Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions.Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology. We have also combined two filters that are based on entirely different principles and this combined filter has a better predictability than the individual components.Testing these functional filters on known and random minimotifs has revealed that they are capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known minimotifs that are not removed by the filter is ∼4.6 times that of random minimotifs. For the molecular function filter this ratio is ∼2.9. These results, together with the comparison with the published frequency score filter, strongly suggest that the new filters differentiate true motifs from random background with good confidence. A combination of the function filters and the frequency score filter performs better than these two individual filters. Minimotifs are short contiguous peptide pieces of proteins that have a known biological function. These functions can be categorized into binding, posttranslational modification of the minimotif, and protein trafficking. While there are many known functional minimotifs, predicting a minimotif in a new protein based on a consensus sequence, position-specific scoring matrix, or other algorithms produces many false-positive predictions. This limits the usefulness of minimotif prediction programs such as Minimotif Miner (MnM) To reduce false positive minimotif predictions, three approaches have been used in MnM Despite these inter-related approaches, false positives remain a concern, thus new types of filters are needed. In considering new strategies that might be used to refine minimotif predictions, we have noticed that some proteins which contain a particular domain are thought to have functions related to similar cellular processes. For example, proteins, which contain PTB or SH2 domain that binds to phospho-tyrosine containing proteins, are typically involved in signaling The Gene Ontology (GO) database yytm is the minimotif sequence, Jak2 is the source protein, binds is the activity, and SHB is the target protein. SH2 domain specifies the region where the minimotif binds the target SHB.We have built a syntactical and semantic structure for minimotifs that enables easy integration of minimotif and GO data For predictions of new minimotifs, the source protein query contains multiple minimotif short sequences that may encode new activities with all of the target proteins. In this paper, the source protein and the set of putative target proteins can be mapped to cellular and molecular functions derived from the GO database to determine whether the source and target proteins share at least one common or similar cellular function and/or molecular function. This approach was first tested on the Minimotif Miner database of experimentally verified minimotifs. Analysis of several variations of the algorithm demonstrates that this approach can reduce false positive minimotif predictions in the test dataset and eliminate many predictions in a set of randomly selected query proteins.Since there are many false positives in minimotif prediction programs, any means of selecting minimotifs with a higher probability of being true is desired. The molecular function filter does provide this advantage. Another important aspect of the filters presented in this paper is that they segregate minimotifs into groups for uses. With the cell function algorithm users can choose minimotifs for target proteins that are involved in the same cellular process or in a different cellular process. For example if the query protein is involved in cell division, one user may want to only look for minimotif predictions for other proteins involved in cell division or may want to identify predictions that are involved in other cellular processes. We have also implemented this for molecular functions as well.Another important contribution of this paper is the novel conclusion that it may be possible to combine more than one filters to get another filter whose performance is better than that of the individual filters. In particular, we have devised two combinations. The first combination has the molecular function and the frequency score filter and the second combination has the cellular function filter and the frequency score filter. The new combination filters have much better p-values than all the component filters involved.To reduce the false positives in the minimotif predictions by MnM with cellular/molecular function information, we obtained this functional data from the GO database. We selected the GO database for this purpose because it has the largest ontologies for these functions and has relationships between functions. The GO ontology (4/09 release) has 16,698 terms and 32,719 edges for biological processes/cellular functions and 9309 terms and 9,924 edges for molecular functions. The edges for functional relationships are directed from the juxtaposed node to the larger node for two neighboring terms. Because identical proteins in the MnM and GO databases may have different accession numbers, we used an alias table to map these accession numbers to the cellular/molecular functions of each protein.To test the effectiveness of the filter algorithms, we ideally needed to compare a dataset of verified minimotifs to known negatives. For experimentally verified minimotifs we used the Minimotif Miner 2 database (MnM 2), for which the total number of minimotifs is ∼5300 We then tested if any of these negative data points was in the positive dataset. In particular, for every minimotif in our database we generated all the  pairs. We assembled all of these pairs into a collection C. Followed by this, for every pair  in the negative dataset we checked if  was in C. None of these 20,000 pairs was in C. This is again a validation of the way we have picked the negative dataset.We did not have access to known negative minimotifs, so we generated a dataset that will serve as “negative” interactions that are comprised of proteins that are most likely not to interact. There are ∼500,000 known protein-protein interactions for >5,000 total proteins, but if all possible pairwise interactions are considered, then the number of true minimotifs is a very small fraction of the total possible number of all interacting protein pairs. For example, if there are ∼30,000 proteins for the ∼500,000 interactions, then the total number of possible pairing is 449,985,000. Thus, it is safe to assume that choosing randomly generated protein pairings represents “negative” minimotifs. Therefore, 20,000 entries of random pairs of source proteins and target proteins were sampled. Of these pairs, 3,153 had at least one cellular function and 3,706 of the pairs had at least one molecular function in the GO dataset. These entries were used as the “Negative” datasets. S, which contains the putative minimotif p, and a known or predicted associated target protein T, with the protein accession number alias table, find the list of cellular functions of each S, and T. Compare the two function lists to identify a set C of common cellular functions. The molecular function filter algorithm is identical except that it utilizes molecular functions F, instead of C.The basic function filter algorithms test whether at least one common or similar cellular/molecular function is shared by the given minimotif source protein and target protein. Given the minimotif source protein sensitivity, while the percentage of negative minimotifs not removed by the filter is the selectivity. The ratio of sensitivity/selectivity is a Discrimination Ratio (DR) that measures the preference for verified minimotifs over that of negative minimotifs. The choice of DR is quite natural. Please note that sensitivity is the % of true positives and selectivity is the % of false positives. Clearly, we want the true positives to be large and the false positives to be low and hence we want this ratio  to be large. Ideally, if an ROC curve could be plotted, that will bring out the statistical significance nicely. For an ROC curve to be plotted there has to be an underlying parameter that changes. For some of the datasets in our analysis, there is no relevant underlying parameter that changes and hence we could not plot ROC curves for them. Also, note that the ROC curve is nothing but a plot of false positives versus true positives. In some sense we can think of the DR as a (single number) summary of the ROC curve.This algorithm was applied to the above datasets for molecular and cellular functions. To evaluate the efficacy of the algorithms we used two metrics. The percentage for the experimentally verified minimotifs not removed by the filter is the DR above 1 indicates a favorable filtering preference, while those below 1 show worse performance in selecting the verified minimotifs rather than the negative minimotifs.Sensitivity and selectivity both range from 0% to 100% with 100 % indicating complete recovery of the experimental minimotifs or of negative minimotifs. A DR of 2.3. The test analysis shows that there is value in using molecular and cellular function filters for reducing false-positives in minimotif predictions.The cellular function algorithm had a sensitivity of ∼11% and a selectivity of ∼3%, with a DR of 3.9, indicating that many motifs were recovered and there was a ∼4-fold preference for retaining a verified instance over a randomly selected negative instance. The molecular function algorithm had a sensitivity of ∼29% and a selectivity of ∼13%, with a While the Cellular and Molecular function algorithms have value in reducing false positives, the structure of the GO database provided us with an opportunity to vary the stringency of function assignment and optimize these algorithms. GO contains neighborhood information of each cellular/molecular function term. The nodes are cellular function terms or molecular function terms in this case, and the edges go from the children nodes to parent nodes. So the “at least one common function” becomes “at least one similar enough function”. That is to say, the predicted target proteins are restricted to those for which at least one cellular/molecular function is similar enough to one in minimotif source protein, or the distance between at least one cellular/molecular function of the target protein and that of the source protein is small enough. We have introduced a distance threshold into the basic algorithm.t, for each pair of cellular/molecular functions, one from the list of S and the other from the list of T following the basic algorithm, examine their ancestors on the directed graph to see whether there exists a common ancestor function such that the total distance or the total number of edges between this ancestor and the pair of functions is smaller than or equal to the threshold t.The expended algorithm works as follows: given the distance threshold DR for verified minimotifs was the highest when the distance threshold was one with a 4.6-fold preference for verified minimotifs, but still showed a 3.4-fold preference for a threshold of two nodes. The sensitivity significantly increased over the basic filter by using a distance of one or two, rather than 0.The basic algorithms used a distance threshold of 0; here we tested 5 additional distance thresholds of 1, 2, 3, 4, and 5. Results from the evaluation of the cellular function filter are shown in To test the statistical significance of the filters we have used ROC curves and p-values. We have employed the programs of the R project DR. The molecular function algorithm is more sensitive, but less selective when compared to the cellular function filter. For the molecular function filter also, we have plotted the ROC curve with distance as the underlying parameter. Results from the evaluation of the molecular function filter are shown in Both filters have value in reducing false-positives in the test datasets and stringency of predictions can be controlled by selecting distances between 0 and 3, whereas the performance of the algorithms degrades at distance values above 3. The above results indicate that the filters differentiate verified data from negative data with a good confidence and strongly suggest when predicting novel minimotifs these filters would help to decrease the number of false-positive predictions.We wanted to compare the performance of the new filters with one of the already existing MnM filters, namely, the frequency score filter. To begin with we have plotted the ROC curve for the frequency score filter. This ROC curve is shown in Note: The above results indicate that the cellular function filter has a poorer p-value than the frequency score and the molecular function filters. As a result, one has to exercise caution while employing the cellular function filter. Both the filters could be of value in clustering the motifs predicted by MnM.A novel contribution of this paper is the conclusion that a combination of several filters can yield a better predictability than the individual filters. In particular, we have devised two combination filters. The first combination filter employs the molecular function and the frequency score filters. Note that these two filters are based on two different principles. The frequency score is based on the number of occurrences of the predicted motif whereas the molecular function filter is based on whether the source and target proteins share a common molecular function. Our tests of the combined filter indicate that the combined filter has a better p-value than the two individual filters.We have employed the either-or-based combination of the molecular function filter and frequency score filter, in the expectation that the two filters can complement each other in some way, which is reasonable since they focus on different aspects and therefore the combined filter may outperform any of the two. Given a motif of some source protein, associated with its target protein, the combined filter examines whether the source and target proteins are retained by the molecular function filter, as well as whether the motif and source are retained by the frequency score filter. If either filter retains them, the combined filter retains them.−10. The ROC curve is shown in This idea was tested on the same positive dataset and negative datasets. The positive datasets have already got experimentally verified entries of motif, its source protein and the associated target protein. For the negative datasets, which are 20,000 random protein pairs, we threw one of each protein pair into Minimotif Miner (MnM) −11. The prediction of this combination is shown in The second combination filter employs the cellular function filter and frequency score filter in the same way. Considering the cellular function filter is more stringent, five distances  were used, together with the same three thresholds  for frequency score filter. As a result, fifteen threshold parameters were formed for this combination of cellular function and frequency score filters. To smoothen the ROC curve, very small noises were also added, which is no more than 6.743894eWe have implemented these new filters with the other filters on the MnM 2 website Figure 3Supporting Information S1). Statistics for predictions from this analysis are shown in We wanted to examine how many predicted minimotifs were filtered by the algorithms. We ran the filter on P53, Cyclin A, and MSH2, which each have different molecular and cellular functions Click here for additional data file.


Cyanobacteria are model organisms for studying photosynthesis, carbon and nitrogen assimilation, evolution of plant plastids, and adaptability to environmental stresses. Despite many studies on cyanobacteria, there is no web-based database of their regulatory and signaling protein-protein interaction networks to date.Synechocystis sp. PCC 6803. It predicts the protein-protein interactions using public interaction databases that contain mutually complementary and redundant data. Furthermore, SynechoNET provides information on transmembrane topology, signal peptide, and domain structure in order to support the analysis of regulatory membrane proteins. Such biological information can be queried and visualized in user-friendly web interfaces that include the interactive network viewer and search pages by keyword and functional category.We report a database and website SynechoNET that provides predicted protein-protein interactions. SynechoNET shows cyanobacterial domain-domain interactions as well as their protein-level interactions using the model cyanobacterium, Synechocystis proteins. SynechoNET is freely available at  or directly at .SynechoNET is an integrated protein-protein interaction database designed to analyze regulatory membrane proteins in cyanobacteria. It provides a platform for biologists to extend the genomic data of cyanobacteria by predicting interaction partners, membrane association, and membrane topology of  Synechocystis sp. PCC 6803 (henceforth referred to as Synechocystis) was determined at Kazusa DNA Research Institute [Synechocystis has been highlighted to integrate "omics" data in systems biology field [Cyanobacteria are prokaryotic microorganisms that perform plant-like photosynthesis as well as carbon and nitrogen assimilation to obtain intracellular energy. Since cyanobacteria are believed to be a prototype organism that changed the ancient anoxygenic environment to oxygenic by photosynthesis, many scientists have used cyanobacteria as an ideal model organism to study adaptation to various abiotic environmental stress . Furthernstitute , which anstitute -8, Synecgy field . HoweverSynechocystis  and were aligned with SCOP (version 1.69) domains using the PSI-BLAST algorithm with a common expectation value  cutoff of 0.001. By applying SCOP domain interaction pairs obtained from the PSIMAP-based interaction information database, PSIbase (build 3) [Synechocystis.3,672 build 3) , 12,748 Synechocystis proteins were collected from SwissPfam [Synechocystis were mapped to Pfam domain-interacting partners from iPfam (version 19), resulting in the construction of 13,448 predicted protein-protein interactions involving 1,541 proteins. They account for 42% of all proteins in Synechocystis. Likewise, Synechocystis proteins were mapped to Pfam domain-interacting partners from InterDom, resulting in 80,319 predicted protein-protein interactions involving 1,760 proteins. They account for 48% of all Synechocystis proteins. Furthermore, 2,658 proteins comprising 72% of all Synechocystis proteins were involved in 26,805 protein-protein interactions directly obtained from STRING (version 6.3). Taken together, SynechoNET revealed that 2,930 proteins participate in 109,532 protein-protein interactions. It is noteworthy that they comprise 79% of all proteins in Synechocystis.Pfam domains of all the wissPfam . All proin silico prediction of protein-protein interactions, we used the number of databases that provide supporting evidence for each interaction as well as the reported reliability scores from InterDom and STRING. As a filter, we selected 509 Synechocystis proteins participating in 1,591 high-confidence protein-protein interactions that were commonly found in all the databases encompassing PSIMAP, iPfam, InterDom, and STRING. Those were further rescaled into the confidence range from 0.0 to 1.0 using the arithmetic means of InterDom and STRING scores. The resultant high-confidence protein-protein interaction network was dynamically visualized in Java applet viewer, a modified version of the public Integrator program [To denote the confidence of the  program .In addition to the interaction information of SynechoNET, it was reinforced to contain the information on membrane proteins that includes transmembrane topology, signal peptide, and domain structure information provided by Phobius and the prokaryotic version of Localizome program. The Localizome program gives an advantage for users to see the transmembrane topology and domain structure of cyanobacterial proteins at a glance.SynechoNET provides user-friendly web interfaces by (i) keyword search Figure  includinSynechocystis positive phototaxis using yeast two-hybrid analysis. The result showed that the hybrid sensory kinase Sll0043 strongly interacts with cognate response regulators, Sll0038 and Sll0039 (data not shown). These experimental protein-protein interactions were consistent with the high-confidence prediction result of SynechoNET. On the other hand, in the analysis of membrane protein complexes of Synechocystis, we found evidence that photosystem II D2 protein (Sll0849) and cytochrome b6 protein (Slr0342) interact directly with photosystem D1 protein (Sll1867) and cytochrome b6f complex subunit 4 (Slr0343), respectively. Furthermore, the experimentally-verified nine transmembrane helices of MntB protein encoded by sll1600 [in silico protein-protein interaction and transmembrane topology information provided by SynechoNET is useful and reliable for the functional genomics study of Synechocystis.To validate SynechoNET, we examined the interactibility between histidine kinase and response regulators involved in  sll1600  was alsoSynechocystis. SynechoNET can be used for the analysis of regulatory membrane proteins by predicting transmembrane topology and domain structure. In particular, approximately one third of the Synechocystis proteome are left to be fully annotated. Thus, SynechoNET can help biologists to annotate them by analyzing their predicted interaction partners, membrane association, and membrane topology.SynechoNET is a database and website that provides predicted protein-protein interactions. It integrates public protein-protein interaction databases that contain mutually complementary as well as redundant data. It is designed for biologists who are interested in the unicellular cyanobacterium  and directly at . All the generated protein-protein interaction lists in tab-delimited and Cytoscape [. The dynamic interaction viewer based on Java applet technology requires Java-enabled web browsers.SynechoNET is freely available at ytoscape  formats The authors declare that they have no competing interests.WK constructed the database, developed the website, and helped to draft the manuscript. SK designed the system, coordinated the project, and wrote the manuscript. BK helped to extract the interaction list. JO manually validated the interaction list. SC modified and improved the Java applet viewer. JB conceived and directed the study and helped to draft the manuscript. JC directed the project, participated in the system design, and helped to draft the manuscript. All authors read and approved the final manuscript.


Most of the existing prediction algorithms take only the polypeptide sequence around the phosphorylation sites into consideration. However, protein phosphorylation is a very complex biological process in vivo. The polypeptide sequences around the potential sites are not sufficient to determine the phosphorylation status of those residues. In the current work, we integrated various data sources such as protein functional domains, protein subcellular location and protein-protein interactions, along with the polypeptide sequences to predict protein phosphorylation sites. The heterogeneous information significantly boosted the prediction accuracy for some kinase families. To demonstrate potential application of our method, we scanned a set of human proteins and predicted putative phosphorylation sites for Cyclin-dependent kinases, Casein kinase 2, Glycogen synthase kinase 3, Mitogen-activated protein kinases, protein kinase A, and protein kinase C families (avaiable at http://cmbi.bjmu.edu.cn/huphospho). The predicted phosphorylation sites can serve as candidates for further experimental validation. Our strategy may also be applicable for the in silico identification of other post-translational modification substrates.Phosphorylation is an important type of protein post-translational modification. Identification of possible phosphorylation sites of a protein is important for understanding its functions. Unbiased screening for phosphorylation sites by  Protein phosphorylation is a kind of post-translational modification which plays key roles in many cellular processes. A protein kinase catalyzes the protein phosphorylation process, in which the γ phosphate on ATP or GTP is transferred to the substrates. Protein phosphorylation has the following characteristics: 1) phosphorylation requires a protein kinase to catalyze the reaction. There are currently 518 known kinase genes in the human genome in vivo usually involves a series of phosphorylation processes This reversible character allows the phosphorylation process to work like a switch in a living cell. When there is an external input signal, protein kinases activate specific substrates. After singla wanes, the activated substrates will be “shut down” by phosphatases, the substrates return to their original state and wait for the next signal. A normal biological function 32p-labeling and mass-spectroscopy are common experimental methods to identify phosphorylation sites, however, both of them are costly and time consuming if applied in an unbiased fashion. Thus, using computational methods to screen for putative sites prior to experimental verification can narrow down the efforts on experimental work. Many computational methods for identifying phosphorylation sites have been developed. In 1998, Kreegipuu et al. found that the primary peptide sequences around phosphorylation sites have strong signals for a collection of known phosphorylation sites. These signals can be used to identify possible phosphorylation sites et al. utilized the information of the peptide sequences in the proximity of the potential phosphorylation sites to develop the first phosphorylation site prediction method based on an artificial neural network algorithm in vivoAlthough most of existing methods predict phosphorylation sites based solely on the primary sequences around the phosphorylation sites, the primary sequences cannot fully determine whether the phosphorylation will occur. There are at least three mechanisms that can affect the phosphorylation process et al. integrated protein secondary structure into phosphorylation site prediction et al. took the protein-protein interaction information into consideration Till recently, some efforts have been made in this direction. In 2007, Gnad In this study, we try to integrate primary sequences with functional features, including KEGG pathways, GO terms, protein-protein interactions and protein functional domains, to predict phosphorylation sites. The final results indicated that for most of the kinase families, integration of functional features can improve the prediction performance, especially for the GSK3 kinase family, in which we can achieve about 10% improvement in accuracy. Finally, we scanned the human proteome for the kinase-specific phosphorylation sites using this new strategy. These identified putative phosphorylation sites can serve as a set of reliable candidates for experimental validation.The phosphorylation sites were extracted from the Phospho.ELM Database (Version 8.2) The background protein set contains all the human proteins of Swiss-Prot database (version 56.0).It is well known that protein phosphorylation is a dynamic event and depends heavily on conditions To estimate the performance of different kinds of features, we randomly selected the negative dataset from the background set. The background set was constructed from all S/T centered 9-aa peptides extracted from proteins of the background protein set excluding those known as phosphorylation sites. It is true that some unknown phosphorylation sites might be included in the negative dataset. Although phosphorylation occurs very frequently in cells, compared to the large amount of peptide sequences, the amount of phosphorylation sites of a specific kinase is still small. So the non-phosphorylated amino acids for the corresponding kinase family should dominate the negative dataset.In addition to primary sequences around phosphorylation sites, protein secondary structure has been found to be informative in phosphorylation site prediction Besides sequence level features, we aimed to find out functional information which could improve the performance of phosphorylation site prediction. We used following data sources: 1) KEGG pathways which were downloaded from the KEGG database (version 2009/09/16). 2) Biological Process, 3) Molecular Function and 4) Cellular Component annotation files from the GO database (version 1.120). 5) Protein-protein interactions (PPI), which were downloaded from the STRING database (version 8.0). Protein functional domain information were extracted from 6) the Pfam (version 23.0) and 7) InterPro (version 18.0) Databases.Functional data contains huge number of features for each protein. To reduce the dimensionality of feature space, we only used the over-represented or under-represented features for the substrate proteins in each kinase family. Two sided hypergeometric tests were used to detect over-represented or under-represented terms in the study set compared to the background protein set. Here the study set is the substrates of each kinase family. P-values derived from hypergeometric distributions were corrected by Bonferroni correction for testing on multiple terms. Terms with Bonferroni corrected p-values less than 1e-2 were taken as significant. The calculations were performed using the R package for statistical computing i , its value ix for the candidate protein is measured from the functional annotations of the protein. We estimated its probability f(ix) in phosphorylated proteins from the positive training set and its probability g(ix) in all proteins from the background protein set. As described above, the background protein set was constructed from all the human proteins in the Swiss-Prot database. The log-odds score for feature i of the candidate protein is defined as the log ratio of the two conditional probabilities:In previous studies, the identification of phosphorylated proteins is usually by the prediction of phosphorylation sites. Here we want to find out whether the phosphorylated proteins can be discriminated from the non-phosphorylated ones based only on the functional features. A simple log-odds ratio approach was used. For binary feature r and s was measured by the Jaccard distance, which is equal to the percentage of nonzero coordinates that differ:j is the sample size.The log-odds for all features can be summed up as the final score for a protein. It measures the log-odds for it belongs to the phosphorylated class versus the background. By summation, we implicitly assumed that all features are independent from each other. But indeed, some of the enriched functional features here should be correlated with each other, for example some annotations in KEGG might be similar to those in GO Biological Process. So we used a modified log-odds ratio score. The central consideration is that the higher the similarity of a feature is to all remaining features, the lower its weight should be. The similarity between two features i, the weight w is the sum of all its similarities to the remaining n−1 features:For each feature The final score for each protein is given by T and threonine (T) is expressed as vector T. Therefore, if the window size of a candidate sequence is 9, the dimension of the feature vector representing it is 20*9.To transform protein sequences into numeric vectors, each amino acid is represented as a 20-bit binary tuple (each bit is an indicator for one of twenty amino acids). For example, serine (S) is expressed as a 20-dimensional vector T , 1 is represented by T, and 6 is represented by T.There are two kinds of structure-related features: secondary structure and accessibility. For each amino acid of the query sequence, SABLE provides three kinds of secondary structure: . Since the phosphorylation sites are preferentially in the coiled sites For all the over-represented or under-represented functional features detected by the Hypergeometric test, if the candidate has the feature it was represented by 1, otherwise it was assigned 0.SVMs were used to evaluate the effects of different kinds of features. High sequence similarity between training and testing sets may cause a bias, so we discarded highly homologous sequences (over 70% identity) in the positive dataset before cross-validation to reduce this bias. After removing the highly homologous sequences, sample size for each kinase family was listed in To evaluate the effect of various kinds of functional features separately, we constructed different feature groups by adding one functional feature at a time to the sequence features; and also considered combined effects by including them all. The feature groups used in evaluation were: 1) sequence and structure features only; 2) sequence, structure plus significant KEGG features; 3) sequence, structure plus significant GO Biological Process features; 4) sequence, structure plus significant GO Cellular Component features; 5) sequence, structure plus significant GO Molecular Function features; 6) sequence, structure plus significant Pfam domain features; 7) sequence, structure plus significant InterPro domain features; 8) sequence, structure plus significant STRING PPI features; 9) sequence, structure plus the combination of all functional features. So for each round of five-fold cross-validation, nine SVM training and testing processes were performed one at a time. The libSVM Using the hypergeometric test, we found over-represented and under-represented functional features for different kinase families. Take the substrates of the CDK family as an example, we found that the CDK substrates are enriched in the Biological Processes “cell cycle”, “cell division”, “mitosis” and “cell proliferation”. Consistent with this result, it is well known that the CDK kinase family is a primary regulator of the cell cycle We evaluate the discriminative power of significant functional features using a simple log-odds ratio approach. For each protein, log odds of observing each feature in the phosphorylated over non- phosphorylated class is summed up. The higher the score is, the more likely the protein can be phosphorylated. With this strategy, we scored both the background protein set and the known phosphorylated proteins. We find that score distributions are significantly different for phosphorylated and background proteins across all the kinase families. As examples, the score distributions for the CDK and MAPK kinase families are shown in In the performance evaluation, an ideal solution to perform an unbiased comparison is by running cross-validations on all methods using the same dataset. SVMs The results of the cross-validation for each kind of functional group were listed in The known phosphorylation sites were limited and some of them are on the same protein. If two sites on the same protein are divided into the training and testing sets, this may cause over-estimate for the effect of the functional features. So we redid the above experiments by dividing the known phosphorylated proteins into training and testing sets first and then extracting their known phosphorylation sites as the training and testing samples. With this strategy, the five-fold cross-validation was also performed 1000 times. The final experiment results are listed in From the results of For the ATM family, adding the functional features resulted in a worse performance. From the results of different functional groups, we can see that the adding of KEGG, BP, CC, MF, IPR and PFAM have little influence on the performance; poorer performance was mainly caused by adding the STRING information. Similarly, for the PKB family, the poorer performance also resulted from adding the STRING information. From the results of http://cmbi.bjmu.edu.cn/huphospho, and the corresponding summary are listed in Saccharomyces cerevisiaeFrom the above analysis we find that functional features are useful for most of the kinase families. In The aim of this study was to evaluate the contribution of functional features in phosphorylation site prediction. Protein phosphorylation is a dynamic process which plays key roles in many cellular processes. It is clear that the recognition of phosphorylation sites should be related to many functional features. In the previous studies, the prediction of phosphorylation sites was mainly based on sequence features. Many researchers have pointed out that the cellular component may contain additional information for phosphorylation site prediction, since the phosphorylation cannot happen if the kinase and its putative substrate are not in the same component. The results in this study support this point in that the Cellular Component features are a powerful predictor of phosphorylation status for some kinase families. Besides Cellular Component features, the PPI information was also an effective predictor. A protein kinase usually binds both its substrates and ATP in the phosphorylation process, so the PPI information should provide additional information. It is expected that the Molecular Function and Biological Process should be more powerful for phosphorylation site prediction, since some known phosphorylation proteins may be annotated by the GO terms related to phosphorylation. But the final result indicated that these two are not so powerful for all the kinase families. This eliminates the possibility that the enhancement of functional features is caused by known phosphorylation related annotations of the test samples.a priori if a protein can be phosphorylated at all which identified by a common phosphorylation antibody or mass-spectroscopy. All these and other types of biological knowledge, when properly coded into a classification model, are promised to further enhance the prediction performance. Besides phosphorylation, all other kinds of post-translational modifications are functionally related, so our strategy should also be extended to predict other kinds of post-translational modification status.This work demonstrates that prediction of phosphorylation site can be more accurate for most kinase families if we incorporate more biological knowledge in the classification model. Such biological knowledge includes, for example, whether the candidate and the corresponding kinase interact with each other which can be obtained by experiments such as immune-precipitation and the yeast two hybrid systems. It is also informative as Figure S1Background protein set (white) and known phosphorylation substrate (grey) score distributions for ATM, CK2, GSK3, PKA, PKB and PKC kinase families. The horizontal axis is the log‐odds ratio score and the vertical axis is the percentage of proteins with corresponding scores. (DOCX)Click here for additional data file.


Research scientists and companies working in the domains of biomedicine and genomics are increasingly faced with the problem of efficiently locating, within the vast body of published scientific findings, the critical pieces of information that are needed to direct current and future research investment.In this report we describe approaches taken within the scope of the second BioCreative competition in order to solve two aspects of this problem: detection of novel protein interactions reported in scientific articles, and detection of the experimental method that was used to confirm the interaction. Our approach to the former problem is based on a high-recall protein annotation step, followed by two strict disambiguation steps. The remaining proteins are then combined according to a number of lexico-syntactic filters, which deliver high-precision results while maintaining reasonable recall. The detection of the experimental methods is tackled by a pattern matching approach, which has delivered the best results in the official BioCreative evaluation.Although the results of BioCreative clearly show that no tool is sufficiently reliable for fully automated annotations, a few of the proposed approaches (including our own) already perform at a competitive level. This makes them interesting either as standalone tools for preliminary document inspection, or as modules within an environment aimed at supporting the process of curation of biomedical literature. The growing body of published scientific findings within the domains of biomedicine and genomics poses, to research scientists and companies alike, the problem of efficiently locating the most relevant pieces of information. The research community is therefore keen to adopt novel text mining solutions, which have the potential of supporting such a discovery process . AlthougSince its pioneering origins , the fie competition , using tools developed within the scope of the OntoGene project . BioCreative is ideally suited to create the conditions necessary for significant scientific advancement in the area of text mining, by providing a framework for testing and evaluation of research tools over shared tasks. In particular, three main tasks were defined by the BioCreative organizers: gene mention (GM), gene normalization (GN), and protein-protein interaction (PPI).In this report we describe experiments performed within the scope of the most recent BioCreative While GM focuses on detecting mentions of gene names in biomedical text , GN provThe OntoGene project aims to develop and refine (semi-)automatic methods for the discovery of interactions between biological entities from the scientific literature. The OntoGene approach is based on dependency-based linguistic analysis of scientific articles . As indiThe original goal of our participation was to test, in a standardized environment, the ability of our system to detect relations among biological entities, for example protein-protein interactions. Although our core target was the PPI-IPS task, we initially assumed that the interaction method would normally be mentioned close to the mention of the interaction itself, and therefore we decided to consider additionally the PPI-IMS task. Although our assumption soon proved to be invalid, in the meantime we did develop an alternative approach to the identification of experimental methods that proved to be very successful .The PPI-IPS can be decomposed into two major subproblems: identification of the protein in text and their normalization to UniProt identifiers (problem A), and selection of the valid interactions among the proteins identified in problem A (problem B). Problem A is functionally equivalent to various tasks of identification and normalization of biological entities (including the GN task of BioCreative). A number of tools have been developed -26, and In the Biocreative GN task, participants have experimented various approaches to tackle this problem, which can basically be classified into two groups. The first type of approach is based on identifying all potential entity names in text (similar to the GM task), based on lexical, morphological and structural properties of the words, and later trying to match, disambiguate and filter those candidates against a shared resource such as UniProt ,30. ThisThe identification of interactions  is a less explored problem. One way to classify different approaches is to consider the amount of linguistic preprocessing that they apply to the input text. For example, systems like those described in -36 tend An alternative approach is to use a generic parser to build a syntactic structure for the sentences to be analyzed, which is then used to extract or confirm candidate interactions. Examples include use of the Link Grammar parser ,38, an LFinally, there are some systems that are based on machine learning, using a number of linguistic features ,44 and t).The first category is linked to the medical field of infectious diseases, in particular disease-host interactions. Examples include interactions between viral and human proteins  and interactions between bacterial and human proteins  and THTR_AZOVI : 'The cysteine-desulfurase IscS promotes the production of the rhodanese RhdA in the persulfurated form. After heterologous expression in Escherichia coli, the Azotobacter vinelandii rhodanese RhdA is purified in a persulfurated form (RhdA-SSH). [...]'The second category is created by cloning and expressing one gene of species A in species B, for example interactions due to 'heterologous expression', such as between ISCS_ECOLI (cysteine desulfurase of 'motif-based search method to identify putative effector proteins' (PubMed: 7493928), determination of a new protein family (PubMed: 9115257), comparison of the activity of homologous proteins (PubMed: 7890767), study of 'a pathway that is conserved from nematode to mammals' (PubMed: 7657591), or an experiment designed to test the presence of 'homodimers' (PubMed: 15131699).The third category contains interactions experimentally created to define the structure and the activity of some protein domains common to several species. For example, the 'murine' is an adjective referring to MOUSE). Names and synonyms for organisms were automatically downloaded from NEWT . The relative frequency of species in the sections of the papers are combined linearly, with weights assigned through a learning procedure over a training corpus, and balanced by the known absolute frequency of species in biological research articles, which are derived from the training data. Interactions involving human proteins are more than 56.3% of the total, followed by mouse (9.3%), yeast (6.5%), C. elegans (6%), with each of the other organism represented in less than 5% of the interactions. Mentions in the abstract tend to have a predominant role in the balanced statistics.For our experiments we have adopted a statistical approach based on the occurrences of mentions of organisms in the various sections of the paper. Just like for proteins, we have stored in our database a number of well-known synonyms for the organism  will simply go through all values for a term, and select those that match the best-ranked organism. If that fails to deliver any result, then it will proceed with the next organism, according to the ranking, until an assignment is found or a given threshold is reached.Over the BioCreative training data (740 abstracts), the initial annotation step delivers 283,556 distinct protein values .  After the species-based disambiguation step, this number is reduced to 45,012 . The remaining ambiguity (intraspecies) must be resolved by other means.With the collaboration of a domain expert, a small set of rules has been developed that reflects the typical naming conventions made by the authors. For example, the term MRGX, even if we know that it refers to a human protein, is still ambiguous among the following: MRGX1_HUMAN, MRGX2_HUMAN, MRGX3_HUMAN, and MRGX4_HUMAN. However, it is a typical convention that, if no further qualifiers are adopted, the term will refer to the first case (MRGX1_HUMAN). Alternatively, where there is a group of proteins characterized by Greek letter suffixes , the convention is that the unqualified name usually refers to the '-alpha' variant .By sequentially applying the variant rules suggested by the domain expert, the second disambiguation step typically selects one value for each term. Over our collection of 740 abstracts, this reduces the number of protein candidates to 6,351 . As the figures reveal, one must accept a significant loss of recall at each disambiguation step in order to reach a minimally satisfactory precision.The annotations of proteins and organisms obtained as described above were further augmented using the ALEx  tool, developed in Research (NIBR) at Novartis, Basel, Switzerland. In ALEx the process of lexical extraction is mainly dictionary based. All terms that are recognized are semantically typed. Currently, ALEx covers concepts such as disease, company, product, gene, target, mode of action, and some more. The entire terminology for lexical extraction sums up to almost 1.7 million terms. The gene dictionary that we used consists of more that 150,000 normalized terms and nearly 0.5 million entries.For the purposes of BioCreative, these additional entities do not need to be distinguished , but they are extremely helpful in the process of syntactic analysis of the sentences, as described in the next section. For an example of annotated abstract, see Figure At Novartis, the lexical extraction capabilities of ALEx are used for various application areas such as text mining, competitive intelligence, and data analysis for disease area, including the Ultralink system . Figure The BioCreative training set contains 740 articles obtained from either the IntACT  or MINT Once reasonable values have been reached in the task of detecting proteins, the next problem to be tackled is that of identifying their possible interactions. A naïve approach would simply consist of generating all possible pairs of proteins mentioned in each single abstract. This results in a recall of almost 35%, but at the cost of an abysmal precision. Another simple approach consists in enforcing a maximal distance (in number of tokens) between any two mentions of the proteins. We have experimented with varying distances from 1 to 50 (without taking into account sentence boundaries), and found the best F measure value at the distance of 9 . The conceptually simpler (and more intuitive) approach of restricting the possible combinations to proteins within the same sentence, without requiring any maximal distance, delivers better results .Still, although recall is relatively good (considering the limitations of the protein detection phase), precision appears to be too low for a practical application of the approach proposed. Therefore, a further filtering phase is required to select from among the proposed interactions only the relevant ones. In this respect, two kinds of false positives must be distinguished. On the one hand, there are pairs that correspond to interactions mentioned by the authors but that are not relevant to the curation task, either because they are well known interactions or because they play a secondary role with respect to the main interactions described. On the other hand, there are genuinely spurious protein pairs, which are not described by the authors as interacting but are simply a product of the simplistic way in which the pairs are generated. The strategies to filter out the false positives must therefore address both problems.'Previous studies have revealed a genetic interaction between DLG and another PDZ scaffolding protein, SCRIBBLE (SCRIB), during the establishment of cell polarity in developing epithelia.' (PubMed: 11937021). An example of a sentence that reports 'curatable' results is the following: 'Here we report the isolation of a new DLG-interacting protein, GUK-holder, that interacts with the GUK domain of DLG and which is dynamically expressed during synaptic bouton budding.' (PubMed: 11937021).In the first case, the approach that we have followed is to try to identify in each abstract the sentences that describe the most relevant results according to the authors, and to distinguish them from the sentences that describe background results. An example of this could be the following: In order to distinguish between background and novel information, we adopted a machine learning approach based on a classifier, which takes as training data the lemmatized version of sentences whose status has been determined on the basis of the gold standard. A sentence is considered positive if it contains at least one pair of proteins belonging to one of the gold standard interactions for the abstract to which the sentence belongs , three possible interactions can be considered : Daxx-Mdm2, Daxx-Hausp, and Mdm2-Hausp. However, on syntactic grounds :- dep, dep, prot(X), prot(Y).int:- dep, dep, dep, prot(X), prot(Z).Only if at least one of such filters applies to the specific case is the interaction considered further. The results that we obtain on the training data are as follows: precision = 0.5437, recall = 0.1839, and F = 0.2749. In order to enhance the usefulness and maintainability of the lexico-syntactic filters, a special type of visualization has been created  showing, for each sentence and each interaction potentially contained therein, which of the filters captures the given interaction, including false positives and false negatives.Our official results in the BioCreative challenge show that the approach is competitive, and can quite reliably extract interactions from PubMed abstracts. Although some of systems that process the full article do inevitably obtain better results , our system achieved the fourth best results in the official BioCreative evaluation. Although the exact results vary slightly according to the type of evaluation performed , they cThe main reason why we focused only on abstracts is their ready availability in a standardized text format. It is conceptually simple  to create converters from the HTML version of the articles to a suitable text format. The main problem lies in the different structuring conventions of each journal, thus requiring a different converter for each format. Once the full articles have been converted in plain text, we could easily apply our tools, opening up the prospect of much higher recall.The original idea for this subtask was to compare two methodologies: pattern matching (supplemented by simple statistics) and machine learning. Because the resources for this subtask were extremely limited and time was running short, this comparison had to be postponed, and so only the results of the pattern matching approach were submitted. Pattern matching was conducted on a full-text version of the articles, because many abstracts do not mention all methods or any hints about them. These are normally mentioned in the 'Materials and methods' section. Therefore we converted the full HTML source version of the papers to text using the command "html2text -nobs". The quality of the result is not sufficient for syntactic parsing, as required by our approach to the PPI-IPS task, but sufficient for the pattern matching approach adopted for the PPI-IMS task.The first important decision for this pattern matching approach was that, considering the limited resources and time budget, patterns for most methods could not be written by hand. So, we started with the method part of the PSI-MI (Proteomics Standard Initiative - Molecular Interactions) ontology  and tookThe selection of methods for which patterns should be written by hand was based on the frequency of the methods in the training data and the recall and precision of the automatically derived patterns. Because just five methods account for two-thirds of all file-method pairs in the training data, these were carefully looked at by our team's biologist, who tried to find additional hints in some of the papers where the methods were not found using the automatically derived patterns. The method 'coimmunoprecipitation' (MI: 0019) and its hyponyms 'anti tag coimmunoprecipitation' (MI:0007) and 'anti bait coimmunoprecipitation' (MI:0006) were most successfully treated in that way, because they are extremely frequent in the training data and at the same time seldom recognized by the automatically derived patterns. After identifying files as containing one of the coimmunoprecipitation methods, the most important problem was the very low precision for most hints with good recall  and the low recall for most hints with good precision .This could be overcome by a back-off algorithm, starting with the patterns with best precision (assigning their methods and excluding other coimmunoprecipitation methods), continuing with patterns with a lower precision (assigning their methods non-exclusively), and ending with a default (MI: 0019).Similar approaches for 'pull down' (MI: 0096) led to much less improvement because the results for the automatically derived patterns were already rather good. This was even more so for the fifth method, 'two hybrid' (MI: 0018), and so the handcrafted patterns for this method were abandoned.'Imaging techniques' (MI: 0428) was selected for a handcrafted pattern because recall was very bad. It was improved significantly by deriving the new pattern from obsolete method names, which must be mapped to MI: 0428 because they do not figure in PSI-MI 2.5 any more. An improvement in precision for 'biochemical' (MI: 0401) could be made by coupling the very imprecise pattern with other, more precise hints.The pattern matching at this stage resulted in about 6.8 candidates per file with good recall (0.734) but bad precision (0.243). Obviously, the number of candidates had to be reduced to a degree comparable to the training data. For this, every candidate (method) was given a weight influenced by its frequency in the training data and the precision and recall of the patterns used to detect it.The BioCreative evaluation framework allowed up to three runs to be submitted for each task. We decided on the following degrees of reduction: run 1, giving only the best candidate (and so the highest precision), was coupled with the results of the highest-precision run for subtask 3.2; run 2, giving the three best candidates  was coupled with the results of the highest-recall run for subtask 3.2; and run 3, giving the best F measure by selecting up to three best candidates  was coupled again with the results of the highest-recall run for subtask 3.2. Because the interactants were identified in the abstracts only, whereas the methods were identified in the full text, no direct allocation of methods to specific interactant pairs could be achieved. Hence, we allocated every method for a file to all its interactant pairs.Pattern matching just on the isolated 'Materials and methods' sections of the articles without candidate reduction yielded much higher precision than did the unreduced pattern matching of the full text, but after candidate reduction the results for the full-text pattern matching were slightly better.The approach described has the advantage of being relatively efficient and self-contained, not requiring additional external tools for preprocessing the documents (save for the conversion into plain text). However, it might be effective only on articles that are thematically similar to those contained in the BioCreative training set. Moreover, experimental methods naturally evolve over time, as new methods are developed and older methods possibly disused, therefore limiting the application range of the tool. An extension to new methods, although relatively easy, requires the concomitant expertise of both a computational linguist and a biologist.Nevertheless, in the official BioCreative evaluation, the tool achieved the best results . Using the parent-node association evaluation measure, which includes the parent node of the expected method as correct answer , the results jump to the following: precision = 0.6794, recall = 0.8548, and F = 0.6375.As a service to the community, we intend to make this functionality available through a web server (integrated into the meta-server described in ). Given This paper presents an approach, directed at the extraction of protein-protein interactions from biomedical literature, based on sequential filtering of candidate interactions (pairs of proteins in sentences). The filters make use of linguistic information derived from a pipeline of NLP tools, in particular including a dependency parser. Furthermore, a pattern-based approach can recognize the most frequently used experimental methods with high reliability. The official results in the BioCreative competition show that the proposed approach is competitive.ALEx, Advanced Lexical Extractor; GM, gene mention; GN, gene normalization; IAS, interaction article subtask; IMS, interaction method subtask; IPS, interaction pair subtask; ISS, interaction sentence subtask; PPI, protein-protein interaction.The authors declare that they have no competing interests.FR coordinated the BioCreative submission and wrote most of the manuscript. He also created tools for protein annotation and disambiguation and for syntax-based disambiguation. TK implemented the IMS approach, and wrote the corresponding section of the manuscript. KK created the XML-based pipeline and tools for visualization. GS is the author of the Pro3Gres parser. MK proposed the classifier-based approach used as a novelty filter for the sentences in the abstracts. J-MvA provided all biological insights. PP is the main creator of the ALEx system. SC is currently responsible for creating an XML-RPC-based annotation service for our IMS approach. MH, MR and TV provided various forms of support.


Identification of protein complexes is crucial for understanding principles of cellular organization and functions. As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.Based on the study of known complexes in protein networks, this paper proposes a new topological structure for protein complexes, which is a combination of subgraph diameter (or average vertex distance) and subgraph density. Following the approach of that of the previously proposed clustering algorithm DPClus which expands clusters starting from seeded vertices, we present a clustering algorithm IPCA based on the new topological structure for identifying complexes in large protein interaction networks. The algorithm IPCA is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm IPCA recalls more known complexes than previously proposed clustering algorithms, including DPClus, CFinder, LCMA, MCODE, RNSC and STM..The proposed algorithm based on the new topological structure makes it possible to identify dense subgraphs in protein interaction networks, many of which correspond to known protein complexes. The algorithm is robust to the known high rate of false positives and false negatives in data from high-throughout interaction techniques. The program is available at  In the post-genomic era, one of the most important issues is to systematically analyze and comprehensively understand the topology of biological networks and biochemical progress in cells. Protein complexes can help us to understand certain biological progress and to predict the functions of proteins. As John Donne pointed out, no protein is an island entire of itself or at least, very few proteins are. Most proteins seem to function within complicated cellular pathways, interacting with other proteins either in pairs or as components of larger complexes ,2.et al.  to be the number of neighbors shared by the vertices u and v. We define the weight of each vertex to be the sum of the weights of its incident edges. After all vertices are assigned weights, however (this is different from DPClus), we also sort in non-increasing order the vertices by their weights and store them in a queue Sq (vertices of the same weight are ordered in terms of their degrees). The complexity of calculating edge weights and vertex weights is O(|V||E|), and the complexity of sorting all vertices by their weights is O(|V| log |V|).Our procedure of vertex weighting is the same as that of the DPClus algorithm . For an Sq and use it as a seed to grow a new cluster. Once the cluster is completed, all vertices in the cluster are removed from the queue Sq and we pick the first vertex remaining in the queue Sq as the seed for the next cluster. There are a number of important differences between this seed selection procedure and the one used in the DPClus algorithm [based on the remaining subgraph. We feel that our approach is biologically more meaningful because a complex is a dense structure in the original protein network thus its seed vertex should be measured in terms of the original graph. On the other hand, a remaining subgraph G' during the process of DPClus may have lost certain biological information . Computationally, our approach also has the advantage of being more efficient. Moreover, our approach has also helped for generating overlapping complexes. This is because the vertices of the generated clusters are only removed from the queue Sq, but not from the original graph G.The notion that vertex weight is a good measure for selecting seeds has been adopted by DPClus  and MCODlgorithm . First, K is extended by adding vertices recursively from its neighbors according to the priority. The priority of a neighbor v of K is determined by the value INvK. This procedure is similar to the one proposed in DPClus [v is added to the cluster is determined by the Extend-judgment test that tests if v is a -vertex. Only when the candidate vertex v is a -vertex, can it be added to the cluster. Once the new vertex v is added to the cluster, the cluster is updated, i.e., the neighbors of the new cluster are re-constructed and the priorities of the neighbors of the new cluster are re-calculated, and the algorithm goes recursively with the new cluster. The complexity of generating a candidate vertex from the neighbors of the cluster K is O(nKnN(K) + nN(K) log nN(K)), where nN(K) is the number of neighbors of K.A cluster n DPClus , except v is added to a cluster K is determined by the two conditions given in Definition 1. First, we calculate the value INvK. The vertex will not be added to the cluster if the value INvK is less than Tin. If the vertex v passes this test, then depending on whether using SP ≤ d or ASP ≤ d, the algorithm computes the diameter of the graph K + v or the average length of the shortest paths between pairs of vertices in K + v, and compares the value with the parameter d. If the computed value is bounded by d, then the vertex v is added to the cluster. If the vertex v fails any of these tests, then the next highest priority neighbor of the cluster is tested, and so on. If all neighbors fail the tests, then the cluster cannot be further extended, and a complete cluster is formed whose vertices are removed from the queue Sq. In this paper, d = 2 is used according to our previous analysis. The complexity of testing whether a candidate vertex is added to a cluster is O matches a known complex (Kc). The overlapping score OS between a predicted cluster Pc and a known complex Kc is calculated by the following formula:To evaluate the effectiveness of the algorithm IPCA for detecting protein complexes, we compare the predicted clusters produced by the algorithm with known protein complexes in MIPS yeast complex database . There aVPc ∩ VKc| is the size of the intersection set of the predicted cluster and the known complex, |VPc| is the size of the predicted cluster and |VKc| is the size of the known complex. A known complex Kc that has no proteins in a predicted cluster Pc has OS = 0 and a known complex Kc that perfectly matches a predicted cluster Pc has OS = 1. A known complex and a predicted cluster are considered as a match if their overlapping score is equal to or larger than a specific threshold. The numbers of matched known complexes with respect to different overlapping score threshold (from 0 to 1 with a 0.1 increment) are shown in Figure Tin = 0.9 for both SP ≤ 2 and ASP ≤ 2. There are 165 known complexes matched when the overlapping score threshold is 0.2. There are 28 known complexes matched perfectly. When Tin ≥ 0.5, the number of matched known complexes is almost the same for SP ≤ 2 and ASP ≤ 2. When Tin ≤ 0.5, the number of matched known complexes is larger for SP ≤ 2 than for ASP ≤ 2. The probability that a known complex is matched perfectly by a cluster in which proteins are picked up randomly is determined by the size of the network and the known complex. The probability that a known complex with size = 3 matches perfectly by a cluster selected randomly in the yeast network used in this paper is 6.39 * 10-11. It is very obvious that more known complexes matched by the predicted clusters implies that the algorithm is more effective to detect complexes. Sensitivity and specificity are two important aspects to estimate the performance of algorithms for detecting protein complexes. Sensitivity is the fraction of the true-positive predictions out of all the true predictions, defined by the following formula:where |TP (true positive) is the number of the predicted clusters matched by the known complexes with OS ≥ 0.2, and FN  is the number of the known complexes that are not matched by the predicted clusters. Specificity is the fraction of the true-positive predictions out of all the positive predictions, defined by the following formula:where FP  equals the total number of the predicted clusters minus TP. According to the assumption in [OS ≥ 0.2. Here, we also use 0.2 as the matched overlapping threshold.where ption in , a predif-measure, has been used in [Another integrated method, called the  used in ,20, whicf-measure of each method can only be taken as a comparative measure rather than its real values, because the reference set MIPS is incomplete and some predicted clusters that may be true complexes could be regarded as false positives (FP) if they do not match with the known complexes. Nevertheless, it is still reasonable to consider a method more effective if it detects more known complexes. The coverage of the known complexes is defined by the following formula:As pointed out in ,20, the NKc is the total number of known complexes. The sensitivity, the specificity, the f -measure, and the coverage of the clusters generated by the algorithm IPCA using different parameters are shown in Table Tin > 0.5. The value TP is 4 times more than the value FN, which implies that the clusters generated by IPCA are reliable. The specificity of an algorithm represents the real positive proportion of all the predicted clusters. As shown in Table f-measure takes into account of both the sensitivity and the specificity, and is determined by the larger one. In this experiment, the f-measure is mostly influenced by the sensitivity. The sensitivity is about 1.6 ~ 1.8 times of the specificity. The coverage of the clusters generated by IPCA increases with the increasing of Tin. Especially, an obvious increase appears when Tin ≥ 0.5. In Table f-measure, and the coverage of the clusters generated by IPCA using SP ≤ 2 are slightly larger than those generated by IPCA using ASP ≤ 2.where 2ing the edges between vertices in the yeast network. More clusters are generated from the random network than from the yeast network, and the clusters generated from the random network have less proteins than those generated from the yeast network. Figure Tin = 0.6 from the yeast network and from the random network. As shown in the Figure, the predicted clusters identified in the yeast network are in various sizes from 2 to 25, while those in the random network are in various size from 2 to 10. Many small clusters are detected in the random network. To evaluate whether all these small clusters in the random network are significant, we compare them with the known complexes. As shown in Figure To evaluate whether the clusters generated by the algorithm IPCA from the protein interaction network are biologically significant, we experiment the algorithm on the protein interaction network of yeast and on a random network of a similar structure. The random network, which has the same size and the same degree distribution as the yeast network, is obtained by shuSince there have been protein complexes that were experimentally determined, a good protein complexes detecting algorithm should identify these known complexes as many as possible. Table CPin = 0.5 and Din = 0.9, DPClus gets the best matching results. Since it is more significant to detect overlapping protein complexes, we consider the best matching results generated by DPClus with overlapping mode.Though the method DPClus can generate clusters with two modes (non-overlapping and overlapping), it does not include the overlapping option at present. We generated by ourselves ten sets with non-overlapping mode using DPClus. Another ten sets with overlapping mode were provided by an author (Md Altaf-UI-Amin) of DPClus. When k = 3.The method CFinder gets the best matching results by setting NA = 0.4.The method LCMA gets the best matching results by setting VWP = 0.1 and Fluff = 0.For the method MCODE, there are 840 parameter combinations, and most of them have similar matching results. The method MCODE gets the best matching results when the "haircut" option is not used and when OS ≥ 0.2. Thus, we consider the matching results of the clusters not merged for the method STM.The method RNSC gets the best matching results when the number of the predicted clusters is about 1200. The authors of the method STM have shown that the method gets the best performance when the merge threshold value is 1.0. However, a huge cluster that includes 4358 proteins and 85 clusters whose sizes vary from 2 to 7 are generated when the merge threshold value is 1.0. Of all the 86 clusters, only 4 clusters match known complexes with the overlapping sore OS > 0.2, the number of matched known complexes by IPCA is about 1.2 times more than that by DPClus, LCMA and RNSC. With the same overlapping score threshold, the number of matched known complexes by IPCA is about 2.7 times more than that by CFinder, and about 4.2 times more than that by MCODE and STM. When OS = 1, there are no more than 10 matched known complexes by CFinder, MCODE, RNSC, and STM. On the other hand, there are 28 known complexes matched perfectly by the clusters of IPCA.As shown in Table The results show that IPCA outperforms all these six previous methods on the performance of identifying protein complexes.N be the number of total known complexes that are matched by any generated clusters (consisting of 2 or more proteins) and N≥3 be the number of known complexes matched by the generated clusters consisting of at least 3 proteins. Then N≥3/N of IPCA is 62.8% and that of DPClus is 58.1%. That is, the clusters generated by IPCA match more known large complexes than that by DPClus.As an additional and interesting example, we compare the performance of IPCA and DPClus for generating large complexes. Since small complexes  have been represented in the protein-protein interaction data, one may be more interested in identifying large complexes. For this, we compare the results generated by IPCA and that generated by DPClus. The comparisons are given in Table In order to detect the functional characteristics of the predicted clusters, we compare the predicted clusters with known functional classification. The P-value based on hypergeometric distribution is often used to estimate whether a given set of proteins is accumulated by chance. It has been used as a criteria to assign each predicted cluster a main function ,16. HereN is the total number of vertices in the network, C is the size of the predicted cluster, F is the size of a functional group, and k is the number of proteins of the functional group in the predicted cluster. The functional classification of proteins used in this paper was collected from the MIPS Functional Catalog (FunCat) database. FunCat [Tin = 0.6 and obtained 132 clusters with the same size when using Tin = 0.9. All these predicted clusters with size ≥ 6 by using Tin = 0.6 and Tin = 0.9 match well with the known functional categories with P-value < 0.001. As the fact that proteins in the same complex are of similar function, we predicted 7 previously un-characterized proteins in the predicted clusters generated by Tin = 0.9 and predicted 50 previously un-characterized proteins in the predicted clusters generated by Tin = 0.6. For example, the unknown function protein YOR264w is included in a 7-member cluster, of which six are the cytoskeleton/structural proteins. Thus, we can predict that the function unknown protein YOR264w is also a cytoskeleton/structural protein. All the clusters of size ≥ 6 generated by Tin = 0.9 and Tin = 0.6 and their main function annotations are given in an additional file where . FunCat  is an anThe un-characterized proteins in these clusters are also given in the additional file Tin = 0.9 and Tin = 0.6) when 80% edges are removed. The analysis strongly shows that the algorithm IPCA is very robust against the high rate of false positives and false negatives in protein interactions.In this analysis, we evaluated the robustness of the algorithm IPCA to various levels of graph alterations. Since all the methods of PPIs (Protein-Protein Interactions) prediction are known to yield a non-negligible amount of noise  and to miss a fraction of existing interactions  , we testTin values from the yeast network, which consists of 4546 proteins and 12319 interactions. The longest running time is 64 seconds when Tin = 0.1, and the shortest running time is 10 seconds when Tin = 0.9. The running time is decreasing as Tin increases. This is because the probability that proteins added to the clusters is decreasing with the increasing of Tin, and because the candidate selection and the judgement whether a candidate can be added to a cluster are time consuming. As a comparison, under the same test environment, the running time of the method DPClus to generate clusters with non-overlapping mode from the same yeast protein interaction network is about 20 minutes.All experiments in this paper are implemented on a PC with 1.7 GHz processor and 512 M RAM. Table SP(K) (or ASP(K)) and INvK, are used that reflect the statistics of topological structures of the networks. As the accumulation of new complexes and protein-protein interactions, the thresholds of the parameters SP(K) (or ASP(K)) and INvK can be changed easily for generating different types of clusters. Moreover, the algorithm IPCA can generate overlapping protein complexes, which is consistent with the fact that many of the known protein complexes are overlapping. Interesting questions for further research include how many functions a protein can have, how many processes a protein can participate in, and how heavily two protein complexes should overlap with each other.It is believed that identification of protein complexes is useful to explain certain biological progress and to predict functions of proteins. In this paper, we proposed a new topological structure for protein complexes and developed an algorithm IPCA to identify protein complexes in large protein interaction networks based on the new topological structure. Interaction networks are represented by undirected simple graphs and we generate predicted clusters in the networks by using seed selection and local search. The seeds in the networks are calculated only once, which has reduced the running time of the algorithm effectively. Two parameters, We applied the algorithm IPCA to the protein interaction network of Sacchromyces cerevisiae. Many well-known complexes were found in the protein interaction network. We predicted the functions for un-characterized proteins and predicted new functions for the known proteins by minimizing the P-values of the predicted clusters. We tested the robustness of our algorithm by adding and removing edges in the network randomly. The results have shown that our algorithm is robust against the high rate of false positives and false negatives in the protein interaction networks. Our algorithm can thus be used to identify new protein complexes in protein interaction networks of various species and to provide references for biologists in their research on protein complexes.The protein interaction data of Sacchromyces cerevisiae was collected from MIPS , represeML developed and implemented the clustering algorithm. JC and JW supervised the work and contributed to the problem formulation and paper writing. BH and GC developed the program for generating random graphs and testing the robustness of IPCA. The manuscript was written by ML. All authors read and approved the final manuscript.size ≥ 6 generated using Tin = 0.9 and Tin = 0.6.P-values for the predicted clusters with  The data provided represent the statistical analysis of the predicted clusters. P-value is calculated for each predicted cluster and a function category is assigned to it when the minimum P-value occurs. When Tin = 0.9, there are 132 clusters (size ≥ 6) generated by IPCA. When Tin = 0.6, there are 443 clusters (size ≥ 6) generated by IPCA.Click here for fileFunctional annotation for a predicted cluster of 10 proteins. This file provides a cluster which is composed of ten proteins: YGL173c, YOL149w, YBL026w, YCR077c, YJR022w, YER112w, YER146w, YDR378c, YNL147w, and YLR438c-a. The functional annotations for each protein in the cluster are listed in this file.Click here for file


We apply our method to two large families of proteins: protein kinase C, and globins, including hemoglobins and myoglobins. We interpret the high-dimensional feature vectors using principal components analysis and agglomerative hierarchical clustering. We find that the feature vector retains much of the information about the original sequence. By using principal component analysis to extract information from collections of feature vectors, we are able to quickly identify the nature of variation in a collection of proteins. Where collections are phylogenetically or functionally related, this is easily detected. Hierarchical agglomerative clustering provides a means of constructing cladograms from the feature vector output.We propose a feature vector approach to characterize the variation in large data sets of biological sequences. Each candidate sequence produces a single feature vector constructed with the number and location of amino acids or nucleic acids in the sequence. The feature vector characterizes the distance between the actual sequence and a model of a theoretical sequence based on the binomial and uniform distributions. This method is distinctive in that it does not rely on sequence alignment for determining protein relatedness, allowing the user to visualize the relationships within a set of proteins without making  Recent advances in biotechnology have allowed sequencing of millions of proteins from a wide spectrum of organisms and this information is rapidly becoming accessible to any researcher with an internet connection. For instance, the UniProtKB database currently contains over 7 million protein sequences and is updated every three weeks One of us (S.Y.) previously designed three types of parameters for use in clustering amino acid sequences In contrast to previous methods of constructing distance measures to determine protein relatedness The selection pressure to which proteins are subjected affects both the number of residues in the protein To describe any protein using the feature vector method, we must first compute parameters of type I, II and III for each of the twenty amino acids which occur in proteins . The parGπ is the probability of glycine occurring in a theoretical dataset of proteins under neutral evolution. This probability is set by the user and can vary for a given dataset. For the current analyses, we use by the number of genetic codons for glycine divided by 64. Alternatively, one can set Gπ to the frequency of glycine in the total dataset of proteins. The variable Gn is the number of glycines in the protein and N is the length of the protein.Here, The following 9 steps detail the specifics of computing the parameters for the feature vector method and of using the feature vectors to categorize and analyze proteins. C++ and Mathematica  code for steps 1–8 is available in the supplementary online materials  and S2. A, nR, nN, nD, nC, nE, nQ, nG, nH, nI, nL, nK, nM, nF, nP, nS, nT, nW, nY and nV. Use these numbers to compute the proportion of amino acids in the protein, the type I measure .1. Count the number of amino acids of each type and note the length of the protein. We denote the length of protein as N and the number of amino acids of each type as n measure : pA, pR,2. For each amino acid, find the indices of the positions in the protein sequence which contain that amino acid (we count the first position as one and not zero).A, mR, mN, mD, mC, mE, mQ, mG, mH, mI, mL, mK, mM, mF, mP, mS, mT, mW, mY and mV. The type II theoretical mean is calculated as the average of all possible positions in a protein of length N.3. Use the indices from step 2 to compute the mean position of each amino acid, the type II measure: mA, vR, vN, vD, vC, vE, vQ, vG, vH, vI, vL, vK, vM, vF, vP, vS, vT, vW, vY and vV. The type III theoretical mean is calculated as half of one less than the square of the length.4. Using the indices from step 2 compute the unbiased variance, type III measure, of the set of indices of each amino acid: va priori estimate of the probability of the occurrence of each amino acid: πA, πR, πN, πD, πC, πE, πQ, πG, πH, πI, πL, πK, πM, πF, πP, πS, πT, πW, πY and πV, This value was taken to be the number of codons corresponding to a particular amino acid divided by the total number of coding codons, although it could also more appropriately be taken as the rate of occurrence of amino acids in the population from which the sample proteins were selected. Use the type I mean to compute the type I theoretical variance of index positions given the observed number of amino acids.5. The type I theoretical mean is the 6. Compute the type II theoretical variance in the mean and the type III theoretical variance of the variance given the observed number of amino acids as shown in 7. Normalize the measures computed in steps 1, 3 & 4 by subtracting the theoretical means, computed in steps 3, 4 & 5, and dividing by the square root of the theoretical variances, computed in steps 5 & 6.8. Assemble the parameters into a 60 dimensional vector.9. Using principle components analysis (PCA) or some other means of high dimensional data analysis, search for clusters or patterns in the protein data.This method can be adapted for use in analyzing DNA or RNA sequences. To create a feature vector for a given DNA sequence, one need only create parameters of types I, II and III for each nucleic acid type, and then combine these parameters into a feature vector. The resulting DNA feature vector will be 12-dimensional, with 3 types of parameter for each of 4 types of nucleic acid, compared to the 60-dimensional protein feature vector  is an online repository of protein sequences; UniprotKB/Swissprot (http://ca.expasy.org/sprot/) builds upon this repository through annotation of protein sequences. Information available in UniprotKB/Swissprot includes citations for related publications, species name, protein family, domain structure and detail on protein variants and structure. NCBI Entrez-Protein (http://www.ncbi.nlm.nih.gov/protein/) is an online protein sequence database curated by the National Center for Biotechnology Information (NCBI).We used three online protein sequence databases to create our protein datasets: Uniprot KB, UniprotKB/Swissprot, and NCBI Entrez-Protein. UniprotKB Click here for additional data file.Sample Code S1Feature vectors computation in C++. Computation of the feature vectors for a protein data set in C++.(0.01 MB TXT)Click here for additional data file.Sample Code S2Feature vector computation in Mathematica. Computation of the feature vector for a single protein in Mathematica.(0.00 MB TXT)Click here for additional data file.Sample Code S3PCA code for Matlab. Principle component analysis code for Matlab.(0.00 MB TXT)Click here for additional data file.Table S1Protein Kinase C Feature Vectors. This file contains the set of all feature vectors for the PKC proteins in our dataset.(0.16 MB XLS)Click here for additional data file.Table S2Hemoglobin Feature Vectors. This file provides the set of all feature vectors for the Hemoglobins in our dataset.(1.03 MB XLS)Click here for additional data file.Table S3Myoglobin Feature Vectors. This file provides the set of all feature vectors for the Myoglobins in our dataset.(0.17 MB XLS)Click here for additional data file.Data Set S1Protein datasets. Accession numbers and taxonomic information for Protein Kinase C (PKC), Hemoglobin and Myoglobin dataset. Each protein dataset is provided as a separate worksheet.(0.16 MB XLS)Click here for additional data file.