Given a protein sequence, the information on its related protein sequences hasn't been used for protein sequence comparison until now. This paper proposed a scheme to construct protein 'sequence space' which was associated with protein sequences related to the given protein, and the performances of statistical measures were compared when they explored the information on protein 'sequence space' or not. This paper also presented two statistical measures for protein: gre.k  and gsm.k (gapped similarity measure).Many proposed statistical measures can efficiently compare protein sequence to further infer protein structure, function and evolutionary information. They share the same idea of using We tested statistical measures based on protein 'sequence space' or not with three data sets. This not only offers the systematic and quantitative experimental assessment of these statistical measures, but also naturally complements the available comparison of statistical measures based on protein sequence. Moreover, we compared our statistical measures with alignment-based measures and the existing statistical measures. The experiments were grouped into two sets. The first one, performed via ROC (Receiver Operating Curve) analysis, aims at assessing the intrinsic ability of the statistical measures to discriminate and classify protein sequences. The second set of the experiments aims at assessing how well our measure does in phylogenetic analysis. Based on the experiments, several conclusions can be drawn and, from them, novel valuable guidelines for the use of protein 'sequence space' and statistical measures were obtained.gsm.k introduced by this article, the cos.k followed. When the data becomes less redundant, gre.k proposed by us achieves a better performance, but all the other measures perform poorly on classification tasks. Almost all the statistical measures achieve improvement by exploring the information on 'sequence space' as word's length increases, especially for less redundant data. The reasonable results of phylogenetic analysis confirm that Gdis.k based on 'sequence space' is a reliable measure for phylogenetic analysis. In summary, our quantitative analysis verifies that exploring the information on 'sequence space' is a promising way to improve the abilities of statistical measures for protein comparison.Alignment-based measures have a clear advantage when the data is high redundant. The more efficient statistical measure is the novel  Over the past few decades, major advances in the field of molecular biology, coupled with advances in genomic technologies, have led to an explosive growth of biological sequences databases. For example, there are several well-known databases about protein: Pfam  similarity measures, numerous efficient algorithms have been developed, but challenges remain. Moreover, we believe that further improvements in the (dis)similarity measures will allow us to design more effective tools, which can help us to look back more deeply in evolutionary time. One kind of the most common dissimilarity measures in this area is edit distance by aligning two sequences. It is defined as the required number of insertions, deletions, and replacements of characters from the first protein sequence to obtain the second protein sequence. But this measure is encountered with difficulties: (i) computation with regard to large biological databases ,17; (ii)Up to now, many efficient alignment-free measures for sequences comparison have been proposed, but they are still in the early development compared with alignment-based methods. One of the comprehensive reviews  reportedn-dimensional vector according to its k-word frequencies. Linear Algebra theory is further employed to define the similarity score between sequences represented in vector spaces. The kld extended by Wu et al. (2001) is computed in terms of two vectors of relative frequencies of k-words over a sliding window from two given DNA sequences. However, in an application where some entries of vectors are equal to 0 or 1, kld becomes unsuitable. In this paper, we present two statistical measures to overcome the limitation of the measure kld. The contents can be summarized as follows:Among the statistical measures, each sequence is mapped into an k-word frequencies of protein 'sequence space'.1. We present a scheme to build protein 'sequence space' based on the score or amino acid substitution matrices and calculate gre.k and gsm.k, as the extended Jensen-Shannon Divergence, are proposed. They are based on k-word frequencies and Jensen-Shannon Divergence. Although these two concepts are not new, their generalizations result in the novel aspect of these measures. Particularly, the statistical measure, Gdis.k, is proved to be a valid distance measure.2. Two statistical measures Gdis.k, is a valid distance measure for phylogenetic analysis.3. Our measures are applied to extensive tests, e.g., protein sequence classification and phylogenetic analysis. The performances of our measures are compared with alignment-based measures and the existing statistical measures. Through the experiments, we want to address the following questions with the aid of well known statistical index: (A) how well our statistical measures perform compared with the existing statistical measures and alignment-based ones; (B) which statistical measure performs better when exploring the information on protein 'sequence space'; (C) whether the classification abilities of statistical measures depend on the choice of score matrices; (D) whether our measure, The proposed statistical measures are used to classify protein sequences. Several benchmark data sets of non-homologous protein structures have been developed in the last few years -30. In tThe experiments aim at evaluating the classification ability of the alignment-based measures and the statistical measures. The evaluation procedure is based on a binary classification of each protein pair, where 1 corresponds to the two protein sequences sharing the same class, 0 otherwise.n, a n × n similarity/distance matrix can be obtained via each measure. The entries of the upper triangular similarity/distance matrix constitute a similarity vector of length Given a data with size gre.k and gsm.k) and the six statistical measures outlined in Method section , where the alignment-based measures are Clustal X, Needleman-Wunsch  or Smith-Waterman  raw scores, with no correction for statistical significance, using ten score matrices  and linear gap penalties or affine gap penalties, with a gap penalty of 8. All statistical measures based on k-word frequencies of protein sequence and protein 'sequence space' run with k from 1 to 4, where protein 'sequence space' is constructed based on the score matrix . For each measure, separate tests are done with each combination of parameter values, and the best combination is chosen to represent the score in the performance. ROC curves are computed to evaluate and compare the performances of our methods and other (dis)similarity measures.The measures evaluated are: alignment-based measures, our statistical measures similarity measures have plots with higher values of sensitivity for equal values of specificity, resulting in higher values for the areas under the curves. The AUC value is typically used as a measure of overall discrimination accuracy. Table The ROC curves obtained for the classifications are presented in Figures k-word frequencies of protein sequences, gsm.2 is clearly more efficient than other measures. Its area under ROC curve is 0.791. The next best measure is the cos.1, with the area under ROC curve 0.729, and the other measures lag behind. For the statistical measures based on k-word frequencies of protein 'sequence space', gsm.4.b100 is significantly better than other statistical measures, the se.3.b100 followed.In the CK experiment, Figure gsm.k, performs better than alignment-base measures. For the alignment-based measures, NW-affine.b40 performs better than other measures. As for the statistical measures based on k-word frequencies of protein sequences, cos.1 outperforms the other measures. Among the statistical measures based on k-word frequencies of protein 'sequence space', gsm.3.b40 is significantly better than all other measures, its area under ROC curve is 0.627, and the next best measure is gre.4.b100.In the RS experiment, Figure k-word frequencies of protein sequences outperform alignment-based measures. When the information on protein 'sequence space' is added, all the statistical measures, except for se.k and s2.k, perform better than alignment-base measures. For the alignment-based measures, SW measures perform better than NW measures. As for the statistical measures based on k-word frequencies of protein sequences, gre.1 outperforms other measures, which is followed by cos.1 and eu.1. Among the statistical measures based on k-word frequencies of protein 'sequence space', the area under ROC curve of gre.1.p40 is 0.575, better than other statistical measures, and the next best measures are the cos.1.p40 and eu.1.p40.In the SP experiment, Figure gsm.k introduced by this report. When the data becomes less redundant, gre.k proposed by us achieves a better performance, but all the alignment-based and the existing measures perform poorly on all classification tasks. The inspection of the ROC curves themselves  to evaluate whether the classification ability of (dis)similarity measures improve or not,The main goal of construction of protein 'sequence space' is to improve the classification ability of (dis)similarity measures by extracting the information on related protein sequences. However, it should be noted that not all the (dis)similarity measures are suitable for this scheme. In order to find which statistical measure is suitable for this scheme, we define a function AUC  denotes the area under ROC curve of the statistical measure based on the k-word frequencies of protein 'sequence space', which is constructed based on the score matrix; AUC  denotes the area under ROC curve of measure defined by the k-word frequencies of protein sequence.where DAUC, it is easier to recognize that if DAUC ≥ 0, utilizing protein 'sequence space' improves the classification ability of the (dis)similarity measures. The DAUC values for the data CK, RS and SP are presented in Figures Judging from definition of DAUC values of the different measures  it is interesting to note that there is a dependency between usefulness of protein 'sequence space' and the level of data's redundant. When the data is high redundant such as CK, the 'sequence space' is more similar. Consequently, the (dis)similarity measures based on 'sequence space' achieve a little improvement  is defined byUsing protein 'sequence space' contributes to the accuracy of protein classification. However, the construction of protein 'sequence space' relies heavily on the score matrix. In order to evaluate the influence of different score matrices, the function AUC  denotes the area under ROC curve of the statistical measure based on the k-word frequencies of protein 'sequence space' that is built based on the score matrix. The MAUC values of all the statistical measures based on ten score matrices for three data sets are presented in Figure DAUC for the data CK, RS and SP are similar. For BLOSUM score matrix, BLOSUM40 and BLOSUM100 perform better in improvement of the statistical measures' classification abilities. As for PAM score matrix, PAM120 or PAM250 improves the classification ability of all the (dis)similarity measures on the high redundant data more obviously, except for the measures eu.k and gre.k. PAM40 or PAM80 contributes to improve the classification ability of the (dis)similarity measures more obviously on the less redundant data.where Gdis.k is a statistical distance measure, it is further tested to analyze phylogenetic relationships. Given a set of protein sequences, their phylogenetic relationships can be obtained through the following main operations: firstly, the k-word frequencies of protein 'sequence space' are calculated; secondly, the statistical distances are calculated and arranged into a distance matrix; finally, the phylogenetic relationships is obtained by neighbor-joining program in the PHYLIP package  represents the 'normalized probability' (score) that amino acid i can mutate into amino acid j. Let i ℵ j denotes that the amino acids i and j are similar. Usually, two amino acids i and j are considered similar if mij > 0. That is to saySubstitution matrices represent similarity of amino acids, where each entry a being similar to b implies that b is similar to a. But this similarity of amino acids is not a transitive relation. For example, a is similar to b and b is similar to c, but a is not similar to c. Therefore, 20 amino acids are not possibly classified into several similarity classes according to this property.where Ω = {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y}. Note that the substitution matrices are symmetric matrices, i.e., S is similar to A, T and N in BLOSUM62 substitution matrix, so S is the center and they can constitute a star set {S, A, T, N} presented in Figure S, A, T, N} as ℵS = {x | x ℵ S, x ∈ Ω}. With the aid of star set, 20 amino acids can be partitioned into 20 star sets presented in Table We shall bypass the above similarity classes and consider a new star set which is easily to implement. A star set assumes that the properties are known between vertices and center. We can construct a star set including all the vertices and the center, and specifically write the center as the first element of the set to distinguish one set from the others. For example, S is ℵS = {S, A, T, N} according to BLOSUM62 substitution matrix. Given two protein sequences P = p1p2 ⋯ pn and Q = q1q2 ⋯ qn,Our work derives a way to build 'sequence space' with the help of star set. From the definition of star set, we know that each amino acid corresponds a star set. For example, the star set of the amino acid P ℑ Q denotes that the protein sequences P and Q are related. Given a protein sequence s, its 'sequence space', denoted by SPs, is defined as follows:where P is a protein sequence, length(P) denotes the length of the protein sequence P. The protein 'sequence space' can be constructed as follows: for each protein sequence, beginning with the first amino acid, we scan through the protein sequence and substitute the star sets for amino acids at each position, respectively. Thus a special set of protein sequences is obtained, which is denoted as the 'sequence space' of the protein sequence. For example, given a protein sequence s = VCST, the star sets of V, C, S, and T are {V, M, I, L}, {C}, {S, A, T, N} and {S, A, T, N} according to BLOSUM62 substitution matrix, and the 'sequence space' of protein s is {V, M, I, L}-{C}-{S, A, T, N}-{T, S}.where k-word frequencies of 'sequence space' can be computed similarly. A segment of k symbols from a finite alphabet, A with 20 letters, is designated a k-word. The set Wk =  consists of all possible k-words that can be extracted from protein 'sequence space', and has Y elements, where Y = 20k. The count of k-words in protein 'sequence space', denoted by k-wide and scanning through the protein 'sequence space'. For example, considering the protein sequence s = VCST, its 'sequence space' is {V, M, I, L}-{C}-{S, A, T, N}-{T, S}, we can get a vector of 2-word countsOnce the protein 'sequence space' is built, the k-word frequencies of protein 'sequence space', denoted as Similarly, one can then calculate We first describe the six previous statistical measures for biological sequences.k, compute the frequencies of all k-words in each sequence, and assess the similarity of the two frequency vectors.Many statistical measures for sequence comparison are to fix a short word length X and Y is the Euclidian distance between their k-word frequencies The Euclidian distance is one of the most common dissimilarity measures of biological sequences. The dissimilarity score between two protein sequences f)In order to derive estimation of relatedness from the vector definitions of biological sequences, Stuart et al. (2002) proposed the pair-wise cosine for generating accurate gene and species phylogenies from whole genome sequences.Cosine is a standard measure of vector similarity, and its application for this purpose can be understood intuitively.The above measures explore the use of Euclidean distances and correlations between k-word frequencies representations of sequences. Standardized Euclidean distance takes into account the data covariance structureij] represents the covariance matrix of k-word frequencies. The standard Euclidean distance forces cov  = 0 for i ≠ j. Therefore, in this distance measure the correlations between different k-words are ignored and only the same k-word variances are accounted for. The standard Euclidean distance was first proposed for sequence comparison by Wu et al. (1997).where S = , ∀ xi ∈ , λi > 0 , Proof: Let x0 ∈ . We expand f(x) around x0, and by Taylor's theorem, we have thatf" (ξ) > 0. Thus we haveSince f(x) is a real convex function f in its domain , xi ∈ , we can obtain thatFor all λi, we haveMultiplying the above inequalities with Summing the above inequalities,Thus, we obtain thatProposition 1. ∀ x, y > 0,Proof: Let f(x) = xlnx, x > 0, we calculate f'(x) and f"(x),f(x) is a real convex function.Thus According to Lemma 1, we haveThenand k-word frequency vectors of protein sequences X and Y, respectively, we define similarity score, denoted by ss.k, as follows:If whereProposition 2.Proof: Firstly, we need to show thatCase 1: Case 2: The entry of Case 3: To find its maximum, we use the Proposition in Method section to get thatTheorem 1. The statistical measure Gdis.k is a distance metric.Proof: Again, by definition ss.k and Proposition 2, we can obtain that it satisfies two important mathematical properties: (1) positivity: Gdis.k ≥ 0 and Gdis.k = 0 ⇔ Gdis.k = Gdis.k. We now need to show that Gdis.k ≥ 0 satisfies the triangle inequality:Case 1: k-word frequency vectors, two vectors are equal. Without loss of generality, assume Case 2: Among three ss.k and Proposition 2, we haveCase 3: SinceThusThe Chew-Kedem data set. The protein sequences used in Chew-Kedem data with the accession numbers of PDB.Click here for fileThe Rost-Sander dataset. The protein sequences used in Rost-Sander data with the accession numbers of PDB.Click here for fileThe Sierk-Pearson data. The protein sequences used in Sierk-Pearson data with the accession numbers of PDB.Click here for fileThe protein data used in phylogenetic analysis. The protein sequences used in phylogenetic analysis with abbreviated names, full names and Accession numbers.Click here for file


Whole PPIs, called the 'interactome', have been emerged in several organisms, including human, based on the recent development of high-throughput screening (HTS) technologies. Individual PPIs have been targeted by small drug-like chemicals (SDCs), however, interactome data have not been fully utilized for exploring drug targets due to the lack of comprehensive methodology for utilizing these data. Here we propose an integrative in silico screening system comprises three independent assessment procedures: i) detection of protein domains responsible for PPIs, ii) finding SDC-binding pockets on protein surfaces, and iii) evaluating similarities in the assignment of Gene Ontology (GO) terms between specific partner proteins. We discovered six candidates for drug-targetable PPIs by applying our in silico approach to original human PPI data composed of 770 binary interactions produced by our HTS yeast two-hybrid (HTS-Y2H) assays. Among them, we further examined two candidates, RXRA/NRIP1 and CDK2/CDKN1A, with respect to their biological roles, PPI network around each candidate, and tertiary structures of the interacting domains.Our novel in silico approach for discovering candidates for drug-targetable PPIs was applied to original human PPIs data. The system excludes false positive interactions and selects reliable PPIs as drug targets. Its effectiveness was demonstrated by the discovery of the six promising candidate target PPIs. Inhibition or stabilization of the two interactions may have potential therapeutic effects against human diseases.An integrative  L for inducing apoptosis in tumor cells  in RXRA and a fragment of the PB064381 domain containing LXXLL motifs in NRIP1 . Figure 3, and the ratio of hydrophobic residues in the pocket is 50%. It is highly probable that CDK2/CDKN1A complex has a tertiary structure not nearly identical but similar to CDK2/CDKN1B complex, and that CDKN1A binds to CDK2 in a similar mode to CDKN1B [We identified domain-domain interaction between the Pkinase domain [Pfam:PF00069] in CDK2 and the CDI domain [Pfam:PF02234] in CDKN1A  of proteins such as membrane receptors and enzymes . Further-helices ,56. Thes-helices , and thu-helices ,5.in silico system can select more reliable interactions as drug targets by excluding spurious interactions via the three independent assessment procedures. PPI data used in the present study were obtained from our HTS-Y2H assays. In general, the false positive rate of HTS-Y2H methods has been believed to be higher than that of other physical, genetic, biochemical, or immunological methods for experimental detection of PPIs, mainly due to 'sticky' proteins that non-specifically interact with various proteins [in silico system, however, can exclude these spurious interactions, because, in these cases, similarity scores for GO-term assignment are not statistically significant in the cellular component category. Therefore, our approach should be widely applicable to PPI data even if a number of false positive interactions are included.Our proteins . While aproteins ), we do In vivo and in vitro validation process of PPIs as drug target, it is more desirable that a researcher can obtain as much information as possible on biology of the PPIs. Since PPIs annotated too little are considered as difficult target in this respect, our system does not select the PPIs in this study. More accumulation of GO annotation will help us select therapeutically important target PPIs that are annotated too little by GO terms at present.Our approach has some advantages described above, but some issues should be noted for further refinement of the approach. For more careful assessment of domain detection, we did not identify interacting partner domains when bait and/or prey fragments have multiple domains, so long as a domain pair was not registered in the public domain-domain interaction databases. However, a large number of human proteins are multi-domain ones, and this is also the case in the bait (> 60%) and prey (> 45%) fragments used in the present study. Several computational methods have been developed in recent years for predicting interacting partner domains from large amounts of experimental PPI data -80. Applin silico system can be further expanded for more precise assessment of candidates for drug-targetable PPIs if other computational methods are incorporated. These methods include the prediction of interaction interfaces on protein tertiary structures, the prediction of disordered regions, and the evaluation of similarities in the expression patterns of messenger RNAs encoding the two interacting proteins in every tissue/organ. In the case of RXRA/NRIP1 and CDK2/CDKN1A, it is fortunate that the interaction interfaces have been well studied by biochemical and immunological approaches [Our proaches ,55,66, aproaches -88 are rproaches  recentlyin silico drug design, although there are various other types of drugs, including peptides, antisense RNAs or DNAs, aptamers, and antibodies. Candidate target PPIs for each type of drugs, as well as small chemical drugs, will be selected by adopting distinct criteria based on the three (or more) independent in silico investigations in our system. For example, to select candidate target PPIs for antibodies, one can adopt criteria so that i) at least one tertiary structure of the interacting domains is known, ii) the interacting domain has an interaction interface predicted to be recognized by antibodies, and iii) the interacting proteins share identical GO terms such as 'extracellular' in the cellular component category and have expression patterns similar to each other.We focused on discovering drug targets for SDCs based on the idea of the structure-based in silico approach for discovering candidates for drug-targetable PPIs in interactome data. The system excludes false positive interactions and selects more reliable PPIs as drug targets. The application of our system to original human PPI data demonstrated its effectiveness by discovering the six promising candidates for drug-targetable PPIs. Advances in HTS technologies for detecting PPIs and the accumulation of high fidelity PPI data in the near future will enable our system to facilitate the more comprehensive exploration of drug-targetable PPIs.In this paper, we propose a novel and integrative etc), those of the Signal Transducer and Activator of Transcription (STAT) family , homeodomain proteins , and zinc-finger proteins . Preys used in the assays were prepared from cDNA libraries derived from various cell lines . Our HTS-Y2H method uses sequence fragments as baits, and preys isolated with the baits are also sequence fragments. This enables us to identify protein domains responsible for PPIs because it is highly probable that protein domains included in the bait or prey fragments are involved in the interactions between the two fragments. Full details of our HTS-Y2H method, including experimental materials and conditions, will be reported elsewhere in near future.The PPI data analysed in the present study consists of 770 binary interactions between human proteins. The data were produced by our HTS-Y2H assays supported by the Genome Network Project from the Ministry of Education, Culture, Sports, Science and Technology of Japan. See Additional file E-value < 0.1 for each detected domain). If the sequence length of a detected domain included in a fragment was < 10 residues, the domain was excluded in the following studies. To check whether a domain pair has been known or predicted as interacting partner in previous studies, all combinations of domains between bait and prey fragments were searched for the public domain-domain interaction databases, iPfam [All domains in the bait and prey fragments were retrieved from the Pfam (version 20.0) database  using ths, iPfam , InterDos, iPfam , and DIMs, iPfam .E-value < 0.001). We considered the fragment to have a tertiary structure nearly identical to the chain, when a bait or prey fragment had sequence identity of ≥ 90% and query coverage rate (length of query sequence showing the identity/full length of the query sequence) of ≥ 90% to a chain in a PDB entry, and if the sequence length showing the identity was ≥ 50 residues. If no nearly-identical tertiary structure was detected for a fragment, the fragment was further searched in the PDB database using the program PSI-BLAST (version 2.2.13) [Using amino acid sequences of the bait and prey fragments as queries, we searched the PDB database  (the ver 2.2.13) . The defv) of a detected pocket was within the range of 150Å3 <v ≤ 2000Å3; ii) in that of MOE Alpha Site Finder, a) the number of atoms comprising the side chains of the amino acids inside the pocket was ≥ 37 or b) the number of hydrophobic atoms inside the pocket was ≥ 22.The search for pockets on protein surfaces was performed for the bait and prey fragments showing high sequence identity (≥ 90%) to a chain in a PDB entry. We used two programs, CASTp  and MOE Si) between a protein pair i is calculated asBased on GO terms assigned to two proteins from which the bait and prey fragments were derived, we evaluated similarities between fragments by counting the number of shared identical GO terms. GO terms assigned to the proteins were retrieved from the QuickGO database  using thLj is the jth level of GO hierarchy  to a specific term (Lj > 1)) and nij is the number of shared identical GO terms in the jth level between a protein pair i. We calculated the scores for the three GO categories; cellular component (SiC), molecular function (SiF), and biological process (SiP).where Statistical significance of the similarity scores was evaluated on the basis of frequency distributions of scores calculated for PPI data composed of 10,000 random pairs of human proteins (see Additional file PPI, protein-protein interaction; HTS, high-throughput screening; SDC, small drug-like chemical; GO, Gene Ontology; HTS-Y2H, high-throughput screening yeast two-hybrid.NS conceived of the study, carried out the studies on domain detection and gene ontology, and drafted the manuscript. KI and TTashiro carried out the protein structure and pocket studies. ST, JO, YI, AS, AT, HN, TTakeda, and TI designed and carried out the HTS-Y2H assays. SK and YS conceived and supervised this study. All authors read and approved the final manuscript.Full results of our analyses of the previously-investigated target PPIs. This file lists the previously-investigated target PPIs and summarizes the full results of domain detection, finding SDC-binding pockets, and evaluating similarities in GO-term assignment.Click here for fileFull results of our analyses of original human PPI data. This XLS-format file lists original human PPIs analysed in the present study and summarizes the full results of domain detection, search for nearly identical tertiary structures and finding SDC-binding pockets, and evaluating similarities in GO-term assignment.Click here for fileFrequency distributions of similarity scores for GO-term assignment calculated for random protein pairs. This file contains a figure illustrating frequency distributions of similarity scores for GO-term assignment calculated for PPI data composed of 10,000 random pairs of human proteins.Click here for filePPI network of original human PPI data. This file is an original version of the PPI network in Figure Click here for file


We want to hear your view of the science being presented.The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the largest meeting of the International Society for Computational Biology (ISCB). In 2010 it was held in Boston, United States, July 11–13. What follows are four conference postcards that reflect different activities considered exciting and important by younger attendees. Postcards, as the name suggests, are brief reports on the talks and other events that interested attendees. You can read more about the idea of conference postcards at Many outstanding talks were given at the ISMB 2010 conference, and the work presented by Dr. Robert F. Murphy during the second day of the conference was certainly one of them. Murphy, who is a professor at Carnegie Mellon University, presented a tool called PatternUnmixer The method presented by Murphy aims to characterize such mixed patterns of fluorescence. It involves several steps, starting with the extraction of all the objects detected in a set of images for a single probe, which constitutes one training set. The features of the objects—such as their size, shape, and distance from the nucleus, for instance—are then collected. These features are used to classify every individual object into categories called “object types” that are defined for a cell type in a given set of conditions and for one probe. Once the object types are defined for all the probes (and their training sets), the tested set of images—which contains mixed patterns—is then investigated. The proportion of signals resulting from each pattern is estimated based on the distributions acquired from the training sets. As such, it means that one can “unmix” the patterns of fluorescence of a protein located in two  compartments.To give a proof of the concept, Murphy and his team used fluorescent trackers for mitochondria and lysosomes in HeLa cells and generated a collection of images. They used these two probes to define “pure” patterns and a combination thereof in order to obtain mixed patterns, using determined concentrations of trackers. In other words, by training their method to identify objects marked by two different trackers , they were able to unmix the two signals in cells with mixed labeling. The proportions of signal belonging to lysosomal tracker or mitochondrial tracker were found to be in good agreement with the expected fractions. This suggested that the method is effective in “unmixing” fluorescent patterns.Using this approach, Tao Peng and colleagues were then able to estimate the accumulation of LC3 (a microtubule-associated protein) in autophagosomes upon bafilomycin treatments  in RT112 cells. In this experiment, the fluorescence of eGFP-LC3 was monitored, and the two training sets corresponded to either untreated or treated cells at a high dose of BAF Overall, I found that this talk was a good illustration of how experimental biology can benefit from computational approaches. The ISMB conference encourages the development of advanced computational methods that resolve biological problems, which I believe was also exemplified by this talk. My personal feeling is that the work presented by Dr. Robert F. Murphy constitutes an important step in the high-throughput collection and analysis of protein localization data at the system level. I think that this method should not only be regarded as interesting for experimentalists who want to quantify and assign protein localization objectively, but also as a starting point to generate new testable hypotheses for computational biologists who aim to understand the impact of protein localization on system-level properties. For instance, the following questions could be addressed: what is the global extent of protein translocation upon varying conditions? Or in distinct genetic backgrounds? Can one identify recurrent spatial or temporal patterns of movements  of proteins? Are there common functions, properties, or sequence motifs in proteins with similar translocation pattern? How variable are protein localization patterns at a single-cell level? Are there distinct trends in the gene expression regulation of proteins whose localization is highly dynamic compared to proteins clustered in restricted subcellular locations?Many—if not all—of the current challenges in molecular and systems biology boil down to the generation of data that are not only wide in scope , but that are also sufficiently accurate and reproducible to establish robust interpretations. There is consequently an increasing need for automated approaches to collect information with high complexity as systems biology progresses in linking information from genotype to phenotype. Recent progress brought by next-generation sequencing keeps improving the accuracy and the depth of our knowledge on genotypic information. However, our ability to accurately and quantitatively collect and describe phenotypic information still suffers a lack of automation. This is precisely what makes the current developments in high-throughput microscopy and computational methods for image analysis very exciting. In this regard, the fast acquisition of protein localization is of great importance, as this is an important aspect to characterize the phenotype of a cell.In short, the meeting of cell biology and systems biology can fuel many applications and open new paths of exploration for computational biologists. This talk was a good indication of how we can get there, provided an intense interaction between the cell biology and computational biology communities takes place.http://www.pdb.org/) do not always give us the whole story. The talk given by James Fraser  at 3DSIG 2010, a satellite meeting of ISMB 2010 that focuses on structural bioinformatics and computational biophysics, did exactly that—it reminded us that there is more to crystal structures than meets the eye. Fraser talked about “the effect of temperature on polysterism in protein crystals”, or in other words, about multiple conformational ensembles that can hide behind the seemingly static view of protein crystal structures.Crystal structures are of fundamental importance to biological research in general and to computational research in particular. Because cellular functions are mostly carried out by proteins in the three-dimensional (3-D) world, and because 3-D structures give us a direct visualization of that world, crystal structures are often perceived as a gold standard when it comes to deciphering protein function. Yet, once in a while, we are reminded that the beautiful 3-D structural models in the Protein Data Bank  intrinsically unstructured proteins that, when monomeric in solution, do not have a fixed 3-D structure NatureIn a recent paper published in A subsequent paper from the Alber lab described the automated method used to identify alternative side-chain conformation, which they named Ringer In the original 2009 study, Fraser et al. observed polysterism in CypA when the structure was solved at ambient temperatures but not when the structure was solved at cryo-temperatures. This observation has far-reaching implications, because nowadays the vast majority of crystallographic diffraction data is collected at cryo-temperatures. The underlying assumption is that such low temperatures result in higher quality data without a significant effect on 3-D structure. To check this assumption, Fraser applied an extended version of Ringer to the electron density maps of a hand-picked dataset of 30 protein pairs—crystal structures of the same protein, solved at both cryogenic and ambient temperatures. This comparison revealed that local temperature-dependent structural differences such as those observed in CypA are also observed in other crystal structures. Furthermore, structures solved at cryo-temperatures were more compact—their volume shrunk by up to 6% and their interior cavities contracted. When Fraser looked at individual residues, he saw that ∼10% of these showed structural differences. These differences included altered minor side-chain states, switching of minor and major states, or sampling of a dissimilar side-chain orientation altogether. In short, Fraser's data casts doubt on the prevalent assumption that a structure determined at cryo-temperatures is equivalent to the structure of the same protein that is determined at room temperature.http://bcb.med.usherbrooke.ca/3dsig10/Program.html). In my opinion, this talk stood out among other 3DSIG presentations in its elegant fusion of experiments and computations, and discussions on the implication of these results for the use of crystal structures in computational research continued well into dinner. The evidence of polysterism, hidden within crystallographic electron density maps, and the effect of temperature on these alternative conformations add to a larger picture—that an assumption of “one sequence equals one 3-D structure” can lead to the loss of important structural and functional information.Fraser gave a riveting talk that was very well received by the diverse audience that attended the 2-day 3DSIG satellite meeting at ISMB 2010 . As the name of the meeting suggests, 3DSIG attracts a wide range of scientists whose research connects to 3-D structures  with the Barton group's findings to let the scientific community explore the predictions that have been made with their system. More specifically, their PIPs framework uses a naïve Bayesian method to combine the predictive capabilities of numerous features to calculate the likelihood of interaction between two proteins. Their predictor uses features such as co-expression, orthology, domain co-occurrence, post-translational modification, and semantic similarity of Gene Ontology terms. They developed two modules that make predictions based on the topology of the predicted PPI network, and several of their predictions have been experimentally validated by external groups. Moreover, they made their predictions available at the PIPs Web site (http://www.compbio.dundee.ac.uk/pips/) so the scientific community can explore them. Users can search with a protein identifier  or a keyword. All predicted PPIs are returned in order of their likelihood of interaction. The Web site allows the user to analyze the evidence used to calculate the likelihood of interaction and provides links to external databases and publications to retrieve the source data.One of the outstanding posters at ISMB was the “PIPs: Human Protein-Protein Interaction Prediction” poster by Mark McDowall (University of Dundee). His work involved using computational techniques to predict new protein–protein interactions (PPIs) as well as developing a PPI Web site  and mass spectrometry help to determine protein interactions. But these methods suffer from high false positive rates, and many protein interaction predictions are not shared among them. For example, of the reported interactions from Y2H in yeast, around 70% are estimated to be false positives. Moreover, only around 3% of the protein interactions are supported by more than one high-throughput method The poster was outstanding because it demonstrated so fully one of the primary themes of the conference, “using computational tools to leverage existing datasets to drive biological discovery”. The work originates from computational analysis of existing data, and then leads to experimental validation of computational predictions, and useful biological insights.I attended the 18th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB 2010) at the Hynes Convention Center here in Boston. We had a great day at the Student Council Symposium (SCS-6) organized by the ISCB Student Council. We started off the session with something called “speed dating”, which was a whole lot of fun. There were about 60 students from all corners of the world who got a chance to interact spontaneously with each other.Speed dating is an event where each person has a colored bracelet on their wrist. There were 5-minute intervals when two people wearing a different color bracelet could interact and get to know about each other's research interests and tell a little bit about their background. Then the whistle goes “shriek” and you are off to talk to another person with a different colored bracelet. When I first heard about the idea, I have to admit I was very skeptical and wondered “what could be accomplished in 5 minutes?” But I was totally surprised by the energy it brought to the event for the whole day. We were no longer strangers from different continents. We felt we knew a lot of people who were sitting with us in that conference hall who shared similar interests.It was a fun activity, where people were talking as fast as they could about where they came from, what work they were currently doing, and so on. Everyone was in a big hurry to finish saying what they wanted in 5 minutes if they did not intend to let the other person talk or, in 2.5 minutes, if they were willing to let the other person say something. We do have to give the other party a chance to say something too, right? But some people got carried away, while others did not feel like interrupting. This experience built social skills in learning how to present your work in a concise manner, being conscious of giving the same opportunity to the other person as one would like to have, how to get a word in when the other person does not seem to be aware of your needs to share your research, and a chance to size up their personality in a few minutes. These are essential skills one should develop to be successful in life. Since 25 to 30 conversations were going on at the same time, there was this big buzz that got louder and louder, of course! Amazingly, people took to it like fish to water, since the organizers did not have to do much in the way of facilitating it other than giving initial instructions and blowing the whistle every 5 minutes. A lot of planning and preparation went into it of course, but it was so seamless that it appeared so easy, thanks to the organizers who carried it out to perfection.In the short time available, I was able to meet students who were from Korea working in the US, from China working in the United Kingdom, a French guy working in Germany, and so on. It was a lot of fun; it was exciting, exhilarating, and exhausting, as if I really walked all those miles around the world! You had a sort of anticipation about whom you were going to meet and from which corner of the world. I was simply amazed at the variety of people I talked to in such a short time. It broke the ice, led to instant and new friendships, and swept away the inhibitions you have in approaching someone for the first time and bragging about your research, which I am sure everyone likes to do! It will hopefully lead to a few collaborations, if the attendees had a follow-up meeting during the conference or have one later, perhaps at another meeting. I felt that I would not have met any of the researchers I chatted with, were it not for this speed dating event.Later on at the business meeting of the Student Council, Burkhard Rost, ISCB President, was excited to hear about the speed-dating event and asked if this event could be arranged on a larger scale for 1,000 people at the next ISMB conference. So, for those who are planning to attend, get your 5 minute pitch ready—or better still, a pitch for 2.5 minutes to share your experiences.So, was the event a success? I think it was, but there is always room for improvement. But let us hear from the participants themselves. This is what some of the participants had to say about the speed dating event (in the order in which I spoke to them):Fadi Towfic from Iowa State University, US: “A great way to meet a lot of people in a very short time…But the time was too short to find what the other person was doing in their research area…it was a very good experience though.”Shweta Shah from Carnegie Mellon University, US: “It was a great experience and you get to meet a lot of new people whom you will not meet otherwise…I would like to do something like this in future conferences.”Adriana Munoz from University of Ottawa, Canada: “I think it is a very good idea and it was interesting to meet people. It made it easy for me to meet others…Great.”Jelle ten Hoeve from Netherlands Cancer Institute, The Netherlands: “I really liked it…I met three very interesting people and the funny thing was I heard all about them, but I could not get to talk about myself… It is perfect though.”Bastian Van den Berg from Delft University of Technology, The Netherlands: “Very nice and easy way to get in touch with PhD students…I was able to explain my project to other students.”


A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.We find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.Given high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN. Protein-protein interaction networks (PPINs) derived from high-throughput studies are known to have many errors ,2. Data Not all interactions occur at the same place and time in all cellular states. This implies that representing a PPIN as a set of binary protein-protein interactions (PPIs) is often incomplete . InsteadPPI-SDDI-PPI triangle network motifs. Triangle network motifs integrate high-throughput PPINs with complementary knowledge, such as structural data, to account for missing edges [PPI-SDDI-PPI triangle network motifs integrate:It was proposed that triangle network motifs represent the basic building blocks of PPINs -32. In tng edges ,33-38. O• PPINs from high-throughput experimental studies, which have considerable coverage but also errors, and• SDDIs that are known to physically mediate PPIs and may be missing in PPINs -49.theme encompasses several PPI-SDDI-PPI triangle network motifs with one SDDI edge as their common organizational principle. Figure A The purpose of PPI-SDDI-PPI triangles is to support revealing biological insights, such as finding complexes of physical interactions occurring at the same time and location -55. BesiE. coli and C. elegans transcriptional network that subgraphs matching two types of transcriptional regulatory circuit triangle – feed-forward and bi-fan – overlap with one another and form large clusters [A rationale for triangles and themes is the observation that proteins with common interaction partners are likely to have common functions -65. Secoclusters ,74-76. Aclusters ,78. Thisclusters .Figure This paper is organized as follows. Next, we present related work on finding errors in PPINs via motifs of interconnection patterns. Then, we present the results on prediction of true positive complexes using triangles. We illustrate this with an example of myosin-actin related activities. Next, we explain the biological basis for triangles: a model for SDDIs that explains the functional similarity of second-level neighbors in PPINs. Finally, we conclude the paper with an outlook of using other data sources to complement interactomes.Several papers aim to find errors in PPINs by completing them for missing edges or finding false positives -83. Our A first category of work involves collecting ensembles of data, such as structural or literature information. Alber et al. (2007)  collect Another large body of work attempted to predict the missing interactions or assign confidences to large noisy interactomes. Some of these use network topology and others use information on SDDIs, while others use Bayesian networks or probabilistic measures. Yu et al. (2006)  describeWe have also experimented with using GO similarities in our approach. Chinnasamy et al. (2006)  present S. cerevisiae. Vidal and colleagues [Another body of work has performed large scale analysis of networks, statistical network motif analysis or error estimation, which is of interest for our work as well. Jin et al. (2007)  use netwlleagues ,100 usedSeveral papers used clustering or graph theoretic methods to predict complexes in PPINs. Bader et al. (2003) detected complexes as highly connected subgraphs . AndreopIn our experiments, we employ three high-throughput PPINs, derived by affinity purification followed by mass spectrometry (AP/MS). Krogan06 is based on . Gavin06complexes. We found Gavin06 ∩ MIPS has 305 overlapping edges, Krogan06 ∩ MIPS has 359 overlapping edges.The biological motivation for our work includes low overlap of high-throughput PPINs with known complexes. We compared the overlaps of two high-throughput PPINs, the Gavin06MATRIX and Krogan06 networks, with the MIPS protein complexes dataset. Table Gavin06MATRIX and Krogan06 had thousands of edges connecting these same proteins, which were not in MIPS. Figure Given the many false negatives (missing interactions) and false positives (wrong interactions) in protein-protein interaction networks (PPINs) derived from high-throughput experiments, we evaluated the success of triangle network motifs and themes in finding known MIPS complexes. With structural domain-domain interactions (SDDIs) representing binding evidence on proteins, PPI-SDDI-PPI triangle network motifs are likely to reflect true complexes. To evaluate this, we examined the overlap of triangles from Gavin06 and Krogan06 with MIPS complexes. For the common proteins we evaluated the interactions that are true positives (overlap) or false positives (no overlap) with MIPS.The first row of table Table We added to PPINs other complementary datatypes, besides structural SDDIs, to form triangles: PubMed literature co-occurrences of protein mentions, and Interpro Pfam domain co-occurrences in PPIs  (see metTable Type I myosin motor proteins (MYO3 or MYO5) have distinct but overlapping functions in multiple cellular processes and locations . Figure MYO3 is one of two type I myosins, which utilize the cytoskeleton for movement, moving along microfilaments through interaction with actin. Deletion of MYO3 causes severe defects in growth and actin cytoskeleton organization . BesidesNext, we explore whether triangle network motifs and themes in Gavin06MATRIX can help reconstruct distinct myosin-actin pathways for cellular localization of biomolecules.Figure RSR1, BNI1, GEA1 play a role in cytoskeletal actin localization ,114. TheWhat is of special interest in this example is the intersection of the neighborhoods of RSR1, ARF2, BNI1 comprising EF1A-RL3, which were previously observed to have a functional significance for F-actin localization . In addiOverexpression of GEA1 or GEA2 was observed to bypass the requirement for profilin in actin cable formation . ProfiliFigure TBA1/RAP1 play a role in nucleus transciption from RNA polymerase II promoter. TBA1/RAP1 is a DNA-binding protein involved in either activation or repression of transcription, depending on binding site context; it also binds telomere sequences and plays a role in telomeric position effect (silencing) and telomere structure. In Figure Figure ARF2, EF1A, IMDH3 play a role in mRNA localization for translation. ARF2 is an ADP-ribosylation factor involved in regulation of coated formation vesicles in intracellular trafficking within the Golgi . In FiguEF1A: Translation elongation factors are responsible for two main processes during protein synthesis on the ribosome [• ribosome . EF1A , since the bait-prey technologies used will cause structurally connected proteins to be detected as prey for similar bait protein(s). Of course this only holds for proteins that are detectable as prey . A SDDI Figure highly connected components. To assess whether the size of the connected SDDI components that overlap with PPINs is significant, we compared the connected SDDI components to randomly selected SDDIs from the SCOPPI database [Table database . We perfx-axis, one would have predicted the true positives for about half of the proteins in the PPIN. However, one would still need to use many SDDIs with the smallest themes, to find all true positives in the Krogan06 PPIN. Therefore, although about half of the true positives could be found with no more than 100 SDDIs, one would need significantly more SDDIs to find all true positives involving all proteins.Figure SDDIs and the PubMed co-occurrences relate to two different aspects. SDDIs are based on experimental results that are likely to imply a structural interaction. In the case of SDDIs, we can use all information found by mapping structural domains to proteins using BLAST sequence similarity and still get good prediction accuracy. On the other hand, for literature we have to apply a strict filtering, keeping only the top 1% of protein co-occurrences appearing in PubMed as complementary data. We observed that the literature co-occurrences appear to give slightly better results than using SDDIs as complementary data. The main limitation of SDDIs at present is the sparsity of known structural interactions. Since PubMed is expected to grow faster than structural knowledge, using literature co-occurrences might give even better prediction accuracy in the future, as long as a strict cut-off is set.With the amount of PPINs from high-throughput experiments, structural data and literature-based interactions on the rise, we studied their combined ability to predict known complexes. We found a low overlap of PPINs derived from high-throughput studies with known complexes, as well as low overlap with structural domain-domain interactions.We proposed PPI-SDDI-PPI triangle network motifs as a model for analysing PPINs and predicting complexes. PPI-SDDI-PPI triangles have higher overlap with MIPS complexes than random second-level neighbors, indicating that structural SDDIs are useful for complementing PPINs in triangles to create a more complete picture of protein cellular involvement. We complemented PPINs with several other datatypes besides SDDIs to create triangle and theme motifs, resulting in similar overlaps with complexes. Themes of PPI-SDDI-PPI triangles helped us to reconstruct complexes in myosin-actin processes that were not detected by PPINs. Our approach is useful for finding true positives in PPINs, as structural knowledge on proteins increases in the future.SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. A SDDI may cause a structurally connected pair of proteins to be observed with common interaction partners in high-throughput affinity purification experiments followed by mass spectrometry (AP/MS) that use bait-prey technologies. We examined why some SDDIs are detected in PPINs, and we found that SDDIs detected by PPINs are part of highly connected components/complexes, therefore they are more likely to be detected by experimental studies.In this section we give an overview of the methods used in this study. Figure complementary datatype edges. For extracting second level neighbors in large networks we used the HIERDENC algorithm, described in [A ↔ B and B ↔ C, while a CD edge A ↔ C exists, such as a structural SDDI. In a PPIN second level neighbors (a pair of PPIs) may be involved across cellular space and time in different processes and locations. Connecting second level neighbors to each other via CD edges gives confidence that the second level neighbors interact at the same cellular space and time [PPI-CD-PPI triangles contain three proteins connected by two PPIs and an edge of a complementary datatype (CD), such as a structural SDDI; in this case, we refer to PPI-SDDI-PPI triangles, as Figure ribed in ,135. Figand time -138. Triand time ,140.σSDDI denote the number of PPI-SDDI-PPI triangles a structural SDDI is involved in. A structural SDDI may be involved in σ ≥ 1 triangles, which we refer to as a theme. A theme is given by the σ common interaction partners (intersecting neighborhoods) of a SDDI's protein pair, and some PPIs in a theme may be False Positives.Let s ≥ 30%. In addition, we required 75% of the domain to appear in the protein.As structural information to complement PPINs, we used the SCOPPI database, which contains SDDIs observed in known protein complex structures . To assiOther complementary datatypes (CD) edges we used included The Genomic Threading Database (GTD) . GTD conp1 and p2 co-occur frequently in PubMed documents: The next CD dataset we used was PubMed literature co-occurrences of protein mentions. To extract these, we used the GoPubMed protein mention extraction algorithm to assign proteins to all PubMed documents . Then, wInterPro . Then, wWe use two yeast PPINs that we denote as Gavin06  and KrogMoreover, for our illustrations we manually curated three network examples from the literature, representing myosin-actin involvement in cytoskeleton organisation, nucleus transcription, and mRNA translocation. Developing these networks involved reading papers from the biomedical literature and recording any interaction(s) described in the articles.It is likely that a PPI is not physical, but a false positive, which may be detected by a GO similarity of zero. PPIs with a GO similarity of zero hint at false positives. For calculating the similarity based on Gene Ontology terms, we searched for GO terms in the current abstract and compared them to the set of GO terms assigned to each gene candidate. For each potential tuple taken from the two sets (text and gene annotation), we calculated a distance of the terms in the ontology tree. These distances yielded a similarity measure for two terms, even if they did not belong to the same sub-branch or were immediate parents/children of each other. The distance took into account the shortest path via the lowest common ancestors, as well as the depth of this lowest common ancestor in the overall hierarchy . The diA and B, where A and B are matrices or vectors of the same size. A matrix entry contains a measure of Gene Ontology similarity (0 – 1) for a protein pair involved in a PPI or SDDI. We used the matlab corr2 correlation coefficient:We computed the correlation coefficient between  or We implemented the HIERDENC online database, which contains all of the datasets we used. HIERDENC helps a user to visualize and find true positives in PPINs via triangles of high-throughput PPINs and complementary data. All authors read and approved the final manuscript. BA planned the paper, carried out most of the experiments, wrote the software, wrote the python scripts for making the networks, built the manually curated networks, and wrote most of the paper. CW helped in conceptualising the paper with discussions and provided the complementary data. DL helped in formulating the paper with discussions. MS supervised the work and contributed discussions and ideas.An excel file with Uniprot accession numbers for all protein names used in the text.Click here for fileManually curated network example from the literature, representing myosin-actin involvement in cytoskeleton organisation.Click here for fileManually curated network example from the literature, representing myosin-actin involvement in mRNA translocation.Click here for fileManually curated network example from the literature, representing myosin-actin involvement in nucleus transcription.Click here for fileMIPS complexes dataset.Click here for fileA script for converting MIPS complexes to a SPOKE model network.Click here for file


With the increase in genomic and proteomic data from genome sequencing projects and structural genomic initiatives, we are faced with an increasing number of sequences and structures in various databases annotated as “uncharacterized,” “hypothetical,” or “unknown function” Biological function can be inferred at different levels depending on sequence identities that exist between the sequences. The success of functional inference, however, depends on the availability of experimentally validated information of related proteins. This relatedness may be at the full-length protein level, domain level, structural level, or motif level. Depending on the type and level of similarity, specific or general functions can be propagated. In fact, it has become widely accepted that percent identity is more effective at quantifying functional conservation than any other scores or means The main objective of this article is to define a ten-step procedure  guided bAquifex aeolicus (UniProt ID O67940_ AQUAE) We will describe the ten-step procedure using an example of an uncharacterized conserved bacterial protein from The analysis performed and results shown reflect the databases at the time of writing of this paper. Unless otherwise mentioned, default parameters were used. Also, because of limitation in space, we have not included other excellent databases and tools that can be used for this type of analysis. The list of tools and resources included in this paper  were choBased on the percent-identity scale  for sequIn the higher end of this range, say above 70% identity, a widely used practice is to see if the query protein belongs to a protein family that has experimentally characterized members. The concept of protein family based on homology was articulated by Margaret Dayhoff in the early days of sequence analysis For sequences whose identities fall in the lower end, say <70% range, in the absence of end-to-end similarity, a safer approach would be to evaluate domain architectures of these proteins, as these can evolve and exist independently of the rest of the protein chain. The most widely used domain database that provides a comprehensive coverage is Pfam Sequence similarity based on full-length sequences has been used as a guiding principle in many classification databases. While this works quite well for closely related sequences whose sequence identities are greater than 50%, it begins to fail for sequences that are related at the three-dimensional structural levels rather than at sequence levels Examination of a protein's structural neighbors and fold comparisons can reveal distant evolutionary relationships that are otherwise undetectable and, perhaps, suggest unsuspected functional properties. Just as proteins with end-to-end similarities may be evolutionarily related, structures with similar folds may also be related. Data resources that provide structural comparisons include Vector Alignment Structural Tool (VAST) Analysis of sequence/structural motifs becomes valuable especially for cases where the overall percent identity goes below 30% for functional inference. These functional motifs/sites form stable units and are evolutionarily conserved relative to the remainder of the protein. Their identification is important in the assignment of protein names and accurate propagation of structural and functional site annotations AQUAE from Aquifex aeolicus is currently annotated as an “uncharacterized conserved protein” in UniProtKB We propose a ten-step procedure  that canAQUAE is blasted against NCBI's non-redundant protein database (nr) in order to retrieve all its related sequences  and 1RQP from Streptomyces cattleya (UniProt accession Q70GK9) are functionally characterized as chlorinase and fluorinase, respectively The amino acid sequence of O67940_ equences , top. Reequences , bottom The results of the BLAST run  of queryResults obtained from the steps so far are not conclusive to determine if the query is a chlorinase or a fluorinase. In this step, we will attempt to see if the query protein belongs to any well-annotated protein and domain families or if the protein has any specific identifiable sequence pattern. The results of scanning the candidate protein against family databases PIRSF and COGS are given in Similarity between related sequences at either the sequence or structural levels may give important clues about their functions since it may be a consequence of functional or evolutionary relationships. Results of the structural searches using the SCOP database is presented in This becomes an important step especially for sequences whose percent identity falls below 30%. Since our query does not have a structure, 2Q6O and 1RQP will be used as starting points to get other related structures. Results of the structural searches using VAST is presented in Transfer of annotations from one homolog to another is not always straightforward. To transfer annotation, one has to identify homologs that can be used for constructing multiple sequence alignments and subsequently used for performing phylogenetic analysis to identify orthologs (next step). More often than not, when many paralogs are present, it becomes difficult to identify a true ortholog. This step is to identify homologs based on results obtained from earlier steps. With the increasing number of genomes being sequenced, it is becoming apparent that restricting analysis to high-quality genomes and sequences from model organisms for generating alignments and performing phylogenetic analysis is important.High-quality multiple alignments are a pre-requisite for understanding the evolutionary relationships that exist between homologous sequences. A structure-guided alignment carried out using Cn3D on the structures and sequences obtained from Step 6 is presented in − to generate 5′-fluoro-5′-deoxyadenosine and L-methionine Streptomyces cattleya is also a chlorinase Structures of complexes provide more functional information than uncomplexed structures. 2Q6O, also referred to as SalL, is a trimer with substrate chloride and ligand S-adenosyl-L-methionine (SAM) bound. 1RQP on the other hand is a hexamer (dimer of trimers) with three molecules of the ligand SAM bound. The functional site in these two related structures reside at the interface between the monomers. SAM-binding residues were obtained from PDBSum Mapping the functional residues from 1RQP and 2Q6O  to queryStreptomyces cattleyaSalinispora tropica, it is likely that the query protein O67940_Aquefix is an enzyme that can halogenate SAM with chloride, bromide, or iodide ions. Based on available experimental information, it is not possible to say if the Aquefix enzyme can also use fluorine. Additional supporting experimental data need to be collected before we can conclude the precise specificity of the query.Based on the conservation of the crucial residues that are involved in catalysis, the query is closer to the chlorinating enzyme 2Q6O than the fluorinating enzyme 1RQP. While it is safe to assume that the binding site for SAM is conserved among the members of PIRSF006779 and that all its members bind to SAM and likely are halogenases, it is not safe to assume that all the members are chlorinases or fluorinases. Their specificities may be to a fluoride, chloride, bromide, or iodide. Judging from the alignment and available experimental evidence on bacterial fluorinating (and chlorinating) enzymes in AQUAE. In addition, we have also identified functional residues.By following all the above steps, we have answered one critical question that we set out to answer at the beginning of this tutorial, i.e. the function of O67940_ The main objective of this article was to define a ten-step procedure, largely guided by the percent-identity scale, that can be followed as a general rule for functional inference of an uncharacterized protein. This procedure is by no means exhaustive but can be used as an initial process for functional assignment. In many cases, additional clues and complementary information may be obtained from pathway analysis, operon information, and other non-homology based methods. We have demonstrated how by following the ten steps a function could be assigned for an uncharacterized conserved protein with its related sequences. In addition, the goal was to provide an overview of the available tools and databases to carry out comparative sequence and structural analysis.


In a systems biology perspective, protein-protein interactions (PPI) are encoded in machine-readable formats to avoid issues encountered in their retrieval for the reconstruction of comprehensive interaction maps and biological pathways. However, the information stored in electronic formats currently used doesn't allow a valid automatic reconstruction of biological pathways.We propose a logical model of PPI that takes into account the "state" of proteins before and after the interaction. This information is necessary for proper reconstruction of the pathway.The adoption of the proposed model, which can be easily integrated into existing machine-readable formats used to store the PPI data, would facilitate the automatic or semi-automated reconstruction of biological pathways.This article was reviewed by Dr. Wen-Yu Chung (nominated by Kateryna Makova), Dr. Carl Herrmann (nominated by Dr. Purificación López-García) and Dr. Arcady Mushegian. The reconstruction of biological systems for computational analyses relies on the existence of data describing components as well as their interactions and relationships ,2. It isNevertheless, it is also well recognized the difficulty of collecting such kind of information through scientific literature, especially when the number of considered proteins is large, because scientific literature contains PPI data in the unstructured format of human natural language. For this reason, a subset of protein interactions gets into PPI databases such as BIND , BioGRIDIn order to store this useful kind of information, we focus on the adoption of a general logical model to make PPI data immediately usable for automatic or semi-automated reconstruction of biological pathways, which can be easily integrated into existing electronic formats used to store the PPI data such as PSI-MI  and BioPThe general, purely descriptive model we propose takes into consideration the protein "state" before and after the interaction, and can be integrated, adapted or adopted in already existing standards for PPI interaction data.With "state" of a protein we mean all the information that can describe the protein properties, in particular those that are significant for the interaction and for the protein functions. For example, if a protein will change its conformation, its function or its cellular localization after the interaction, this information should be recorded. We want to stress that we are interested in that kind of changes (or its absence) in the interacting protein also before the interaction. In this way, we can define under what conditions will happen the interaction .Although these data are not necessary in systems biology approaches that want to analyze the network of interactions in order to study its topological properties and for identify functional modules and motifs, these are essential for pathways reconstruction.UML  is a lanClasses and objects are concepts derived from the Object Oriented Programming (OOP), a programming paradigm that uses "objects" and their interactions to design applications and computer programs.A class defines the characteristics and behaviours of a thing. For example, the class Car defines the possible characteristics and behaviours common to, and shared by all cars such as the type of engine, the shape of the body and the maximum number of passengers.An object is an instance of a class. Then, for example, we can have different objects of type Car, and each object represents a particular automobile having certain defined characteristics.In UML, a Class Diagram gives an overview of the system by showing its classes and the relationships among them. The relationships between classes are shown as connecting links. Class diagrams are static diagrams because they display the elements that are interacting, but not what it is happening when the interactions occur. Class names begin with a capital letter.In our model Fig. , the intThe class Protein represents the generic protein. Note that between the class Protein and the class Residue there is a "composition" relationship. In UML, a composition relationship is used when an object is made up of other objects and the whole and parts have coincident lifetimes.The multiplicity of the relationship is 1 for Protein and (0..*) for Residue. This means that there can be zero, one or more Residue for each Protein. In fact, we don't need to store the information of all the residues of the protein because we would like to define the interaction between two proteins and not the exact residue sequence.Thus, for example, if we know that the interaction takes place only if a certain residue is phosphorylated, it is enough to consider only the information about this residue and its state (phosphorylated).The class Residue represents the generic residue of the protein. There is a "composition" relationship between Residue and Protein.The class Interaction represents the generic interaction between two proteins. Note that between the class Protein and the class Interaction there is an "association" relationship. In UML, an association relationship is used when a class must be necessarily associated to another class.The multiplicity of the relationship is 1 for Interaction and 2 for Protein. This indicates that for every object of type Interaction there must be two objects of type Protein. This is because we want to model the interaction that takes place between two proteins.The class DataReferences stores the information on the article reference where the interaction is described.We show an example of interaction in the Object Diagram in Fig. In the example we consider as interaction the case of the phosphorylation of the residue "Ser177" of a protein "B" by the protein "A", that leads to the activation for a given function of the protein "B".It is important to say that we store the "state" of both proteins before and after the interaction. In the example, for the protein "A" we have an indicator "copy" of the object "A" because the interaction doesn't change any of the properties (considered by the model) of the protein "A". Finally, we have an indicator "become" for the objects "B" and "Ser177" because the properties of these two objects are changed after the interaction.This example also shows how the model is able to take into account the "direction", simply defined as the direction of the arrow in the binary relation between A and B. In fact, it is clear that the direction is from A to B because is A that change the state of B. During the reconstruction of a hypothetical pathway in which are involved the proteins A and B, starting from these data we could say that the protein A, in its native conformation, can acts on the protein B when it is in its native conformation, and that the result is the activation of the function of the protein B.The proposed model was kept purposely simple to easily transmit the idea that adopting a model that takes into account the "state" of interacting proteins is important for pathways reconstruction. However, as the UML can handle even highly complex models, it can be easily expanded to consider other information. In general, a model may be more or less complex depending on many factors and in accordance with the purpose to be achieved by the use of the model.For example, we may want to add in the "state" of the interacting proteins the knowledge of the conformations assumed by the proteins. In fact, the amino-acid sequence of a protein determines its native conformation, which in turn determines its biological function. In our case, since the aim of the model is to facilitate the reconstruction of biological pathways from PPI data, and not to perform complex simulations like molecular docking, a simple solution might be to insert a variable into the class Protein to store the type of the assumed conformation .Another more complex way would be adding the variables dX, dY and dZ in class Residue. In the native conformation the variables dX, dY and dZ are set to zero for all residues. In a given alternative conformation, for the i-th residue, the variables dXi, dYi and dZi contains the distance from the central point of the residue in the native conformation respectively along the axis X, Y, Z. Gradually complicating the model, we can get up to consider the coordinates of each atom of the protein, but of course this data is rarely available and, in addition, it would be excessive for the purpose of the model.As shown in the example above, the model takes into account the "state" of interacting proteins before and after the interaction. Thanks to this, the model is able to provide necessary information to reconstruct the pathway from the PPI data. Instead, as described above, the only binary information of the type "protein A interacts with protein B" is not sufficient.Finally, the data of the previous example may be presented in a series of XML code lines although, as mentioned above, these information could be easily integrated into currently adopted formats used to encode PPI data instead of creating new.It is important to emphasize that the information considered by the model should be included within the PPI data. Although current standards are available for modeling of biological pathways such as SBML  and mEPNhttp://bioinfo.homeunix.org/forum/We propose that this project will be developed by the community as an open-software project. The project web site is accessible at the following URL: Encoded protein-protein interactions in a machine-readable format avoid waste of time and uncertainties, difficulties and typical issues encountered in the retrieval of such data and in the reconstruction of comprehensive interaction maps and biological pathways based on interaction data.However, the knowledge of only the "binary" interactions between the elements belonging to a pathway is not sufficient to its reconstruction. For example, it is necessary to know the direction of the relationship and the conditions under which it occurs.Our simple logical model of PPI shows how to take account of this essential information considering the "state" of interacting proteins before and after the interaction. The lack of these data (that would be saved in machine-readable standards adopting this model) translates into a long work by the researchers that are forced to manually search in published papers. This model can be easily extended to describe the interactions between proteins and small molecules, between proteins and protein complexes etc. and can be integrated into the existing standards encoding protein interactions. Its adoption will allow for an automatic or semi-automatic reconstruction of biological pathways from the interaction data, decreasing the probability of making errors and reducing the time required for this task.The UML diagrams were created with the design tool Visual Paradigm for UML v7.2  .Main comments1) The proposed work described a logic model that collects critical information for protein-protein interactions. The authors stated this method would be able to assist reconstructing biological pathways. The idea of logic model fits well with systems biology. One can model the system using elements such as input, output and activities. The simple model authors proposed is able to save residue information that is essential for the interaction.2) The classes and attributes described in the manuscript did not capture the complexity of protein interactions. For example, the structure conformation is not included. But, the authors presented that an extension could be done by inserting variables in the class Protein, so the knowledge of conformation changes can be preserved. Other more complex scenarios can be added in similar ways in the future to obtain a comprehensive model.3) The authors emphasized that the logical model is different from Systems Biology Markup Language (SBML), such that their method focused on the first step of building pathways, naming retrieving "raw" data from PPI databases. In this aspect, the two approaches could potentially accompany each other since first they targeted at different steps of pathway construction, and second both employ XML in practice.Minor issues not for publicationhttp://sbml.org/.1) In "Results and Discussion", at "Adoption of the model and pathway reconstruction", the last paragraph, the citation  for SBMWe have corrected the URL.2) Please indicate in the manuscript whether a more detailed manual or examples will be available, and if so, where.We have added the link to the project website.Carl Herrmann, TAGC & Université de la Méditerranée (Aix-Marseille II), Marseille, France .In this article, the authors present a logical model of protein-protein interactions that allows to take into account the modifications induced by the interaction at certain residues. This information is usually not available in common PPI databases such as Intact or BIND. The model is based on the UML modeling language, and they present a Class diagram representing the various classes defined. This approach is interesting, as, as emphasized in the introduction, most description of such processes are available in the literature in natural language, which makes its systematic use difficult for bioinformatics approaches. Hence, any attempt to introduce a systematic nomenclature or description of biological processes is welcome.My first comment is that studies analyzing interactomes are not necessarily aiming at reconstructing pathways. Functional modules extracted from PPI data could represent e.g. protein complexes acting as a cellular machinery. In this case, the exact nature of the interaction  are usually not needed. Therefore, the information currently available in PPI databases is sufficient for many studies.We partially agree. Although this information is not always necessary, as in systems biology approaches in which you want to look at the topology of the network of interactions for properties of the network and for identify functional modules and motifs, there are cases where the lack of information that would be saved from machine-readable standards adopting this model translates into a long work by the researchers that are forced to find it manually from published papers. This model is designed to improve the existing standards so that you can use this kind of information in case they are needed, such as in the case of the reconstruction of pathways.The authors mention other projects in the introduction which go into similar directions: they cite SBGN, BioPAX, and HUPO-PSI, but one very important project in this field is not mentioned: SBML.We have changed the manuscript accordingly.However, general interaction/pathway databases such as Reactome use the SBML representation for "reactions", which is grounded on UML. It seems to me that SBML already allows for the introduction of informations such as the modification of an entity, e.g. the phosphorylation of a protein in a reaction. In particular, the Systems Biology Ontology (SBO), which is closely integrated into SBML, contains an "interaction" branch, which contains terms such as phosphorylation (SBO:0000216), a sub-term of the "Addition of chemical group" term (SBO:0000210). Also, the PSI-MOD ontology contains terms such as "phosphorylated residue" (MOD:00696). Hence, my concern is that the model presented by the authors might be redundant with already existing representations or models, as those cited above. If this is not the case, the authors should clearly explain the difference of their approach with respect to existing ones, and make clear why it does not fit into e.g. SBML type descriptions. Indeed, the danger is that multiple independent initiatives will fragment the field instead of converging to a common, unified, description scheme.We don't agree: we are considering the previous step. That is when, starting from protein-interaction data present in PPI database, you want to reconstruct a pathway. It is at this step that that information that could be memorized by the implementation of the proposed model becomes essential. After that, once the pathway has been reconstructed, it can be stored in appropriate formats such as the Systems Biology Markup Language (SBML), the modified Edinburgh Pathway Notation (mEPN) and so on.I acknowledge that the authors are aware of this, as they explain that "The general, purely descriptive model we propose [...] can be integrated, adapted or adopted in already existing standards such as the PSI-MI" (first paragraph of the discussion) or "[...] these information could be easily integrated into currently adopted formats used to encode PPI data instead of creating new." (last paragraph of the discussion). I would appreciate if the authors would make these statements more precise, and give details about the possible integration of their approach into existing projects.As previously said, we don't want to insert our model at the level of the languages used for simulation and modeling of biological pathways. We want to place our model at the previous level, when you need to reconstruct a pathway starting from the data available in PPI databases. The models used in these databases could integrate the concept of "state" of interacting proteins before and after the interaction to present this valuable information in electronic format.Arcady Mushegian, Department of Binformatics, Stowers Institute for Medical Research, Kansas City, Missouri, USA.I have very little to say about this manuscript, not being in expert in automatic text processing and relational modeling. I expect that improvement in machine-readability of the data on protein interactions will be useful for more accurate reconstruction of the pathways, but I do not see that this study has shown how this approach makes this actually happen in any specific case.Currently, to our knowledge, there is no model that takes into account the "state" of interacting proteins before and after the interaction. Consequently, there are not databases where this kind of information is present in machine-readable format, and reconstruction of pathways thus requires more work as this information should be sought directly in the papers. Being a logical model, it is natural that does not resolve a particular case, but its adoption and implementation in one or more of the standard electronic formats protein interaction would allow other researchers to solve in less time with fewer errors and an unlimited number of special cases of interest.


The proteomes that make up the collection of proteins in contemporary organisms evolved through recombination and duplication of a limited set of domains. These protein domains are essentially the main components of globular proteins and are the most principal level at which protein function and protein interactions can be understood. An important aspect of domain evolution is their atomic structure and biochemical function, which are both specified by the information in the amino acid sequence. Changes in this information may bring about new folds, functions and protein architectures. With the present and still increasing wealth of sequences and annotation data brought about by genomics, new evolutionary relationships are constantly being revealed, unknown structures modeled and phylogenies inferred. Such investigations not only help predict the function of newly discovered proteins, but also assist in mapping unforeseen pathways of evolution and reveal crucial, co-evolving inter- and intra-molecular interactions. In turn this will help us describe how protein domains shaped cellular interaction networks and the dynamics with which they are regulated in the cell. Additionally, these studies can be used for the design of new and optimized protein domains for therapy. In this review, we aim to describe the basic concepts of protein domain evolution and illustrate recent developments in molecular evolution that have provided valuable new insights in the field of comparative genomics and protein interaction networks. The protein universe is the collection of proteins of all biological species that exist or have once existed on Earth . Our sam1]) indeed fully rely on such sequence similarity and motif identifications [A comparison of sequences for evolutionary insight is best achieved by looking at the structural and functional (sub)units of proteins, the protein domains. By convention, domains are defined as conserved, functionally independent protein sequences, which bind or process ligands using a core structural motif -13. Examications , 19.The algorithms that are used for domain identification are built around a set of simple assumptions that describe the process of evolution. In general, evolution is believed to form and mold genomes largely via three mechanisms, namely i) chemical changes through the incorporation of base analogs, the effects of radiation or random enzymatic errors by polymerases, ii) cellular repair processes that counter mutations, and iii) selection pressures that manifest themselves as the positive or negative influence that determines whether the mutation will be present in subsequent generations , 21. By 1A) in which multiple sequences are compared simultaneously are commonly created with for example ClustalX and MUSCLE (see Table 1) [Although already informative in its own right, mutation data can be significantly different among species due to dissimilar metabolisms, generation times, population sizes, lifestyles, reproductive strategies, or the lack of apparent polymerase-dependent proofreading such as in positive-stranded RNA viruses -25. ConsTable 1) -34.1B), depend heavily on rate variation models, such as molecular clocks or relaxed molecular clocks , which are calibrated with additional evidence such as fossils and may therefore also provide valuable information on aspects like divergence times and ancestral sequences [1. Close relatives, sharing an overall sequence identity above for example 50% and a set of functional properties, can also be grouped into families and subfamilies. In turn, these families share also evolutionary relationships with other domains and form together so-called domain superfamilies , 35. Evoequences -38. CommA limitation of all inferred phylogenetic data is that it is directly dependent on the alignment and less so on the programs used to build the phylogenetic tree . One of Although sequence and phylogenetic analysis provide a relatively straightforward way for looking at domain divergence, comparison of solved protein structures has shown that protein tertiary organizations are much more conserved (>50%) than their primary sequence (>5%) . For thiAs described above, the evolution of a protein domain is generally the result of a combination of a series of random mutations and a selection constraint imposed on function, i.e., the interaction with a ligand. The interaction between protein and ligand can be imagined as disturbances of the protein’s energy landscape, which in turn bring about specific, three-dimensional changes in the protein structure , 45. BinThe folding of a domain is essentially based on a complex network of sequential inter-molecular interactions in time . This haHowever, distant sites also contribute to the thermodynamics of catalytic residues. This is achieved through a mechanism called energetic coupling, which is shaped by a continuous pathway of van der Waals interactions that ultimately influences residues at the binding site with similar efficiency as the thermodynamic hotspots , 54. IndThrough selective mutation, protein domains have been the tools of evolution to create an enormous and diverse assembly of proteins from likely an initially relatively limited set of domains. The combined data in GenBank and other databases now covers over 200.000 species with at least 50 complete genomes and this greatly facilitates genome comparisons -59. Follvia a power-law, which is essentially the relation between the frequency f and an occurrence x raised by a scaling constant k  ~ xk ) [2) [There are various mechanisms through which protein domain or whole proteins may have been duplicated. On the largest scale, whole genome duplication such as those seen in the vertebrate genomes duplicated whole gene families, including postsynaptic proteins, hormone receptors and muscle proteins, and thereby dramatically increased the domain content and expanded networks , 63, 64.) ~ xk ) , 70. A sxk ) [2) , 72. Given the amount of domain duplication and apparent selection for specific multi-domain encoding genes in, for example, vertebrates, it may come as little surprise that not all domains have had the same tendency to recombine and distribute themselves over the genomes , 73. In Some of the most abundant domains can be found in association with cellular signaling cascades and have been shown to accumulate non-linearly in relation to the overall number of domains encoded or the genome size . AdditioFrom a mutational point of view, protein-protein interaction domains are different from other domains as well and this appears to be particularly true for the group of small, relatively promiscuous domains like SH3 and PDZ. These domains are promiscuous in the sense that they both tend to physically interact with a large number of ligands , 80 and The overall phenomenon that the number of protein domains and their modularity increases as the genome expands has not been linked to a conclusive biological explanation yet. A rationale for the increase in interactions and functional subunits, however, may derive from the paradoxical absence of correlation between the number of genes encoded and organism complexity, the so-called G-value paradox . There i3). In the next step, extensive network rewiring may take place, driven by the effect of node addition or node loss in the network  and mutations in the domain-interaction interface [It is clear that some of the above characteristics are underappreciated in the phylogenetic analysis of linear amino acid sequences. Moreover, the effects of evolution extend even further than these aspects and entail transcriptional and translational regulation, intramolecular domain-domain interactions, gene modifications and post-translational protein modifications -96. New nterface , 103-105Beyond mutations at the domain and protein level, regulation of protein expression provides another vital mechanism through which protein networks can evolve. Microarray studies are now well under way to map genome-wide expression levels of related and non-related genes under a variety of conditions , 94-96. via a merge of conserved and newly evolved modules [To overcome the limitations mentioned in the previous paragraph, the analysis of co-expression data has been developed to supplement the direct comparison of individual gene expression changes . In this modules , 113.Finding evolutionary relationships protein domains is mostly based on orthology and thus commonly performed on best sequence matches. Identifying these and categorizing them depends largely on multiple sequence alignments and this will in most cases give good indications for function, fold and ultimately evolution. However, this approach usually discards apparent ambiguities that arise from species-specific variations  and may therefore introduce significant biases . Biases AV is supported by The Netherlands Organization for Scientific Research (NWO) through Toptalent grant 021.001.037.