representation in SCOP superfamilies, we make the distinction between two types of over-represented structural words within loops: structural words over-represented in multiple superfamilies, called ubiquitous words, and structural words over-represented in one or few superfamilies, called superfamily-specific words. To assess the role of these words, we (i) investigate the correspondence between a subset of ubiquitous words and known recurrent motifs, such as turns and niches and (ii) check the link between a subset of superfamily-specific words and functional sites of proteins, provided by Swiss-Prot functional annotations. This validation step confirms that superfamily-specific words are involved in some functional sites of proteins, such as the binding sites of small ligands. Our method thus allowed the identification of structural motifs important for protein function. Some were previously known as involved in protein functions, others are new structural motifs with a putative functional role. Our results indicate that our statistical approach is a promising approach for the detection of new structural motifs of interest in protein structures.

Methods Protein data sets Initial data set

A list of 8 119 protein structures was extracted from the PDB of May 2008 with PISCES software [40], using the following criteria: data obtained by X-ray diffraction, with a resolution better than 2.5 Å, longer than 30 residues, less than 50% sequence identity between any pair. We restricted this list to the 5 429 structures classified in SCOP [19]. As it is assumed that proteins grouped in the same SCOP superfamily have similar structure and function, this level was chosen for our analysis. For statistical

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analysis, we further restricted the list to proteins classified into superfamilies with at least two members in the data set, corresponding to 4 911 proteins from 1 493 superfamilies. On average, a superfamily contains 7.90 proteins (±13.78). Annotation data set

To validate the functional role of over-represented structural words, we analyzed their correspondence with functional annotations extracted from the Swiss-Prot database. Swiss-Prot is a curated sequence database providing a high level of annotation (description of protein function, domain structure, post-translational modifications, variants, etc.), a minimal level of redundancy and a high level of integration with other databases [41]. To extract functional annotations from our initial data set, we used the PDB/UniProt Mapping database [42], which consists of several files mapping the PDB and UniProt codes, and PDB and UniProt sequence numbering. Only 1 487 of the 4 911 protein structures of our initial data set are present in the PDB/UniProt Mapping database. From this set of 1 487 proteins, called annotation data set, we extracted the Swiss-Prot annotations. We focused on the feature table listing post-translational modifications, binding sites, enzyme active sites, local secondary structure or other features. We extracted only the following annotations: “Repeat” (Positions of repeated sequence motifs or repeated domains), calcium, DNA, nucleotide-binding sites, metal-binding sites (cobalt, copper, iron, magnesium, manganese, molybdenum, nickel, sodium), zinc finger, active sites, and binding sites for any chemical group (coenzyme, prosthetic group, etc). Validation data set

This data set was used to double-check the correspondence between structural motifs and Swiss-Prot annotations. From PDB/UniProt Mapping database, we extracted a set of 2 640 proteins classified in SCOP. From this protein set, we retained the 2 636 proteins obtained by X-ray diffraction, with a resolution better than 3 Å, longer than 40 residues and presenting less than 95% sequence identity between any pair. Extraction of over-represented structural motifs from protein loops

Our approach, summarized on Figure 1 is based on two components: (i) the structural alphabet HMM-SA that allows the simplification of protein structures into structural-letter sequences, (ii) the SPatt software that allows the computation of exact pattern statistics in simplified structural-letter sequences. We describe below these two components. Simplification of protein structures by HMM-SA and extraction of structural motifs

HMM-SA is a structural alphabet of 27 structural prototypes of four residues, called structural letters, established

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with hidden Markov models. The main steps of HMMSA construction are the following (see [34,36] for details): 1. the backbone of protein structures of a large data set are split in overlapping fragments of four residues, 2. each four-residue fragment is described by the three distances between the non-successive a-carbons and the projection of the fourth a-carbon on the plan formed by the first three ones, 3. four-residue fragments are classified according to their geometry and their succession in protein structures, using a hidden Markov model where the inputs are the vectors of distance descriptors of each fragment. 4. the optimal structural alphabet model is selected using the parsimony principle to choose the model that better fits the data with the smallest possible complexity. In this goal, structural alphabets of different lengths are compared using the Bayesian Information Criterion, which balances the log-likelihood of the model and a penalty term related to the number of parameters of the model and the sample size. The optimal HMM-SA resulted in 27 classes of fourresidue fragments and the transition matrix between these classes. For each class, labelled by letters (a, A-Z) and named structural letters, a representative four-residue fragment, presented in Figure 2A, is computed. It has been shown that four structural letters (A, a, W, V) are specific to a-helices, five (L, M, N, T, X) are specific to b-strands and the remaining 18 describe loops [36]. HMM-SA can be used to simplify a protein structure of n residues into a sequence of (n - 3) structural letters. This simplification takes into account the structural similarity of four-residue fragments with the 27 structural letters. It is achieved by a dynamic programming algorithm based on Markovian process to obtain maximum a posteriori encoding using the Viterbi algorithm. The input is the sequence of distance descriptors of the four-residue fragments of the input structure. The output is a sequence of structural letters, where each structural letter describes the geometry of a four-residue fragment. We used HMM-SA to extract structural motifs from protein loops using the protocol established in a previous study [39] and summarized in Figure 2. We first simplified all the 4 911 structures of our initial data set in sequences of structural letters. Since we focused our analysis on protein loops, regular secondary structures were removed, based on the fact that some structural letters are specific to regular secondary structures [36,37]. From the initial data set, we obtain 90 811 protein loops

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Figure 1 Protocol used in this study. Non redundant protein structures were simplified using the structural alphabet HMM-SA and structural motifs extracted using the protocol presented in Figure 2. Over-represented structural motifs in SCOP superfamilies in protein loops were detected using the SPatt software. Based on SPatt statistics, two types of words were distinguished: ubiquitous words, over-represented in several superfamilies, and superfamily-specific words, over-represented in few superfamilies. Some ubiquitous words were compared with known structural motifs: b-turns identified by the ExtractTurn software and structural motifs presented in the Motivated Proteins database. Some superfamily-specific words were compared with functional sites, using Swiss-Prot annotations and external softwares.

Regad et al. BMC Bioinformatics 2011, 12:247 http://www.biomedcentral.com/1471-2105/12/247

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Page 5 of 23

  

  

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Figure 2 Protocol used for extraction of structural motifs. A: the 27 structural letters of HMM-SA. B: input 3D structure. C: sequence of structural letters resulting from the simplification. D: extraction of loops based on regular expressions of structural letters; the geometry of a loop encoded by SPBDRPI is shown on the right side. E: systematic splitting of loops into overlapping words of four consecutive structural letters. The geometry of two structural words, KGDR and DRPI, are shown with superimposition of their fragments. Fragments are superimposed with ProFit software http://www.bioinf.org.uk/software/profit and represented with Pymol http://www.pymol.org.

encoded into structural-letter sequences. In these 90 811 protein loops, we chose to study the structural motifs formed by four consecutive structural letters (i.e., seven residues). The choice of the length of four structural letters is motivated by our previous work [39], where we showed that it allows a compromise between considering long fragments on the one hand, and avoiding data sparsity on the other hand. The 90 811 protein loops are split into 238 158 seven-residue fragments, described by 25 304 different words of four structural letters. As we have previously shown that structural words with low frequencies are linked to structural flexibility and regions with uncertain coordinates [39], we did not consider structural words seen less than five times in our initial data set. This results in a set of 11 294 different structural words, grouping 224 148 seven-residue fragments. Each word is seen on average 20 times (±32), meaning that it groups on average 20 seven-residue fragments.

Computation of pattern statistics using SPatt

We used the SPatt software [38,43], available from http://stat.genopole.cnrs.fr/spatt/index.html to identify structural motifs over-represented in SCOP superfamilies. Here, we computed the over-representation of fourstructural-letter motifs in sets of protein loops grouped by SCOP superfamilies. The considered sequences are typically short. The SPatt approach allows the calculation of exact statistics in sets of short sequences [44,45]. The over-representation of a word w in a set of sequences is assessed by comparing its observed occurrence (N obs ) with the theoretical occurrence (Ntheo) expected under a background model. The over-representation score Lp of w is given by Lp(w) = −log10 (p − value)

(1)

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where the p - value is defined by: p−value = P(Ntheo ≥ Nobs ) = P[Ntheo = Nobs ] + P[ Ntheo = (Nobs + 1) ] + P[ Ntheo = (Nobs + 2)] +. . .

(2)

where P denotes the probability of the events. For instance, a Lp score of 3 means that a word is over-represented with a p - value of 10-3. SPatt allows the exact computation of the distribution of the word occurrence Ntheo and thus the corresponding p - value. The approach implemented in SPatt is based on the notion of automata. We briefly present it below, see [44,45] for details. Let us consider, for example, the word PZCD. The first step in SPatt consists in building an optimal Markov chain embedding through a Deterministic Finite Automata (DFA) shown in Figure 3A. The second step in SPatt consists in passing the structural-letter sequences in the DFA, resulting in the corresponding state sequence as illustrated in Figure 3B. By definition these state sequences are a heterogeneous first order Markov chain embedding over the alphabet   Q = states of the DFA , with a starting distribution md (d Î [1, r]) and a transition matrix T. The computation of md and T are explained in [44]. Then, these corresponding Markov chain embedding parameters allow the

computation of the generating function of Nw in each structural-letter sequence. From the generating functions, GNtheo, of Ntheo, all terms of equation 1 are deduced, see [44]: GNtheo (y) = GN1 (y) × GN2 (y) × . . . × GNr (y)

=

+∞ 

(3)

P(Ntheo = Nobs )yNobs

(4)

Nobs =0

A simple example of the computation of p - value of word using DFA is presented in details [44]. Note that, contrary to approaches based on the hypergeometric distribution approximation, the exact approach does not require any correction to take into account the size of the data set in which the patterns are searched. This is explicitly taken into account during the exact p - value computation. In this work, we computed the over-representation scores for four structural-letter words, in the loop regions of proteins classified into SCOP superfamilies. In each of


A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,Q,R,S,T,U,V,W,X,Y,Z,a A,B,C,D,E,F,G,H,I,J,K,L,M, N,O,Q,R,S,T,U,V,W,X,Y,Z,a

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  Structural-letter sequence: DFSKPZCDSKGIKH State sequence : 00001234000000 Figure 3 Example of Markov chain embedding for the PZCDpattern. A: Deterministic Finite Automaton (DFA) associated to PZCD. Initial state is highlighted in green, transiting states in blue and final state in red. One proceeds in this DFA according to the labels associated to the rows between states. Each occurrence of PZCD will reach the final state. B: state sequences obtained after passing of a structural-letter sequence to the DFA.

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the 1 493 superfamilies, we computed the Lp scores of those words, among the 11 294 that meet the condition of being observed at least five times in the superfamily. In order to take into account multiple testing, we used the Bonferroni correction to set the significance threshold, resulting in a final threshold equal to 5.97. We further considered two criteria: • Lpmax: the maximal Lp score of a word among all superfamilies, • nbsf*: the number of superfamilies in which a word is significantly over-represented. These two criteria enabled us to differentiate two types of over-represented structural words, as defined in Table 1: words over-represented in a large number of SCOP superfamily, with Lp max > 5:97 and nb sf* >= 5, which we refer to as ubiquitous words and highly overrepresented in one superfamily, with Lpmax > 5.97 and nbsf* < 5, which we refer to as superfamily-specific words. For comparison, we also calculated these criteria over randomized data sets obtained by randomly reassigning loops to SCOP superfamilies. Extent of coverage of structural words

Let us consider a data set of protein structures encoded in structural-letter sequences and a subset of structural words. The coverage of the data set by the subset of structural words can be calculated at various aspects, illustrated in Figure 4: • word coverage: the fraction of structural words included in the word subset, • fragment coverage: the fraction of fragments encoded by words from the subset, • loop length coverage: the fraction of residues in loops covered by words from the subset, • protein coverage: the fraction of proteins containing at least one of the words from the word subset.

Validation of structural or functional role of structural words

Our protocol enabled us to extract over-represented structural motifs in from loops. Then, we tried to assess the implication of these words in a structural or a functional point of view. Specifically, we investigated (i) the link between ubiquitous words and known structural motifs and (ii) the link between superfamily-specific words and known functional sites. This step of validation was performed on the annotation and validation data sets, only for a subset of the most significantly over-represented structural words, called extreme words, as defined in Table 1. Validation of the structural role of extreme ubiquitous words

Ubiquitous words were compared with well-characterized 3D motifs: b-turns, niche and nest motifs. b-turns are detected in protein structures with ExtractTurn software [46]. Turns are defined as tetrapeptides with an Cαi − Cαi+3 distance lower than 7 Å, with the two central residues i + 1 and i + 2 in a non helical state [47]. Nest and niche motifs are identified using the Motivated Proteins database [48]. Nest motifs are fragments of three consecutive residues, in which the main-chain NH of residue i and the main-chain NH of residue i + 2 have the potential to interact weakly with an anionic group [49]. Niche motifs are formed by three or four consecutive residues in which the main-chain CO of residue i and the main-chain CO of the last residue i + 2 or i + 3 have the potential to interact weakly with a cationic group [50]. The Motivated Protein database stores the nest and niche motifs detected in a data set of 400 representative proteins. Only 249 of these 400 proteins are also included in our initial data set. The comparison of structural words with nest and niche motifs is thus restricted to these 249 proteins. The Motivated Protein database was also used to detect ends of b-turns. For a pair formed by a structural word and a known structural motif, we computed a precision measure given by the

Table 1 Definition of word types Name

Definition

Structural word

Sequence of four successive structural letters

Over-represented word

Structural word with Lpmax ≥ 5.97

Ubiquitous word

Structural word with Lpmax ≥= 5.97 and nbsf* ≥= 5

Extreme ubiquitous word

Structural word with Lpmax ≥= 10 and nbsf* ≥= 5

Superfamily-specific word

Structural word with Lpmax ≥ 5.97 and nbsf* < 5

Moderately superfamily-specific

Structural word with Lpmax ≥= 10 and nbsf* < 5

Extreme superfamily-specific word

Structural word with Lpmax ≥= 50 and nbsf* < 5

Functional word

Extreme superfamily-specific word with a precision≥ 40% for a Swiss-Prot annotation

*: extreme structural words are subject to further examination to validate their structural or functional role.

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Figure 4 Definitions and illustration of coverage rates. We considered a set of seven words of four structural letters (SPBD, UQRS, RBTU, DOCI, ZPCD, PCDU, DUGO), grouping 14 sevenresidue fragments. Let us consider that these words and their occurrence are examples and not the real occurrences in the data set. From this set of words, we focused on three words, named restricted set and presented in red in A, grouping seven sevenresidue fragments. Various coverage rates were calculated for these words. A: word coverage, the fraction of structural words included in the restricted set. B: fragment coverage, the fraction of fragments encoded by words from the restricted set. C: loop-length coverage, the fraction of residues in loops covered by words from the restricted set. D: protein coverage, the fraction of proteins containing at least one of the words from the restricted set.

proportion of fragments encoded by the structural word that contain the known structural motif. Validation of the functional role of extreme superfamilyspecific words

The functional implication of superfamily-specific structural words was explored using the biological annotations from the Swiss-Prot database extracted from the annotation data set. The comparison of structural words with Swiss-Prot annotations extracted from annotation data set is limited to the 1 487 proteins. In an effort to limit this gap, we built a second data set, named validation data set composed of 2 636 proteins and favoring the selection of annotated proteins. In order to quantify the correspondence between structural word and biological annotations, we computed precision and sensitivity measures of the detection of annotations using words. We considered two levels of annotation: the first level, named annotation, corresponds to the “Feature key” and the second level, named second-level annotation, corresponds to the “Description” that provides a description of the annotation. For

example, when the annotation is “binding”, the secondlevel annotation indicates the ligand type. The precision is defined as the proportion of fragments encoded by a structural word that are annotated by a given annotation considering the two levels of annotation. A structural word with high precision is said to be functional. In order to take into account the sparsity of Swiss-Prot annotations, we set a permissive threshold of 40% precision. The sensitivity (also called recall) is defined by the proportion of a given annotation that is covered by a structural word. To compute the sensitivity, we retained only annotations extracted from protein loops, annotations seen in regular secondary structures regions are discarded. In complement to Swiss-Prot annotations, which are of high quality but far from complete, we used various external tools to identify putative functional motifs. • The Catalytic Site Atlas (CSA) database [51] documents enzyme active sites and catalytic residues in enzymes of known 3D structure. It identifies the residues directly involved in the enzymatic reaction. • The Ligplot software [52] allows the identification of interactions between proteins and ligands, by providing schematic diagrams of protein-ligand interactions from a given PDB file. • The REP software [53] is used to predict repeat regions from protein sequences. This software uses an iterative homology-based repeat finding method. • The SitePredict software [24] http://sitepredict. org/ is used to predict nucleotide and calciumbinding sites. SitePredict is a machine learning method based on diverse residue properties, including the spatial clustering of residue types and conservation during evolution. Only residues with a score above 0.5 are considered to be involved in the binding site.

Results Extraction of structural motifs over-represented in SCOP superfamilies

The goal of our study is to systematically identify structural motifs of interest, i.e. motifs with structural or functional implication, in protein loops. We made the hypothesis that structural motifs of interest are subject to selective pressure during evolution, which should result in structural words with unexpectedly high frequency in protein structures simplified into structuralletter sequences. In order to make the connection with protein function, we surveyed the over-representation of structural words in SCOP superfamilies, by computing over-representation scores for all structural words seen at least five times in a SCOP superfamily.

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We counted a total of 1 705 structural words overrepresented in at least one SCOP superfamily in the initial data set, corresponding to a coverage rate of 15% of the words and 30% of the fragments, as reported in Table 2. Based on the over-representation in SCOP superfamilies, we built two statistical criteria to classify the structural words: Lp max , which is the maximum over-representation score Lp observed among SCOP superfamilies, and nbsf* indicating the number of superfamilies in which a structural word is over-represented. For example, structural word GSUS has a Lpmax value equal to 140 and a nbsf* value equal to 3, meaning that this word is over-represented in three SCOP superfamilies and very strongly in one of them with a Lp score equal to 140, i.e. a p - value equal to 10 -140 . Average values observed for Lp max and nb sf* are reported in Table 3. Globally, structural words display an average Lpmax equal to 4.3 ± 5.6, with extreme values observed for the words PCDS (Lpmax = 0.39) and UODO (Lpmax= 210). The mean value of nbsf* is equal to 0.2 ± 0.7, ranging from 0 to 25, indicating that many of these words are not exceptional in any superfamily. We assessed the relevance of these numbers by comparing them with those obtained with randomized SCOP classifications. The number of over-represented words using random SCOP classifications is significantly smaller than that for SCOP: only 47 words are over-represented for the random SCOP classification, see Table 3. We can therefore conclude that over-represented words significantly depart from random regarding their repartition in SCOP superfamilies. Figure 5 presents the values of Lpmax versus nbsf* for all structural words seen at least five times in a SCOP superfamily. Interestingly, this representation reveals that some structural words are over-represented with very high scores in a small number of superfamilies, whereas others are over-represented with more moderate scores but in several superfamilies. Accordingly, we define two classes of words: ubiquitous and superfamilyspecific words, as detailed in Table 1. Ubiquitous words are over-represented in several superfamilies, suggesting that they may be involved in protein structures. By contrast, superfamily-specific words are over-represented in few superfamilies, suggesting a possible association with functional sites. We then carried out an analysis of (i)

the link between ubiquitous words and known recurrent structural motifs, and (ii) the link between superfamilyspecific words and functional sites in proteins. This analysis was carried out only for a subset of the ubiquitous and superfamily-specific words, the extreme ubiquitous words and extreme superfamily-specific words as detailed in Table 1. Link between extreme ubiquitous words and known structural motifs

We focused on extreme ubiquitous words, defined by Lpmax ≥= 10 nbsf* ≥= 5. As reported in Table 2 these 24 words account for only 0.2% of words but cover more 5% of loop-length and are seen in 63% of proteins (see Figure 4 for the definition of coverages). These words are highly recurrent, with a mean occurrence equal to 326 (± 216). They are seen in 32 to 285 superfamilies and over-represented in 5 to 25 superfamilies. Some recurrent structural motifs in loops are well characterized and described in the literature. These motifs include b-turns [54,55], a-turns [56] and g-turns [57,58], nests [49] and niches [50]. They may play a role in protein folding and stability [59,60] or in the biological function of proteins, within the enzyme active sites or binding sites [49,61]. We thus investigated whether extreme ubiquitous words correspond to some of these small structural motifs. The results of this analysis are reported in Table 4. b-turn motifs

We compared extreme ubiquitous words and standard b-turns [54,55]. As b-turns are four-residue long and we consider seven-residue motifs, the question is to know whether b-turns are included in, or overlap with extreme ubiquitous words. As shown in Table 4, eleven structural words (PZCD, HBDS, ZCDS, UFQK, GYUQ, YBDS, FQLG, YZDS, GUDO, FFFI, FQKG) are clearly associated with b-turns, and two words (SLGI, QLGI) contain the three last residues of a turn motif. To evaluate the structural diversity of this set of eleven extreme ubiquitous words, we computed the a-carbon RootMean-Square Deviation (RMSD) between all word-pairs. The RMSD between two words is measured by the average RMSD between 30 fragment pairs randomly selected within pairs of seven-residue fragments encoded by the two words. The set of eleven words clearly associated

Table 2 Coverage rate (%) of different word subsets in the initial data set Word subset

Number of words

Word coverage

Fragment coverage

Loop-length coverage

Protein coverage

Over-represented

1705

15

30

44

61

Extreme ubiquitous

24

0.2

3.4

5

63

Extreme superfamily-specific

23

0.2

0.7

1

17

Relaxed ubiquitous

40

0.4

4.5

6.5

72

Moderately superfamily-specific

114

1

3

5

77

Regad et al. BMC Bioinformatics 2011, 12:247 http://www.biomedcentral.com/1471-2105/12/247

Page 10 of 23

Table 3 Statistics for the various word subsets Data set

Word subset

Word number

Lpmax

nbsf*

Initial data set

All words

11 294

4.3 (5.6)

0.2 (0.7)

Over-represented words

1 705

11.3 (12.1)

1.3 (1.4)

Extreme ubiquitous words

23

26 (14)

10.33 (5.5)

Extreme superfamily-specific words

24

89 (47)

1.4 (0.4)

All words

11 294

2.5 (0.9)

0.006 (0.4)

Over-represented words

45 (7)

10.7 (11.9)

1.9 (2.2)

Initial data set+random SCOPa

a

We report average values with standard deviation between brackets. : twelve random SCOP classifications were generated by permuting the loops in the real SCOP classification.

25

with b-turns comprises structural words with very different conformations, with a mean RMSD of 2.12 Å (± 1.05). This reflects the diversity of b-turns motifs. For example, word PZCD contains two type I turns, whereas word UFQK contains one type II turn. An example of an extreme ubiquitous structural word corresponding to b-turn motifs, word PZCD, is illustrated in Figure 6 (upper panel). The superimposition of PZCD-fragments and the amino-acid logo [62] associated to the PZCD-fragments, presented in Figure 6A and 6B, shows that PZCD-fragments are very similar in terms of structure and present some amino-acid specificities at positions 2, 5 and 6. As shown in Figure 6C, this word is very frequent (seen 560 times in the initial data set), and over-represented in 25 superfamilies with

PZCD

15

20

ZCDS HBDS

Nest or niche motifs

UFQK

nbs** f

DRPI SKGI

10

GYUQ HBBQ BQGI DSPI DSKG YBDS FQLG SLGI

OIPI

QLGI DSKHDSGI YZDS

DOIP

GUDO FFFI

5

DGPI FQKG IYCQ QKGI DFOQ DQKH DRGI DSLG DSLH HBZE PBEG GBCI PPZC SKKU CDSK SUOD ZSUS BCDS USLH GDZI

UODO

UGBQ FQKH OQGI QPSY GQYH BVQG PBQP BBQG GSUQ ZDOQ HBVQ DSGQ PBVQ RYZQ CGBQ BQKH UQXY UQTK GQHQ DOQG PRJP ZRJP KLGI JLGI JKGI USGI HADO UEDO UBCZ BCZR DSKP GUBZ GZOG YADS DFOB YWDS GBCD SNKU KUSG FFFB SLHB CDRP GSHB BZSP SFFR BBZO GDSH GIYC GITF

GSUS

GZDO

QUOR QKUQ PQPI QUOD PQKH BBQK BBQP DSPQ DQKK DGGQ IJYQ RUQU UOBQ QUQP QKNP QGQY QFFF GQHI QFFR QMYU QPRJ QXYB ZQNH QTYU QUED UGZQ PQYP ZDQH SPQP UZCI UOQU ZQLH CDQJ GFFQ BBCQ HSGQ OSUQ IHZQ DQMK IPQY ZIHQ BQNH UQMK QLYU UQYH GQPQ RJUF RUEE KTUF UEIP RUBE UILH BIUD IMUF FRUZ ZRJH IFRU IMYU UODS UFSP UGBB GGUD GIGO GIHZ DFOI PSYU DSNY DSNJ DSNK DFOS DFOZ KKUS IPSY ITFD GFSU JJGI BSGI UBSY YKKH YZDO UODG KFFD IKFF RURG GRFI DRNH FOZE GZZD GZRJ DILG ZFFF GORJ GIJJ OCIY IKYP BDOI UFFO GUEG GUEI GUBC FFOI CEBC EIYK FRNH PGBC KPOD FOEZ ZCPF ZCNU SKUS FFRF CNUE BZCG BVDS ZWVZ KUOD DOGB BZZC YHBZ PCXF CIYK GBZD KLKK USTU FSKK BZZZ RGUD ZCRJ GZDF CZRY PEDO SLYK SPCG BBFF GBBE STUS OBBE URGI OIKH ZCRY ITGF PBCD USXY GBSG GSUO GILJ GBOG GSUD SNKG SHSG CRYZ SNYU UOSU KKLH SNUS SUEI SHBB SUDO YPBB PIPS GFGG GFFR IUDO RLUO FEIY YKUS RJKG EBCC FFBB SKGR ECZR KYUO HBBE HBBF HBBC IPSH GDFS JUFO BCCE USNG PRPI DOIK FSLG GIMU

USLG

0

PQPZ PQPC PQUZ PQLG PQUS QKXK PQUG PQYH PQPB QKXP GIGQ GFUQ GGDQ GGBQ BSPQ UODQ UDEQ IYQH IYZQ UIUQ RJPQ JYQK JGQH JFCQ JPCQ UBQP JFQP RUQJ UEGQ UEDQ URZQ SGGQ SGFQ RFQK RFQU QGDG QFGG QUOC QMGB QUGE EQUQ QUSG OQUS BOQY QGZO OQMH OQTY QPSH QMUD QGFG QTHD RFBQ QKUO QYGO QKGD QKHD QUEB QFRK CQUS GZQH OQGU QPZD QPVS QJPC QPBQ OQUG QYKK QKHB QJYO QNYU QKKU QJUR GGQP QKKH OQMJ UEQU QYRY QURZ GZQG QUOS SYUQ QPBD PQHS EQYZ QKKP OQHP QXUR EQTY ZQMY QJUF QHZD GQUI QUIU QGZQ GQFI QTKY QFEZ QNYB HRFQ GQUS QXFZ QUZC QGRJ OQYH QYBI QPSO QNHB QUBZ QYHS QPER QHBI OQUQ QPZI CQUE QHEE QYUO QGUD QPEQ QJMJ BQYH QMJF QKPO QPQU QHSP QUGB HQUQ DGZQ QTPR QHIK QFOI QPED QYZD QTUQ QGOI QYPZ QYKF GQFO QGQP QNGI OQXK HQXY BCQU OQUE QGRU GQHF RUBQ QNHZ QHFO QKLH QGOB QJMH QUGI GQGO QXYZ YQUG EQTU GOQY QJFI QJUQ PQHI HQKH QKGB QTYB QXPO QUQJ CQHD QNKU HQXP PQKG QPQP QJFR QYKH QTUF QTJY GQFF QTJU QHBS QHEZ DQYK QGDO QPIJ QUIK CQUI QHSG QXUS OCZQ UFCQ BDFQ BDQK BDQY URUQ GUBQ FFQJ FFOQ YHQH FRUQ JNFQ SUQK SUQH PFUQ BEOQ BECQ IGQY IGQU YWBQ RYQH CCEQ SPOQ SPQH FCQU FCQP RYQU YZQY ZCQG FCQJ SYHQ RYQP OBQK OPGQ FDEQ PGFQ CIPQ PGQF PEQP OBQY FOSQ FOQP GPQY PEQH PIYQ YZZQ YUQJ YQHB YQLH PZDQ EWBQ GBQG OUOQ UOQG URGQ KUQG YBQG SHQP GEEQ ZQGR OUQK PQGO KNGQ YUDQ UQFZ DZQY SLUQ GSPQ CXPQ EZQH YGQU GBBQ ZQKG EBQH OWBQ PBQG HBQG YBQJ DRUQ FDZQ BBQJ CCOQ KQLU EQPC RPQP OGGQ KHDQ ZQFE PIHQ ZOZQ GBQP OBBQ ZQGI DEQG SPZQ PCDQ GEQP PFQU GRYQ DEQH OSQH PZQP BDZQ EGQP PBBQ EFFQ RGFQ OGEQ KGPQ YEQH EQPE PFFQ RPFQ FBVQ EIPQ CGEQ GRUQ SLGQ SGQG PBDQ EQHI CLUQ UQHB ZGBQ KPFQ YBDQ YCDQ KUQH BBOQ HEDQ ZQGD UQHS HZQS SGZQ SNYQ SUGQ BZQG YPBQ PIPQ PPBQ FEZQ GFQY GFQU EBBQ FFDQ BBBQ HBDQ DQPR DQTK DQKP DQLP DQLY CPOQ CQFE DQHI DQHE DQHS DQJF DQJU ZSHQ ZSUQ FSYQ JUDQ BCQK BQMU BQKK BQPI BQKY BQNK BQPR BQXU BQPE UQTH BQGQ QLUQ UQKP QLHB FQUO QLKP FQTY UQTJ QLJF FQPO QLUS QLUR FQYP UQPG BQGO UQPO QKYK UQPI QLHI BQJY FQTU FQUG FQPI UQJU UQUI UQUB UQUQ UQTY YZDQ FQLH FQNH GQHO ZQYK ZQSU ZQNJ ZQTY ZQXU GQPB GQLJ GQNG PRPQ PQYR PQYU PQYQ PRGQ FSPQ FSUQ KYUQ GEBQ GDQG GDQK DOQP DOSQ DOQU BBRJ KKYU JJXY JJUI JJUZ JJPZ JJNH JJMP JJLH JJMJ RKFU RJXK JYER UBZZ ZIUZ ZIMU ZILU IYKU JYZD UBRU KRUR JXYP IXUF UIUZ JXPI RUZR RJPR UBZE RXYU JUBZ JHBB JPRF IURF IJUR UIYK RUEB RUEI RJTP RJUD KTJP JXJU YJUD UBIU RJTU JTUI JTUF JKYK ZIJJ JXKY JUBD IJXH RURJ RUZE RURU KUEI JTYI JKLY JKNJ UEEB UEEE UBER UBDF UBFF RTUB PRUB IKNU ZERU UIHZ RUBB UIKH PRUE HaaF RUBZ RUBR UEZR UEBB ERJJ UERJ BRJP UBBB IKUF UFIJ UEWB UEBE URPE URPF URUR URUB URNH URXH FRUB YHRU FFIU EIYU URLH URLU BEIU HIYU YWRU KXJU JNJU IMKU FFRU PVIJ ERNU PFRU ZNYU ZWaF URJK JMJJ PZRJ ERUE KPIJ RNYU YBIU PZIJ YKTU JMJU FDRU FZIU IUBZ URHB EIUB PIKU JHZP UZDR YPRU JHER BZRJ KNUI PILU KNUR UZZI BIUZ RYUD KYUF IUBD KURU KURP PIUD YUFR FIJK BIJP PIJP HIJP HIJY ILKU EIJJ ILJY ITUF ITYU EIJU RJJY KNJU RJJU EaRY YUFF HBIU IPRJ JUIH KKUR FUFR HWRJ KKNU JUER ZIFU YKNU KFUF PRJU KPRU IJLU IJFR IJJP ZRJJ UOPF UOEF UOGZ UOEI UGGU UGIJ UGEB GGWD GIGG GGUS GIFF GGZD BBSP DFSG DFRK DFSF DSUG DSXH DSTK DSPS DSUR DZCI DSUI DSUO DZER DGGZ DGSJ IPXP IPZE JJPS GGPO JJKG GGFO GGBC BSNH BSKU BSYP BSUS BSLH UODR RJUS RKHI RKHB RJUO JYBC UBZS UBZC UDOZ UIYG UDOI UDOD UDOE UCNH IUZS IXFD IYPB IYNK IYZI ZITY IUZO IUZG RUDG RJPO RJPC JLUS UIPG IYKF RJNG IYEC JUBS ZIKF IYKG IJPGIJPI IYLG IYFC IYGI RUDO IUSG UOBB RYBD RYES JKGE RYCG JGXJ IYBP JFCG JYOP RYBF JKGG RXYB RYCD YKFI YKFF UOCG YZCG YKGG JPGI JGRU YKGD JPSP RUZO IKKG IKHI RUGU RUSK RUGI RUGB ZEBB JKGS KUEO KUFS UBEC HaDS HADS DRNY PRUG PSJU PSFF PSHI PSJH EDSY EEBE RFIG UDSG RFOR EDOZ SGFG EDGS RGBD SGES SGIG BRTK SGNK SGOG GOBZ SGIH GOCI SGEI GOBB RFZD RFGI UGUR GOCS GNHG UGVD RFFO USKH GGUB ZEZO YNKP BRFS GODE YRGP GRJK USKK GRFD DRLK IKNG BRPR EDFO SXUI USHI ZFDG SUZE UGWD GNGI SURP BPOI ERLH BERG DRHS ZZZS DRLP GGRU RFBD UFSK ZFDS USKG GRJP GOGE GRJJ GNPI BRFE GORY UGZD DIMG DIYI ZEZD ERGI SGDO SXGI UaGB BCSU SXUS SXJJ SYRJ SYRY GOEI ORJJ DGZS ERGF BERH ZFFD USGZ RFFF USFF GOCZ DRLH PBBI GODF ZFFO USHS SUSH GZSG UGRU UFOR UFOG UFOZ UFOP GLUO BESO GOZR GOUS GOZIGOZO GKUS GIJG YNYH RFDI OZRF YOPI OCXP RFCS BEZO BESH DOBD GOZZ UESU OZSY OCPO OCGB DOCI OCIK GIJF GOSU GOZS BFFF SXYR OZRJ GOZD BFFR UFCG UIMG BFRF UECE ILFF BDOD ILGI ILFR BDFE BCZZ BDEO DSLK DSMH URUS HBZF HBZS HDGZ GSYU HDOR GUDG ZDOZ FSGE FSGI FSFF FRYZ FSFG FFIY GZOP GZGF GZED YHWR GZGG HBZC FRPI PEFD YHVE GZGS GZFF FRXY FRYH JMPG KUSH JNFO FFFE KUSK FFFY CSJF FFIF KXFI CDSO JNGI KXHS FFIH FFFZ FFIK FFFC EIUS FFFD CDSU ESGD EODO YBCZ CECS YBBE FFFR ESHB SUOE ERYB EIYH EIYG SUOG CDSY EIYF BZSU RYUS ESKU RLUS IUDS RMKF SFFD BEBD BECE BEGS BEOI BEEB BEGE BEGZ BECZ HRUS RYUO DSHS FRNF ZDIF FIUS ZDOI IGSU HIYH ZDOB ZDOD FFZE FFRY PFRY PFSF CCGU RYKG DSHD CCDO PVEG FORK PESO GPPB ZCCD PFEG PXKG OZOG OZGU SKXP KYKG RLGD PSYP DFOG PGGD HVSK FCXP HVES OPFS PSUS ZDFO KXPG PSYH ODOD KYKK PXPG YLPF KYKF RLHB CIPC RKNK FOGG IGPZ ZDFF KPIY FDIK FRLY IGGU DFOE IGOZ CCCZ HRMK CCDS SPIH JJFC SPES FDGG YIYZ CILK YLKY GPFS GFFE HRYC FITK ODZE GPRJ ZCCO ZCLP SKYP OBVI FCZG SPOP PERP YERY PERY FIXP BIMK ZCGZ DSHP SPGI PEIY OPZD IGOB ZZIK IGKU PEOI ODRG DFOD CIPS CCDR ZCOZ FOPG PEPB OBZF IGNH OZES PFEZ SYPZ ODOC OEBB HVDS DRXY HIUO OPRG HVEG EZDO ZCLU ZCIP PSUD HVEZ ZCLH PIYK OBZI HIMY SYLP YFCP DSHB OZGB PSYR PGOZ PWAB PVZZ EWVO HVDO RGSU DODG KYGG OGBS CCOB PGEO CCCO GZCD YGPG GRYE OBDO CGZD YGSP CGBB CRKG CNPC ESNY CGUE ZWBC UZGZ ECZG KXKH RPPS PBEC OEEG DRSK FOBB CGZS GWDR CNKG FZGB KHBB USUG KYPO RYGE YGSU SNPB SLGF CCCD KKGE GRXY CZFE HSJP BBDE EGEZ PVDO SJYC EBZF PBES KGRJ DEOP YGOZ CZGB ZGBZ KHBZ RGFF CNFE CLHE ZCGB EZPO SJXG ODEZ CEZC PZPB DSOB HPBB ZCXP GEOB YGOI CRJP YUOZ EBZZ FODO CGEI PBSG ZGBC PSKU OGIG KNHZ IMKP HFGI HEBC PWVD FCPE PZES POBS OZOR SYBZ HZIP JMGI CRJF ZZCC SHRZ GRUE PFFR RYZD CLJG KYLH ILHB HSUZ YBFR SYGO EBZS FDSH KGIJ KKGI HBVO GRUZ OZCU ZCDD DSPG HFIY IHRH BBCZ PBSU YBPO PIHS EBVD GIKG OWBD RJGI KXPR HFIP SLPI ESOU ZVSH KNGR YIHE RGIH PBBZ EGBS CXPF GSNK KHIH EFDS SPVI GEIU HIFD PBDS KXKP YECZ YZOP POCI CIHD CXPI CCCP GEOD FGIJ ZGBB RHSH OSFF BBEI ITFF ZZDS FGSH ITKG GRUI ZOZS PZCR RGDS FZOB DOEB OPBI GRPI FFZI SPEP FBBE ZCGE ITFS ZCGP SJXH SHIJ GXHB GRYC PILP OSGI ZZGB PPRG CIKP CZCD PODO PSPE FDSK YBZE BVOZ GFDO PSPZ CUBI RGIF GRYZ SMUS ERPI KGIH GILF FGFF IGFO PIFG FGGI DSOD HSNU CDRG ESOS ZDEO PBZS KGRY SJUB HSXJ DFFO ESUS PZZD FIGG OEFF EIGK SGSH SKHO SNJJ HSPE ECZC HSHB GRUS SPEO GSHS YLPR SPEG HSXG PIFD ECOB FDZC RGEI GPFO SHPB CIKK CGPG BIGS BZCO OIJF SKPZ SLPO PZDE GBBB SJFC HRGD ECSJ KYPI GXJU CERY OERG GBBS DFCD CRJG EGBC PIHD YECO FBDS OGBO PPOS FDOS CLUO GRUB FDZI ITGO PZDO OZPO HFOZ BDSK PZSG HBSU KNHB HZIF ZSYP YGOC PZFD SKPF FCPO IHSH KPZS HEZO EGWD ZGGU YRYK KXPS DSMP EZOG EZFF EGZS DFEI GWDO ODFO IHDG FCDO RYZZ PGZG FCLH OSFB OZDO RHBD GXUS SMPG RJLG YLGI EIPI DODO GSHF DSNH RNHB OEIJ GRUD HSGIEGZD IMPR YPZD YPZO KHOD OGRF CCRK SNPR SOUS PCNK OURG PEBR OGRP STYR POGR STYZ HZPB COBB UOZP KNPF UORU ZOCS PCOB GBZR KNPO BZEZ UOWB GDaI RGRF BZCR SHDS KHOI PECO BZDR GYCD CNUR BZFF POIH ZOGZ ZODI BZIF KKHI CZVS IFBD BZDF KKGR CRUR KNPG DDGG SGZG SUIY PCER OGZO SOIH PCLH CCPR UZSY RJGR SGZC UOZG SUED OGZS ZOGI SHFO PIMP COZE YPBO CCRU COUR CCRY SPBC CCZZ CDDG SOWB POPR UZPS POUO POVS PIPP YUOB YUOE YUFS YUOC PPBC YPOU ITYZ POZR ILUG ILUS GFOG GFGZ KPBE FEZE GFOI KPBB ILPZ GFOD ITYH HILF ILPG ILPI HIKK IMGU YUFO GFOR EBCZ EBBB EBBR SKHB FFDZ FFED FFDR FFDI FFDS EBEE EBDR EBEG DFOR BBCI BAVC HBBO BBCE BAAB HBDZ HBEC HBFF JUOB JUZS JUOR HZEG HWVZ HZDO IPSU HWVO JUGG JUES DGGO CPSF GUGZ CPIH DGGI CPFS GVZO CPRK GVZD CPOD GUFO CPBD ZSGD DGFO ZGPB GDEF KKPE CPED KKPZ KKNG ZSXP ZSNP JUDO DFSP IPGF ZSPC BBZI ZSLH CPFO COZO IPGI HVSP DGDZ DGBZ IFDG FSYP ZSLG IPIH IFCZ IFFS IFDE ZSPG DOZP ZSXH ZSUE GDFG ZSUO FUaG DFSK GDFO ZSGZ FUDO ZGSH ZSGI JUEO ZIFZ FZDR ZGSU ZGUE IFGZ IFZZ ZGXH ZIFC IPPO ZIFR BCCR BCEZ BCIY BCIP FRHG FRFF FRFI FRFZ YKLH YKKY KFFF YKKK YZFF KFFE YKNH YZFS YZGB KFFR KGEI KGDS USMG USLP USMU PRKP PRFD PRFB PRFC PRFG PRGD PRGE PRFR PRGI PRHB KPOE KPZC OITG FSPF OILK DOIF FSPB GIMJ FSKP FSPE FSXU IPFF FSPI OIKK FSXK OILP OITK FSUF IPBS DOGE OIMP GILK OILH PIPZ IMUG IMUS IJJG IJHO IJLG IHZR IHZS ZRLH KYUS ZRYI KZWZ GEBB GDGF GDIH GDZS GDGB GDOD USPI USPC USNY USNH USNU USOI DOSP DOZG DOSF GIXP GITJ GITU GIYZ GITG GIMY GIPG

SLGS

DODQ

YUOD

UOGQ UOPQ UOEQ UFUQ UFSQ UGaQ UFZQ UGQG QUOG PQMY PQNU PQTU QKYF PQTY PQYE QUOQ QKXU QKYG PQNF UGGQ PQPG PQKK PQPF PQPS PQMK UGDQ PQMU GGUQ QKYB UGFQ PQKQ PQMF PQNP PQUD PQUE PQYO QKYC PQPR PQNH PQYG QUOP QKXF PQUQ PQPO QKUZ QKYH UGQF PQYB PQMJ PQNK PQKR QKXG PQUO PQUR PQUF PQTP PQKY PQPP PQPE PQNG PQXH PQLY PQXF PQXJ PQNY PQUB PQLF QUOE QKYE PQTF UGPQ PQXU PQUI PQTH UGEQ PQMH PQMP PQKP QKXY PQYK PQYC PQMG PQKU PQYI QKXH PQLK PQTK PQLH PQXY PQPQ PQNJ PQSJ QKUS PQLU PQLP QUOI PQXK QKUR PQTJ PQLJ PQTG PQXP PQYJ QKXJ GIHQ GGZQ GGVQ GIFQ PVCQ BBSQ PSZQ BBVQ BBQS BBQY BBQU DFQH DFQU DFQP DFQG DFQY DFQF DFQK DFQJ DSUQ DSYQ DSQK DSQH DZEQ DWVQ DSQY DSQU KKYQ KLFQ KKZQ KLGQ DGQP DGPQ DGQU DGQG DGQJ DGQY DGSQ DGQH DGQK DGOQ IPZQ IPVQ JJUQ JJPQ GGQH GGQG GGQF GGPQ GGFQ GGGQ GGOQ BSHQ GFZQ BSKQ JJKQ GGEQ BSGQ JJGQ JJHQ BSQH BVCQ BSUQ RKHQ RKGQ RJUQ RJYQ UDQF JYZQ UDOQ UBVQ JXUQ UBZQ JYBQ JYEQ JXPQ JYCQ JYHQ UCGQ JYGQ UCQK UDGQ UDFQ UCQY IXGQ IXFQ IXHQ IUZQ RUDQ IYPQ IYQK IYQY IYQU IYOQ IYQF IYQJ 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RFFR RFGB RFRP BRZZ RFSG BPSY ZFIG GOCE ZFFR RFZS BRYI ZFFS SGNH SGOI SGIP RFFS RFZO SGIK DRGO GRGE GGRY ORGO GODS OCDO ORPO FOWZ GRGB YNHO HABD ERGS USGB GRFR OCDS GRMY GOEE ERMP DRMG ORMP ORPG BRNH GRNG USHB GOEZ BCRH USHO SGBB SGBS FOWD ERGB GGSG USGR ZEOR BCXH GZZG ORKG GGSO GOEG ORMY ORGB GOSH SGBR GZWB YRHZ OCDE ORNP GRLH GRNU BCRG BRKH UGSG ORNH GOGO DRMY BOSU UGRG DRHD BOGZ BOWZ DRFS YNHB RFFD GGRF GODG GGSU SGBD YRGE DRGG BCSY GOGD BCRP HABC GOPR ORHZ ZEPB GZWD ORNG SXKH EDFF SGBC BRHO GODO ERFO YNHS BORG GRLP HADG GNGB RTPE ZESH SYUF ORMK GRMP GOES SFRF BCRK OZZP GOSJ GNGO GRGO RSKH SXHG GZRU GOSG ERFR YRFF SGDG YNKF ORKK HRFO GRGD USFR GNKH GRGZ BOWB BOSK GRGP BCRU GOPO GGRG EDFG BCRY SXYE GZSO GOGS ORMH BCXU ORPS GGSP BOGS SFRU GOSK ORGZ DRKY RTKK OZWB ERFC GOPE YNHG SURG YRGD GRNK GRHE DRHR GGRP GRKG DGVS DGZR EDFD GRNJ SYWZ DRGS GOER GRMK BCXY GNKU BOUO ERKF UaDS PADS YRLG GNJH ERGR USHG ORNY GRNY UGZO GORG UGWZ GNKG GORP UGSO GOGG BRPZ UGWB BRKG OCEE DGZC GOPG ERKK PaDO GRKU GOIG DRLF USFO YNGO GOIY USJG UGVE SUSO GZWE ORGU BRKP SFRH DGZZ SXGO YNGD OCCP UGRK DGWZ GGSH RFCC ORGE GRHR SYXK ORPB SURH OZWE USGU SYZE UGZP GOGF USJF ERFB YNHR DGZD GRMJ GORH BRMP OCEC SXPO HADE SYWB ZEWD GNHD SYZZ HABS GGSK PRTH OZZR ORPC UGRP ORGG GZRK GRLU GRHD USHD DRFB UGZG RTKG SUZO GRGY GRHZ GZSJ SUZR ORFG ORTG DRGB SGBE OZZO BRGZ RFDR DRGR UGXP SXUO YNHD ORFR DRGZ SGBO DRGP ZZWD OZPR USHE ORLH RFCD GRGS GRMG ERGP BOZO YNGB GNPB GRLG FOWB GNHB YRHG SFSP RTKF BRGF GOEC ORNU USFE ORPE GGSJ ERLG USKY BCSK HABB BCUG GRFG UGZE GOPI SFRJ BORP BORK YNGE UESG SXUG HRFG UESP GZSK GZSY RFBC YNHZ GNFF UFSH UGWR BCSH SYRH GNPG UFSJ ORHI SXYK BRLH GZZS GRPB SYUG ERKP BRPB UESK BRFR DRJG ORHE UIGB SXJG ERKH HAAD GNGS RTPS RSKG BOSH GNPE DIHB ORMJ GGRH ZFEB UIGG SURF GZRG GODZ ERLK ERLY BRLY BRPC YNKH GZSH RTHR ORMF DRMP ERJG OZZS SFRY YRLH BORY GRKY RTGD ORFD BPGZ BOZE SYRP SXHS SXPR USFD GRFZ GOSP BCUO ORHS GRNP SUZZ RTGB OZZG SYRU SUSU SYXJ OCCR PRTK RFBE GZSF SYZD USJK YRMG SUSY 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SXKR RTKH GNHS ORNK BRLK BOXK BOUZ BRNP GOPC SXYP BORJ GNJK ZFDR YRGS DRFF YRHD RSKY HAAB ORFF UGZZ UGSH BRLP YRLK SXPF RFFB USJU RTGP RTPG YRGF ORJY SYZR RSGB BOVD GOSF BPFF USJY GOIU ZEOS USHF ZEVS IKTG GNJG DIXP DRNF DIMK ERKG ZFCC GOSO GRLJ RSKK PBBC BCSP ORJH GNGY GOIP ZEZZ GRNH DGWB DGYP OCEB UGXK UESJ GRGU ERHF ZESY YRFZ OCEZ ORFB OCEG GOGZ RFES ZESU USFS BOIP OCES ERHR PaSK GODI GNJU BRGI USGD GNHE RFEF BPGB SFRG GRFB YRGG SXKY RTHZ BRKK SXPC ORPI PAVZ SURK ZEZG SGDE DIKH HADI BOZS SXGF RFBR BOIH YRMY BOXY RFBZ ERFG DIKP HRFD YRMH YNJG SFSH BOIK GOPZ GRNF SYXH SXHP RFED ORPR SFSY ZEPS DIMY BCZE DRLS USGE ORTH DRGF BCXJ GORU ERNF ERHG ZEWE PBAZ SYXY RFEB IKPG BCSG BCUD BOXF ZESO ZFCG BCZC DILH IKMP SXJF FOZO GRKF YNGG SXJY HABF RFFC BOPR BORE DIFG GZRH IKMG SXYH USJH PRSH YNGZ GZZO GODR RTFF YRKP RFCP YNHF GNFZ BCRF GOPB RTGO YNGU OCED YRGI YRLY BCRS GNHI SFZO DGXJ GREE YNHI ZEZP ZERH GRHB RTPZ ORJF ERGZ HABI SFZZ SYZI BCXG ORFI UGUF BOSO SGDI YRMP DRMF ZERZ GRFE HRFI PBBB RSPZ ZERY ORGR SXJU ORLK SYUE ORKR PADR BOZR DIPB BOZZ BCSF IKRH BRHI RSYP ORLY SYUS SXYI ZEZR BRFO UESF SYUZ GRKP ORHF GRGR BCZD GZRF GRKH RFEZ SXYG ORPF GOIJ SXJP SXJK PAAB ZEZF IKTY BERF DRLY YNGS HRFF YRHI SXFZ YRFR ERGD BOSP BOZI DRLG DIUG BOIY ZFEE UIFS GNPC GOIF GRGG YRGZ UGSF PBBE HAAZ RTGI DIHS DRHP UGUE SXGP IKNY GZRY ORTY RTPP USKF YRFS SGBI UIHS PaBC REGZ GZRP SYRF DGXY ORLG ORKY GOEO REBE GZZI DIMF HREB UGZS SUZG REEZ IKPO DRHB EDES BCXK BRFC ORJZ USFG SXUR ZFFB OBZS RTHS HRFS RFEE ORMG GRJY DILP GRKK DIPI DIMH RTKR DIYF OZZF FOZZ GZZZ USGO HaBC YRKK FOZI BOPE IKPC YRHB SYZC OCBB SXJH ZFFC IKMK IKLP EREC BPIH YRKG GRLY OZZC REDO GNFO OCEI PBBF ZFCP OBZR PaOB GRHI IKLH BOPI RUBC SFZS PBBD UIJG RECG ZEPE HaaO BCRJ REZO BPES UIHG SXPS RFDZ GRPE SFSF ZEVZ IKPP USKP RTPC ORHB PaFO DRKK USFB SXFD DGZO ERLP ZFEZ BRFB BOUS SUSG IKLF ZZVS ERFZ ZEZI EDER OZZE YNFS GNKY FOXK SXKU ERHZ RSLP SYXF DIKK GZRE HaaS OBZZ DILK GRJU SXGG OZZZ ERHB ZZWZ GNJJ ZZVZ GNKK UGVS USHR GGRJ IKRY OCEF DIFR SFUB ERFI OZZI BOZC PBaO GZWZ SXKK BPFO BREE SXGD ZFBS UGSU GZRS GRFS DRFI ZZZI FOZC DIYG ZZZF PaEG UIFG SXHB ZESS HRFR ORKP BOVI UGRF YRLS ZFEC HaDO USGS FOZR RSJH FOZG RFaO PaSP ZEZS ZZZD SUZC IKPR OREZ RFBB SZIP ERHI RSJP DIFE UGRJ OZWZ ZZZP ZEVI UIFO ZEOU DIHO HREC OCFF UFRG YRFI RUBO DGZE RTHI ZFEF SUSK RFER BRFI ORFZ GZVZ GZSP RSPC UFSG IKNH IKPB RTHB DITK ORKH GRFO UaSU RSJU GRJF RECE DIPP DITH BPEO SZGI REZE YRNH UaFS USFC SXPE ORSJ RFFI IKPE DRKG ORMU ZZWE PRSK ORJU ORUB ZZSU DIKY SXYB BOPB IKNP ZFFE IKNF USFI DIPF ZZVC USGF ORJG DRKH ZEZE GRGI SXFF DIPO SXFS RFBF GZSU DIXY IKTP DILY BRJG ORTU USLF BPEI ORFE IKPS PaGI ZZZR SUZI BORU SXKG DIYE RSHI UIKG BORH SFSK DIHI USKU RFCI UGRH ERMH UIGI ORJK YRGB SXHO ERLF IKPZ PaaS REZI DRKZ IKLK UGXJ ZESK USJJ YNGI SZIJ SXKP SXHI FOZP ZEWZ GRJH IKTF ORJP DIPG UaGZ BCZG OZPZ FOZD ZZZZ BCZF SXPI ZEWB YRFE UaOI RTKP GRFF UGUS IKPF SYUO IKKY RFBS YNKK SXHE DIHZ PaFG OCEO SZZP UGZF DIPE USKZ SUSP GRJG SXPP ZZZO IKNK ZZZE ZZWB FOVS RFEI ZEZC DIFI DIHG BPBB RSKP ZFFI DRHI DIHE UGZI GOIH DIJG RTPI IKLG IKPI IKLY DIFF UFGB UFOS UFOD UFGS UFGR UFGZ UFGG UFGP UFGE UFOI UFOC UFOB UFGI UFOE UFFS BEWD GOWD OCNU OCSH OCGG GOSY DOCG BEZD DOBR DOBS GOUO OCRF OCZD YOEZ OCXH YNYR GOUB OCNH GOUR OZRG OCOZ YNUO BEWB OCSK OCRY BEZC YNPC OCGP OCZO BEZR OCNK OCNY GOVD GOUE YOBZ YOBE GOWB OCGO YODO OCGR YOBB BERK BEWZ OCOG OCXK YOEB OCZC OCGE GKXH YNYP RFDE YOPG GOXP OCRP GLPZ YNPG GOVZ DOBE OZSK BESG GLHB OZRP OCRU YOPR YOGI YOGZ RFDG OCOD OCRH YNPO DOBC GLKG YNUS GOVG BFFO RFDO OCNG OCOI GOZC YOIP YOBD BERS GOXY DOCE YOBC OCRJ OZSF RFDF YNYE YNUG YNYZ GLPB OCOR YNYC YNKY OCZZ OCNP YNPR OCOB YNPS YOGS BEZE YOIJ GOWZ OCOC OCZS OCGY OCSG OCGD OCXU YNPF BERP YOBO OCGZ OCPZ GOZG GOVC BEVE GOUD GOVS GOVO GOVE GOVI YNYG BEZG DOaR BESY BEVS DOaS BEZP DOBB YNPZ OZSS OCSP BFEG YOGB OCIP BFFE YOPE OCZE BFDS RFCZ OCSY BEZZ YOPB GLGB OZRS GLHZ OCIH BFFG OCGU OCIG GPaD YOIK GLGD YNPE OZRY BEZF GLGE OCSJ BEVZ OCXF OCXJ OZRH OCZI BERY BFCC BFBZ GLHS BEXH GLGS GLHE BFEB GLKK DOBP BESU GIJK DIYR OCNJ OCPE BESS OCIU YODS BEZI OZSP BESF BESJ BEXY GOZE BEVC YNPB BEZS OCGI OCIJ GKXJ OCZF YOCI RFDD OZSG YOIH OZSJ BFEZ OZRK GLPI GOUI YOIY GLGI OCIF DIYP YNPI YOCS OZRU GPaG DOBZ YNYI BESP OCSU BESK YODI OZSH DIYK GLUI OZSU GIJH BFFI RUCD RUCO RUCZ RUCS RUBS RUCU BFOB UEZC UEZG BFOG BFFZ UEZO BFOR BFOE BFOI BFFS BFIK BFIY BFOP BFIF UFDO UFCZ UFEC UFCE UEZS UFCO UFEO UFEG UFCC UFCP UFCI UFFG BFRY BFSY BFRH BFRP BIFO BFRK BIFF BFSK BFSO BFSF BFSG BIFD BIFG BIFC BIFE BIFI BFSP UECD UEDG UECS UECC UECZ UECG UECO UIPC UEBC UECI UBCG UBCD UBCC UBCO UBCE UBBS UBCU UBCS IKYR IKYZ IKYG UBBC IKYH IKYF IKUG ILFD IKYE ILFE IKXK IKUO IKXY IKXH IKZS UBCI ILFG IKYI IKXF UBCP IKYK ILFI ILFO IKXP IKXG IKUS BDOP BDOG BDOS BDOB BDGZ BDFF BDRK BDRY BDRG BDFR BDOC BDOE BDOZ BDGF BDGD ILGO BDOU BDRF BDIK BDGG BDFC BDGB BDGS ILGB BDIF BDIH BDGR BDGI ILGE ILGF BDGP ILGP ILGG BDRH ILGD BDOR BDFO BDIY ILGK BDRP ILFS BDFS BDSF BDEE BCZS BDDR BDEI DSKU DSMG DSKR DSLU DSMJ DSLY DSLJ DSLF DSKY DSLP GUFF GUED GUEF GUEO GUEE GUER GUES GUEB GUDS GUEZ GUFE GUFI DSKK URPO URUO URPS URTG URPG URPC URUG URYO URYG HDOG HBZG HBZR HBZP HDGR HDOB HDOE GSYC GSYK GSYG HDOC GSXY GSYP GSUZ GSYE GSXU GSUR GSXP HDGS HBZZ HDED HDGB GSYB HDFF HDOI HDER HDEB HDFO HDGI HDIY GSYF HDIH HDEF GSXK HDFD GSXH HDOD HDIK HDFS HBZO HDDO HDEE HBZI HDaS HDEI GSYH HDSG HDSH GSYZ GUBO GUDF HDOS HDRH HDOZ GUBE GUBS HDSJ GUBR GTHB GUBI GUDE GTKK GSZI GUBB ZDRF ZDRH ZDRG URMG URNG ZDOR ZDOS FSHG FSHB FSGD FSGO FSGR FSGG FSGP FSGB FSGZ FSGF FSHF FSFR FSGS FSFZ FSHD GZOU GZPR GZPG GZPB GZPZ GZOS GZPS GZOR GZPO GZPI GZPF GZOZ GZPE FFOU FFOD FSFB FFOP FFOG FFOB FRZE FRZO FFOR FRZC FFOS FFOC FFOE FFOZ YHRH FRUO GZOC YHWD YHDS YHVC GZEB GZOD YHOZ PEGR YHRP GZFR GZGR YHOR YHWB YHSG GZGP GZNH YHGB YHOD PEEG GZER YHGZ GZGO GZES PEFO GZGI YHSK PEGZ PEEO YHWZ YHOB GZOE YHRG YHRK GZEC GZNK HBVR GZFE PEDS PEGI PEFR HBVS YHSH YHVZ GZEO FRPC FRPO FRPP FRTH PEGS FRNP YHRY YHPI HBVZ YHEB YHEZ YHVO PEFG FRNY PEGO YHGI GZGD FRTK PEEE FRPS GZEF GZFS YHIG FRPF PEDR YHES PEER YHPE GZEE GZNG PEFE GZDS PEES YHFR PEFS GZIH YHSP FRPZ YHSY PEEC PEIH PEEP YHDZ FRUC YHSF PEIG PEGG YHOE FRTY YHGE GZNP YHOI GZGE PEGE GZGB GZOB GZIF GZDR GZFZ FRTP YHIP GZIP YHEF GZGZ YHEC YHVR PEEI PEEF YHZC YHFS PEIK YHVS YHIY YHIH YHIF PEEZ YHIK GZFI FRPR YHVI GZIJ FRPG GZIY YHVP GZNU GZEG PEIF GZIG FRNK GZFO GZIU GZNY PEFF GZOI GZEI FRPB PEEB GZEZ GZIK HBZD FRUG YHSJ PEGB GZNJ FRPE FRYF FRYO FRYR FRYG FRYK FRYC FRYI FRYE FRYB FRYP KUSU CSHO KXGG EODR EOEC JNHG KXGR JNGB KXFR KXGP FFGG KXHG KXHO CECE CEBR EOER CDSP CSHZ KXJG CSHD EOEG CSGZ KXFG KUZG CSGE CSGP CSJU FFGO CDZZ CECC FFGU KXGS KXHF KXGZ KXFC CSGS CSFF JNHS JNPG FFGP FFGZ EODZ KXGO FFGE KXHZ KXFF KXHE CSHB KXHD CEBO KUZO KXFS JNPO EOED EOEO FFIG KUZS JNUG KXFE KXGE EODS KXFO CECG CSGI CSHG CEBE FFGS KXHB CDZS EOEB KXGI CSHI FFGI CSGU JNPC FFGF CECI FFGY EOEI CEBB JNGZ JNGP FFIP JNGS CSJJ FFFG CEBS FFGB KUZC JNFS CEBZ JNKG KUSY CEBI EOEE CECO KXHR JNGK KXGF KUSJ KXFD FFGR FFFO JNGY KUSP KXHI FFFF FRXF FRXP FRXH FRXK FRUS SUOU ERUG ERXH YBCG ERUO YBCD CEDS YBCC EOBC CEOB EOCC CEGO JMUO YBAD YBBR CEEC URHO JMPS SUOS EOBS ERYF ESHE ESGG CEDR EOBB ERYR YBCO URHS ERUS YBBB ERXY EOBE JMPO JMUG SUOR CEEG CEGE EOCE ESGU CEEE JMPC CEGZ SUOZ ESGE EODF EOBZ CEGR EOBD ESHS JMKG YBAB ESKH ESFC ESGS ESGR ERXP ESHR EOCS ESGZ EODE URKG ESFS YBBD YBBC CEFS CEGB ERYZ CEFR ERYH ESHZ ESGO YBBO ESFO CEIG CEES EIXK EIXH ESHI YBDE CEEB URHG EIYZ ERYK YBBS EOCZ ESJH ESHP CEFE EIXY CEIK CEEZ JMUS ESGB ESKK EIYC EIYE ESFG ESJY ERYO ERXF EOCI CEDZ ESJP CEIH CEGI CEGF EIYR ESHG ESJJ ERYE CEFO EIXP CEDO CECZ CEDG CEFF URJG YBBI CEEI ERZS EIYO EOCP CEFG CEIY EIUG ESKP ESFF CEDF CEIF CEIP YBCS YBBZ CEDI ESJU CEIU EODI EIYP URLG ERXK ESFI FFFS ESGI ERYG SUOI CEGG SUOP YBCE ERYI YBCI EIYI ESLG KKMP KKLS KKNF KKLY ESLK KKMH ESMF ESLP ESMK ESLF ESMJ ESLJ ESMP KKLP KKMG KKLK ESLY ESKY ESLH RMUO RMPG RMKH RMYC RMUS SFFO RMKG RMPZ RMPR RMPO RMYB RMYP RYWD RMYO SFCC RMPE SFDO 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RLYG YFOD DSJP DSJY DSJH RLYC RLYH HIUS HIYC HOGR YFFD ZDOG IGUS IGRP YFRY FRNG IGRG RLYK RLYI YFRF HOBD IGRU RLYR HOEZ ZDGD RMFF HIXF DSJK YFUO YFOZ HODF HOIH YFGE ZDGP YFRH RLYP YFGZ ZDOC IGVZ YFFF DSJF HIXY YFUS HOEI HOES ZDGI YFSP HOGI HODS ZDIY YFSY HOBI SKYR YFSH IGSK HIXH YFOE HOEF IGRF YFOS HIYK HOCG ZDFS IGXH HIYO YFFS HIYF YFFB HIYZ HOCI YFED YFZE RMHI ZDIK YFCZ YFGG YFEI HIYI IGRY IGRH HODZ HOIJ YFGI ZDIH YFCS YFDF YFGP ZDIP YFFR ZDGE YFIY IGRZ YFOI HIYE IGXF HIXP IGVO IGUF ZDOE IGSJ YFFZ DSJU YFFI YFZR HOCF HOEO YFSJ EZEC HIYB YFFE IGUD ZDGB DSJJ YFIF IGSP IGRJ DSHR ZDFR EZDS HIYP IGSY IGSH FFZC FFUO FFSK FFYP FFUG FFWB FFSO FFSG FFUS FFSF FFSS FFYK FFWZ FFRZ FFSU FFSH FFZD FFYR FFSJ FFSP FFSY PGDS PGDR PFSG PGEC PFWB PGDG PGBE PGBD PGBR PFSO PGBF PFZG PFSU PFSH PGBI PGDF PGDZ PFSK PGDE PFZC PGEB PFRZ PGDI PGBP PGDO PGBO PFSJ PFYG PGBS PFSY PGBB PGBZ PFSP PFZI PGGO POGG ODOG HRNP HGGB DRYC YZOS ODSH OPWB HRNH ODSU OBRH GPSU CCGG ODGZ EWVS PSUG PEWZ PFEB ZCGS CCGO PGGE SPGS 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DFSY ZSTJ ZSMY CPEZ ZSPZ COZZ ZSMG ZSGF CPCD ZSHI HVSU GDEO HVZS ZSHE DGBS ZSKR CPFF GDFF ZSUI ZSLY HVZF HWBI ZSUB CPBB CPCS ZSKF ZSJP ZSPS ZSUD ZSTK DGDE ZSSU ZSJF ZSKK DGDR HVZO IFDO ZSHO HVWE IFFO ZSHP ZSNK ZSLJ IFDR IPGR GDER ZSJY ZSNU ZSXF IPGU HWEC IFEG IPPR ZGPC ZSKH CPBS ZSJJ CPES DGBI ZSLF CPEI IPIG KKNP ZSXJ FSYR IFFE ZSLK IFEE DGDS ZSKY KKPG IFES ZSNH HWED CPEC CPER ZGPI KKPC GDEZ DGDI CPEE ZSPE KKPB IFEB COZR ZSKP IPIF ZSJU ZGPF IFFC ZSTY HWBC ZSLP DFSU IFED ZSHG IFDF KKNH DGEC IFEZ ZGRJ ZSMP ZGPZ DGBE ZSPF ZGSJ ZSGS IPPZ IFFR ZSNY IFFF IPGG ZSPB ZSPI IFFI IPGO DGBB CPBI ZSGR IFFD ZSLU ZSUZ GDFI GDES HWBB ZSXK ZSNJ GDEI ZSUF IFEF KKNY ZSKG IFEI KKPO HVWZ KKPI ZSPR ZSUR IFDI DGEB IFFB IFDS ZIGD ZIGZ FZEZ FZEG ZIHZ ZIGB ZIHB FZER FZEE ZIHD FZES ZIGS ZIGR ZIHO ZIHR FZDS ZIHG ZIHE FZEP ZIHP FZEI FZEF ZIHF FZFC ZIHI ZIGO ZIGF ZIHS ZIGP FZEC FZFE ZIGI ZIGE ZIGG ZGZO ZGXY ZGWD IGBB ZGZD ZGZE ZGZR IFOG IFRG IFUO ZGWB IFSK IFRH ZGZG ZGVE IFSP IPOU ZGXU ZGSY IFOE IFOD IFZD FUBC ZGUB IPOD IFOR ZGVS IFSH IFRY IPOC IPPC ZGUO ZGXP ZGXJ IPOR IPPI ZGVC ZGZS IPPB ZGUS IPPF IPIK ZGVO IFOC IFRP IPNG IFSG IFFZ IFZO IFOS IPPG ZGZI IFGE IFSY IFRK ZIFO IFGD ZGUI IFZG IFOZ IFGG IFIH IFGS IFWB ZGVZ IFOB ZIFE IFZC ZGZC IPNK IPOG IFUS ZIFF IFZE IPOP FUBO IPNP IFRZ IFGR ZIFG ZIFD IPOS IFSF IPOZ IFGF ZGYU IFSJ IFZP ZIFB IFSU IPPE IFSO IFIK IFZI IFRF IPOB IPIY ZGWZ IFGI IGaR IPNH IPNF IPOI IFZS IFIY ZGZZ IPIP ZIFI IPOE IFOI IFOP IFGB IFIP JUEC JUEG JUDS ZIFS BBZP BCCD BCCI BCCC BBZR BCCP BCBC BCCG BBZZ BCBB BCCO BBZS BCDO BCNG BCGD BCNP BCGP BCDR BCNY BCCZ BCEO BCNK BCDF BCOG BCGG BCCU BCGY BCOR BCPG BCOP BCGR BCGU BCGI BCGF BCPO BCOZ BCLP BCOC BCEG BCPE BCDG BCCS BCPR BCPI BCGE BCOI BCEF BCLU BCEE BCOS BCER BCEI BCEP BCEC BCGO BCPB BCNJ BCOU BCIG BCNF BCPS BCFF BCLH BCIH BCGB BCOB BCNU BCFO BCNH BCFG BCIU BCED BCGS BCOE BCGZ BCDI BCES BCPF BCIK BCIJ BCEB BCIF FRGD FRGU FRHR FRKR FRGZ FRFG FRKK FRGS FRKY FRFC FRFD FRFB FRGP FRFE FRGO FRKF FRFR FRHB FRGG FRGE FRGI FRHS FRHE FRHO FRFO FRGK FRHD FRHI FRHZ FRKG FRHF FRFS FRKP FRGF FRGR FRKH FRJG URGB URGE URFO URFS URFG URGD URFC YKLG YKLF YKNF YZER YZEZ YZEC YKNG YZED YZEG YZDR KFFO YZEE YZES YKMG YZEB YZFO YZEF YZEI YZEO YZDZ YKLK YKLY YZFI YKLP KFFI YKKP KFOG KFSH YKRH YZGZ YZGO YZIH KFOZ YKPG YZNP YKPP KFRF YKNY YKNK YKPS YKPO KFOB YKRY KFRK KFOE KFSK KFOD KFOR KFRP KFGZ YKPF KFSU YKPR KFOP KFGB KFGI KFGF YKPC KFFZ KFSP KFOI KFGE KFIK YKPB KFSJ KFRY YZIK KFIP YZIG YZIF KFGP KFIY YZIY YKPE KFFS YZIP YKPZ YKNP YZOB YKPI KFIH YZGI KGBD KGBC KGBE KGDO KGBR KGBS KFZO KGDG KFZZ KGBB KFUS KGBZ KGDF KGDI KFYP KGDE KFZI KGED KGEG KGEB KGEF KGER KGEO KGEE KGEP KGDZ KGEC ZRGE ZRGU ZRFS ZRGR ZRGG ZRGO ZRGP ZRGI ZRFR ZRFB ZRGS ZRGD ZRFC ZRFF ZRFZ ZRFO ZRFD ZRFE ZRGF ZRED ZRFI ZRFG USMH USMY USMK USLS USMP USMF USMJ USLJ USLY USNF USLK USLU PRMP PRMG PRPF PRMK PRPG PRPO PRLP PRMY PRNF PRMF PRKY PRPZ PRNP PRNY PRPR PRPC PRNG PRMH PRLF PRNH PRPE PRLG PRPB PRLK PRLH PRLY PRPS PRPP PRNK PRKK PRKG PRKH PRKF PRFE PRFF PRFO PREG PREZ PRFI PRGO PRGF PRHO PRGG PRHR PRGZ PRGY PRHE PRHZ PRFS PRHP PRHG PRHD PRHI PRHF PRFZ PRGR PRGS PRGP PRGU PRJG PRHS PRGB KPSG KPOS KPSY KPOR KPZD KPWZ KPSK KPRG KPPE KPOP KPOZ KPPR KPRK KPPG KPRY KPWB KPVD KPRF KPRP KPVZ KPXH KPSO KPPO KPPB KPRH KPPI KPVE KPVR KPPF KPVO KPRZ KPXK KPOI KPPC KPPZ KPOG KPSP KPSF KPRS KPVC KPSJ KPSU KPPS KPVG KPVI KPOU KPVS KPSH FSOZ FSMU DOGO FSNP FSUD FSKY FSYG DOGF DOIH FSUR FSMY OIYG OIMF IPGD DOGS FSPG FSMG FSOI FSYC OIYB OIXH DOGG FSOC FSMP IPFR IPBD FSKR OIYP GILU FSOE OIPS FSXF FSMK OIXF FSPC IPBF FSNU FSXY FSUB FSNG FSLU OIUO FSYF FSPZ OIPC DOGU FSLY FSYH FSNF IMYZ FSTP FSUZ FSNY FSPO FSTY FSXJ OIKU OIMG DOIG OIPG FSUE OIUB OIUG OIUE IPCE DOGR FSYB OIMK OIUR OIPB DOGZ FSPP FSSP OITP OIYK IPCD FSNH IPCO OIPZ IPGB OIYC FSTH OIYO IPCR DOGP GILP FSMJ OIYH FSKU FSUO OITY OIXJ OIXK OILY IPBZ FSMF FSYE OIPF FSLP FSNK IPEG OIYR IPBI IPBO IPCC FSLF OIPR FSPS FSYI OIXP IPEE OIMH OIMJ IPER IPFG FSYJ OILF FSYK IPEO OILJ FSLJ IPFS GIMH IPED OIKR OITJ IPCP IPBC OITH OIYF IPEC DOGI FSNJ IPCI OIUF OIKP IPFO FSLH FSLS FSUG FSUI OITF IPEI OIYJ OIPE OITU IPBR IPFB IPEB OIYI GIMG OIYE FSUS OIPO FSXP FSTK IPFE OIKY IPBE IPES GIMP FSTU GILY GIMF IPGE OIXY FSTJ IPCG FSPR OIPP DOIJ OIMY FSXH OIXU GIMK IPEZ FSLK OILU IPBB OILG IPCS IPFI IJMG PITF IMYO IHZZ IMYE IMYP IMYH IMYB IMYK IMYC IMYG IJFC IMUO IJFO IMYF IJGZ IJGG IJGE IJHS IJGB IJGR IJHG IJGO IJGS IJGP IJGI IJLS IJFS IJGU IJKG ZRKG ZRKK ZRKP ZRKY ZRKF ZRLG ZRKH ZRMH ZSGB ZRPB ZRMG ZRYR ZRMP ZRUG ZSFE ZRNP ZRPG ZSFR ZRNH ZRXH ZRPS ZRUO ZRNF ZRYB ZRYP ZRYE ZRXP ZRXK ZRYO ZRTK ZRNK ZRLY ZSFS ZRTP ZRYG ZRYH ZRPF ZRMK ZRSP ZRXY ZRPR ZRZS ZRYK ZRLP ZRNY ZRTH ZRPE ZRTY ZSFG ZRUS ZRYZ ZRSK ZRPC ZRPZ ZRYC ZRPI ZSFF ZSFI KYWB OaDO KYZC OAAD KYZE OaDS KYXY OBAB KYZR KYXK KYZO KYXP KYWD KYZZ OaDR KZZE KYXG OaSG KYZD KYXH KYXF KYZI KZOB OBaB OaDF KZOG KYZS KZZO OaRJ OaaZ OaZC KZSK OaaD OaaR KZSF KZVS KZZZ GEBR GECC GEBS GECP GECO GECS GECD GEDG GECZ GEBC GECB GEaZ GECR GEDF GEBI GECF GECG GEaS GEDE GECE GEBO GEBE GEBZ GECI GEaR GEBD GDSG GDOB GDOR GDSU GDRG GDGE GDOZ GDRY GDSY GDOE GDZC GDOS GDRH GDGR GDOC GDRF GDGP GDGD GDOP GDGS GDGZ GDRK GDRZ GDRJ GDZO GDGI GDRU GDZR GDIU GDOI GDIP GDSK GDSP GDRP GDOG GDWR GDIJ GDIY GDIK USOR USPP USPB USOZ USPO USPE USPG USPF USOG USOC USOE USNK USOB USNJ USNP DOPR DOZF DOWB DORP DORG DOPS DOWE DOSG DORU DOZD DOPO DORH DOWD DOPI DOSO DOSY DOZC DORJ DOXK DOXY DOVE DOSS DOPG DORS DOSZ DOVR DOVZ DOIY DOSJ DOSH DOZO DOZI DORY DOSK DOIU DOVS DORF DOSU DORK DOZE GIUE GKHB GIUB GKUE GKNG GKNP GIUD GIYU GIYE GKNK GIUF GIXY GKNU GKPB GKGG GIXH GIYK GIYF GIUI GIUR GKGI GIYO GIUC GIXK GIUZ GIXJ GKLU GIYP GKPZ GKNF GIUS GITY GIUG GKPC GKHI GIYB GIYI GIXG GIYR GIUO GIYH GIXF GITH GKUI GIYG GITK GITP GIXU GIPC GIPO GIPP GIPB GIPR GIPI GIPZ GIPS GIPF GIPE

0

an Lpmax equal to 34.82. The representation of two proteins containing PZCD-fragments shows that this ubiquitous word is present in superfamilies with different folds. As reported in Table 4, 99.8% of PZCD-fragments contain b-turns. Specifically, they contain two b-turns, at positions 2:5 and 3:6. However, some of the fragments encoded by the eleven words strongly associated with b-turns, given in Table 4, do not contain turns as assigned by the ExtractTurn software. This represents a small fraction of the fragments: only 342 fragments out of 8 369, i.e. 4%. Out of these 342 fragments, 79 fail the turn assignment because they have a Cαi − Cαi+3 distance greater than 7 Å and 263 because they have an internal residue in the helical state. For example, only one of YZDS-fragments is not identified as a turn because the distance is equal to 7.08 Å (2ahu_A: 259-262). Our structural words therefore group together fragments including fragments identified as turns and some that narrowly fail the turn assignment. This suggests that structural motifs could be used to assign “relaxed” turns and supports the notion of turn-like conformations, introduced by Fuchs et al, corresponding to four-residue fragments with a Cαi − Cα4 distance around 7 Å [63].

50

100

150

200

Lpmax

Figure 5 Plot of statistical criteria Lp max and nb sf* for the structural words seen at least five times in a SCOP superfamily. Black: words with Lpmax ≤= 5.97. Red: extreme superfamily-specific words (Lpmax ≥50 and nbsf* > 5). Orange: extreme ubiquitous words (Lpmax ≥10 and nbsf* ≥ 5). Pink: over-represented words with Lpmax > 5.97 not discussed in this study.

We also compare extreme ubiquitous words with the 12 small hydrogen-bonded 3D motifs extracted from the Motivated Protein database [48]. Results of this analysis are reported in Table 4. As stated in the Methods section, there is very little overlap between our initial data set and the proteins stored in the Motivated Protein database. Even on such a small number of fragments, the comparison reveals that seven extreme ubiquitous words (DRPI, DSPI, DSGI, DSKG, DSKH, DOIP and OIPI) correspond to nest motifs, with precision greater than 93% and two words (BQGI and HBBQ) correspond to niche motifs with precision greater than 95% precision. The set of words corresponding to nest motifs includes structural words with similar conformations, such as DRPI, DSPI and DSGI or DSKG and DSKH. We also note that some structural words overlap: in 81% of cases, structural word DOIP is immediately followed by letter I, forming the five-structural letter word DOIPI.

Regad et al. BMC Bioinformatics 2011, 12:247 http://www.biomedcentral.com/1471-2105/12/247

Page 11 of 23

Table 4 Correspondence between extreme ubiquitous words and small structural motifs Statistics in the initial data set Word

Occurrence

Lpmax

Comparison with known motifs

nbsf*/nbsfa

Known motif

Matchb

Precision (%)

b-turn comparison PZCD

903

34.82

25/211

b-turn

902

100

HBDS

1588

21.97

22/285

b-turn

1588

100

ZCDS

1112

27.55

22/246

b-turn

996

88

UFQK

449

27.77

15/134

b-turn

441

98

GYUQ

278

14.40

11/96

b-turn

278

100

YBDS FQLG

391 242

20.60 25.37

9/136 8/77

b-turn b-turn

391 236

100 98

YZDS

397

10.30

7/130

b-turn

394

99

GUDO

43

27.55

6/11

b-turn

43

100

FFFI

265

21.62

6/80

b-turn

206

78

FQKG

237

32.77

5/71

b-turn

223

94

Motivated Proteins comparisonc SLGI QLGI

258 185

15.60 15.16

8/114 7/89

b-turn end b-turn end

DRPI

232

14.95

14/94

DSPI

541

27.15

9/158

DSGI

387

32.45

DSKG

346

DSKH

411

DOIP OIPI

11 (13) 4 (4)

85 100

Nest

9 (10)

90

Nest

14 (15)

93

7/115

Nest

20 (20)

100

23.16

9/145

Nest

9 (9)

100

20.46

7/145

Nest

10 (10)

100

219

63.30

7/82

Nest

10 (10)

100

201

69.81

8/71

Nest

11 (11)

100

HBBQ

616

23.29

10/219

Niche

23 (23)

100

BQGI

337

21.06

9/130

Niche

18 (19)

95

SKGI

34

18.93

12/127

-

NA

DGPI

56

15.77

5/32

-

NA

a

: nbsf denote the number of SCOP superfamilies in which a structural word occurs. b: match denotes the number of fragments containing a known motif. c: comparison with Motivated Proteins motifs is restricted to the set of proteins common to our database and the Motivated Proteins database. In this case, the number between brackets denotes the number of fragments involved in the comparison.

Figure 6 (lower panel) provides an example of a structural word, DRPI, containing a nest motif. We observe that DRPI-fragments are very similar in terms of structure and present some weak amino-acid specificities in positions 3: 5 and 7. This word is recurrent (seen 232 times in the initial data set and in 94 superfamilies) and over-represented in 15 superfamilies with a Lpmax equal to 14.9. The representation of two proteins containing the DRPI word shows it is present in superfamilies with different folds. Like turn motifs, nest and niche motifs are detected by applying geometrical thresholds. In this case also, the fact that a very small proportion of our fragments fail the assignment suggest that structural words could be used to assign nest- and niche-like motifs. Extreme ubiquitous words not associated to known structural motifs

Two ubiquitous words, DGPI and SKGI, are extracted from proteins not listed in the Motivated Protein database. It is therefore not possible to compare them with

niche and nest motifs. Let us note, however, that DGPI is structurally close to the structural word DRPI (RMSD equal to 0.74 ± 0.24 Å), which contains nest motifs. In the same way, SKGI is similar to SLGI (RMSD equal to 0.76 ± 0.24 Å), a word containing the end of a b-turn. Link between ubiquitous words and functional annotations

In the previous part, we have shown that extreme ubiquitous words contain some known motifs such as turns, nest, niche. It has been shown that these small motifs could be involved in protein functions such as active sites or binding sites [49,61]. We thus surveyed the association between extreme ubiquitous words and Swiss-Prot annotation by computing the precision of the extreme ubiquitous words toward biological annotations. As reported in Additional file 1: Table S1, we obtained low precisions, suggesting that ubiquitous words are not strongly associated to functional features.

Regad et al. BMC Bioinformatics 2011, 12:247 http://www.biomedcentral.com/1471-2105/12/247

Page 12 of 23







     

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Figure 6 Illustration of two ubiquitous structural words. Upper part: structural word PZCD. Lower part: structural word DRPI. A: geometry of several word fragments, optimally superimposed. B: amino-acid conservation of the word generated by WebLogo http://weblogo.berkeley. edu/. C: word statistics. D: example of structures containing the structural word. The location of structural word is indicated by arrows.

Regad et al. BMC Bioinformatics 2011, 12:247 http://www.biomedcentral.com/1471-2105/12/247

Page 13 of 23

Link between extreme superfamily-specific words and biological annotations

Unlike ubiquitous words, superfamily-specific words are highly over-represented in few superfamilies, suggesting a possible implication in function. In this section, we focus our analysis on the extreme superfamily-specific words, defined by Lpmax ≥= 50 and nbsf* < 5, and investigate their correspondence with biological annotations provided by Swiss-Prot extracted from the annotation data set. We complement the analysis based on SwissProt by the use of external softwares (Rep, SitePredict, CSA and LigPlot) for functional site identification/ prediction. As reported in Table 2, extreme superfamily-specific words account for 0.2% of the structural words, 0.7% of the seven-residue fragments, and are seen in 17% of the proteins of the initial data set. Their average Lpmax score is equal to 88.9 ± 46, ranging from 51.7 to 210, and their

mean nbsf* is equal to 1.4 ± 0.4. The results of the comparison between extreme superfamily-specific words and Swiss-Prot annotations are reported in Table 5. We present below these results grouped according to the SwissProt annotations identified during the comparison. For each annotation, we computed the precision, i.e. fraction of the fragments encoded by a structural word that actually correspond to the annotation. A structural word associated to a precision greater than 40% with respect to a functional annotation is said to be functional. For these functional words, we also computed the sensitivity, i.e. fraction of the annotation that is actually covered by the structural word. Disulfide annotation

Two overlapping extreme superfamily-specific words, RNHB and URNH, are strongly over-represented in the immunoglobulin superfamily (SCOP id = 48726). They correspond to regions covalently linked by disulfide

Table 5 Correspondence between extreme superfamily-specific words and Swiss-Prot annotations in the initial data set Statistics in the initial dataset a

b

c

Comparison with Swiss-Prot d

Word

Occ

Lpmax

nbsf*/nbsf

URNH

43

54.95

1/17

48726*

Disulfide

7/14 (50)

RNHB

59

51.33

1/28

48726*

Disulfide

9/20 (45)

6

UQHS

53

75.07

1/16

52058*

Repeat

12/22 (55)

41

SUQH

70

63.42

1/25

52058*

Repeat

11/26 (42)

38

QHSG

37

51.75

1/12

52058*

Repeat

4/10 (40)

14

HSGI

63

76.26

1/18

52058*

Repeat

5/12 (42)

17

QXUS ZSGI

43 99

52.05 52.22

1/10 1/49

51735* 52058*

Repeat Repeat

1/15 (7) 7/36 (19)

Superfamilies

Annot

Match/total (Precision (%))

Sensitivity (%) 4

GSUS

169

140.49

3/59

141571*, 52047, 52058

Repeat

6/38 (16)

GZDO

115

84.72

3/49

47473*, 52833, 52935

Repeat

1/35 (3)

DODQ

73

157.01

1/17

47473*

CA_BIND

15/23 (65)

ZDOD

48

91.27

1/13

47473*

CA_BIND

11/16 (69)

58

YUOD

111

184.67

1/11

52540*

NP_BIND

39/41(95)

35

UODO

142

210.14

4/14

52540*,53659, 54211, 55729

NP_BIND

49/60 (82)

38

OEIJ EIJU

33 48

53.84 51.68

1/4 1/13

51735* 51735*

NP_BIND NP_BIND

6/7 (86) 7/15 (47)

14 20

USLG

121

137.35

2/47

141571*, 51206

NP_BIND

2/22 (9)

UZCI

99

63.70

2/28

103025*, 56784

NP_BIND

1/13 (8)

RUDO

27

55.55

1/4

53335*

Binding

5/10 (50)

UGRU

37

60.07

1/8

53335*

Binding

4/12 (33)

EGZD

48

51.68

1/5

51735*

GRUD SLGS

33 60

70.55 118.45

1/6 1/17

53335* 141571*

75

18

This comparison is made on a subset on the initial set: 1487 proteins that can be mapped to biological annotations using the PDB/UniProt Mapping database. a: word occurrence. b: nbsf denotes the number of SCOP superfamilies in which the structural word is seen. c: superfamilies in which the word is over-represented. d: match and total denote the number of fragments annotated and the total number of fragments, respectively. Bold font indicates a match/total ratio greater than 40%. Italic font indicates a match/total ratio lower than 40%. Abbreviations used: NP BIND = nucleotide phosphate-binding site, CA_BIND = calcium-binding site. SCOP ids: 103025 = Folate-binding domain, 141571 = Pentapeptide repeat-like, 47473 = EF-hand, 48726 = Immunoglobulin, 51206 = cAMP-binding domain-like, 51735 = NAD(P)-binding Rossmann-fold domains, 52047 = RNI-like, 52058 = L domain-like, 52540 = P-loop-containing nucleoside triphosphate hydrolases, 52833 = Thioredoxin-like, 52935 = PK C-terminal domain-like, 53335 = S-adenosyl-L-methionine-dependent methyltransferases, 53659 = Isocitrate/isopropylmalate dehydrogenase-like, 54211 = Ribosomal protein S5 domain 2-like, 55729 = Acyl-CoA N-acyltransferases (Nat), 56784 = HAD-like. “*” denotes the superfamily in which the word is most over-represented.

Regad et al. BMC Bioinformatics 2011, 12:247 http://www.biomedcentral.com/1471-2105/12/247

bridges and identified by the “Disulfide bond” Swiss-Prot annotation with a precision of 50 and 45%. This annotation provides no functional information per se, but might indicate that these structural motifs result from structural constraints induced by the disulfide bridge. However, the very low sensitivity observed (4 and 6%) shows that a only small fraction of the disulfide annotations are encoded by these words. Repeat annotation

Four overlapping extreme superfamily-specific words SUQH, UQHS, QHSG, HSGI are strongly over-represented in the “L domain-like” superfamily (SCOP id = 52058). This superfamily groups proteins containing repeat regions, which are regions of 20 to 30 amino acids unusually rich in leucine [64]. Repeat regions have strong implications for the biological role of protein, as they are often involved in protein-protein interactions in plant and mammalian immune responses [64]. A number of human diseases have been shown to be associated with mutations affecting leucine-rich repeat domains [64]. These repeat regions may therefore be of functional relevance. Structural words SUQH, UQHS, QHSG,HSGI often occur in the same proteins, allowing the formation of longer motifs, like illustrated in Figure 7: in protein







Page 14 of 23

1ogq A, SUQH and UQHS overlap to form the five-structural letter words SUQHS. Figure 8A illustrates the example of the word UQHS. It is a recurrent word (seen 52 times in the initial data set), strongly over-represented in one superfamily (SCOP id = 52058), with a high maximal score (Lpmax = 75.07). The superimposition UQHS-fragments shows that they are very similar in terms of structures, with a turn conformation. The amino-acid logo indicates that UQHS presents amino-acid conservation at positions 1, 4 and 6, resulting in an amino-acid profile close to the consensus sequence of LRR (LxxLxLxxNxL or LxxLxLxxCxxL [65]). The comparison with Swiss-Prot annotations reveals that the four structural words SUQH, UQHS, QHSG and HSGI correspond to the “repeat” annotation with precision greater than 40% (see Table 5). According to our definition of functional words, these four words are thus functional. Some fragments encoded by these functional words, however, do not correspond to repeat annotations. For example, in the initial data set, 10 UQHS-fragments are unannotated. To determine whether these 10 fragments might still correspond to repeat regions unannotated in Swiss-Prot database (i.e., false negatives), we used the REP











  
   
  


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Figure 7 Illustration of the word UQHS corresponding to the repeat annotation. A: position of UQHS word in protein 1ogq A. B: structuralletter sequence of the protein 1ogq_ A. C: representation of the 3D structure of this protein. Blue: UQHS-fragments. Orange: odd-numbered repeat regions. Yellow: even-numbered repeat regions.

Regad et al. BMC Bioinformatics 2011, 12:247 http://www.biomedcentral.com/1471-2105/12/247







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Figure 8 Illustration of four functional words. A: structural word UQHS. B: structural word DODQ. C: structural word YUOD. D: structural word RUDO. For each word, we provide word statistics (frequency, Lpmax, nbsf*), the name of the superfamily in which the word has highest Lp score, the superimposition of fragments associated with this word, and amino-acid conservation data.

software to predict repeat regions. Two repeat regions are predicted: 1dce A:484-507 and 529-553. Region 1dce A: 484-507 actually contains the word UQHS, whereas the second region: 529-553 does not (see Table S2). The sensitivity measure for the repeat annotation for the four structural words SUQH, UQHS, QHSG and HSGI ranges from 17 to 41%, meaning that repeat regions correspond to a variety of conformations, not only the ones encoded by SUQH, UQHS, QHSG and HSGI. By definition, repeat regions are formed by the repetition of a motif. Calcium-binding site annotation

Two overlapping extreme superfamily-specific words, ZDOD and DODQ, are over-represented in only one superfamily: “EF-hand” (SCOP id = 47473). This superfamily contains proteins with EF-hand units, which consist of two helices connected by a calcium-binding loop. The words ZDOD and DODQ are frequently overlapping: in 66% of cases, DODQ is preceded by the letter Z, forming

the word ZDODQ. Figure 8B presents the statistics, geometry and amino-acid sequence conservation of the word DODQ. The amino-acid logo shows that DODQ presents amino-acid conservation at positions 2, 3, 4, 5 and 7, with a strong conservation of an aspartic acid or asparagine residue at positions 2 and 4 and of a glycine residue at position 5. This conserved sequence is in close agreement with the consensus sequence of calcium-binding motifs [DxDxDG] [66]. The two words ZDOD and DODQ correspond to the calcium-binding site annotation (CA_BIND) with precision greater than 65%, they thus are functional motifs. As shown in Figure 9A, DODQ contains residues directly involved in the binding of calcium ions. Five ZDOD-fragments and nine DODQ-fragments are not annotated as calcium-binding sites in Swiss-Prot. However, six of these unannotated DODQ-fragments are identified as putative calcium-binding sites by the SitePredict software (see Table S3). The sensitivity of the calcium-binding site

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Figure 9 Illustration of the functional role of three words. A: DODQ corresponds to calcium-binding sites. B: YUOD contains residues involved in nucleotide-binding sites. C: RUDO contains residues involved in SAH/SAM-binding sites. Structural words are highlighted in red and ligands in blue.

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annotations with respect to ZDOD and DODQ ranges from 58 to 75%, meaning that the majority of calcium-binding sites actually correspond to these structural words. These two structural words could thus be used to predict calcium-binding site candidates. Nucleotide-binding site annotation

Five extreme superfamily-specific words are associated with nucleotide-binding site annotations (NP_BIND) with precision greater than 47%. Some correspond to ATP/GTP-binding sites, others to NAD(P)-binding sites. We discuss these two cases separately. ATP/GTP-binding sites Structural words YUOD and UODO are strongly over-represented in the superfamily “P-loop-containing nucleotide triphosphate hydrolase” (SCOP id = 52540), grouping proteins with a phosphatebinding site. These two words are often found in the same proteins: in 90% of cases, the structural word YUOD is followed by the letter O, forming the word YUODO. Figure 8C illustrates the statistics, geometry and amino-acid sequence conservation of the YUOD word. This word displays clear amino-acid conservation: glycine in positions 1 and 6, lysine in position 7, and threonine or serine in position 8, consistent with the consensus sequence of P-loops: [AG]XXXXGK[TS] [10]. Structural words YUOD and UODO correspond to the nucleotide-binding site annotation with precision greater than 80%. YUOD and UODO are thus functional words with residues directly involved in ATP/GTP-binding sites, as shown in Figure 9B for YUOD word. In the initial data set, two YUOD-fragments and eleven UODO-fragments are unannotated. SitePredict indeed predicts ATP/ GTP-binding sites for four of the eleven unannotated UODO-fragments (see Table S4). The sensitivity is equal to 35 and 38%, meaning that roughly one third of the ATP/GTP-binding sites adopt conformations described by these structural words. NAD(P)-binding sites Two structural words, OEIJ and EIJU are strongly over-represented in the “NAD (P)-binding Rossmann-fold domain” superfamily (SCOP id = 51735) grouping proteins with NAD(P)-binding sites. These words are often overlapping: in 95% of cases, OEIJ is followed by the letter U. Word OEIJ is associated with the NP_BIND annotation with precision equal to 86% and 47% respectively, they thus are functional words. One OEIJ-fragment and seven EIJU-fragments are unannotated. Two of the seven unannotated EIJU-fragments are predicted as NAD(P)-binding sites by SitePredict (see Table S5). The sensitivity is quite low, ranging from 14 to 20%, meaning that NAD(P)-binding sites probably adopt various conformations, and not only the ones encoded by OEIJ and EIJU. S-adenosyl-L-methionine binding sites

The superfamily-specific word RUDO is strongly overrepresented in the “S-adenosyl-L-methionine-dependent

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methyltransferase” superfamily (SCOP id = 53335), grouping proteins with SAH/SAM-binding sites. Figure 8D presents the geometry of the structural word RUDO and its amino-acid signature, with glycine residues preferred at positions 1, 3 and 5. Figure 9C presents an illustration of a SAH/SAM-binding site for a RUDO-fragment, showing the residues involved in the SAH/SAM-binding site. This word corresponds to the “binding” annotation with a precision equal to 50%, therefore it is a functional word. Three out of the five unannotated RUDO-fragments actually correspond to SAH/SAM-binding sites according to our analysis using LigPlot. The sensitivity is equal to 18%, suggesting that SAH/SAM-binding sites adopt other conformations than the one identified by the RUDO word. Unannotated extreme superfamily-specific words

Ten superfamily-specific structural words QXUS, ZSGI, GSUS, GZDO, USLG, UZCI, UGRU, EGZD, GRUD and SLGS, indicated in italics in Table 5 could not be validated as functional motifs because they have low precision values toward Swiss-Prot annotations. This could be due to (i) the limited number of proteins of the initial data set that are annotated in Swiss-Prot and (ii) the incomplete annotation of Swiss-Prot, since annotations for a given protein simply reflect our current knowledge about it. Double checking the link between functional words and biological annotations using the validation data set

The previous analysis was based on the Swiss-Prot annotations of the annotation data set. Since many proteins of the initial data set are lost in the UniProt/PDB mapping step, we complement our results using a data set specifically built to maximize the coverage by Swiss-Prot: the validation data set composed of 2 636 proteins. In the validation data set, 17% of seven-residue fragments in loops are covered by a Swiss-Prot annotation versus only 2% in the initial data set. For the functional words identified in the previous section, we compute the precision and sensitivity measures presented in Table 6. We do not consider the words associated to disulfide and the repeat annotations since they are non specific to annotations. The seven functional words considered have precision greater than 40%, the threshold used for their validation in the annotation data set. These two criteria are stable on the annotation and validation sets with sligth global increase for the validation set: on average 70% to 76% for precision and 37% to 39% for sensitivity. The precision values are high indicating that most of the fragments encoded by these words are annotated by the corresponding annotation.

Discussion In this work, we used a structural alphabet-based simplification of protein structures and applied an exact statistical approach to identify structural motifs over-represented in

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Table 6 Precision and sensitivity for functional words computed in the validation data set Words Annotation

Second-level annotation

Precision (%)

Sensitivity (%) 95

DODQ

CA_BIND

-

82

ZDOD

CA_BIND

-

92

64

YUOD

NP_BIND

ATP/GTP

91

29

UODO OEIJ

NP_BIND NP_BIND

ATP/GTP NAD(P)

80 94

40 7

EIJU

NP_BIND

NAD(P)

54

10

RUDO

Binding

SAH/SAM

44

30

loops in SCOP superfamilies. Our underlying hypothesis was that structural words with unexpectedly high frequency are probably linked to structural or functional implication. We discovered two distinct trends: some words, termed ubiquitous words, are over-represented in several superfamilies, whereas others, termed superfamilyspecific words, are over-represented in a small number of superfamilies. We then investigated the link between these structural motifs and known structural motifs and functional sites annotated in Swiss-Prot, on a subset of structural words with extreme over-representation scores. We focused on structural motifs formed by seven consecutive residues, i.e. four structural letters, since it is the optimal length to have a good description of the 3D conformations and enough data to allow statistical treatments [39]. However, our findings revealed longer motifs formed by overlapping four-structural letter words, such as YUODO, ZDODQ, corresponding to eight-residue motifs or shorter motifs consensus as LGI common to SLGI, QLGI. These results suggest that this motif approach could be extended to motifs of different lengths. Interpretation of ubiquitous words

Since ubiquitous words are over-represented in several SCOP superfamilies with various functions, it is likely that they are the result of structural rather than functional requirement. A comparison of ubiquitous words with extreme scores and known small 3D motifs showed that extreme ubiquitous words contain b-turn, nest or niche motifs. Several studies have shown that turns, nest and niche motifs may play a functional role in determining the conformation of enzyme active sites and binding sites [13,49,61]. We were not able to confirm this point using our extreme ubiquitous words. However, among the functional words identified in the subset of extreme superfamily-specific words, three words (ZDOD, UQHS, UODO) actually contain turns, which is in agreement with the fact that turn motifs could be involved in binding sites [13]. Let us note that turns, niches and nests are shorter (three or four residues) than our structural words (seven residues). The fact that we capture them using

structural words suggests that structural motifs longer than previously described are important for protein folding and stability. Long structural motifs are thus part of a “basic structural repertoire”, similarly to regular secondary structures which are used in protein structures regardless of the overall fold and function of the protein concerned. In addition, structural words allow detecting structural motifs without computing hydrogen bonds, or dihedral angles, and without explicit pairwise comparison of fragments. This could thus be very useful to detect structural motifs with relaxed parameters like turn-like motifs. Interpretation of superfamily-specific words and their link with function Usage of superfamily-specific words for functional site prediction

The analysis of the correspondence between extreme superfamily-specific words and Swiss-Prot annotations revealed that some of superfamily-specific words are linked to functional sites. For example, we found superfamily-specific words associated to repeat annotations and binding sites to ATP/GTP, SAM/SAH, NAD(P), calcium and iron. Thus functional words allow a reliable prediction of some binding sites. Limitations introduced by the Swiss-Prot mapping

Some annotations, such as metal-binding sites (cadmium, lithium, mercury, potassium, vanadium) are very rare and not represented in our data set. This explains why these functional sites are not detected at all by superfamily-specific words. Moreover, only a fraction of the annotation data set is covered by Swiss-Prot annotations (2% of seven-residue fragments) and the step of mapping annotations to PDB structures using the PDB/UniProt Mapping database further reduces significantly the data available for comparison. The link between structural words and functional sites is thus established on a limited amount of data and is probably under-estimated by our analysis. For example the structural word UGRU, over-represented in the “S-adenosyl-L-methionine-dependent methyltransferase” superfamily (SCOP id = 53335), is not characterized as “functional word” in the annotation or validation data sets (precision = 33% and 36%). The manual analysis of the functional annotations of UGRU-fragments show that 69% of them are actually involved in SAH/SAM-binding sites, see Table S6. This illustrates the case of a functional motif missed by our analysis due to a defect of biological annotations. In this paper, the link between superfamily-specific words and functional sites is established only for the 23 extreme superfamily-specific words. These 23 words cover 1% of residues in loops and they are seen in 17% of proteins. If we consider superfamily-specific words with moderate scores (565 words with Lpmax ≥ = 10, see Table 2),

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the coverage can be increased to 10% of residues and 90% of proteins. From these moderately superfamily-specific words, 13 words are clearly associated with a functional Swiss-Prot annotation ("binding site” or “active site” annotations), 17 correspond to a repeat annotation and 16 to a disulfide annotation (data not shown). For example, word ZCLH is over-represented in the superfamily SCOP id = 53474 with a Lpmax equal to 12. This word has a precision for the detection of “active site” annotation of 67% (see Table S7). This suggests that over-represented words with moderate Lpmax score may be functional too. Intrinsic limitation of the structural word approach

However, some functional sites were not detected by structural words. To be identified by our structural word approach, a functional site must meet two conditions: (i) at least one part of the functional site must be located in protein loops and (ii) it must correspond to recurrent structures across different proteins. Indeed, structural words can only identify a functional motif if structural conformation spanning at least seven or more consecutive residues. Thus, superfamily-specific words cannot detect DNA-binding sites or zinc finger motifs because these functional sites are preferentially seen in a-helices. In the same way, some metal binding sites (cobalt, copper, magnesium, canganese, colybdenum, nickel, sodium) are not detected because they display a high flexibility [67] or a structural conservation restricted to few residues. To quantify the correspondence between extreme superfamily-specific words and Swiss-Prot annotations, we computed the precision and sensitivity of annotation detection by these words. We observed that sensitivity values depend on the functional sites and structural words. For example, two overlapping words DODQ, ZDOD present a high sensitivity for calcium-binding sites, meaning that most of these binding sites can be detected by these two structural words. Other structural words have lower sensitivity, e. g. YUOD detects only one third of ATP/GTP-binding sites. However, we checked, on randomized data sets, that these sensitivity measures are significantly greater than expected by chance (see Table S8). Indeed, random sensitivities are very low and the sensitivity of structural words reported in this study are higher in any case. Thus, even if the sensivity measures reported in this sudy may seem modest, they are still significant, meaning that all the superfamily-specific structural words presented here are significantly enriched in functional sites. These low sensitivity values indicate that some functional sites actually correspond to several conformations encoded by different structural words. These different conformations of a functional site could be explained by (i) its flexibility or (ii) the fact that it can span several segments in a protein. Figure 10 presents an illustration of flexibility of

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binding-site through the four calcium-binding sites of protein Calcium-dependent protein kinase 3 (pdb code 3k21). This flexibility results in the encoding of these functional sites into two close words: ZDOD and WDOD, with a RMSD of 0.419 Å. A way to take into account the flexibility of binding-site could be to consider “degenerated words” (for example [W/Z]DOD) instead of “exact” word. This would certainly increase the ability to detect functional sites. In Figure 10, we also present an example of protein Translation initiation factor if2/eif5b (pdb code 1g7s) data, illustrating a binding site involving different 3D regions. This protein contains a GTP-binding site involving three regions, which two are annotated by one NP_BIND annotation, resulting in two NP_BIND annotations for this protein. Each annotated region is detected by a superfamily-specific word: YUOD and UGBB. This indicates each word can detect one part of the GTP-binding site, thus each word is expected to detect to 50% of the NP_BIND annotations at most. Thus, the weak sensitivity value of some functional words shows that these words can detect one part of the functional site. To identify the entire functional sites, we could couple the different functional words associated to the same annotation. Comparison with existing approaches

Several approaches address the link between local structures and protein function. These methods can be clustered into three groups. The first group corresponds to the characterization of structural motifs specific to functional sites [22-28]. Such methods consist in learning the structural motifs of known functional sites and are therefore dedicated to the prediction of those sites. The second group corresponds to the discovery of conserved structural motifs in proteins with the same function. These methods start from protein superfamilies and search for structural motifs specific to superfamilies [20,21,68]. They can identify conserved motifs in different proteins with the same function. In these approaches, the extraction of structural motifs is based on the comparison of structural fragments using RMSD. These methods are able to discover new functional sites within superfamilies. However, they cannot identify functional motifs common to several superfamilies. The third group corresponds to structural classification of local conformations, followed by an analysis of the association between clusters and functional sites [14,17,18,69]. These methods do not focus on the description of a particular functional site, or restrict the analysis to a particular superfamily. Instead, they analyze a posteriori the association between fragment clusters and protein superfamilies or GO annotations. Our approach is based on the same philosophy as these methods.

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Figure 10 llustration of the binding sites, which correspond to different words. A: Illustration of the flexibility of calcium-binding sites in the Calcium-dependent protein kinase 3 (pdb code 3k21), which is cristallized with 3 calcium atoms (colored in blue). Among these 3 calciumbinding sites two are detected by overlapping words ZDOD and DODQ, colored in red. The third binding site is detected by overlapping words WDOD and DODQ, colored in magenta. B: Illustration of a GTP-binding site involving different 3D regions in the Translation initiation factor if2/ eif5b (pdb code 1g7s). The GTP is represented in blue. The binding site is composed of three 3D regions (15-20, 130-133; 198-199). In red are colored the two regions, which are detected by superfamily-specific words: YUOD and UGBB over-represented in the superfamily “P-loop containing nucleoside triphosphate hydrolases” (52540). In magenta is colored the third region, which is not detected by superfamily-specific word. In Swiss-Prot this protein is annotated by two NP_bind annotations (12-19, 76-80, 130-133).

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Compared to Espadaler et al. [14], Tendulkar et al. [17], and Manikandan et al. [18], our method is original in three ways: (i) the extraction of structural motifs is based on a structural alphabet, which allows defining structural motifs without using geometrical thresholds or extensive pairwise structural comparison, (ii) the functional role of a motif in a particular superfamily is assessed by its statistical over-representation within the superfamily, and (iii) it can deal with all loops, irrespective of their length or secondary structure types. This last point is particularly important: in a previous study, we have shown that 64% of structural words display no specificity for loop length [39]. It is also the case of the functional motifs identified in the present study: for example, 60% fragments of the word DODQ, involved in calcium-binding sites are extracted from short loops, and 40% from long loops. The fact that we made a systematic decomposition of loops into structural words, instead of clustering full-length loops as done by Espadaler et al. [14] makes the comparison with their study difficult. Two studies by Tendulkar et al. [17] and Manikandan et al. [18] aimed at the extraction of structural motifs specific to a protein function. Contrary to our approach, they considered all structural motifs including a-helices and b-strands. In these two studies, structural motifs were extracted by a systematic classification of eightresidue fragments based on geometric invariants [17] or dihedral angles [18]. They then analyzed the association between structural clusters and protein functions provided by SCOP superfamilies [17] or GO terms [18]. Tendulkar et al. [17] defined a cluster as functional if at least 70% of its fragments are found in a same SCOP superfamily. Manikandan et al. [18] identified functional clusters on the basis of the over-representation of GO terms in clusters. These two definitions restrict the definition of functional motifs to motifs specific of one superfamily or GO term. By contrast, the statistical treatment presented here allows the extraction of motifs shared by several families, even if the superfamily contains few members. Recently, Wu et al. [69] have proposed an approach to extract functional structural motifs from DNA-binding proteins using a structural alphabet. As in our approach, the structural alphabet is used to simplify 3D structures into uni-dimensional sequences. The structural alphabet used in [69] is composed of 16 structural letters, named protein blocks. Wu et al. focused on DNA-binding sites by searching structural words present in DNA-binding proteins binding and absent in others, and considered long and degenerated structural words (26 residues) without secondary structure restriction. In the present study, we discarded helices and strands. In addition, our statistical treatment is radically different from theirs, and

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allows retrieving structural words shared by several superfamilies, even in superfamilies with few proteins. Even if based on a similar method of protein structure simplification, both these works thus pursue quite different objectives and consider different structural motifs.

Conclusion In this study, we present a systematic extraction of 3D motifs from loops likely to be important for protein structure or function. This method is based on the structural alphabet HMM-SA and an advanced method for pattern statistics. We identified ubiquitous structural motifs over-represented in several superfamilies, and superfamily-specific structural motifs over-represented in few superfamilies. Some ubiquitous words correlate with known 3D motifs such as b-turns, niches and nests. The link between the word over-representation and functionality was proved for some superfamily-specific words. Thus, some of these structural words allows the detection of calcium-binding sites, some part of nucleotide, SAHbinding sites, or active site. As in DNA sequence analysis, statistical over-representation can be related to functional features. These results could be used for the prediction of functional sites in protein structures: the identification of these structural motifs in uncharacterized proteins could provide useful clues to protein function in complement to usual methods based on homologous proteins. As some functional annotations are supported by regular secondary structures, current perspectives include the consideration of regular secondary structures. Also, some functional words present sequence specificity, which opens the perspective to the prediction of these functional motifs from their amino-acid sequence. Additional material Additional file 1: Supplementary information. This file is a pdf file. It contains different information about the comparison between some over-represented words and biological annotations: • Table S1: Precision of annotation dectection by extreme ubiquitous words. • Table S2: Analysis of UQHS fragments. • Table S3: Analysis of DODQ fragments. • Table S4: Analysis of UODO-unannotated fragments. • Table S5: Analysis of EIJU fragments. • Table S6: Analysis of UGRU fragments. • Table S7: Analysis of ZCLH fragments. Table S8 present the results of the computation of a random sensitivity for each functional word.

Acknowledgements We would like to thank Dr. Christelle Reynès for critical reading of the manuscript and Dr. Gaelle Debret for her assistance. We thank Grégory Nuel for statistical discussions. We thank the three anonymous referees for their constructive comments. Author details INSERM, U973, Paris F-75013, France. 2Université Paris 7 - Paris Diderot,UMRS973, MTi, F-75013 Paris, France. 3Université Lyon 1, Univ Lyon, France; CNRS,

1

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doi:10.1186/1471-2105-12-247 Cite this article as: Regad et al.: Dissecting protein loops with a statistical scalpel suggests a functional implication of some structural motifs. BMC Bioinformatics 2011 12:247.

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