Background Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones. Results To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Proteins account for measures of protein identification as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures. We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions or in combination with other AP-MS scoring methods to significantly improve inferences. Conclusions Our method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or especially in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called "ROCS", freely available from the CRAN repository http://cran.r-project.org/. We employed our newly adopted SVM method in the continuous measurement of the cortical width of the mandible on dental panoramic radiographs to identify osteoporosis. We compared the diagnostic performances between this newly developed method and our trimmed mean method in identifying women with low BMD at the lumbar spine and the femoral neck in 100 postmenopausal women , of whom 60 to the training and 40 to its testing with no previous record of osteoporosis. Cortical width below the mental foramen of the mandible on dental panoramic radiographs was measured continuously with CAD system by enhancing the original image of the X-ray, determining cortical boundaries, evaluating all the distances among upper and lower boundaries and finally discriminating by radial basis function - support vector machine method.The sensitivity and specificity of the cortical measurements of the lumbar spine were 90.9%  (85.3-96.5) and 83.8% (76.6-91.0), respectively and 90.0% (84.1-95.9) and 69.1% (60.1-78.6), respectively with femoral neck BMD. In addition, the sensitivity and specificity for the combination of data of both the lumbar spine and femoral neck for identifying women with low BMD were 90.6% (92.0-100) and 80.9% (71.0-86.9), respectively. We assessed that the diagnosis and classification of women using support vector machine employing the average and variance of the continuous measurements provide excellent discrimination ability in comparison with the estimation of cortical width using the trimmed mean method. Conclusion: Results showed that our newly developed CAD system with SVM method improves the overall performance of the identification of high risk group of osteoporotic patients.Overall, most promising results in terms of best overall response rate (BORR) were obtained with 10 mg/kg of ipilimumab, every 3 weeks for a total of 4 doses (induction phase) followed by maintenance period in which ipilimumab was administrated every 12 weeks (maintenance phase). This was the reason for the choice of such a schedule for the front line phase 3 study. The most common treatment-related adverse events (AEs) associated with the use of ipilimumab were immune-related and specific algorithms have been subsequently developed, showing that early recognition and correct therapeutic approach with steroid therapy make most of these AEs manageable and reversible The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/13/1009/prepub


Recent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species. An important challenge for the analysis of these data is to extract functional modules such as protein complexes and biological processes from networks which are characterised by the present of a significant number of false positives. Various computational techniques have been applied in recent years. However, most of them treat protein interaction as binary. Co-complex relations derived from affinity purification/mass spectrometry (AP-MS) experiments have been largely ignored.This paper presents a new algorithm for detecting protein complexes from AP-MS data. The algorithm intends to detect groups of prey proteins that are significantly co-associated with the same set of bait proteins. We first construct AP-MS data as a bipartite network, where one set of nodes consists of bait proteins and the other set is composed of prey proteins. We then calculate pair-wise similarities of bait proteins based on the number of their commonly shared neighbours. A hierarchical clustering algorithm is employed to cluster bait proteins based on the similarities and thus a set of 'seed' clusters is obtained. Starting from these 'seed' clusters, an expansion process is developed to identify prey proteins which are significantly associated with the same set of bait proteins. Then, a set of complete protein complexes is derived. In application to two real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes and well-characterized cellular component annotation from Gene Ontology (GO). Several statistical metrics have been applied for evaluation.Experimental results show that, the proposed algorithm achieves significant improvement in detecting protein complexes from AP-MS data. In comparison to the well-known MCL algorithm, our algorithm improves the accuracy rate by about 20% in detecting protein complexes in both networks and increases the F-Measure value by about 50% in Krogan_2006 network. Greater precision and better accuracy have been achieved and the identified complexes are demonstrated to match well with existing curated protein complexes.Our study highlights the significance of taking co-complex relations into account when extracting protein complexes from AP-MS data. The algorithm proposed in this paper can be easily extended to the analysis of other biological networks which can be conveniently represented by bipartite graphs such as drug-target networks. Protein-protein interactions (PPIs) are believed to be fundamental to the biological process and metabolic functions in the cell . As advak-cliques share k-1 nodes. It exploits the topological feature of the network by using the direct link between a pair of nodes.Over the past decade or so, various clustering algorithms -16 have Most of these algorithms have been developed by modelling protein interactions as binary, i.e., interactions only exist between pairs of proteins. Results from the Y2H approach are inherently modelled as binary since the Y2H approach detects physical pair-wise protein-protein interactions. Although AP-MS data contains non-binary information, as it directly identifies co-membership of complexes by purifying proteins  that are associated with tagged proteins which were used as bait -6, it alTwo well-known binary models for AP-MS data are 'Spoke' and 'Matrix' models which have been proposed in 2003 by Bader and Hogue . The 'SpIn 2006, Gavin et al.,  devised The study of Gavin et al.  highlighThe first study of modelling AP-MS data as non-binary was conducted by Scholtens et al., . They buMost recently, in 2011, a novel algorithm called CODEC  has beenAs pointed out by Geva and Sharan , AP-MS dThis paper presents a novel algorithm for detecting protein complexes from AP-MS data. The algorithm intends to detect groups of prey proteins that are significantly co-associated with the same set of bait proteins. We first construct AP-MS data as bipartite network, where one set of nodes consists of bait proteins and the other set is composed of prey proteins. We then calculate pair-wise similarities of bait proteins based on the number of their commonly shared neighbours. A hierarchical clustering algorithm is employed to cluster bait proteins based on the similarities and thus a set of 'seed' clusters is obtained. Starting from these 'seed' clusters, an expansion process is developed to identify prey proteins which are significantly associated with the same set of bait proteins. Then, a set of completely formed protein complexes are derived.The organization of the paper is shown below. In Section 2, we first introduce the methodology of our proposed algorithm. In Section 3, we will present and discuss experimental results. We validate biological significance of predicted protein complexes by using curated complexes and well-characterized cellular component from GO . SeveralThe AP-MS experiment directly detects complex membership by purifying prey proteins which are co-associated with tagged bait proteins ,5. Thus,Though protein complexes are considered to contain proteins having most similarities, there is no one standard definition of protein complexes from the perspective of topological structure. A set of proteins which are highly connected is intuitively considered as a complex, e.g., a clique is an ideal structure for a complex. However, ensuring density of internal interactions only is not enough to identify a complex, as discussed in . Figure Here, a module is referred to as a term of a complex structure. In a strong module each vertex has more connections with the cluster than with the rest of the graph. In a weak module, the sum of all links connecting from each node within the cluster is larger than the sum of all links connecting from each node inside the cluster toward the rest of the network. In PPI networks, there exist complexes which have structure of a strong module, or of a weak module, or a combination of the two. However, all complexes should meet requirements in the definition of weak modules.B represents the set of purification with bait nodes which is on the one side, V represents the set of prey nodes on the other side that have been detected by purifying via the bait nodes. If let V is the union set of bait nodes and prey nodes, which, in other words, V is the set of nodes in the network. Note that, there exist nodes that are preys of some baits but also baits to other preys, thus we assign them a bait instance and prey instance respectively. E represents pair-wise interactions between baits and preys. A potential protein complex or functional module corresponds to a sub-graph We represent AP-MS data as a bipartite graph. The graph is denoted as Figure The process of detecting complexes in our proposed algorithm consists of the following steps:1. Calculating pair-wise similarities between bait proteins;2. Clustering bait proteins to obtain preliminary seed clusters;3. Expanding process to form complete clusters;4. Filtering clusters and outputting final set of clusters.Jaccard Similarity Coefficient [We estimated pair-wise similarity between two baits based on the number of their common neighbours using fficient . Let b1 B represents the set of bait proteins.We calculate the similarity between every pair of bait proteins in the graph. Thus, let In order to identify the set of prey proteins that are significantly associated with the same set of bait proteins, we first need to obtain sets of bait proteins as 'seed clusters'. Using the similarities calculated above as metric, we apply Agglomerative Hierarchical Cluster algorithm to cluster the bait proteins. We employ an open source tool called MultiDendrograms  to clustAs shown in the work published by Gavin et al., , a proteSc be a seed cluster, and let u be a prey protein connecting with proteins in the seed cluster. Let u to Sc; u to proteins that are not in Sc; let u is included in Sc; Sc.Let We start from the neighbourhood of bait proteins in the seed cluster. If a prey protein is able to be included into the strong module, it should satisfy:u should connect to at least half proteins inside the seed cluster, that is,1) Sc.where u to proteins inside the seed cluster should be more than connections linking to other proteins which are not in the seed cluster, that is,2) The connections from u should be less than the internal links, that is,3) The out-links of seed cluster which includes The process ceases when there is no matched protein.After the expansion process of finding strong modules, the process of forming final clusters starts. It will iteratively explore matched proteins in the neighbourhood of the proteins in strong modules.v is a candidate protein, Mc is the strong module cluster after expansion from Sc, v should meet following criteria:Suppose v to proteins in Mc should be no less than those from v to other proteins, that is,1) The connections from v, the internal links inside the new cluster should be more than the external links, that is,2) After included Actually, there exist "seed clusters" only consist of one bait protein; we just add its neighbours if the neighbour protein meets the two conditions above.|C| is the size of the cluster. If the overlap rate is above a given threshold, we merge the two clusters. In our algorithm, we use 0.2 as the threshold value.In the set of clusters obtained from expansion process, there exist overlapping clusters. We calculate the overlap rate between two clusters, that is, k times of expansion, thus the time complexity for the whole expansion process is The general time complexity of the entire algorithm is We implemented the proposed method using Java programming language with JDK 1.6. The proposed method is applied on a desktop computer with Inter(R) Core(TM)2 Duo CPU E8500 @3.16GHz 3.17 GHz processor and 8 GB memory. The amount of running time depends on the size of dataset. The running time of the proposed method on Gavin_2006 dataset was 34653 milliseconds, and the application to Krogan_2006 dataset was 61336 milliseconds. This running time only contained the time of process of calculation of pair-wise similarity for bait proteins, expansion process and post-process, excluding the time of application of hierarchical clustering method to generate seed clusters since we utilized the software toolkit, MultiDendrograms , for thiWe applied our method on two recently published datasets in bait-prey relationships in Yeast. One is the dataset obtained by Gavin et al  with 199IEA evidence codes due to their lack of reliability. Thus, a total of 319 protein complexes were derived accordingly. To fairly evaluate performance of different methods, we only considered the set of benchmark complexes that contain at least 2 proteins which are in PPI networks. We employ similar method of pre-processing benchmark complexes in literatures [We built the benchmark set of complexes from hand-curated complexes derived from the Wodak lab CYC2008 catalogue  which coeratures ,30, thatWe utilize evaluation metrics, i.e., accuracy and homogeneity suggested by Broheé and Helden . These tC*, and th irow stands for predicted cluster Let Accuracy measures the general correspondence between predicted clusters and benchmark complexes, which contains two components, sensitivity (Se) and positive predictive value (PPV).Sensitivity is defined to calculate the proportion of proteins in benchmark complex PPV reveals the fraction of proteins annotated in benchmark complex Accuracy is defined as the geometric mean of the product of general sensitivity and positive predictive value,Thus, high precision value requires a high performance for both measures. The higher precision values the better quality of a clustering result.j over all the generated clusters. Meanwhile, cluster-wise homogeneity i over annotated complexes. These two measures can be represented as:Complex-wise homogeneity Similarly, in order to reflect a clustering result as a whole, the average values of Thus clustering-wise homogeneity Homogeneity reflects relative ratio of distribution of overlapping intersections between annotated complexes and generated clusters. When proteins are allowed to be assigned to multiple clusters, the value To further assess the quality of predicted clusters, we also measure the specificity and sensitivity of predicted complexes with respect to the set of benchmark complexes. We utilize the overlap score introduced by Bader and Hogue  to measuivity in , the numandF-Measure is the harmonic average of sensitivity and specificity. In our experiments, based on the study of Bader and Hogue , we consWe selected the parameters following a trial-and-error procedure. Unless indicated otherwise, the results reported in this paper were derived based on the following parameter settings: the hierarchical clustering was implemented with un-weighted average linkage and the cut-off values set to 0.3 and 0.25 for Gavin_2006 and Krogan_2006 networks, respectively.We choose the set of parameters of MCL and MCODE recommended by Broheé and Helden . SpecifiWe compare performance of our method with that of several state-of-art clustering methods, which are categorized into two groups. One includes MCL ,13, MCODEvaluation results of accuracy and homogeneity, using benchmark complexes from CYC-2008 catalogue and GO-CC, of different clustering methods in Gavin network are listed in Tables The results shown in Table PPV value indicates the fraction of clustering results which have also been identified and annotated in the benchmark complexes so far. It favours smaller clusters. In order to be fair, as stated above, we excluded clusters whose size of overlap with curated complexes is less than two proteins. Results show that our method obtains the highest PPV value in comparison to other algorithms. The better accuracy suggests that the proposed algorithm can achieve a much better performance as the value of the accuracy reflects the general performance of a clustering algorithm based on the estimation of the overall correspondence between the set of predicted clusters and the set of annotated complexes.Homogeneity is the product of the fraction of members in a cluster found in an annotated complex by the fraction of members in the complex found in a cluster. High homogeneity indicates a bi-directional correspondence between a cluster and a complex . The maxSimilar observations can be made when analysing the Krogan_2006 data as shown in Tables Table In the results obtained from Krogan_2006 network (shown in Table To further validate biological significance of the results obtained by the proposed method, we next discuss several predicted complexes that are found by our method but not detected by other methods, which are also biological relevant. Here, we present examples of clusters obtained from Krogan_2006 network.One example of fully-matched clusters identified by the proposed algorithm but not found in results produced by other algorithms, includes four proteins, that is YJR112W, YPL233W, YAL034W-A, and YIR010W. This protein complex has been defined as a kinetochore complex that binds to centromeric chromatin and forms part of the inner kinetochore of a chromosome in the nucleus ,32. AnotThese cases exemplify that, by the incorporation of information of bait proteins in the clustering analysis of AP-MS data, the propose method has the advantage to discover significant functional modules from the networks.In this paper, we propose a new algorithm for discovering functional modules and complexes in AP-MS PPI networks. It has been tested on two real AP-MS PPI networks, i.e., Gavin_2006  network Currently, our proposed algorithm only considers the topological features of PPI networks. Incorporation of other biological information such as semantic similarity derived from GO into the clustering process would be an important part of our future work.In this study, the determination of the parameters was based on trial and error. Integration with other techniques such as Genetic Algorithm for the dynamic determination of learning parameters provides another direction of our research.The authors declare that they have no competing interests.BC conceived of the study and carried out all programming and analyses as a Ph.D student in the University of Ulster. HYW supervised the study and provided valuable input for experiments. HZ and HW supervised the study and helped to draft the manuscript. All authors read and approved the final manuscript.


Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones.Indicator Prey Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Prey Proteins account for measures of protein identifiability as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures.To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions, or in combination with other AP-MS scoring methods to significantly improve inferences.We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS a very promising tool for this purpose, either on its own or in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called “ROCS”, freely available from the CRAN repository http://cran.r-project.org/.Our method addresses important issues encountered in AP-MS datasets, making  Affinity-purification mass spectrometry (AP-MS) is a powerful tool for identification of protein complexes and interactions and enables the larger scale analysis of protein networks and cellular processes ,2. AP-MScarefully removes outlier experiments in AP-MS data before applying final statistical procedures aiming at removing contaminants, i.e. non-specific prey proteins.Principal challenges in interpreting AP-MS data are the presence of false-positive interacting proteins, misidentification at the MS database search level, and the variability of replicated experiments ,7. FalseMASCOT and that higher RI represents greater glob reproducibility across Experimental Replicates.where Reproducibility Index (RI) is expected to vary as a function of the MASCOT score, when used as a threshold. So, we define the Reproducibility Index Threshold (RIT) as the peptide MASCOT score threshold maximizing the Reproducibility Index (RI), i.e. formally Reproducibility Index Threshold (RIT) is used for subseting a set of Q uniquely identified proteins from the Prefiltered Prey Proteins, which we termed Indicator Prey Proteins, denoted by MASCOT scores are greater than RIT:It follows from the above that the Indicator Prey Protein, we define its marginal inclusion probability, denoted pM(j) for j ∈Experimental ReplicatesIndicator Prey Proteins for which their marginal inclusion probability is greater than a given marginal inclusion probability threshold joint inclusion probability Experimental ReplicatesIndicator Prey Proteins can be identified for which their marginal inclusion probability is greater than the joint inclusion probability is relatively high. How this threshold is chosen in any bait or control experiment is described in the subsequent subsection “Setting a Marginal Inclusion Probability Threshold”.Next, for each Indicator Prey Proteinsjointly appear in each individual Experimental Replicate Ek for k ∈ {1, … , K}. We claim that these are the most reproducible experiments, which we term Reproducible Experimental Replicates. We denote this subset of L experiments by One may determine a subset of Reproducible Experimental ReplicatesR of uniquely identified and most reproducible Prefiltered Prey Proteins, which appear at least once in Reproducible Experimental ReplicatesReproducible Prey Proteins and denote it by R”, standing for “Reproducible”.From the reduced sets of Indicator Prey Proteins as well as Reproducible Experimental Replicates. Conversely, this threshold can be set to lower probability levels in order to remove outlier experiments as thoroughly as possible. So, the setting of this threshold controls the level of experimental reproducibility and is a matter of tradeoff between specificity and sensitivity and the goals of the experiment.The choice of the marginal inclusion probability threshold Reproducible Experimental Replicates, that is L > 0, where for simplification reasons dependency with respect to Second, in every experiment the marginal inclusion probability threshold In practice, we noted that the choice of the marginal inclusion probability threshold has relatively little influence as long as it remains within admissible boundaries  score of individual bait-prey interaction specificity, which we term the Reproducible Prey Protein relative to the bait and the control experiments, weighted up/down by the marginal inclusion probability of each Reproducible Prey Protein in the bait experiment alone. In other words, the Confidence Score accounts for measures of bait-prey specificity and bait-prey frequency altogether. Note that by definition non-specific and specific interacting prey proteins respectively. In keeping with previous notations, a subset of Specific Prey Proteins may be found by taking the Reproducible Prey Protein in Confidence Score is greater than a specificity cutoff, denoted Specific Prey Proteins by As can be seen, this score is an individual bait-prey interaction specificity measure. It is a standardized ratio accounting for the probability of occurrence of each S”, standing for “Specific”. In practice the Confidence Score is computed after the aforementioned identification of unique Indicator Prey Proteins and Reproducible Experimental Replicates, that is, after pre-specifying marginal inclusion probability thresholds in both control and bait experiments: Confidence Score cutoff is to be estimated from the data. We show in the following section how to do so automatically by simultaneously controlling the False Discovery Rate (FDR) and the Gene Ontology (GO) semantic similarity of the candidate preys to the bait.We denote this last procedural stage by “Confidence Score cutoff To objectively validate any Protein-Protein-Interaction (PPI) identification procedure in AP-MS data analysis, one needs to simultaneously assess the sensitivity/specificity of the final sets of prey proteins identified. In the case of our PPI identification procedure, an optimal FDR analysis does not control by definition the overall False Positive detections, it can indirectly enable the control of the specificity inherent to such PPI procedure (Specific Prey Proteins). By definition, the FDR is the expected proportion of the number of erroneous rejected null hypotheses to the total number of rejected null hypotheses in the context of multiple hypotheses testing .Figure B and C correspond to the bait and the control experiments respectively. The control dataset consists of Experimental Replicates with an initial number of Experimental Replicates: VHL small numbers of Experimental Replicates  and filter out un-reliable peptides (and corresponding proteins) according to the method described in the Methods Section. We have estimated three quartile curves for the B-spline model for visual purposes, but we only used the median regression function for the computation of the MASCOT Score Threshold. In the instance of the CTNNBIP1 bait experiment, the median MASCOT Score Threshold corresponding to a 50% Peptide Probability Threshold was MST = 22.34, leaving MASCOT Score Threshold in the control experiment was MST = 20.91, leaving Peptide Probability ThresholdMASCOT Score Thresholds (MST).We first plotted the peptide Reproducibility Index (RI) is not monotonic as a function of the MASCOT score threshold. We computed our Reproducibility Index (RI) in the control and all bait experiments and plotted it against a range of peptide score thresholds RI) always peaks at a certain optimal value of the peptide score threshold, which we have termed Reproducibility Index Threshold (RIT) . This was carried from the initial “Naïve” stage (“N”), to the “Reproducible” stage (“R”), and to the final “Specific” stage (“S”), i.e. with datasets restricted to the set of uniquely identified proteins from either (i) the entire dataset (NB), (ii) the reproducible dataset (RB), (iii) or the specific dataset (SB). Results are reported in Additional file metric space) that is being used: whether one considers the entire Prey Protein space  vs. a subspace of it, such as the Reproducible Prey Protein space (stage “R”), or the final Specific Prey Protein space (stage “S”). Indeed, notice the stability or the decrease in all cases that is achieved from procedural stage “N” onward (with the exception of NME2 bait experiment which is discussed below) [Additional file Further, to check the reproducibility in all bait experiments, we compared the overall Coefficient of Variations (CV) of the mean marginal inclusion probabilities of prey proteins across ROCS scoring in all bait experiments, the corresponding lists of Specific Prey Proteins were matched against the BioGRID references database (version 3.1 - http://thebiogrid.org/) [Additional file BioGRID database references into the ROCS list as well as the reciprocal matching of the ROCS list into the BioGRID references. Altogether, results for all bait experiments are indicative that when a protein bait interaction has been characterized and published, it is largely confirmed by our ROCS scoring [Additional file To test the validity of the ROCS Specific Prey Proteins and those found in the literature (BioGRID), we first looked at the GO semantic similarity measure between the two protein lists (denoted c1 and c2) of uniquely identified Specific Prey Proteins being compared (ROCS vs BioGRID) for all gene ontologies , Molecular Function (MF), and Cellular Component (CC)), and in all bait experiments. This was done by computing Resnik’s semantic similarity between the two protein lists ) according to Wang’s algorithm [ROCS Specific Prey Proteins and those found in the literature (BioGRID) [Table To quantify the overlap and biological coherence between lgorithm . ResultsSpecific Prey Proteins (ROCS vs BioGRID). Under the null hypothesis that the two lists are unrelated or that any intersection is due to chance alone (i.e. ROCS lists are randomly sampled under the null without replacement), the random variable of the number of common (intersecting) proteins between the two lists, denoted X, follows a hypergeometric distribution with parameters NB, SB, and B: X ∼ P, where P is given by ROCS Specific Prey Proteins and BioGRID proteins are denoted by SB and B respectively. The overlap analysis results with corresponding rejection probabilities  are reported in Table all bait experiments at the α = 0.05 significance level, meaning that we can reject the null hypothesis that the intersections found with the literature is due to chance alone, or that the ROCS lists are drawn at random [Table Second, we determined the statistical significance of the overlap between the two lists of om Table .ROCS's effectiveness as a stand-alone method for finding specific protein-protein interaction (PPIs), we also compared its performance to similar methods such as SAINT[ComPASS[ROCS’s lists of Specific Prey Proteins in all bait experiments with those obtained by SAINT and ComPASS. These lists are given in Additional file SAINT and ComPASS lists into the ROCS list [Additional file GO semantic similarity as above, we determined the significance of the overlap between (i) ROCS and SAINT, (ii) ROCS and ComPASS for all gene ontologies , Molecular Function (MF), and Cellular Component (CC)) and in all bait experiments. Overlap analysis and GO semantic similarity results in Table ROCS identified Specific Prey Proteins compares very similarly to these methods and that the overlap with SAINT and ComPASS is statistically significant in all bait experiments tested.To further assess  as SAINT and ComPT[ComPASS. We compSAINT developed by Choi et al. [SAINT, we tested the datasets of all bait experiments at every procedural stage of our method, while keeping the same set of control experiments. SAINT works indeed with a small set of control experiments, which were chosen as the five most replicated ones from the initially pool of KC = 200 control Experimental Replicates (unfiltered dataset). Also, in the latest implementation of SAINT  we were able to use the MASCOT scores directly as quantitative input rather than spectral counts as used in earlier versions.Next, we combined our method with an existing procedure for analysis of AP-MS data in the following way. Although there are multiple methods for evaluation of AP-MS data (see Introduction), we use the probabilistic scoring approach i et al.  since itROCS method is to do a side-by-side comparison of SAINT protein-protein interaction scoring (ranked by SAINT posterior probability PSAINT) between SAINT-only and SAINT applied in conjunction with ROCS at different procedural stages, namely, the initial “Naïve” stage (“N”), the “Reproducible” stage (“R”), and especially the final “Specific” stage (“S”). The comparison is given in Additional file sim between a Gene Ontology (GO) term from the bait protein (denoted cB) and for each Specific Prey Protein (denoted cP) as described in the methods section. This was computed for all Gene Ontologies , Molecular Function (MF), and Cellular Component (CC)) and all bait experiments [Additional file An objective way to assess the performance of our sim is given between a Gene Ontology (GO) term from the bait protein (denoted cB) and for each Specific Prey Protein (denoted cP) as described in the methods section. This was computed for all Gene Ontologies , Molecular Function (MF), and Cellular Component (CC)). Results show a statistically significant increase in the GO bait-prey semantic similarity as ROCS is applied in conjunction to SAINT, i.e. between SAINT-only , and ROCS-SAINT  [Figure We next plotted the 95% confidence interval (CI) of the median bait-prey semantic similarity as described in the methods section. The pairwise Resnik semantic similarity measure ROCS method in any bait experiment is to compare the behavior of the False Discovery Rates (FDR) between ROCS procedural stages, independently or in combination with another scoring method. Here, we used the posterior probability PSAINT of true protein-protein interaction from the SAINT output to get 1 − PSAINT, which serves as the local False Discovery Rate [lFDR). Aggregate FDR up to the chosen rank is the integral of local FDR across the selection, or, in practice, the average of 1 − PSAINT across the selection. The latter is closer to what's called Bayesian FDR, and conceptually to lFDR/FDR. Results show that when ROCS is used in conjunction with SAINT, one substantially reduces the FDR [Figure An alternative way to empirically assess the performance of our ery Rate  , CTNNBIP1 (KB = 9), NME2 (KB = 8), PPM1B (KB = 6), STK24 (KB = 5). Table To demonstrate the usability of our identification procedure, we tested its performance on several AP-MS bait datasets with varying numbers of VHL KB = 3, CTNNBIIP1 KB = , NME2 , an arbitrary α = 0.5th-quantile of peptide probabilities, and an arbitrary marginal inclusion probability threshold of e.g. unbiased “multiscale” joint inclusion probability estimates are very stable across a wide range of experimental scale values. Only the unbiased multiscale unbiased joint inclusion probability estimate should be trusted. In fact, although regular (biased) bootstrap estimates show a similar behavior over the range joint inclusion probability estimate for lower experimental scale values (data not shown). As expected, the stability of the unbiased estimate is lost for the smallest k values [Figure To test the stability of our method on a larger set of replicated AP-MS experiments, we make use of a set of 200 replicated control AP-MS experiments. Figure 5 Figure . As demos Figure . The plo5 Figure .Indicator Prey Proteins, which enables identification of the most Reproducible Experimental Replicates from a larger dataset. Importantly, we show that the method uniformly scales up and down, making it quite versatile to accommodate realistic studies with a range of numbers of Experimental Replicates. The identification of subsets of reproducible AP-MS experiments significantly improves the ability to distinguish specific from non-specific prey proteins. In the future, this approach may be used as a general selection method for quality control purposes in proteomics studies and proteomics databases, where experimental reproducibility issues arise.The described method identifies and selects reproducible AP-MS experiments as well as bait specific preys when experimental replicates and control experiments are provided. The method is able to identify a subset of We show that at least on larger AP-MS datasets, the frequency of occurrence of prey proteins provides good sensitivity for discriminating specific from non-specific prey proteins. In our study we use the prey protein frequency as a surrogate measure for more specific protein confidence metrics such as search-engine scores , spectraExperimental Replicates [Figure at least greater than the Experimental Replicates) are always needed to increase statistical power and reduce false discovery rates. Lastly, since frequency is a standardized metric, our method may be useful when attempting to compare different AP-MS datasets.We were also able to estimate the number of required replicate AP-MS experiments. From our analysis of stability on multiscale sets of s Figure , this nuIndicator Proteins and the related Reproducible Experimental Replicates rely on an objective measure of peptide reliability, which we chose to be the MASCOT peptide identification score [PROPHET[Indicator Proteins may be applied beyond AP-MS experiments to other types of mass-spectrometry based proteomics.The concept of on score . However [PROPHET might beThe authors declare that they have no competing interests.J-ED and SS developed the statistical methodology and J-ED implemented the method as an R package. J-ED lead the statistical aspects of this paper, and RME lead the experimental and biological aspects. All authors formulated the problem, wrote and approved the manuscript.Data set and Database Search. Determination of Protein Spectral Counts, Protein MASCOT Scores, and Protein Marginal Inclusion Probabilities. Raw Input Dataset Structure. Initial Pre-filtering. Derivation of Marginal and Joint Inclusion Probabilities of Indicator Prey Proteins. Identification of Reproducible Experimental Replicates and Reproducible Prey Proteins. Confidence Score and Identification of Specific Prey Proteins. Automatic Estimation of an Optimal Confidence Score Cutoff Derivation of Coefficient of Variations Formulas. Testing Stability on Multi-scale Sets of Experimental Replicates.Click here for fileFigure S1. Scatter plots of protein spectral counts vs. protein MASCOT scores (left-hand-side) and protein MASCOT scores vs. protein marginal inclusion probabilities (right-hand-side) in all AP-MS control and bait experiments. Figure S2: Scatter plot of peptide PROPHET probabilities (Prob) onto the peptide MASCOT scores (Score) in all AP-MS control and bait experiments. Figure S3: Empirical Probability Density Function (PDF - left) and Cumulative Density Function (CDF - right) plots of peptide scores (top) and peptide probabilities (bottom) in all AP-MS control and bait experiments. Figure S4: Optimizing the determination of the peptide MASCOT score threshold in all AP-MS bait experiments. Figure S5: Number of Indicator Prey Proteins and Reproducible Experimental Replicates and the joint inclusion probability for the protein-based analysis in all AP-MS bait experiments. Figure S6: FDR sensitivity as a function of Confidence Score cutoff and marginal inclusion probability threshold in all AP-MS bait experiments. Figure S7: FDR and GO semantic similarity analyses in all AP-MS bait experiments. Figure S8: Density distribution plots of bait-prey Confidence Scores at procedural stages “N”, “R” and “S” in all AP-MS bait experiments. Figure S9: Quantile-Quantile plots of bait vs. control marginal inclusion probabilities in all AP-MS bait experiments. Figure S10: Correlation and regression relationships between protein MASCOT scores and protein marginal inclusion probabilities at different procedural stages from the initial “Naïve” stage (“N”), to the “Reproducible” stage (“R”), and to the final “Specific” stage (“S”) in all AP-MS bait experiments. Figure S11: Stability of the Coefficient of Variation (CV) of the mean marginal inclusion probability as a function of procedural stages from the initial “Naïve” stage (“N”), to the “Reproducible” stage (“R”), and to the final “Specific” stage (“S”) in all AP-MS bait experiments. Figure S12: Confidence Intervals (95% CIs) of the median bait-prey semantic similarity for SAINT-only and SAINT in conjunction with ROCS at the different ROCS procedural stages “N” (SAINT-only), “R”, and “S” (ROCSSAINT) in all AP-MS bait experiments. Figure S13: FDR computed from SAINT posterior probability output (PSAINT) as a function of procedural stages: from the initial “Naïve” stage (“N” – SAINT-only), to the “Reproducible” stage (“R”), and to the final “Specific” stage (“S” – ROCS-SAINT) in all AP-MS bait experiments [eriments ,32,35-37Click here for fileROCS lists of Indicator Prey Proteins (IPI) and Reproducible Experimental Replicates (RER) in all AP-MS control and bait experiments (each on a single Excel tab-sheet in a single file).Click here for fileSpecific Prey Proteins between SAINT (Posterior Probability PSAINT), ComPASS (D − score) and our method ROCS (C − score) in all AP-MS bait experiments (each on a separate Excel tab-sheet in a single file).Biological validation of ROCS protein-protein interaction (PPI) scoring results for the Click here for fileSpecific Prey Proteins between SAINT (Posterior Probability PSAINT), ComPASS (D − score) and our method ROCS (C − score) in all AP-MS bait experiments (each on a separate Excel tab-sheet in a single file).Comparison of protein-protein interaction (PPI) scoring for the Click here for fileSAINT protein-protein interaction (PPI) scoring for the Specific Prey Proteins at the different ROCS procedural stages “N” (SAINT-only), “R”, and “S” (ROCS-SAINT) in all AP-MS bait experiments (each on a separate Excel tab-sheet in a single file).Comparison of Click here for file


Background: Non-synonymous single-nucleotide polymorphisms (nsSNPs) within the coding regions of genes causing amino acid substitutions (AASs) may have a large impact on protein function. The possibilities to identify nsSNPs across genomes have increased notably with the advent of next-generation sequencing technologies. Thus, there is a strong need for efficient bioinformatics tools to predict the functional effect of AASs. Such tools can be used to identify the most promising candidate mutations for further experimental validation.Results: Here we present prediction of AAS effects (PASE), a novel method that predicts the effect of an AASs based on physicochemical property changes. Evaluation of PASE, using a few AASs of known phenotypic effects and 3338 human AASs, for which functional effects have previously been scored with the widely used SIFT and PolyPhen tools, show that PASE is a useful method for functional prediction of AASs. We also show that the predictions can be further improved by combining PASE with information about evolutionary conservation.Conclusion: PASE is a novel algorithm for predicting functional effects of AASs, which can be used for pinpointing the most interesting candidate mutations. PASE predictions are based on changes in seven physicochemical properties and can improve predictions from many other available tools, which are based on evolutionary conservation. Using available experimental data and predictions from the already existing tools, we demonstrate that PASE is a useful method for predicting functional effects of AASs, even when a limited number of query sequence homologs/orthologs are available. A non-synonymous single-nucleotide polymorphism (nsSNP) is a single-nucleotide change in a protein-coding region of a gene that causes an amino acid substitution (AAS) in the resulting protein. The importance of nsSNPs has been demonstrated in many studies , and in Table 1). The information obtained from PASE can also be combined with knowledge about sequence conservation to further improve functional predictions. Several examples are provided to illustrate the predictive ability of PASE both independently and combined with conservation information.As the functional effects of most AASs are experimentally unexplored, there is a need for development of tools to efficiently predict substitution effects on protein structure and function. Currently, several methods exist that are based on the degree of evolutionary conservation as an indicator of the functional effect of an AAS. Widely used software tools are, for example, SIFT , 2003 anFigure 1). To calculate the AAS-induced change in the physicochemical properties, we imported seven physicochemical properties from the AAindex database previously selected by (Table 1), which gives every AA a unique profile. To illustrate how PASE can be combined with information about sequence conservation, we calculate a simple conservation score from a given protein sequence based on the alignment of homologous and orthologous sequences.The PASE algorithm uses physicochemical properties of AAs and sequence conservation to estimate the effect of each AAS on the protein . In other words, the selected properties span a nearly orthogonal 7D coordinates space in which every AA is a unique point .The seven physicochemical properties: (1) transfer of free energy from octanol to water, (2) normalized van der Waals volume, (3) isoelectric point, (4) polarity, (5) normalized frequency of turn, (6) normalized frequency of alpha-helix, and (7) free energy of solution in water used by PASE, have been selected from the AAindex database of protein indices , 2008 usHighly conserved sequences often indicate a functional importance of the AA and substitutions tend to be deleterious, whereas those within areas of low conservation are often tolerated. We calculate a simple sequence conservation score , by searching for functionally related protein sequences by using NCBI-blastp  and geneN is the number of assessed sequences of MSA, and r is the proportion of the AAs of interest in MSA. The formula  gene have been associated with cataracts. It has been shown that these two mutations cause the disease through different mechanism  as well as high conservation scores (MSAC scores for both of 0.93). The combined PASEC scores are 0.74 and 0.52 for W45S and G46V, respectively, indicating considerable expected effects on protein function. This is consistent with the disease association of both these AASs (Table 2).As shown in ese AASs  as well PRKAG3) gene encodes the regulatory γ subunit of adenosine monophosphate-activated protein kinase (AMPK), which is prevalently expressed in white skeletal muscle fibers . The combined PASEC functional prediction scores are 0.50 and 0.12, respectively, thus consistent with the observed phenotypic effects and with the SIFT and PolyPhen predictions for these mutations (see Table 2).Exploration of the conservation and changes in physicochemical property of the AASs in the porcine PRKAG3 revealed large physicochemical property changes for R200Q (PASE score 0.54) and smaller for I119V (PASE score 0.14) as well as high degrees of conservation at both 199th (MSAC score 0.85) and 200th (MSAC score 0.93) sites (see Table 3). The distributions of PASE, MSAC, and PASEC scores are shown in Figures C, respectively.To further evaluate the PASE and PASEC predictions, we used 3338 AASs in the Ensembl database representing nsSNPs on human chromosome 22 with functional effects previously predicted with the widely used SIFT and PolyPhen tools. For AASs predicted with SIFT as “tolerated” (1987 AASs) or with PolyPhen as “benign” (1637 AASs), PASE/PASEC showed average scores of X/0.18 and Y/0.16, respectively, whereas AASs predicted with SIFT as “deleterious” (1351 AASs) or with PolyPhen as “probably damaging” (1162 AASs) showed average scores of Z/0.3 and A/0.33  and protein families. We are planning to use statistical learning methods to take into account the specific microenvironment of the substituted AA and to infer more general, interpretable rules describing the degree of AAS impact on protein function. To this end we need to collect larger datasets of carefully curated examples of different classes of AAS and to integrate information available from several sources, like databases, structure prediction servers, etc.Our future research will focus on tailoring PASEC to specific protein classes : Linux and UnixPrograming language: Python and BiopythonOther requirements: Python 2.6 or higher, Biopython 1.58 or higher and ClustalW (1.82) or higherLicense: GNUAny restrictions to use by non-academics: noneMarcin Kierczak initiated the study. Marcin Kierczak, Xidan Li, Stefan Marklund, and Örjan Carlborg planned the study. Xidan Li, Marcin Kierczak, Xia Shen, Stefan Marklund, and Örjan Carlborg developed the algorithm that was implemented by Xidan Li and Marcin Kierczak. Xidan Li and Muhammad Ahsan evaluated the algorithm. Xidan Li and Marcin Kierczak drafted the manuscript and all authors contributed to the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The type 2 diabetes has increased rapidly in recent years throughout the world. The insulin signal transduction mechanism gets disrupted sometimes and it's known as insulin-resistance. It is one of the primary causes associated with type-2 diabetes. The signaling mechanisms involved several proteins that include 7 major functional proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4. Using these 7 principal proteins, multiple sequences alignment has been created. The scores between sequences also have been developed. We have constructed a phylogenetic tree and modified it with node and distance. Besides, we have generated sequence logos and ultimately developed the protein-protein interaction network. The small insulin signal transduction protein arrangement shows complex network between the functional proteins. The occurrence of type 2 diabetes, especially in the developing countries has increased rapidly during the last decade. The International Diabetes Federation has estimated that the number of diabetic patients in India alone more than doubled from 19 million in 1995 to 40.9 million in 2007. It is projected to increase to 69.9 million by 2025. Presently, 285 million people throughout the world are living with diabetes and a vast majority (90%) is caused by the type 2 diabetes Various proteins are associated with the signal transduction of insulin of which the major ones are insulin receptor, insulin receptor substrate 1, insulin receptor substrate 2, type 1A phosphatidylinositol 3–kinase, Akt2 protein, and glucose transporter type Mapping the protein-protein interaction is an essential step towards the framework of biological signaling pathway In this paper, we have applied computational biology to map out the protein-protein interaction. We have highlighted 7 principal proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4 in insulin signaling pathway and we have collected data on their genes from the global databases. Using the 7 primary proteins, we have carried out multiple sequences alignment to develop scores between sequences. We have successfully constructed phylogenetic tree and also modified phylogenetic tree with node and distance. Besides, we have generated sequence logos and ultimately introduced the protein-protein interaction network. The small insulin signal transduction protein network presented in this paper shows a complex network pattern among the functional proteins.www.ncbi.nih.nlm.gov). The functional protein sequences in FASTA for these genes were used for further analysis.The first step toward a protein interaction network-based modeling of insulin signaling pathway involves listing of human proteins and related genes. We have identified 7 principal proteins  in insulin signaling pathway and collected information about their genes from the database. The functional protein sequences in FASTA format for these genes were collected from the National Center for Biotechnology information , a general purpose multiple sequence alignment computer software for proteins, which produces biologically meaningful multiple sequence alignments of divergent sequences. It calculates the best match for sequences, and lines them up so that the identities, similarities and differences can be observed and analyzed. Evolutionary relationships can also be observed by viewing Cladogram or Phylogram. Using the multiple sequence alignment technique, we have observed the similarity in the sequences and their respective alignment scores were elucidated. In this analysis, seven sequences were used . We have used notation Seq (x:y) meaning alignment score between sequence x, and sequence y. The scores were further used for analysis. Based on these results of the sequence alignments, phylogenetic tree was constructed We have used ClustalW (We have used MATLAB (7.3 version) programming to depict the modified phylogenetic tree with distance. The tree was constructed using the distance between the nodes of the phylogenetic tree. Furthermore, we have developed an algorithm for the generation of this tree. The ribbon structure regarding the proteins used in this study was collected from the Protein Data Bank http://string-db.org), a database of known and predicted protein interactions, we have developed the protein-protein interaction in insulin resistance in signaling pathway. This web-based database dedicated to protein-protein interactions that include direct  and indirect  associations Using STRING We have used this program for MATLAB (7.3 version) and also used sparse matrix of our phylogram. The MAT LAB program is as follows:UG  =  sparse View )There were 12 nodes in the tree and unknown distance was mapped as .50. We have collected 6 major protein ribbon structures form the protein data bank. Ribbon diagram of the structures have shown alpha helices and beta sheets of six functional proteins . The graThe insulin signaling mechanism is complex and highly integrated network that relates to several proteins and downstream regulation of proteins. Taniguchi et al. The evolutionary history and the phylogenetic relationships pattern can be explored through the molecular approach like amino acid sequences in vitro as well as in vivo as a target protein for future drug development research.With the advancement of computer science, mapping proteins onto signaling pathways could be done easily, which would provide scientists an advantage of the rapid accumulation of proteomics information. Using computational methods, large-scale proteomics information can be analyzed faster and this way scientists could reduce the number of experiments required detecting the main interactive proteins of a pathway. The major interactive proteins can be validated


Protein function prediction is an important problem in the post-genomic era. Recent advances in experimental biology have enabled the production of vast amounts of protein-protein interaction (PPI) data. Thus, using PPI data to functionally annotate proteins has been extensively studied. However, most existing network-based approaches do not work well when annotation and interaction information is inadequate in the networks.In this paper, we proposed a new method that combines PPI information and protein sequence information to boost the prediction performance based on collective classification. Our method divides function prediction into two phases: First, the original PPI network is enriched by adding a number of edges that are inferred from protein sequence information. We call the added edges implicit edges, and the existing ones explicit edges correspondingly. Second, a collective classification algorithm is employed on the new network to predict protein function.We conducted extensive experiments on two real, publicly available PPI datasets. Compared to four existing protein function prediction approaches, our method performs better in many situations, which shows that adding implicit edges can indeed improve the prediction performance. Furthermore, the experimental results also indicate that our method is significantly better than the compared approaches in sparsely-labeled networks, and it is robust to the change of the proportion of annotated proteins. The past decade has witnessed a revolution in high-throughput sequencing techniques, resulting in huge amounts of sequenced proteins. However, experimental determination of protein functions is not only expensive but also time-consuming. As a consequence, there is an increasing concern about using computational methods to predict protein functions. Though many efforts have been made in this regard, the functions of most proteins in fully sequenced genomes still remain unknown. This is true even for the six well-studied model species. Taking yeast as an example, approximately one-fourth of the proteins have no annotated functions . TherefoThe most common approach to computational prediction of protein functions is to use sequence or structure similarity to transfer functional information among proteins. According to a recent survey , homologet al. [Recent advances in experimental biology have enabled the production of vast amounts of protein-protein interaction (PPI) data across human and most model species. These data are commonly represented as networks, where a node corresponds to a protein and an edge corresponds to an interaction between a pair of proteins. Thus, using PPI data to assign protein function has been extensively studied. Approaches based on PPI data assume that proteins with similar functions are topologically close in the network. In a review of the existing computational approaches based on PPI data for protein function prediction, Sharan et al.  distinguMajority and Indirect neighbors are two neighborhood counting approaches. Majority [Indirect neighbors [Functional flow [et al. [Direct annotation schemes predict the functions of a protein from the known functions of its neighbors, representatives are neighborhood counting approaches -15, grapMajority  is the seighbors  assumes nal flow  is a gra [et al.  devised et al. [Instead of predicting functions for individual proteins, module-assisted schemes first identify modules of related proteins and then annotate each module based on the known functions of its members, examples include hierarchical clustering-based approaches ,23 and get al. , edge-beet al. , clique et al.  and overet al. .et al. [et al. [Additionally, Chua et al.  presente [et al.  hybridizHowever, most existing network-based approaches do not work well if there is not enough PPI information. In view of this, we proposed a new method that combines PPI information and protein sequence information to improve the prediction performance based on collective classification. Our method divided function prediction into two phases: First, the original PPI network is enriched by adding a number of edges that are computed based on protein sequence similarity. Second, based on the new network, a collective classification algorithm is employed to predict protein function. The main idea behind this method stems from the observation that existing network-based approaches ignore protein sequence information. Therefore, we increase the amount of useful information in the networks by adding a number of computed (or implicit) edges, which consequently improves the prediction performance.S.cerevisiae and M.musculus functional annotation datasets. Compared to four existing protein function prediction methods, our method performs better in many situations, which shows that adding implicit edges can indeed improve the prediction performance. Furthermore, the experimental results also indicate that our method is significantly better than the compared methods in sparsely-labeled networks, and it is robust to the change of the proportion of annotated proteins.We conducted experiments on l proteins are labeled as y1, ..., ly, each i yis a vector with ij y= 1 in case that the protein i Pis associated with the j-th function jF, otherwise ij y= 0. Our goal is to predict the labels y1+1, ..., n yfor the remaining unlabeled proteins lP+1, ..., nP. In this study, we denote the PPI network as a finite undirected graph iEj,∈ ε denotes an observed interaction between protein i Vand j Vand a weight i,j w∈ W indicates the interaction confidence between i Vand jV. Here, we employ collective classification to tackle this problem. In addition, we use both explicit edges that are extracted from PPI datasets and implicit edges that are computed from protein sequence information. In what follows, we present the method in detail.Protein function prediction is a multi-label classification problem where we have a set of functions As we pointed out above, most existing network-based approaches do not work well when there is not enough interaction information in the PPI networks. Considering this, here we propose a novel method that combines PPI information and protein sequence information to improve the prediction performance based on collective classification. The first step of our method is to enrich the original PPI network by adding a number of computed edges based on protein sequence similarity. Note that the similarity between two proteins is not a reliable proof that the two proteins interact, nevertheless, enriching PPI networks by adding a number of computed edges can increase the amount of useful information to the original PPI network and hence improve the prediction performance. In this paper, the basic local alignment search tool (BLAST) is employed to compute the similarity score between each pair of proteins.xV, we define its sequence similarity scores with other proteins like this:For the protein x,i sdenotes the similarity score between protein x Vand protein iV. We set x,i s= 0 if x = i, which means that we do not consider self-similarity. For each protein x Vin the original network, we create k edges to the k vertices that have the highest similarity scores with xV, and use the similarity scores as the weights of these created edges in the enriched network. Thus, we havewhere implicit edges) and explicit edges that are already there. Here, two questions need to be answered. One is how many edges be added for each protein, that is, how to set the value of parameter k, and another is how to combine the weights of these two types of edges with different semantics. We will answer the first question in the experimental evaluation section and the second question in the next subsection.It is worth noting that there are two types of edges in the new network: BLAST-inferred edges   based coAlgorithm 1 Gibbs sampling based collective classification for protein function prediction with implicit and explicit edges in PPI networks.1:     // bootstrappingfor each query protein x Vdo2: 3:         compute the initial end for4: 5:     // burn-in periodfor i=1 to B do6: for each query protein x Vdo7:      8:             update end for9:      end for10: 11: // sampling periodfor i=1 to S do12: for each query protein x Vdo13:      14:             update m-rank result15:             create end for16:      end for17: for each query protein x Vdo18: xM19:      calculate the final result end for20: According to the observation that proteins with shorter distance to each other in the network are more likely to have similar functions, weighted voting is employed to predict an initial functional probability distribution for the query protein. Note that there are two types of annotated neighbors to vote: implicit neighbors (BLAST-inferred neighbors) and explicit neighbors. Thus, we introduce a combination parameter λ ∈  to control the tradeoff between these two types of neighbors.x Vthat has k implicit neighbors and x Nexplicit neighbors, we define the corresponding edge weights like this:Formally, for a query protein x Vassociated with the j-th function j Fis computed like this:Above, where x Vis associated with the j-th function jF. Given a query protein xV, its initial functional probability distribution is denoted as an m-dimensional vector:The larger the value of xV, we consider only its labeled neighbor proteins (either implicitly connected or explicitly connected). That is the reason why we use Bootstrapping phase in Algorithm 1 are from Line 2 to Line 4.Note that when predicting the functions of the query protein burn-in period and the sampling period. The burn-in period consists of a fixed number B of iterations where we update S iterations. In each iteration, we not only update j-th function j Ffor protein xV. This period is implemented in Algorithm 1 from Line 12 to Line 20.The iterative classification process is divided to the following two periods: the xV, its most likely function can be computed as follows:It is worth noting that most proteins in vivo often perform more than one function, thus, protein function prediction is a multi-label classification problem. For the query protein j that maximizes the value of m-dimensional vector x Vto record its ranking result in the i-th iteration as follows:where S of iterations is reached, we can get a matrix x Mwith S rows and m columns for the query protein xV:When the threshold number xM, the most frequently sampled function is denoted by xM, the most frequently sampled function  used in this study is based on Gene Ontology (GO) annotation scheme . GO annoProtein interactions of Dataset A were downloaded from the Biological General Repository for Interaction Datasets (BioGRID) . BioGRIDS.cerevisiae and the other three for M.musculus) in Dataset A. In these networks, a node corresponds to a protein and a un-weighted edge corresponds to an interaction between two proteins. Each node was assigned with at least one Go term, and proteins without interaction data or sequence information were deleted. The details for these networks are shown in Table In this study, we constructed one protein interaction network for each GO namespace using only physical interactions. Therefore, there are totally six PPI networks (three for http://www.helmholtz-muenchen.de/en/ibis). Fun-Cat is organized as a hierarchically structured annotation system and consists of 28 main functional categories. FunCat annotations for S.cerevisiae were downloaded from Comprehensive Yeast Genome Database (CYGD) [M.musculus were downloaded from Mouse functional Genome Database (MfunGD) [The second dataset (denoted as Dataset B) used in this study is based on Functional Catalogue (Fun-Cat) annotation scheme  taken fre (CYGD) . CYGD is(MfunGD) . MfunGD S.cerevisiae and another for M.musculus), proteins without interaction data or sequence information were deleted. As a result, in the S.cerevisiae interaction network, there are totally 388846 distinct interactions among 4687 proteins, and in the M.musculus interaction network there are 14269 proteins and 832124 interactions. Additionally, protein sequence information for Dataset A and Dataset B were also downloaded from the STRING database.Protein interactions of Dataset B were downloaded from STRING database , which ikNN classifier to predict the functions of un-annotated proteins. We also conducted comparison with a graph based method: Functional flow, as well as two neighbor counting methods: Majority and Indirect neighbors. Functional flow [Majority [Majority are its simplicity and effectiveness. Indirect neighbors [We compared our method with a sequence similarity based approach (termed BLAST-mined) that does not take the PPI network into account. The BLAST-mined approach was performed in two steps. First, BLAST was adopted to compute similarity score between each pair of proteins. Second, we employed the nal flow  treats eMajority  makes useighbors  exploitsTP/FP, which depicts the relative magnitude between the number of true positives and the number of false positives. In this setup, we define the i-th rank overall true positive (TP) as the number of proteins whose i-th rank predicted function x Vand the i-th rank overall false positive (FP) as the number of proteins whose i-th rank predicted function xV. To evaluate the prediction performance of our method, leave-one-out cross validation was used to compare the performance of our method with that of the competing approaches. The idea behind leave-one-out cross validation is simply to treat each annotated protein as un-annotated in turn, then run the algorithm and compare the predicted functions to the known functions of the protein. It is worth noting that the iterative classification step is omitted in leave-one-out validation, this is because the label vector of the query protein is never updated after bootstrapping. However, in real PPI networks, there are a significant number of un-annotated proteins, thus leave-one-out cross validation seems impracticable in reality. Therefore, we also compared the performance of our method with that of the competing approaches in sparsely-labeled networks. In our implementation, the proportion of annotated proteins was varied from 10% to 90%, we ran 10 experiments for each given proportion of annotated proteins and reported the average performance. Moreover, the burn-in period and the sampling period were set to contain 20 and 100 iterations respectively.For traditional classification problems, the standard evaluation criterion is accuracy. However, in this paper we can not simply determine whether a prediction is correct or wrong because of the partially correct phenomenon in multi-label classification problems . Therefok. We varied k from 1 to 15 for BLAST and compared the prediction performance. Table k in dataset A, and Table k in dataset B. We can see that when using k=5 for both dataset A and dataset B the proposed method performs best. This is because adding BLAST-inferred edges with low sequence similarity (when k is large) may produce false predictions. Hence, in our rest experiments, the value of k was set to 5.We studied the effect of two parameters used in our study. The first one is the combination parameter λ that controls the tradeoff between implicit (BLAST-inferred) edges and explicit edges. We varied λ from 0.1 to 0.9 and compared the prediction performance. Table S.cerevisiae in Dataset A, there are three PPI networks (corresponding to the three GO namespaces). The average number of functions that each protein has in these networks is 1.24, 1.33 and 1.64, respectively. So we considered only the top 2  predictions. Figure M.musculus in Dataset A, as the average number of functions that a protein possesses in the three original networks is 2.16, 3.96 and 2.79, we considered only the first 3, 4, and 3 predictions, respectively. The results are shown in Figure To evaluate the prediction performance of our method, leave-one-out cross validation was used to compare the performance of our method with that of the competing approaches. For S.cerevisiae and M.musculus data in Dataset A, and Figure S.cerevisiae and M.musculus data in Dataset B. These results clearly show that our method performs better than the four compared approaches in most cases. The experimental results also validate that generally for all approaches the prediction performance gets better as the number of annotated proteins in the network increases. However, very interestingly we noticed that in Figure Here we compared the performance of our method with that of the competing approaches in sparsely-labeled networks. In our implementation, the proportion of annotated proteins in PPI networks was varied from 10% to 90%, with which we predicted the functions of the remaining (un-annotated) proteins. We ran 10 experiments for each given proportion of annotated proteins and evaluated the average performance. Figure S.cerevisiae and M.musculus functional annotation datasets. The experimental results demonstrate that our method outperforms the existing approaches across a series of label situations, especially in sparsely-labeled networks where the existing approaches do not work well due to PPI information inadequacy. Experimental results also validate the robustness of the proposed approach to the number of labeled proteins in PPI networks.In this paper, we proposed a new method to protein function prediction that combines PPI information and protein sequence information to improve prediction performance. It first reconstructs PPI networks by adding a number of BLAST-inferred implicit edges, and then applies the collective classification method to predicting protein functions based on the new networks. The key idea of our work is to enrich the PPI information of PPI networks by adding a number of computed edges, which subsequently improves the prediction performance. We carried out experiments on In this paper, we used a very simple scheme  to infer implicit edges. Actually, there are some other methods that can be used to mine useful implicit edges, such as random walk. Random walk exploits both local and global network information, should be able to discover more useful hidden edges. We will explore this direction in the future.The authors declare that they have no competing interests.Shuigeng Zhou and Jihong Guan conceived the study, and revised the manuscript. Wei Xiong performed all experiments and data analysis, and drafted the manuscript. Hui Liu prepared the datasets.


Catalogue of Human Protein Complexes). CHPC2012 achieves a higher coverage for proteins and protein complexes than those individual databases. It is also verified to be a set of complexes with high quality as its co-complex protein associations have a high overlap with protein-protein interactions (PPI) in various existing PPI databases. We demonstrated two distinct applications of CHPC2012, that is, investigating the relationship between protein complexes and drug-related systems and evaluating the quality of predicted protein complexes. In particular, CHPC2012 provides more insights into drug development. For instance, proteins involved in multiple complexes (the overlapping proteins) are potential drug targets; the drug-complex network is utilized to investigate multi-target drugs and drug-drug interactions; and the disease-specific complex-drug networks will provide new clues for drug repositioning. With this up-to-date reference set of human protein complexes, we believe that the CHPC2012 catalogue is able to enhance the studies for protein interactions, protein functions, human diseases, drugs, and related fields of research. CHPC2012 complexes can be downloaded from http://www1.i2r.a-star.edu.sg/xlli/CHPC2012/CHPC2012.htm.Protein complexes are key entities to perform cellular functions. Human diseases are also revealed to associate with some specific human protein complexes. In fact, human protein complexes are widely used for protein function annotation, inference of human protein interactome, disease gene prediction, and so on. Therefore, it is highly desired to build an up-to-date catalogue of human complexes to support the research in these applications. Protein complexes from different databases are as expected to be highly redundant. In this paper, we designed a set of concise operations to compile these redundant human complexes and built a comprehensive catalogue called CHPC2012 ( Protein complexes are a form of quaternary structures that are of great importance for understanding cellular organization and functions. They are involved in many essential biological processes, such as the transcription of DNA, the translation of mRNA, signal transduction and other processes. For example, the RNA-Induced Silencing Complex (RISC complex) With the recent development of experimental techniques, such as tandem affinity purification with mass spectrometry (TAP-MS), the information and knowledge of these biologically important units become more enriched and are stored in a number of databases. In 2004, a database named PINdb et al.et al.As data for protein complexes are identified and classified, various computational applications utilizing those protein complexes have recently been proposed for different purposes. For example, Yang Redundancy is a challenging problem which we will definitely encounter when we integrate human protein complexes from different databases. Even records within the same database  are highly redundant et al. built disease-specific drug-protein connectivity maps using protein interaction networks and literature mining et al. established an integrate pharmacological network of proteins, diseases and drugs It is a growing recognition that network-based approaches are suitable to describe the complexity of human diseases and assist the development of new drugs In addition, while experimental methods  exist for detecting protein complexes, they have several limitations, e.g., time-consuming, false-negative and false-positive detections. Therefore, computational prediction of protein complexes can fill up the map of protein “complexome” and is thus an interesting topic in bioinformatics. In another application of our CHPC2012 complexes, we evaluate the performance of state-of-the-art computational methods for predicting human protein complexes using CHPC2012 as golden standard. These high quality protein complexes predicted by computational methods may further serve as interesting putative candidates for experimental verifications. Furthermore, analysis on these evaluation results may guide us to streamline future directions in this topic.In this section, we will first introduce our CHPC2012 in more details, including the support for CHPC2012 from binary protein interactions. Then, we will show the application of CHPC2012 in drug-related systems. Lastly, we will evaluate the quality of protein complexes predicted by various computational methods using our CHPC2012.redundancy of each database, which is measured by the number of redundant pairs. In particular, two complexes with Jaccard similarity higher than the overlapping threshold redundancy for the raw CUROM is 2727, which means there are 2727 redundant pairs among CORUM complexes. Since protein complexes in each raw database themselves are redundant as mentioned previously, we also applied our Algorithm  complexes from CORUM, thereby decreasing the number of covered proteins from 2507 to 1808. We observe that these 1170 filtered complexes (64.07% of 1826 complexes) only cover additional 699 proteins (27.88% of 2507 proteins). These results demonstrate that the Algorithm . However, it is our CHPC2012 that has the most druggable proteins with the lowest p-value. As shown in We model the relationships between drugs and protein complexes as a network, where both drugs and complexes are nodes, an edge between a drug and a complex indicates that this complex contains at least one target of the drug. We thereafter call this network as drug-complex network. The drug-complex network plays important roles to analyze the features of multi-target drugs and polyphamacological drugs and to unveil novel associations between diseases and protein complexes The drug-complex network for CHPC2012 complexes consists of 2648 nodes (1835 drugs and 813 complexes) and 9916 edges as shown in However, some drugs and targets might be over-counted. For example, suppose that three complexes  share a protein D which is a target of drug E, so drug E is counted 3 times  and target D is also used 3 times. To avoid the over-counting problem, we should delete the repeats, i.e., both drug E and target D should be counted only once. As previously reported in in silico prediction of drug-drug interactions.Drug-drug interaction is a situation where a drug affects the activity of another drug when taken together. In our drug-complex network, we found several drugs are interacting with the same complex . Our hypothesis is that drug-drug interactions tend to be co-complex drug pairs. To verify this hypothesis, we perform the following random test. Among 1901 known drug-drug interactions, there are 375 co-complex drug pairs. We also generate 1901 random drug pairs for 1000 times. In this case, the average number of co-complex drug pairs in these random pairs is only 88. We link phenotypes or diseases to protein complexes based on their enrichment in these complexes see the  section.We applied the literature mining techniques to verify the above identified drug-disease associations et al.The association between the drug Imatinib and type 1 diabetes is a verified example predicted in our disease-specific complex-drug networks. Type 1 diabetes is an autoimmune disease that destructs insulin-producing beta cells of the pancreas with subsequent lack of insulin and leads to increased blood and urine glucose. The complexes linked to Type 1 diabetes as shown in Currently, there are various computational approaches for predicting protein complexes from protein interaction data, e.g., MCODE In this work, CHPC2012 will be utilized to evaluate the quality of protein complexes predicted by five of the above approaches . In this case, all the proteins in those real complexes were pretty much covered by this very big cluster. Meanwhile, COACH achieved the highest F-measure due to its balanced precision and recall in both Saccharomyces cerevisiaeAfter showing the comparative results of various methods in the above table and figures, we can find that these results are consistent with those collected for the model organism yeast However, a difference between the results of human and yeast is that the performance of each individual method is much lower in human than in yeast. For example, many methods including MCL, DPClus and COACH achieve a recall higher than or close to 0.6 on Krogan PPI data while all of them have a recall lower than 0.4 on both HPRD and BioGrid datasets. The recall of MCL on BioGrid data is even lower than 0.3. In addition, all the methods used in the paper have very low PPV values, which motivated the following strategies to improve the performance of protein complex prediction for human. First, we may improve the quality of our inputs, i.e., the PPI data. Due to the high false positive and false negative rates, it would be necessary to assess the reliability of PPI data used for protein complex detection Protein complexes are key entities to perform cellular functions and associate with specific human diseases. However, they are highly redundant within and across various existing databases, such as CORUM and HPRD. In this paper, we processed these redundant complexes and compiled a non-redundant catalogue for human protein complexes called CHPC2012. CHPC2012 is verified to be a high quality set of protein complexes as it has a high coverage for proteins and protein complexes, as well as protein-protein interactions in existing PPI databases. It reveals extensive advantage for applications in drug development, especially in the drug-target prediction, drug-drug interactions, and drug repositioning. CHPC2012 also provides a multidimensional view of associations among biological components, including drug-complex networks and disease-specific complex-drug networks. In addition, CHPC2012 can be used as a golden standard benchmark to evaluate the performance of various methods that are designed to predict protein complexes from human PPI data. The evaluation results also provide several useful hints to fill up the current map of human protein “complexome”.silico will provide guideline for in vitro and in vivo studies. Furthermore, disease-specific drug-complex networks (taking disease-gene associations into consideration) reveal novel associations between drugs and diseases . With these achievements of CHPC2012 for drug development, we reasonably expect that broader applications of CHPC2012 will be conducted for translational bioinformatics in the future.Translational bioinformatics is dedicated to translate scientific discoveries into better therapeutic outcomes through integrative methods We applied literature mining techniques to verify our drug repositioning results. Around a third of our predicted drug-disease associations have at least one literature support, which suggests our prediction is reliable and believable. Meanwhile, two thirds of the associations are completely new discoveries, showing the novelty of our analysis. However, we must be very cautious with the computational verification of drug repositioning. A co-occurrence of a drug and a disease sometimes implies that the drug might cause the disease instead of cure it . Therefore, more sophisticated literature mining techniques  are needed for a more precise computational verification. Although such false positive predictions cannot be ignored, our analysis nonetheless can provide valuable information for drug repositioning.Lastly, Gene Ontology also provides some information for protein complexes in the “cellular component” sub-ontology. We didn't utilize the complexes in GO to compile our CHPC2012 for the following reasons. First, we define the significance scores for known complexes based on their GO term enrichment, which would bring bias for complexes in GO. Second, some complexes in GO are computationally inferred and they may not be accurate. Third, the support from binary interactions shows that complexes in GO have much lower quality than those in other databases as shown in After human protein complexes from individual databases are collected, we compile them to construct a more comprehensive catalogue for further reference. However, many of those collected complexes may overlap with one another. In order to process these redundant complexes, we define a significance score for each complex. Since proteins within the same complex tend to cooperate with each other to perform a common function, the significance score of a complex here will show the extent of its functional enrichment. In particular, a protein pair can have a semantic similarity based on their GO annotations. The significance score of a complex The above strategies are presented in the Algorithm , which iDisease-gene associations were collected from 118 GWAS articles Based on the above collected disease-gene associations, we extract disease-complex associations using the following hypergeometric distribution In addition, the drug targets and drug-drug interactions were downloaded from Drugbank (Release 3.0) By constructing disease-specific drug-complex networks, we are able to predict novel drug-disease associations for existing drugs . Here, we briefly introduce the computational verification for those predicted drug-disease associations.As we know, MeSH  is a comprehensive controlled vocabulary for the purpose of indexing scientific articles and it can also serve as a thesaurus that facilitates searching in PubMed. To search drugs and diseases in PubMed, we will map them to MeSH terms. For example, 315 drugs and 59 diseases have MeSH terms (there are 600 drugs and 62 diseases in 1400 predicted drug-disease associations). They can be automatically linked to PubMed records . For those drugs or diseases without MeSH terms, we will directly use their names for searching in PubMed. As such, 550 drugs and 59 diseases in 1103 drug-disease associations have at least one PubMed record. Finally, if a drug and a disease have a co-occurrence in the PubMed record , they are considered to be a verified drug-disease association. Otherwise, they are considered as a novel drug-disease association.Supporting Information S1Supporting Information S1. Includes supplementary figures and tables.(PDF)Click here for additional data file.