Accurate and fast tools for comparing protein three-dimensional structures are necessary to scan and analyze large data sets.The method described here is not only very fast but it is also reasonable precise, as it is shown by using the CATH database as a test set. Its rapidity depends on the fact that the protein structure is represented by vectors that monitors the distribution of the inter-residue distances within the protein core and the structure of which is optimized with the Freedman-Diaconis rule.χ2 test, the probability density function of which can be accurately estimated.The similarity score is based on a  Although numerous methods for comparison protein three-dimensional 3D) structures were designed, we still lack a unique, commonly accepted procedure to measure the structural diversity between proteins . In partD structuIn the present communication, some improvements of the original PRIDE technology are presented. They make it more accurate than the original version without decreasing its speed. The classification ability of the method was tested on the CATH database.αi and Cα(i+n) atoms, where n, which ranges from 3 to 30, is the number of Cα atoms between them in the backbone joint. The comparison between two protein 3D structures is reduced to the comparison between distributions of inter-residue distances. This is performed by chi-square contingency table analysis, which estimates whether two distributions represent the same overall population and allows one to compute a probability of identity P, ranging from 0 and 1. Since 28 pairs of histograms are compares, 28 P values are obtained and then averaged to give the overall PRobability of IDEntity (PRIDE) between the two protein 3D structures. Such a similarity score can range, by definition, from 0 to 1, the latter value indicating the identity between the two protein structures. In the next sections, four modifications, introduced into this computational procedure, will be described.In original PRIDE version, a protein structure in defined by the distributions of the distances between Cmax, where nmax is the value for which there are only 20 Cαi-Cα(i+n) distances. Clearly, if n > nmax, the histograms would contain less than 20 observations and they were thus ignored. Therefore, the numbers of histograms are different for proteins of different length in the modified PRIDE version. In the comparison of two domains, represented by series of Cαi-Cα(i+n) histograms, with 3 ≤ n ≤ nmax1 for the first domain and 3 ≤ n ≤ nmax2 for the second domain, the maximal value of n (nmax) was defined asThe maximal value of n, which was equal to 30 in the old PRIDE version, is now selected as a function of the protein dimension. Obviously, the histograms, in which inter-residue distances are binned, must have a sufficiently high number of observations to be compared via any statistical tool. The number of observations in the histograms increases with the length of the protein and decreases with n. Therefore, histograms were generated for all n values larger than 3 and lower than nMoreover, only distances between residues belonging to helices and/or strands were taken into account in the modified PRIDE version, in order to increase the computational speed of the method. The STRIDE package, based on the detection of hydrogen bonds patterns and backbone torsions, was used for secondary structure assignment .The building of a regular histogram from continuous data demands a cautious specification of the number of bins. In the old version of PRIDE, each bin width was arbitrarily set to 0.5 Å, and adjacent bins were merged together so that at least 5% of the observations were included in each bin. Here a more rigorous approach was followed. Firstly, inter-residue distances were binned in the histograms with a fixed bin width of 0.1 Å, a value close to the average expected uncertainty of protein atomic coordinates obtained with crystallographic methods . Then biwhere k is the number of observations in the sample x; iqr(x) is the interquartile range of the data of sample x, that is the range between the third and first quartiles. The iqr is expected to include about half of the data. The optimal BS values are computed for a query protein structure, and then they are used to change the histogram bins for all domains in the scanned database. New optimal BS values must be recomputed for a new query. Despite this might seem to be rather complicated and time consuming, we verified that once the histograms for the entire database are pre-computer and stored with very small bins of 0.1 Å, all of them can be re-shaped to the optimal BS very rapidly .αi-Cα(i+n) distance distributions were compared using the contingency tables [i is the number of observations in bin i for the first protein and Si is the number of observations in the same bin i for the second protein, then the chi-square statistics isWhile in the original version of PRIDE, the Cy tables , anotherwhereandχ2 ranges from 0 to the positive infinity. A large value of χ2 indicates that the null hypothesis is rather unlikely and that the two proteins are considerably different, and χ2 can thus be used as a statistical measure of proximity between two protein 3D structures. On the contrary, two identical protein 3D models are associated with a χ2 value equal to 0.Furthermore, the degree of proximity between two protein structures can be also expressed by an incomplete gamma function determining the chi-square probability density function:b is the number of histogram bins, that corresponds to a number of degrees of freedom for histograms with an unequal number of observations. In this case the proximity measure P ranges from 0 to 1 corresponding, respectively, to the completely different and to the identical protein folds. n are computed for each pair of histograms of the Cαi-Cα(i+n) distances for 3 = n = nmax. Then they are averaged to estimate the global degree of protein structural proximity. It must be observed that while χ2 is a distance measure of proximity, with lower values associated with two domains that are similar, P is a measure of similarity, with higher values associated with two domains that are similar. Beside this difference, both can be used as structural similarity scores and monitor exactly the same protein structural features. However, P has the definite lowest and highest limits that are equivalent to the similarity score used in the old PRIDE version.where Nαi-Cα(i+n) distributions. However, they can be pre-computed and stored in about 850 seconds (Xenon 3 GHz processor) for the 34,035 protein domains of Table Given the extreme simplicity of the algorithm, it is not surprising that computations can be very fast. The most time consuming step is the computation of the histograms of the Cαi-Cα(i+n) distances, and the other with smaller domains, for which 10 < nmax < 30. Domains containing more then one polypeptide chain were disregarded since, by definition, PRIDE cannot handle them.The new structure comparison method was benchmarked against the CATH v3.0.0 database , which imax > 30) and other small domains (10 < nmax < 30). About half of them were considered to be "easy" queries, in the sense that they belong to a CATH fold cluster containing at least 50 domains, and the others were "difficult" queries that belong to small CATH fold groups having no more than 3 domains.A non-redundant series of CATH entries were randomly selected from different superfamilies to be used as queries, by ensuring that all the three principal classes C of the database are equally represented Table . Some weThe performance of the new PRIDE version can be examined by the computation and the analysis of the ROC curves. The P value, which is a similarity score, is used to calculate ROC curve in the present study. A threshold similarity is consecutively decreased, with subsequent decrements equal to 0.01, in the entire range of possible P values, from 1 to 0. At each step, each of the queries  a true positive (TP), if the similarity between the query and the entry is higher that the threshold value and if the query and the entry belong to the same CATH fold; ii) false positive (FP) if the similarity between the query and the entry is higher that the threshold value despite the fact that they have different CATH classification; iii) a false negative (FN), if the entry and the query are in the same fold cluster despite their estimated similarity is lower than the threshold value; iv) a true negative (TN), if the similarity is estimated to be smaller that the threshold value and if the query and the entry are actually classified into different CATH fold groups. On the basis of these definitions it is possible to compute, for each threshold value, the sensitivity and the specificityαi-Cα(i+n) distances, and smaller (0.82) for the second dataset, which contains smaller proteins that are represented by a lower number of histograms. Such values are considerably better than that obtained by using the old version of PRIDE (0.55). These values are also comparable to those obtained with two other procedures for evaluating protein structure similarity – SHEBA (0.93) and VAST (0.90) that are computationally much more demanding then the methods described in the present manuscript [α, β, and α/β classes within the CATH database in order to estimate the performance of PRIDE on different types of proteins. Values of 0.90, 0.90, and 0.83 were obtained by scanning the database of 29,098 domains with the query sets containing 49 α proteins, 50 β proteins, and 50 α/β proteins (dataset number 1 of Table and the ROC curve is obtained by potting Sensitivity against (1-Specificity) for the entire range of possible threshold values. Figure nuscript . The areThe authors declare that they have no competing interests.OC supervised and coordinated the project. SK developed the algorithm, carried out the analyses, and prepared, with OC, in the writing of the manuscript. All authors read and approved the manuscript.


How to detect protein complexes is an important and challenging task in post genomic era. As the increasing amount of protein-protein interaction (PPI) data are available, we are able to identify protein complexes from PPI networks. However, most of current studies detect protein complexes based solely on the observation that dense regions in PPI networks may correspond to protein complexes, but fail to consider the inherent organization within protein complexes.To provide insights into the organization of protein complexes, this paper presents a novel core-attachment based method (COACH) which detects protein complexes in two stages. It first detects protein-complex cores as the "hearts" of protein complexes and then includes attachments into these cores to form biologically meaningful structures. We evaluate and analyze our predicted protein complexes from two aspects. First, we perform a comprehensive comparison between our proposed method and existing techniques by comparing the predicted complexes against benchmark complexes. Second, we also validate the core-attachment structures using various biological evidence and knowledge.Our proposed COACH method has been applied on two different yeast PPI networks and the experimental results show that COACH performs significantly better than the state-of-the-art techniques. In addition, the identified complexes with core-attachment structures are demonstrated to match very well with existing biological knowledge and thus provide more insights for future biological study. Higher F-measure is achieved in the first range , which sacrifices the Coverage Rate, resulting the lowest Coverage Rate compared with those in other two ranges. Additionally in this range, COACH only generates a small number of complexes . On the contrary, higher Coverage Rate are achieved in the third range , which sacrifices the F-measure. However, as stated above, the redundancy in our predicted complexes would become more severe with the increase of t. To reduce the redundancy involved in predicted complexes, as well as to encourage one-to-one matching between predicted complexes and real ones, we recommend that the suitable setting of t would be in the second range i.e., t ∈ . In fact, the performance of COACH does not change significantly in this range. For the setting of t = 0.225 in our experiments, COACH can achieve a good balance of both F-measure and Coverage Rate.Secondly, as we increase the values of t = 0.225) are shown in figure We also performed our COACH on Krogan et al.'s PPI data (see Methods section). The F-measure and coverage rate of each method using this data (Ncp and Ncb (see Method section) of DECAFF and find that DECAFF generates protein complexes with some redundancy on both DIP and Krogan et al.'s PPI data. In other words, the complexes predicted by DECAFF overlap a lot with each other. For example, DECAFF predicts 2190 complexes using DIP data, 605 of which match 243 real complexes. Even, it predicts 2143 complexes using Krogan et al.'s data, 759 of which only match 192 real complexes . The ratio Ncp/Ncb of DECAFF is much higher than that of other methods, which suggests that some post-processing  is needed for DECAFF.In particular, we check both Protein complexes are key molecular entities to perform cellular functions. The increasing amount of protein-protein interaction (PPI) data has enabled us to detect protein complexes from PPI networks. However, current computational methods only focus on detecting dense subgraphs in PPI networks as protein complexes but ignore their inherent organization. Hence, new approaches that can provide insights into the organization of protein complexes are greatly desired.In this paper, we proposed a core-attachment based method to detect protein complexes from PPI networks. We first mined the protein-complex cores from the neighborhood graphs and then formed protein complexes by including attachments into cores. The evaluation and analysis of our predictions demonstrated the following advantages of our COACH method over the state-of-the-art techniques. First, Our proposed method is fundamentally different from existing methods. It provides insights into the inherent organization of protein complexes while existing methods mainly focus on detecting dense graphs. Proteins within the same protein-complex core detected by our method have high functional similarity and tend to be co-localized, indicating that protein cores are possible biological hearts of protein complexes. Second, COACH has achieved significantly higher F-measure and Coverage rate than existing methods. Thus, our predicted complexes match very well with benchmark complexes. In addition, COACH also performs very well in terms of other measures such as co-annotation, co-localization and p-values, indicating that COACH can predict protein complexes very accurately. Our identified complexes, therefore, could be probably the true complexes to help the biologists to get novel biological insights. As we know, a protein complex is often formed by multiple proteins which have permanent and stable relations with each other. However, current PPI networks do not differentiate the types of interactions. Recent studies demonstrate that proteins structures and binding interfaces ,47 are oIn our experiments, we performed our COACH method on two yeast PPI networks. One is from DIP (the Database of Interacting Proteins ), which p and a real complex b in the benchmark, NA, is used to determine whether they match with each other. If NA ≥ ω, they are considered to be matching  between a predicted complex  0.20 in ,14, whic follows :F-measure, as the harmonic mean of precision and recall, can be used to evaluate the overall performance of the different techniques ,14.n benchmark complexes and m predicted complexes, Tij is the number of proteins in common between ith benchmark complex and jth predicted complex. Coverage rate is then defined as:Coverage rate ,53 is apNi is the number of proteins in the ith benchmark complex.where MW and XL conceptualized and designed the method and drafted the manuscript together. MW was responsible for the implementation. CKK and SKN participated in discussion and conceptualization as well as revising the draft. All authors read and approved the manuscript.Remark 1. A preliminary core detected from vertex v's neighborhood graph Gv will definitely contain the vertex v.v links to all other vertices in Gv and has the maximum degree, it is easy to understand the remark 1.Since vertex Remark 2. Each interaction in a complex core tend to be reliable.pc =  be a preliminary core detected from Gv. We discuss the reliability of interactions within this complex core, based on following 2 cases.Let a complex core Case 1: Avdeg(Gv) = 2. In this situation, Gv is protein-triangle and also a preliminary core itself, i.e., pc = Gv. Moreover, each protein pair in Gv has a common neighbor  and protein interactions within this topology tend to be reliable [reliable .Case 2: Avdeg(Gv) > 2. For every interaction e =  ∈ Epc, e will be in one of two following cases.Case 2.1, u1 = v, u2 ≠ v. Since deg(u2) ≥ Avdeg(Gv) > 2, u2 will have at least another 2 neighbors besides u1, which are common neighbors between u1 and u2. In this case, e is demonstrated to have higher reliability than those in Case 1[Case 2.2, u1 ≠ v, u2 ≠ v. u1 and u2 will have at least a common neighbor, namely v. e =  thus has a reliable alternative path {u1 - v - u2}. e is also shown to be with high reliability in [in Case 1,54. Caseility in .The running time of our COACH method over random graphs. Additional file Click here for fileThe comparison between our COACH method and the CoreMethod. Additional file Click here for file


We have recently released a comprehensive, manually curated database of mammalian protein complexes called CORUM. Combining CORUM with other resources, we assembled a dataset of over 2700 mammalian complexes. The availability of a rich information resource allows us to search for organizational properties concerning these complexes.As the complexity of a protein complex in terms of the number of unique subunits increases, we observed that the number of such complexes and the mean non-synonymous to synonymous substitution ratio of associated genes tend to decrease. Similarly, as the number of different complexes a given protein participates in increases, the number of such proteins and the substitution ratio of the associated gene also tends to decrease. These observations provide evidence relating natural selection and the organization of mammalian complexes. We also observed greater homogeneity in terms of predicted protein isoelectric points, secondary structure and substitution ratio in annotated versus randomly generated complexes. A large proportion of the protein content and interactions in the complexes could be predicted from known binary protein-protein and domain-domain interactions. In particular, we found that large proteins interact preferentially with much smaller proteins.We observed similar trends in yeast and other data. Our results support the existence of conserved relations associated with the mammalian protein complexes. Knowledge of constraints governing systems provides a means to predict events and mechanisms that cause their breakdown. It may also allow one to speculate how such systems might have evolved. One class of biological systems that have captured much interest involves protein-protein interactions and protein complexes. Protein complexes are groups of two or more proteins that physically interact. Such interaction serves to spatially join, modify or create novel functional capability from component proteins. Once in a complex, proteins can achieve greater structural stabilization and protection from proteases, which result in significantly longer half-lives ,2. A breWhat forces have shaped the formation of complexes and what is their effect? The formation of quaternary structure is associated with greater constraint in the evolution of proteins -8. RecenRecently, complexes have been investigated in the context of intrinsic disorder  and aggrAlthough these characteristics of complexes are dynamic, their relation to properties of component proteins derived in their isolated state can be studied. By deriving these relations, we hope to gain insight into certain constraints governing the organization of complexes. Here, we find evidence that protein length, predicted secondary structure and isoelectric point, as well as the nonsynonymous/synonymous substitution ratio of genes are associated with our measures of complex complexity and participation.Available data on protein-protein interactions have been used extensively to predict complexes -25. A st1732 experimentally verified protein complexes annotated at MIPS  and storThe focus of this study was on mammalian complexes but we also analyzed the MIPS yeast complexes stored in CYGD . We use We assembled 94906 pairwise eukaryotic protein-protein interactions from BIND, DIP , HPRD, MWe refer as domain-domain interactions in the text as those derived from known 3D structures of proteins, obtained from the iPfam  and 3didWe generated random complexes using two different random models to evaluate significance of trends associated with non-stochastic protein complex organization. For the random model in the main text Model 1), we generated the same number of random complexes as found in annotated complexes by picking the same number of proteins as found in each of the annotated complexes from a pool of all proteins collected from these complexes. We picked proteins randomly with replacement throughout the process of random complex generation to simulate neutrality of protein reuse in the complexes. A second random model (Model 2) of the complexes which uses the complex complexity and participation preserving rewiring method, first introduced by Maslov and Sneppen  was also, we genedN/dS) ratios computed from coding sequences of ortholog pairs from Ensembl ). When the number of unique proteins increases past 4, complexes become increasingly rare following a power law-like distribution Figure . When we51). WhenNot all proteins are exclusive to a single complex. Having defined complex participation as the number of complexes that a particular protein is found in, we find that it also follows a power law-like distribution  but are present in annotated mammalian and yeast complexes. For example, the 81 subunits of the human ribosomal complex [CORUM:306] have a mean length of 169 amino acids and the 37 subunits of the mouse mitochondrial NADH dehydrogenase complex [CORUM:381] have a mean length of 234 amino acids.In contrast to randomly generated complexes, the complexes that we have collected had much more variable mean lengths, especially for complexes with large numbers of subunits. We observe this as an increased scattering of points on the right side of the plot Figure . This ph-57) in the Eukaryotic Subcellular Localization Database  are encoded by two genes with a mean dN/dS ratio of human genes of associated constituent proteins tends to decrease suggesting that complex complexity negatively correlates with purifying selective pressure on genes . A contributing reason can be derived by examining the distribution of dN/dS ratios. The distribution of human-mouse dN/dS ratios is skewed towards small values considering all known human genes with over 40% of the values < 0.1 . For both mammalian and yeast complexes, the trends appear to be conserved using dN/dS ratios generated from a variety of ortholog-pairs with some exceptions.Because such results could potentially be sensitive to the ortholog pairs compared, the quality of the gene models, and peculiarities of human/mouse evolution, we also repeated such analyses using a variety of other mammals [see Additional file dN/dS ratios is the gene conservation of orthologs of the proteins in complexes. Compared to dN/dS ratios, statistically significant differences in gene conservation are even more difficult to detect in closely related genomes. However, over large evolutionary distances we noticed that highly complex complexes tend to have conserved their subunits. For example, between yeast and human, yeast complexes with more than 60 subunits had more than 80% of their subunits also encoded in human. This was not observed in random complexes [Additional file -70). A significant difference in complex participation was not observed for model 1 randomly generated complexes. These results concerning dN/dS ratios and gene conservation portrays similar relationships between gene evolution, complex complexity and participation at different time and constraint scales.Related to dN/dS with complex complexity and dN/dS with complex participation using such an extended complex dataset [Additional file Because our conclusions are based on a limited sample of complexes, we sought for extra datasets of protein complexes. The combination of the annotated MIPS yeast complexes with those that were predicted  allowed dN/dS with complex complexity and dN/dS with complex participation [Additional file Often in a cell, complexes exist along side their subcomplexes. In our data, subcomplexes of complexes are present. If we repeated our analysis with subcomplexes removed, we still observed a significant negative correlation between mean dN/dS ratio should fluctuate as proteins are added or subtracted from the complex. Nevertheless, there also seems to be an asymptotic bound for mean dN/dS ratios as complex complexity increases and dN/dS ratios as complex participation increases. The conserved trends we observe may be a reflection of these bounds. We find that genes associated with our human complexes which are considered essential (see Methods) tend to have lower dN/dS ratios (computed by human-mouse orthologs) and encode proteins that participate in more complexes  compared with all genes in the human complexes. We see the same significant trend for yeast (dN/dS ratios computed with S. cerevisiae – S. paradoxus orthologs). These results suggest that for proteins participating in increasing number of complexes, lower dN/dS ratios tend to be associated with purifying selection to maintain organism fitness. Essential S. cerevisiae complex proteins tend to be found in complexes with more unique subunits  compared to other proteins in the yeast complex data. However, such a trend was not observed for human possibly because a significant number of proteins which contribute to complex complexity are not known to be essential. We conclude that there is strong evidence of non-random association between dN/dS ratios of genes and complexes of their protein products.As complexes are assembled or disassembled, the mean dN/dS ratios, in agreement with these results.The finding that the number of protein-protein interactions positively correlates with the level of conservation of orthologs between species ,65 is sidN/dS ratios of proteins in the collected complexes tend to decrease with increasing complex complexity. There seem to be at least two major phenomena associated with this observation:In summary, we observed that the mean dN/dS ratios amongst genes as depicted in Figure dN/dS ratios of proteins in complexes tend to stabilize towards the mean of this skewed distribution.1) The skewed distribution of 2) increased selective pressure on proteins involved in the organization and function of more complex complexesdN/dS values . These observations agree with previous results providing evidence that yeast proteins belonging to the same functional module tend to have more similar evolutionary rates than those belonging to different modules [Similar to protein properties, we also found that the  modules . We obse modules  and whendN/dS ratios can be used to help predict the probability that a protein belongs to a particular complex. We did not observe a significant difference in standard deviations of protein length between proteins belonging to annotated versus Model 1 random mammalian complexes. It may still prove useful for the separation between real and erroneous complexes when combined with other properties.To conclude, we suggest that pI, secondary structure and The overlap between binary protein-protein and predicted domain-domain interactions with proteins in our complexes was assessed Figure . On aver192 PDB structures are available for our complexes (103 of which consist of at least 3 different proteins). A large proportion of these structures are solved with protein fragments rather than the full-length proteins. Many of the interactions between peptides in these structures (see Methods) are supported by binary protein-protein interactions. The IntAct interaction set (excluding interactions derived only from NMR or X-ray diffraction) covered on average ~40% of detected interactions in a given complex containing PDB structure. Interactions in 34 (18%) PDB structures were fully explained by these IntAct binary interactions. A similar coverage . dN/dS ratios of genes associated with model 2 random complexes are examined with respect to complex complexity and participation.Click here for fileBinned Plots for Figures 3D and 3F.3Click here for fileProtein complexes and dN/dS based on orthologs from a variety of species. dN/dS ratios of genes based on human-dog, human-chimp, human-rat, S. cerevisiae-S. mikatae and S. cerevisiae-S. paradoxus orthologs are examined with respect to complex complexity and participation.Click here for fileComplexes and gene conservation. The fraction of orthologs conserved in yeast and human complexes is examined against complex complexity.Click here for fileExtension and subsets of the protein complex data. Extended yeast complexes, complexes with subcomplexes removed, nuclear and non-nuclear complexes are examined with respect to complex complexity and participation.Click here for fileAnalysis of protein complex data using the median. Analysis of protein complex data with respect to mean dN/dS ratios and sequence length is compared with analysis using the median.Click here for fileHomogeneity of Protein Properties in Yeast Complexes. Deviation in pI, secondary structure and evolutionary rate between proteins in annotated yeast complexes are compared to those in random complexes.Click here for fileLength difference of interacting proteins. Large proteins tend to interact with much smaller partners.Click here for file


S.cereviciae validation networkwith 1622 proteins against 90 Gene Ontology terms of different levels ofabstraction. Compared to three other protein function prediction methods, ourapproach shows very good prediction performance. Our method can be directlyapplied to protein-protein interaction or coexpression networks, but also can beextended to use multiple data sources. We apply our method to physical proteininteraction data from S. cerevisiae and provide novelpredictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and weevaluate the predictions using the available literature.Inference of protein functions is one of the most important aims of modernbiology. To fully exploit the large volumes of genomic data typically producedin modern-day genomic experiments, automated computational methods for proteinfunction prediction are urgently needed. Established methods use sequence orstructure similarity to infer functions but those types of data do not sufficeto determine the biological context in which proteins act. Currenthigh-throughput biological experiments produce large amounts of data on theinteractions between proteins. Such data can be used to infer interactionnetworks and to predict the biological process that the protein is involved in.Here, we develop a probabilistic approach for protein function prediction usingnetwork data, such as protein-protein interaction measurements. We take aBayesian approach to an existing Markov Random Field method by performingsimultaneous estimation of the model parameters and prediction of proteinfunctions. We use an adaptive Markov Chain Monte Carlo algorithm that leads tomore accurate parameter estimates and consequently to improved predictionperformance compared to the standard Markov Random Fields method. We tested ourmethod using a high quality  Functional annotation of proteins is an important goal in post-genomics research.However, despite the many recent technological advances that have allowed theproduction of various types of molecular data at a genome-wide scale, the functionof large numbers of proteins in fully sequenced genomes still remains unknown. Thisis true even for six of the most-studied model species, in which the proportion ofunannotated proteins varies between 10% and 75% e.g. the metabolicpathway or biological process that the protein is involved in. Such contextualinformation can be derived from large-scale data on interactions between genes orgene-products, such as proteins. These data are commonly represented as networks,with nodes representing proteins and edges representing the detected interactions method proposedby Deng et. al.Lanckriet et. al.et. al. used adiffusion kernel Mus musculus genomic datasets leading to the conclusion thatGenemania, MLHC and KLR showed appealing performance.Lee The application of diffusion kernel based KLR or SVM to large networks is difficultor even impossible because of the huge computational cost of the required matrixexponentiation. In this paper we therefore try to improve the original MRF-Dengmethod without introduction of diffusion kernels.We discovered an important potential problem with MRF-Deng. The parameter estimationstep of MRF-Deng is problematic in that proteins with known function(“annotated proteins”) have unannotated proteins as neighbors sothat the predictors used in the logistic regression carry uncertainty due to theunannotated proteins .This pri.e. in a way that the computational cost is still modest comparedto diffusion kernel based KLR. Joint analysis is a standard approach to deal withmissing data in the context of semi-supervised learning and can be performed byiteratively estimating the parameters by maximizing the PseudoLikelihood Function(PLF) using logistic regression as a first step and estimating the unknown functionby optimizing the objective function of the MRF in the second step, till convergenceis met i.e.when half of the proteins in a network is unannotated. We show that BMRF outperformsMRF-Deng, and is competitive to diffusion KLR. Using a high quality protein-proteininteraction data set of S. cerevisiae proteins in terms of 340 nodes(“GO terms”) of the biological process ontology of The GeneOntology Consortium Here we amend the MRF-Deng method, by performing joint parameter estimation andprediction  assuggei.e. MRF-Deng, LK We compared the prediction performance of BMRF with three other protein functionprediction methods,  of BMRF . This le of BMRF . A furthet. al. data Another important aspect of our comparison is the computational cost of themethods. BMRF has by definition larger computational cost than MRF-Deng, sinceit uses MRF-Deng for labelling initialization and also involves the additionalparameter updating step, but the improvement in prediction performancecompensates this increased cost. We did not compare with LK because our Rimplementation of this method was not sufficiently optimized for the speed. Wecompared KLR and BMRF in five networks of different sizes, constructed from theCollins S. cerevisiae proteins. Lists of proteinnames, GO terms probabilities and ranks per GO term are provided assupplementary material  substrate YOR093C is related to increased stress levels caused by the accumulation ofunfolded proteins in the endoplasmic reticulum Information from SGD, based on the work of Proteins YGL128C (1st), YBL104C (2nd), YHR156C (3rd), were co-predicted to fourhierarchically dependent GO terms concerning the nuclear spliceosome mRNAsplicing. They interact with proteins related to mRNA splicing in a very denseneighborhood of the protein interaction network. Information from SGD suggeststhat YGL156C is located in the snRNP U5 compartment and probably linked to mRNAsplicing. This compartment is known to be connected with spliceosome complexesthat are involved in mRNA splicing. YGL128C is annotated in SGD as putativelyinvolved in pre-mRNA splicing, while there is an IEA annotation (Inferred fromElectronic Annotation) to the RNA splicing GO term. This is a parent node of ourprediction and thus we provide a more detailed prediction. Also, this protein islocated in the spliceosome and therefore in principle associated with thesplicing processes. SGD does not provide information on the protein YBL104C.However, using BLAST we found the protein YPR178W to be a distant homologue.This protein is assigned to the GO term nuclear mRNA splicing, via spliceosomeand contains a splicing factor motif in its sequence. The region of similaritywith YBL104C is however located outside of this motif.YOR227W is involved in the organization of the endoplasmic reticulum SGD states that YBR227C is possibly a mitochondrial chaperone withnon-proteolytic function while our predictions place this protein as firstranking for cation transport. This mismatch does not necessarily imply that ourprediction is false, since functional evidence from SGD can be still weak andalso it is rather common that proteins have multiple functions.Development of computational methods for protein function prediction based oninteraction data is a challenging problem in bioinformatics. Here, we present amethod to tackle this problem based on MRF. We followed the seminal work by Deng etal. (2003) in formulating the problem but we solved it in a significantly improvedway. Our MCMC algorithm samples the MRF parameter values jointly with functionalinference, whereas these are estimated in a single, questionable, training step inthe work of e.g. orthology networks e.g.In this study we focused on the methodological aspect and limit our experiments to asingle data source. In this way, we could clearly show that our method is morepowerful than its predecessor. Our method can handle multiple data sources such asexpression correlation datasets, co-occurrence of protein names in literatureobtained via text-mining, or cross-species sequence comparisonsand each pixel corresponds to a variable whose value (i.ecolor) depends on the values of its neighborhood pixels. In image restorationproblems, MRF methods are used to restore the missing parts of the images. Themost probable coloring configurations of the missing pixels can be inferred fromthe full joint probability distribution. The colors of the missing pixelsthereby are predicted simultaneously, allowing prediction in cases where theentire neighborhoods of pixels have to be predicted. MRF is thus particularlysuited for a guilt-by-association approach.MRF methods provide the framework for probabilistic modeling of dependent randomvariables. They are widely applied to a variety of problems with spatialdependencies, such as image analysis e.g. one GO term), where the coloringnodes of unannotated proteins remains unknown  used three classes of interactions. If both of theinteracting proteins perform the function of interest then The framework for protein function prediction based on MRF was originallyproposed by  unknown . The colitten as (2)where MRF-Deng ng units . Thus, tIn summary, MRF-Deng disregards the neighborhood uncertainty in the parameterestimation step, but takes it into account during the labeling step. Bydisregarding unannotated proteins in the first task, neighborhoods are prunedcompared to the full network. We expected that this strategy will work worse asthe proportion of unannotated proteins in the network is large.In this study we develop a Bayesian strategy and draw from the joint posteriordensity of https://gforge.nbic.nl/projects/bmrf/.The labelling vector S. cerevisiae interaction network using thephysical protein-protein interaction dataset of We constructed a i.e. a partially labelled network, containing the masked,the unmasked proteins and unannotated proteins), resulting in posteriorprobabilities for each protein and for each method. The masked proteinsconstituted the test set and their corresponding probabilities were used toconstruct ROC curves and to calculate the AUC score  terms. The top ten ranking proteins per GO term are shown(0.14 MB TXT)Click here for additional data file.


The classification of protein domains in the CATH resource is primarily based on structural comparisons, sequence similarity and manual analysis. One of the main bottlenecks in the processing of new entries is the evaluation of 'borderline' cases by human curators with reference to the literature, and better tools for helping both expert and non-expert users quickly identify relevant functional information from text are urgently needed. A text based method for protein classification is presented, which complements the existing sequence and structure-based approaches, especially in cases exhibiting low similarity to existing members and requiring manual intervention. The method is based on the assumption that textual similarity between sets of documents relating to proteins reflects biological function similarities and can be exploited to make classification decisions.-3 error rate. Finally when only the highest scoring predictions were used to infer classification, an extra 4.2% of correct decisions were made by the combined classifier.An optimal strategy for the text comparisons was identified by using an established gold standard enzyme dataset. Filtering of the abstracts using a machine learning approach to discriminate sentences containing functional, structural and classification information that are relevant to the protein classification task improved performance. Testing this classification scheme on a dataset of 'borderline' protein domains that lack significant sequence or structure similarity to classified proteins showed that although, as expected, the structural similarity classifiers perform better on average, there is a significant benefit in incorporating text similarity in logistic regression models, indicating significant orthogonality in this additional information. Coverage was significantly increased especially at low error rates, which is important for routine classification tasks: 15.3% for the combined structure and text classifier compared to 10% for the structural classifier alone, at 10We have described a simple text based method to classify protein domains that demonstrates an improvement over existing methods. The method is unique in incorporating structural and text based classifiers directly and is particularly useful in cases where inconclusive evidence from sequence or structure similarity requires laborious manual classification. Advances in structural biology have increased the rate at which new protein structures are being determined, thus creating a need for automated methods for protein domain classification. The main computational tools for classification in protein structure databases such as CATH  and SCOPSupport Vector Machines (SVM) are one of the newer machine learning approaches in wide usage today, and they are based on statistical methods to minimise the risk of error and offer solutions for optimal generalisation performance . SVMs exet al. [et al. [Caenorhabditis elegans corpus using rules, but the same classification engine can be applied to other domains [There are several applications of SVMs for document classification. PreBIND is an information extraction system based on SVM technology for the detection of protein-protein interactions in the literature . Stapleyet al.  used SVM [et al.  develope domains .Several approaches already exist that incorporate text mining methods and other bioinformatics tools. Indeed, the combination of sensitive sequence similarity searches with functional annotations has been successfully implemented for the functional prediction of proteins, based on experimental knowledge of remote homologues . Other aet al., have developed an unsupervised method for recognizing biological properties in free text (FiGO) [et al. [Various methods are available for automatic functional annotation of proteins from text, and several of these have been evaluated in the second task of BioCreAtIvE . Couto et (FiGO) . The syst (FiGO)  is able t (FiGO)  which is [et al.  which coWe have developed a novel text based approach for protein classification, which is based in text similarity of documents related to proteins, with a view to support the curation of protein structure databases. It is assumed that textual similarity between sets of documents relating to proteins mirrors structural and functional relationships and therefore can be used to make protein classification assignments. Such documents usually contain a description of the protein function and protein structure. In addition, they may mention specific characteristics, related or homologous proteins and their classification. In general, they represent concepts that report on functional and structural details. Several of the words used in these descriptions are specific to each protein or protein class. The method exploits the presence of such protein specific words (features) in document classification. It is expected that documents related to proteins that belong to the same class, and therefore have similar function and structure, are more similar than documents related to proteins in different classes. A classification assignment for a query protein can be inferred by assessing text similarity between a set of documents relating to the unclassified protein and sets of documents relating to classified proteins. For instance, if documents related to an unclassified protein are similar to documents related to a classified protein, then it could be that they belong to the same class, or in this case, the same protein superfamily.The documents can be abstracts or full text articles, although only abstracts have been used here due to the large variety of access control methods employed on journal full texts. An SVM model was developed to discriminate sentences in the abstracts containing functional, structural and classification information that are relevant to the protein classification task. The SVM model was used to remove text that is irrelevant to the classification task, thus reducing noise and increasing the prevalence of informative terms, allowing for more accurate classification predictions. The SVM model presented is novel and optimised for the discrimination of sentences in text that contain useful information for protein classification.The text similarity algorithm was firstly optimised using a previously described gold standard dataset . Several-3), compared to the structural classifier alone. Additionally, it makes an extra 4.2% of correct classification decisions when only the highest scoring predictions were used to infer classification. This method is useful for the challenging task of classification of such 'borderline' cases that usually require manual curation involving time-consuming study of the relevant literature.The method is unique in incorporating structural similarity searches with text mining directly. In a dataset of 'borderline' proteins, which lack clear structural and sequence similarity to classified proteins, the combination of the structural and text similarity classifiers resulted in an improvement in coverage by up to 50% at low error rates  and Homologous superfamily. The latter level groups together domains where evidence from sequence, structure and function similarity suggest they have evolved from a common ancestor. Classification is currently guided by structure and sequence similarity measures such as CATHEDRAL , SSAP 2, profileIn CATH, an existing classification is inherited if a protein domain displays "clear" structural and sequence similarity (over 35% sequence identity and/or SSAP score over 80) with a classified domain. However, if there is no domain in the database that fulfils these requirements, the classification is performed manually by considering the results of structural and sequence similarity and analysis of relevant literature. The literature often contains references to the function of the protein and even which evolutionary family it is thought to belong to. It is this information that can be highly useful for classification and lends itself to a text mining approach.An all-versus-all text comparison was performed using DC1.1993 as the query set and textCATH as the reference set according to the optimal conditions identified in the gold standard enzyme dataset. Performance was assessed using the AUC and MCC measures  by exploiting any orthogonality in our data, we generated a series of logistic regression models. The inclusion of the text similarity (TEXT) variable was significant p <= 0.001) as judged from the increase in the likelihood ratio which reflects the difference between error not knowing the independent variables and error when the independents are included in the model (Table  as judge2 was improved (0.34) compared to SSAP (0.30), indicating that the model is useful in classification predictions (Table The discriminative power of the models (how well matches are distinguished from non-matches) was evaluated using the AUC statistic. Its value is the total fraction of cases where a classification match is ranked higher than a non-match. Values of AUC for models that contain structural classifier SSAP are already over 0.90 indicating significant extant predictive capacity from structure alone. In contrast, the AUC for the text similarity classifier alone was only 0.79, thus indicating lower classification power when textual information is considered in isolation. Nevertheless, this indicates that the text classification pipeline is extracting almost 88% of the structural information when assessed against the opinion of an expert curator. Clearly highly relevant texts are being extracted, which would be useful to a human curator in the intended practical application. Interestingly, inclusion of the TEXT variable to the structural classifier improves the AUC value to 0.91 for the combined SSAP and TEXT model , the SSAP, TEXT and SSAP+TEXT classifiers detect 10%, 4.21% and 15.33% of the true matches. The SSAP+TEXT classifier consistently outperforms the structural classifier when used on its own, especially in the range of low error, with more than double the number of matches found for error rates below 10-4. The TP-FP breakeven point is the number of true positives that equals the number of false positives. In the test set for the SSAP+TEXT, the breakeven point is reached after 2098 matches are detected (12.51%). Both SSAP and TEXT predict more false than true positives for all cutoffs in the 'borderline' dataset.The data in Figures Aside from the ability of each classifier to separate true from false positives across the range of scores, we were also interested in the power of each method to assign correct structural classifications to proteins of the 'borderline' set in CATH using the top scoring hit. Only the best scoring domain comparison was considered for each of the 2436 domains of the DC1.1993 set (tophit), in order to simulate classification tasks. The combined SSAP+TEXT classifier increased coverage by 4.2%, capturing 1694 (69.5%) correct classifications relative to 1591 (65.3%) by SSAP alone.Our results demonstrate that although much work is required to address fully-automated classification of proteins that lack clear structural or sequence similarity, the combination of text and structure similarity can improve classification performance for 'borderline' cases. When text and structural similarity scores were compared for their classification power in a set of simulated 'borderline' cases of the CATH protein structure database, structural similarity scores performed best in the H (superfamily) level of CATH. This may be expected for structural databases, where structural relationships primarily define class membership. Although the SSAP scores perform better than text similarity in classification as single predictors, the performance improved considerably when structure and text similarity classifiers were combined in a logistic regression model.On a dataset of 'borderline' cases, a significant improvement in coverage by up to 50% at low error rates was observed. For equivalent coverage, the combined classifier benefits from a significant reduction in the number of false positives compared to SSAP. For instance, when SSAP captures 1000 true matches, it also includes 1612 false positives, while the SSAP+TEXT classifier includes 559, which is a 65% reduction. Moreover, the combined classifier was able to make an additional 4.3% of correct classification predictions based only on the top scoring hits, compared to SSAP alone. The combined classifier is therefore a better starting point than SSAP on its own, when classifying a 'borderline' protein. Although the error rate is not sufficiently low to enable fully automatic protein classification of 'borderline' cases, the combined classifier can have practical application in the support and evaluation of manual protein domain classification assignments. More development of the text similarity algorithm using a more elaborate weighting scheme combined with more efficient text retrieval and information extraction is required to improve performance further.The text similarity algorithm was benchmarked and optimised using a manually curated dataset of enzyme superfamilies, before it was applied on a large dataset of 'borderline' cases in CATH. Removal of sentences which were semantically irrelevant to protein classification using an SVM model resulted in better performance of the text classifier, while the dimensionality of the feature space of the document reference set of CATH was reduced by 32.7% making calculations speedier. Although all terms were used in this implementation, performance may improve further with feature selection based on thresholding metrics, such as document frequency, information gain, mutual information or chi-square and more involved weighting schemes. For example, the weights of a specific set of highly informative terms can be boosted. For the CATH database reference set, these terms would include selected names and synonyms of the superfamily and their protein members and function annotations from UniProt. Performance should also benefit by using a more sophisticated term generation scheme that accounts for synonymy.All words of the abstracts, including title and authors, were used in our method. Typically abstracts contain a brief description of the protein structure. A more detailed and extensive description of important features may only be mentioned in the main article, so full text is expected to yield better results. Irrelevant sentences can be removed by classifying each sentence of the full text article using the SVM filter. The inclusion of additional abstracts from related articles in the text related to each protein of the reference set improved performance. Abstracts which are related to the main reference usually contain additional terms that are likely to be characteristic of each class. As full text articles are not always available in public databases, this is an alternative way to muster additional relevant text for each protein and leverage the lack of full text.A more comprehensive selection of texts by inclusion of abstracts of relevant articles sourced from UniProt and GenBank records may provide more comprehensive text sources for the query proteins. Annotations from PDB and UniProt may also be used alternatively or additionally to abstracts in the query proteins. In this implementation, abstracts were used as text relating to the query protein of the test set because they are more readily available. Moreover, it is common for annotations to contain technical terms (for instance '3D structure'), which may introduce bias to the text similarity classifier. They also lack consistency and reliability because not all proteins are fully annotated yet, while several annotations are propagated based on high homology rather than experimental observations.Structural database entries are protein domains, however domains lack a one-to-one relationship to PubMed abstracts. In fact, it is assumed that each abstract possesses information in all domains of its related protein. In reality it is quite frequent for certain domains within a protein to attract greater interest, and text, than others. Inclusion of additional abstracts or full text articles related to the query protein will increase the likelihood that information relevant to all domains of the protein is present in the text.Combination of the text similarity algorithm with other structural similarity algorithms, such as CE , DALI 2 and MSDFFinally, we demonstrated the practical application of a text based classifier in protein structure database curation. The model resulting from its combination with the structural classifier is superior to the structural classifier alone, thus providing an improved way to classify 'borderline' proteins in the CATH protein structure database. Although in the context of full automation the improvements might at first sight look relatively moderate, it is important to bear in mind that in the principal application domain, the textual results are intended to be used by manual curators or users of a structure classification server as a guide to manual classification. Putting it another way, we need the textual results to be confirmatory of the structural similarity results rather than being entirely novel. The fact that our text classification scheme reproduces around 88% of the purely structure-based AUC, and in combination increases the AUC by a small but significant amount, shows that we are indeed extracting the most relevant texts and that some of these texts are key to making better informed decisions on superfamily membership. A user of a hybrid system would therefore be provided with a highly relevant shortlist of texts from which he or she can make an informed decision as to the correct superfamily classification of the protein being analysed. In other words, the fact that we can improve automatic classification is actually far less important that the fact that we are able to select the relevant texts which can be further analysed by the user in semi-automatic classification. Consequently, we are planning to build this text classification functionality into the user interfaces of our existing fold recognition web servers: both the sequence-based GenTHREADER , and theOverall, our findings show a useful combination of a structural similarity with a text mining approach and demonstrate the value of the text based approaches in protein classification.In summary, a text based classifier was developed and implemented for the classification of proteins in the CATH database. Although the structural similarity scores perform better than text in classification of proteins in structure databases, it was proven that the combination of the structure and text classifiers in a logistic regression model provides a more powerful classifier, significantly increasing coverage especially at low error levels compared to using structural similarity alone. The benefit is particularly useful in cases where structural similarity is not high enough to be conclusive.We found that, for 'borderline' matches with SSAP scores below 80, which are notoriously difficult to classify, it is preferable to use the combined structure and text similarity classifier than SSAP alone. This result should be useful in the development of servers which aim to classify proteins automatically and reliably.TF) in the document, weighted by the inverse document frequency of the term (IDF) in the document collection. IDF is defined as follows:Text was represented according to the bag-of-words model  as an unN denotes the number of documents (abstracts) in the collection and DF is the document frequency of the term. The vectors were of unit length  to compensate for variable document length which may favour long documents in the text similarity calculations. The base of the logarithm used in calculations is 10. Similarity c between two documents is defined as the cosine of the angle between two vectors v A and v B representing each text:where c is high when the compared documents share several rare words. For ease of calculations, the range of c was transformed to range 0–100.The value of Lucene : Lucene R : PlottinThe performance of the method was assessed using the AUC (area under ROC curve) metric, the Matthews correlation coefficient (MCC), F-measure and coverage versus error plots. The ROC plots were generated by sorting the scores and plotting the number of correct assignments (true positive rate) versus the fraction of erroneous assignments . For TP = true positives, TN = true negatives, FP = false positives, FN = false negatives, the recall (rec), precision (prec), MCC and F measure are defined as follows:MCC is a measure of overall classifier accuracy. A value of 0 indicates random performance, whilst a value of 1 implies perfect classification. The F-measure is the weighted harmonic mean of precision (prec) and recall (rec). For α = 0.5, which is the value of α used in the assessment of this method, the mean is balanced.et al. [Brown et al.  describeet al.  and sequAnother set of abstracts PDB145) was derived from the primary references (JRNL field) of the PDB structures that correspond to the REGS352 sequences. The primary references of the 282 PDB files described in the gold dataset of enzyme superfamilies were checked in order to remove PDB files without references published in MEDLINE, and files with identical references to avoid duplicate references appearing in the set. The filtered set consists of 145 PDB files, each with a unique reference, and their distribution across the enzyme families and superfamilies of the gold set is shown in Table 5 was derAbstracts relating to a protein were downloaded from PubMed using a set of Perl scripts. The title and authors with their affiliations were also collected with the body of the abstract. The primary reference for each protein was either specified by a PMID contained in the corresponding Genbank or UniProt annotation, or, if a structure for the protein was available, in the JRNL field of the relevant PDB file. Additional abstracts were collected from the 'Related Articles' hyperlink of PubMed.To perform text comparisons, the documents were converted into IDF weighted term vectors using one of Lucene's analysers. The cosine similarity of the angle between the normalised text vectors was used to assess the similarity between text documents and was calculated according to equation (2).To improve performance by reducing noise and increasing the prevalence of informative terms, a machine learning approach was developed to discriminate informative sentences from any text irrelevant to the classification task within the abstracts of the reference set. Sentences from the abstracts of the REGS352 dataset were used to train an inductive linear SVM model using SVM-Light [Protein, family and superfamily names were removed from the positive and negative examples to avoid unfavourable training of the model on these terms. SVMs were used to learn the features of the training set and classify new unseen sentences. Binary and frequency feature representations as well as a range of values of the c parameter of SVMlight from 0.1 to 2 were tested to identify the optimal SVM model. The frequency representation gave better results and was selected over binary. The optimal c value in our dataset was 0.25. Leave-one-out cross validation of the optimal SVM model estimated the recall 83.45% and precision 82.88%. The SVM model was used to classify each sentence in the abstracts as relevant or not-relevant. Only relevant sentences were used in the text comparisons.To benchmark the method, the text similarity scores of the abstracts of the REGS352 (test set) and PDB145 (reference set) were calculated  and assessed for the prediction of classification matches in the superfamily level of CATH. Scores were labelled as matches if the CATH superfamilies of the proteins corresponding to the two abstracts were matching, or non-matches otherwise. In order to optimize the performance of the classification method several conditions of the reference set were considered and tested Table .The SVM model was used to filter the abstracts of the PDB145 reference set for informative sentences. The SVM filtered reference set performed better (F measure: 0.56) than the set of intact abstracts (0.53).Perl scripts were implemented to download 19 additional abstracts for each primary reference from the 'Related Articles' hyperlink of PubMed. This function of PubMed identifies other publications in MEDLINE which resemble to the primary reference. Retrieval is based on the evaluation of similarity between two documents over all vocabulary terms using a probabilistic model . The incThree types of text analysis were tested using Lucene's Standard, Stop and Simple analysers. All analysers lowercase text, however only the first two remove stop words, and differ in the way they split the text into terms. The Standard has rules for acronyms, hostnames, etc. which are retained intact as a single term, while the Stop and Simple analysers split the words in special characters. The Standard analysis produces a large number of uniquely found, non-informative terms compared to the shorter, fewer and more abundant terms generated by the Stop analyser. Performance was improved when words were split in special characters as well as white space (AUC 0.74) instead of splitting in white space only (0.70). It also benefits from removal of 'stop' words, common words that occur in similar frequency across the reference set. There was no improvement when the Porter stemming algorithm was applied (results not shown) . HoweverA set of Perl scripts was used to retrieve annotations from the PDB  and UniProt  databases and concatenate them to the abstracts of the reference set. The inclusion of annotations from the UniProt and PDB databases produces a negligible improvement over the performance of the plain 20 abstracts (MCC 0.51 with annotations versus 0.50 without). The method was also tested using UniProt database annotations instead of abstracts in the test set. When relevant annotations from UniProt  were used instead of abstracts as text relating to the query proteins, performance was improved . However, abstracts were used in this implementation because annotations are not immediately available in the databases, in contrast to abstracts that usually accompany structural biology papers describing protein structures upon their release.The classification performance of each of the conditions is shown in Table .A reference set of abstracts was assembled to assess the ability of the method to classify domains at the homologous superfamily level of CATH. CATH v3.1 contains structural classifications for domains from 30028 PDB files. Using PubMed IDs (PMIDs) indicated in the JRNL field of these PDB files of protein domains already classified at the superfamily level of CATH, a total of 15154 unique abstracts with their related articles from PubMed and annotations from UniProt and PDB were downloaded, analysed, indexed and filtered for informative sentences using the SVM model. There are 27069 PDB files (90.1%) with primary abstracts within this document set (textCATH set), because several PDB files share the same primary reference. The textCATH set included 139171 terms and covered 1806 of the 2090 superfamilies in CATH. The textCATH reference corpus is available for download from our website An additional reference set was compiled from the intact abstracts, without any filtering, in order to compare the effect of using the SVM model in the performance of the system. The latter set was also analysed using the Stop analyser and contained 204904 terms.In order to compare the performance of the structure and text similarity measures in protein classification in the superfamily level of CATH, an all-versus-all structural comparison using SSAP was performed on the protein domains corresponding to the 15154 abstracts of the textCATH set. The majority of the protein domains have "clear" homologues in the database displaying clear structural similarity (SSAP > 80). In the benchmark, the 'easy' pairs are ignored in order to focus on protein domains without clear homologues. Only domains whose matches in the database displayed SSAP scores below 80 and sequence identity below 30% were retained in order to simulate 'borderline' classification assignments. As a result, there were 2436 'borderline' protein domains with a total of 6207493 comparisons, of which 33577 were true matches (0.54%). The set of 1993 unique primary references contained in the PDB files of such domains comprise the DC1.1993 set.Logistic Regression is used for prediction of the probability of occurrence of an event by fitting data to a logistic curve. The logistic model can be written as follows:1, x2,..., xi are the explanatory, independent variables, which in this case are the SSAP structural similarity and the text similarity (TEXT) scores. The resulting score is thus defined as the natural logarithm of the odds.where p is the probability of a classification match, and xFor validation purposes, the set of comparisons was split into a training set using the comparisons of 1000 randomly selected abstracts and a test set comprising the comparisons of the remaining 993 abstracts. The training and test sets included 1224 and 1212 domains from the DC1.1993 'borderline' cases dataset, respectively. There were 3130887 (3076606) domain pair comparisons in the training (test) set, of which 16812 (16765) were domain pairs in the same CATH superfamily or true matches and the remaining 3114075 (3059841) were non-homologous pairs or non-matches.2 measure. The R2 statistic is a measure of the effect size and indicates how useful the explanatory variables are in predicting the response variable.The predictive ability of the models was addressed using the Nagelkerke RProject Name: textCATHProject Webpage: Operating System: Linux x86Programming Language: Perl, Java)Other requirements: Lucene (available from License: GNU General Public LicenseAK designed and carried out the study and wrote the paper. OCR prepared the structural and sequence comparisons for the CATH dataset. DTJ conceived and coordinated the study and participated in writing the paper. All authors read and approved the final manuscript.


Analysis of the pathogen interactome is a powerful approach for dissecting potential signal transduction and virulence pathways. It also offers opportunities for exploring new drug targets.Mycobacterium tuberculosis H37Rv was constructed using a homogenous protein mapping method, which has shown molecular chaperones, ribosomal proteins and ABC transporters to be highly interconnected proteins. A further analysis of this network unraveled the function of hypothetical proteins as well as a potential signaling pathway. A hypothetical protein, Rv2752c, which was linked to a metal cation-transporting ATPase, was characterized as a metal-beta-lactamase, through domain analysis in combination with an in vitro activity experiment. A second hypothetical protein, Rv1354c, and an unknown protein kinase, PknK, interacted with a similar group of inner membrane-associated ABC transporters in the PPI network. The interactions of Rv1354 with these proteins were also confirmed by a further bacterial two-hybrid analysis. According to protein domain structures, the unique M. tuberculosis Rv1354c gene was proposed, for the first time, to be responsible for the turnover of cyclic-di-GMP, a second messenger molecule in this bacterium. A further structure-based inhibitors screening for Rv1354c was also performed in silicon.In this study, a protein-protein interaction (PPI) network of M. tuberculosis consisting of 738 proteins and 5639 interaction pairs. Our analysis unraveled the function of hypothetical proteins as well as a potential signaling pathway. The group of ABC transporters, PknK, and Rv1354c were proposed to constitute a potential membrane-associated signaling pathway that cooperatively responds to environmental stresses in M. tuberculosis. The study therefore provides valuable clues in exploring new signaling proteins, virulence pathways, and drug targets.We constructed a comprehensive protein-protein interaction network for  Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), is responsible for nearly two million human deaths worldwide every year. In addition, one-third of the world's population is currently infected with the TB bacillus. The situation has worsened in recent years with the emergence of multi-drug resistant TB and co-infection with HIV [M. tuberculosis is therefore critical for the identification of new drug targets and the development of new drugs.The intracellular pathogen with HIV . UnderstM. tuberculosis bacterium confronts a highly hostile environment during infection, including restricted access to nutrients and reduced oxygen tension [The  tension . Its abiProtein-protein interactions (PPI) play significant roles in many biological processes, such as in the assembly of molecular complexes or in signal transduction. At a more applied level, protein interaction networks also provide tools for exploration of novel drug targets . HoweverM. tuberculosis PPI network consisting of 738 proteins and 5639 interaction pairs using a homogenous protein mapping method. Analysis of this network has provided a number of valuable clues for exploration of new virulence pathways and drug targets.In this study, we constructed an M. tuberculosis protein interactions at the proteomic scale. The rationale is that, for any pair of interactive proteins validated experimentally in the Database of Interaction Protein (DIP) [M. tuberculosis. The schematic diagram shown in Fig. M. tuberculosis H37Rv was constructed, consisting of 738 proteins and 5639 non-redundant interaction pairs  to predict in (DIP) , two proSeveral computational methods have recently been developed to predict protein interactions ,11,12. BM. tuberculosis to be able to resist damage from various pressures exerted by the host cells, which makes molecular chaperones especially critical for the survival of M. tuberculosis in its host.In protein interaction networks, the term degree represents the number of proteins that interact with a given target protein. The most highly connected proteins are usually the most important  and are M. tuberculosis have been reported to differ from those of other species [M. tuberculosis has two groEL genes, located in distinct regions of its genome, while most eubacteria contain only a single copy of this gene. The protein with the highest degree of connection to both groEL genes was Rv0685 (Tuf) and this protein showed increased expression inside macrophages in the BCG strain [M. tuberculosis in its host, suggesting that these may be potential new drug targets.In this respect, it is notable that molecular chaperones of  species . For exaG strain . This alM. tuberculosis [In contrast to what is known regarding molecular chaperones, the functional importance of ABC transporters has not well been addressed, although these proteins account for about 2.5% of the genome of rculosis ,19. TheiM. tuberculosis genome from NCBI; these also appeared in the PPI networks  and phosphodiesterase (PDE) activity [M. tuberculosis.An Rv1354 homologous protein from the activity . TherefoM. tuberculosis that contains GAF-GGDEF-EAL domains and interacts with membrane-associated ABC transporters. Thus, Rv1354c may play an important role in sensing extracellular signals and in regulating the transcription of the M. tuberculosis genes. Additionally, Rv1354c is localized near to the inner membrane [The cyclic-di-GMP signal system is ubiquitous to bacteria but is absent in archaea and eukaryotes, making this an ideal system to target for potential therapeutic drugs. It also provides a basis for a selective inhibitor design. The Rv1354c appears to be a unique gene in membrane , a locatM. tuberculosis have not yet been identified experimentally. For this reason, a structure-based inhibitor screening was performed, which targeted the GGDEF domain to inhibit its cyclic-di-GMP synthesis activity. At present, the 3D structure of Rv1354c is not known. Using the automated comparative protein modeling web server SWISS-MODEL [2V0N) from Caulobacter vibrioides as a template (Identities = 41%), a structural modeling of the GGEDF domain of Rv1354c  and all antibiotics were obtained from TaKaRa Biotech. All reagents for the two-hybrid assay were purchased from Stratagene, while all PCR primers were synthesized by Invitrogen (Table S1). DNA purification kits were purchased from Watson Biotechnologies, and the Ni-NTA (Ni2+-nitrilotriacetate) agarose columns were obtained from Qiagen.M. tuberculosis protein interactions at the proteomic scale. With a pair of interactive proteins validated experimentally in the Database of Interaction Protein (DIP) [To integrate accumulative protein interaction information obtained from the high throughput experiment, a computational method called homologous protein mapping (HPM) was used to predict in (DIP) , two proThe information collected in DIP as source data were selected because they integrated many types of evidence on protein-protein interactions including those from several model organisms .blast [blast package (2.2.16) and amino acid sequence of M. tuberculosis were downloaded from the FTP Web site of NCBI. Using these amino acid sequence as input, a local blast database was subsequently constructed using the program formatdb released in the blast package. The amino acid sequence of proteins in the DIP database was downloaded from the DIP Web site (update to 20071007). Subsequently, all of the DIP amino acid sequence was used in the query. Then, the previous blast database was employed as target database to perform a large-scale blast search using blastp . The preliminary blast results were further processed with our custom script for a convenient mapping procedure in the next step. Consequently, the linkage between these two sets of proteins was procured. Thereafter, the mapping process was completed according to the strategy described above. The Protein interaction network was visualized using Cytoscape [The homology of proteins was assigned using the sequence similarity searching tool, blast . The latytoscape .Escherichia coli BL21 (Novagen) was used as the host strain to express and purify proteins as described in a previously published procedures [2/KI and with or without 10 mM ZnSO4. The reaction mixture was further rotated for up to six minutes at room temperature. Rapid decolorization occurred if the penicillin was hydrolyzed by beta-lactamase, which indicated positive beta-lactamase activity.The vectors expressing mycobacterial protein were constructed using the pET28a vector and primers described in Additional file ocedures . Purifieocedures . The staThe BacterioMatch II Two-Hybrid System Library Construction Kit (Stratagene) was used to detect protein-protein interactions between mycobacterial proteins. Bacterial two-hybrid analysis was carried out according to the manufacturer's directions. The pBT and pTRG vectors containing mycobacterial genes were generated. All primers used for PCR amplification are described in Additional file in silicon. The structure of the GGDEF domain of Rv1354c was modeled computationally using the automated comparative protein modeling web server SWISS-MODEL [According to protein interaction information and domain structures, we propose that Rv1354c is a suitable target for new anti-tuberculosis drugs, and a docking-based inhibitor screening was performed SS-MODEL . LikewisSS-MODEL . The comSS-MODEL .PPI: Protein-Protein Interaction; HPM: Homologous Protein Mapping; DIP: Database of Interaction Protein; TB: Tuberculosis; DGC: Diguanylate Cyclase; PDE: Phosphodiesterase.The authors declare that they have no competing interests.TC performed data analysis and wrote the manuscript. LZ and XW performed the experiments. ZGH performed the data analysis, helped in the experimental design, supervised the experiments, and wrote the manuscript. All authors read and approved the final manuscript.M. tuberculosis H37Rv. A protein-protein interaction network of The data provided represent the protein interaction network of M. tuberculosis H37Rv generated from HPM method which composed with 793 individual proteins and 6091 interaction pairs.Click here for fileTop 20 highly connected Nodes in the network. The data provided represent the top 20 highly connected Nodes in the network.Click here for fileHypothetical proteins in the network. The data present the hypothetical proteins in the network.Click here for fileTop 10 hits compounds in structure based virtual screen. The data provided top 10 hits compounds in structure based virtual screen.Click here for fileMycobacterium tuberculosis obtained based on the experimentally verified interactions from DIP.The interactome of  The data present all protein-protein interactions of Mycobacterium tuberculosis obtained based on the experimentally verified interactions from DIP.Click here for fileMycobacterium tuberculosis to the hit protein in DIP. Identity, coverage and E-value of each query protein of The data described the identity, coverage and E-value of each query protein of Mycobacterium tuberculosis to the hit protein in DIP.Click here for filePrimers and plasmids used in the study. The data present the primers and plasmids used in the study.Click here for file