Pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. Among its various strains, pathovar tomato strain DC3000 (PstDC3000) is asserted to infect the plant host Arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets.Every year pathogenic organisms cause billions of dollars' worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Arabidopsis and PstDC3000 in genome scale. The interolog method relies on protein sequence similarity to conduct the PPI prediction. A Pseudomonas protein and an Arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If a Pseudomonas and an Arabidopsis protein contain an interacting domain pair, one can expect the two proteins to interact with each other. The interolog-based method predicts ~0.79M PPIs involving around 7700 Arabidopsis and 1068 Pseudomonas proteins in the full genome. The domain-based method predicts 85650 PPIs comprising 11432 Arabidopsis and 887 Pseudomonas proteins. Further, around 11000 PPIs have been identified as interacting from both the methods as a consensus.In this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between Arabidopsis thaliana and Pseudomonas syringae pv. tomato DC3000 in a genome wide scale with a high confidence. Although the predicted PPIs may contain some false positives, the computational methods provide reasonable amount of interactions which can be further validated by high throughput experiments. This can be a useful resource to the plant community to characterize the host-pathogen interaction in Arabidopsis and Pseudomonas system. Further, these prediction models can be applied to the agriculturally relevant crops.The present work predicts the protein-protein interaction network between  Pseudomonas syringae is a Gram-negative bacterium causing economically important diseases in a wide range of plant species leading to severe agricultural losses worldwide. Each strain of Pseudomonas shows a high degree of host specificity and infects only a limited number of plant species or even a few cultivars of a single plant species . In addition, Pst DC3000 also produces non-proteinaceous virulence effectors, including coronatine (COR), which are crucial for pathogenesis. However, the virulence function of a large number of potential effectors encoded by the Pst DC3000 genome and their mode of action is still unknown. Similarly, in Arabidopsis it has been seen that approximately 3000 proteins are directly related to plant defense [et al. [ Arabidopsis proteins and 83 pathogen effectors including very few interactions with Pst DC3000. Till now, only nearly 10 % of the full genome of Arabidopsis has been evidenced for interaction. Therefore, to functionally characterize the dynamic interactions of plants with bacterial pathogens, there is a need for genome-wide study of the host-pathogen interactions. Knowledge of such novel resistance interactions provides the backbone of the understanding of plant resistance mechanisms and will aid in the further analysis of plant immunity [ species ,2. Amongractions ,3,4. Theed genes . One of ed genes . Basical defense . Many of [et al.  reportedimmunity .Generally, pathogen attacks host tissues, secreting degradation enzymes and toxin release. Many of such mechanisms involve the protein-protein interactions (PPIs). PPIs are essential process in all living cells and play a crucial role in the infection process, and initiating a defense response. In this context, understanding the PPI network (interactome) between plant proteins and pathogen proteins is a critical step for studying the molecular basis of pathogenesis ,11. In pIn the past decade, a series of PPI prediction methods have been elegantly developed and are playing an increasingly important role in complementing experimental approaches. Diverse data types or properties, such as gene ontology (GO) annotations , proteinPseudomonas syringae and Arabidopsis thaliana. The domain-based approach infers inter-species protein-protein interactions by known domain-domain interactions from various databases and the interolog approach identifies protein-protein interactions based on homologous pairs of protein interactions across different organisms. We present the prediction pipeline in detail and the functional analysis of the predicted results.In this work, we used the interolog and the domain-based methods to jointly predict the protein-protein interactions between Pseudomonas syringae pv. tomato DC3000 is downloaded from Pseudomonas genome database (http://www.pseudomonas.com/download.jsp) which contains 5619 protein sequences. Similarly, the full genome of Arabidopsis thaliana containing 35386 protein sequences is extracted from the TAIR10 database (http://www.arabidopsis.org/). To infer the prediction from the interolog, we have used two types of datasets: the HPIDB dataset and DIP dataset. Database of Interacting Proteins (DIP) is a collection of experimental determined interactions between proteins in intra-species [The whole proteome of -species . As of J-species . As of J Pseudomonas are processed through the Psortb3.0  [PstDC3000 are filtered for interaction prediction.All proteins ofbacteria ) and thors.org/) , which i Arabidopsis and a Pseudomonas protein is inferred from two approaches: the domain based and the interolog method individually. The prediction framework is shown in Figure In this study, the probability of interaction between an Arabidopsis and Pseudomonas, HMMPfam is used in interproscan5 [ Arabidopsis proteins and 7253 domains are collected for PstDC3000. If a protein pair between Pseudomonas and Arabidopsis contains an interacting domain pair from iPfam and 3DID, then the pair is expected to interact with each other.The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If two proteins contain an interacting domain pair, it is expected that these two proteins may interact with each other. To get the domains inproscan5 . In totaA and B are two different interacting proteins of one organism, and A' and B' are two different interacting proteins of another organism. Then the interaction between A and B is an interolog of the interaction between A' and B', if A is a homolog of A', B is a homolog of B', A and B interact, and A' and B' interact. Thus, interologs are homologous pairs of protein interactions across different organisms. Each protein in Arabidopsis and Pseudomonas is BLASTed against all the protein sequences in the DIP and HPIDB database to identify the homologs with E-value, sequence identity and aligned sequence length coverage of 1.0E-4, 50 and 80% respectively. Each protein pair between Pseudomonas and Arabidopsis is predicted to interact if an experimentally verified interaction exists between their respective homologous proteins in DIP or HPIDB databases.The interolog method relies on protein sequence similarity to conduct the PPI prediction. An interolog is a conserved interaction between a pair of proteins which have interacting homologs in another organism . The ill Arabidopsis and Pseudomonas are paired up, which constitute ~97M PPIs. The interaction probability of each pair is assessed through the domain-based model and interolog-based model separately. The predicted interactions from these methods are reported in Table  Arabidopsis proteins and 1337 Pesudomonas proteins. Out of these, 85650 PPIs are predicted by domain based method involving 11432 Arabidopsis and 887 Pseudomonas proteins. Similarly, the interolog method predicted ~0.79M PPIs including 7766 Arabidopsis and 1068 Pseudomonas proteins. Nearly, 11000 PPIs are consistently predicted by both methods as consensus which comprises 2043 Arabidopsis and 93 Pseudomonas proteins. The interaction network of the consensus predicted PPI is shown in Figure  Pseudomonas protein has around 118 Arabidopsis interacting partners, whereas an Arabidopsis protein interact with around 6 Pseudomonas proteins. The reported results are coherent with the previous studies in which it is demonstrated that a few pathogen proteins involved in interaction in the host interactome [To predict the genome wide interactions, all proteins oferactome . All pre Pseudomonas with the highest number of edges (hubs) are PSPTO_0135, PSPTO_0400, PSPTO_0540, PSPTO_0808, PSPTO_1510, PSPTO_2303, PSPTO_2529, PSPTO_2632, PSPTO_3161, PSPTO_3583, PSPTO_3890, PSPTO_3912 and PSPTO_4001 with more than 400 PPIs in the Arabidopsis-Pseudomonas interactome. There are also several effectors with more than 40 predicted PPIs. These are PSPTO_4497, PSPTO_1482, PSPTO_4868, PSPTO_4602, PSPTO_3882, PSPTO_0405, PSPTO_1492, PSPTO_4093, PSPTO_1949, PSPTO_4776, PSPTO_3130, PSPTO_3900, PSPTO_5014 and PSPTO_4090. In contrast to these hub proteins, several effectors are predicted to interact with very few proteins. These hub proteins play important role in pathogenesis, hence can be further investigated for deciphering virulence mechanism.The effectors of Arabidopsis and Pseudomonas proteins in the predicted PPIs are listed in Tables  Arabidopsis shows that many proteins involved in the biological process, response to cadmium ion and metal ion. In literature, it has been shown that metal ions are required for pathogen virulence and plant defense [Pseudomonas syringae pv. maculicola [Functional enrichment analysis is an important assessment for elucidating the functional relevance of the host and pathogen proteins involved in the PPIs. The presence of enriched (over-represented) functional categories that are closely related to host defense and pathogen infection support the validity of the predicted PPIs of the prediction models. Gene ontology (GO) is a comprehensive functional system to annotate the gene products. We used the biological process GO term enrichment to see the relevance of the predicted proteins. The Database for Annotation, Visualization and Integrated Discovery (DAVID) is used to conduct the enrichment analysis. The ove defense ,32. Foneculicola . Block aculicola . Qiu et culicola  show thaculicola . These e Arabidopsis proteins targeted by the predicted Pseudomonas will be helpful in deciphering the mechanism of host-pathogen interactions. If the targeted Arabidopsis proteins are located in cellular compartments that are very relevant to the pathogen's infection or very likely to be involved in interactions with the pathogen, then the prediction result supports the host-pathogen predictions.Pathogens suppress host immunity by directing a range of secreted proteins or effectors, to the cytoplasm of host cells. Once these effector proteins traversed the host plasma-membrane, are transported to many subcellular locations where they subvert the host immune system to enable pathogen growth and reproduction. The knowledge of cellular compartments of the Arabidopsis proteins from both the domain based and interolog methods using the AtSubP [ Arabidopsis proteins on a genome-scale with high accuracy for seven locations. The subcellular locations of all predicted Arabidopsis proteins are listed in Table Pseudomonas syringae target the plant proteins mostly in plasma membrane, chloroplast and mitochondrion. Citovsky et al. [Agrobacterium tumefaciens interact with A. thaliana, it hijacks VIP1 protein and use it to shuttle transfer-DNA (T-DNA) into the nucleus for its reproduction. Tao et al. investigated that TIP, an Arabidopsis protein, interacts with the coat protein (CP) of Turnip crinkle virus (TCV) in yeast cells in nuclei [ Arabidopsis proteins by our approach are in close agreement with the earlier findings. Also the localizations for a large number of proteins are still unknown which need a special attention for experimental characterization.To have a clear understanding the location of the interactions in host, we extracted the subcellular localization of the predictede AtSubP  availably et al.  showed tn nuclei . Thus, tArabidopsis thaliana and Pseudomonas syringae pv. tomato DC3000 based on the domain-based and interolog approaches. The functional annotations of both Arabidopsis and Pseudomonas proteins involved in the predicted PPI are analyzed and it shows the relevance of the proteins for host defense and pathogen infections. The present work may provide some useful information and resource to the plant community to understand the molecular mechanism of the plant immunity system against pathogen virulence. The quality of the predicted interactome could further be improved by combining these methods with other computational approaches and biological data sources. The reliability of the predicted interactions can be further assessed through experimental validations.In this study, we have demonstrated that the sequence and domain similarity to known interactions are valuable information in predicting the host-pathogen interactions. We identified ~11000 PPIs between  Pseudomonas syringae Pst DC3000,pathovar tomato DC3000; DIP, Database of Interacting Proteins; HPIDB, Host Pathogen Interaction Database; PPI, protein-protein interaction; GO, Gene Ontology; TTSS, type III protein secretion system; 3DID, three-dimensional interacting domains; iPfam, Protein family interactions; DAVID, Database for Annotation, Visualization and Integrated Discovery.The authors declare that they have no competing financial interests.SSS collected the host and pathogen proteome datasets, developed algorithms and models, performed the calculations, figures and tables, and wrote the draft manuscript. TW helped in data analysis and setting up the pipelines on High-Performance Computing Center. RK conceived the study, participated in its design and coordination, and edited the final manuscript. All authors read and approved the final manuscript.Pseudomonas syringae based on the domain model.Predicted protein-protein interaction pairs for Arabidopsis-Click here for filePseudomonas syringae based on the interolog model.Predicted protein-protein interaction pairs for Arabidopsis-Click here for filePseudomonas syringae.Consensus protein-protein interaction pairs predicted based on both the domain and interolog models in Arabidopsis-Click here for file


Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small.In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures  are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins.This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end.The online version of this article (doi:10.1186/s12859-016-1166-7) contains supplementary material, which is available to authorized users. Genome-wide gene deletion studies show that a small fraction of genes in a genome are indispensable to the survival or reproduction of an organism , 2. ThesSaccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster [With the development of high-throughput experimental technologies, such as Y2H and mass spectrometry, large amounts of protein-protein interaction (PPI) data have been produced, which makes it possible to study proteins from network level. In order to overcome the experimental constraints, recently researchers have paid more attention to computational methods based on PPI network topological characteristics. These computational methods aim to mine and capture the correlations between network topological features and protein essentiality. It has been observed in the PINs of many species, such as nogaster , 11, thanogaster . This phnogaster , which dnogaster –16. Althnogaster –16, mostnogaster .Besides the degree centrality (DC) , many otWhile these proposed centrality measures have demonstrated that network topological properties correlate with protein essentiality, low accuracy and low overlap exist when separately using these centrality measures to predict protein essentiality. It is expected that protein essentiality relates to multiple biological factors. Each of the proposed centrality measures often captures only one or a few topological properties which correlate with the protein essentiality. In addition, the currently available PINs for each species is incomplete  and noisy , which further hinders the performance of these centrality measures.In order to improve the prediction accuracy of essential protein discovery methods, biological and data quality information have been integrated with network topological properties, such as gene expression, cellular localization, biological process information, protein complex, and PPI confidence , 27–33. According to the explanations for hub proteins’ essentiality either in the uniform essential PPI assumption  or in thAs we know, aside from technologically produced false positives, PINs provide in fact a set of putative interactions occurring between gene products. In the biological sense, these interactions are physically possible, but we don’t know if they occur inside the cell. This can be due to a number of possibilities, for instance, the protein pairs may not share the same cellular compartment, could not be expressed simultaneously, the interaction could occur but be biologically irrelevant, the interaction happen for short intervals. The integration of PINs with gene expression data is to provide a more reliable approximation to an in-vivo scenario. Recently several essential protein discovery methods by integrating PINs with gene expression data have been proposed, which outperform the centrality measures solely based on PINs. Most of these methods are based on two or more of the following assumptions: (1) A highly connected protein is more likely to be essential than a low connected one; (2) essential proteins tend to form densely connected clusters; (3) essential proteins in the same cluster have a higher chance to be co-expressed; (4) party hubs and date hubs have the similar probability to be essential while they have very different clustering property. PeC integrates edge clustering coefficient with gene co-expression correlation so as to capture both the co-clustering and the co-expression properties of a protein with its neighbors . In CoEWSaccharomyces cerevisiae, a well-studied species by knockout experiments.In this paper, we propose an ensemble framework aiming to increase the prediction accuracy of basic centrality measures. It integrates biological information (gene expression) with PINs. In our framework, a series of PINs are generated based on the original one using a gene expression correlation-guided bagging strategy. Five centrality measures  are applied in the PINs and a set of scores for each protein are obtained according to each centrality measure. A final score is computed for each protein by using a weighted voting strategy, which reflects the probability of a protein being essential. Differing from other proposed methods, a protein’s essentiality in our ensemble framework is determined not only based on its connectivity in the original PIN , 18, butThis paper is organized as follows. We first show the materials used to test our framework, including the protein-protein interaction data, gene expression data, and the information of gene essentiality. Then the proposed framework is depicted in detail. The experimental results are presented and discussed in the following section. Finally we present our conclusion and future directions.G, where a node v∈V represents a protein and an edge e∈E denotes an interaction between two proteins u and v. In the context of PINs, protein centrality is used to characterize the importance or contribution of an individual protein to the global structure or configuration of the PIN [In this paper, a PIN is represented by an undirected graph  the PIN . In the Saccharomyces cerevisiae were used. This model organism has been characterized by knockout experiments and widely used in the evaluation of essential protein discovery methods. The test data used in this paper are as follows.To evaluate the performance of the ensemble framework, the PPI and gene expression data of The PPI data were downloaded from the DIP  and BioGThe Saccharomyces genome deletion consortium reports a total of 1156 essential open reading frames (ORFs)  and SGD Saccharomyces cerevisiae were retrieved from [Saccharomyces cerevisiae exist, most of them either have small sample size  or are devoted to specific special treatments. The collection of gene expression data from [The gene expression data of ved from , which hved from , 23, 36.ata from  spans thIn machine learning, ensemble methods use multiple models to obtain better predictive performance than could be obtained from any of the constituent models alone . BootstrIf we take protein-protein interactions (PPIs) in a PIN as the training information, false positive PPIs as noise introduced in the training information, and each centrality measure as the base learning model, then we can construct an ensemble framework for each centrality measure with the expectation of an improved performance just as it does in other application domains. The ensemble framework in this study aims to lessen the effect of noise contained in the PPIs as well as to emphasize the effect of PPIs which are considered important for their interacting proteins’ essentiality. To this end, we construct the ensemble framework by using a correlation-guided sampling method which combines the thoughts of both bagging and boosting.Data partition: from the original PPI data, a series of, say m, PINs, are constructed according to certain criteria (see below). This step is similar to that used in bagging to generate training data for each of the ensemble models. (2) Grading: compute a score for each protein on each of the m generated PINs using a centrality measure. Then we can get m scores for each protein by using each centrality measure. (3) Integrating: integrate the m scores for each protein into one final score according to some strategy (see below). (4) Ranking: rank the proteins according to their final scores. For the top n ranked proteins, more true essential proteins contained, more effective the ranking method is.The ensemble framework consists of the following four steps. (1) Data partition is one of the most important steps in the proposed ensemble framework. In this paper, we use a correlation-guided data partition strategy. The basic ideas behind this data partition strategy include: (a) essential proteins are more likely to be co-expressed with some of their neighbors; (b) PPIs between two highly co-expressed proteins are more important to the proteins’ essentiality. Pearson correlation coefficient (PCC) is used to evaluate how strong two interacting proteins in a PIN are co-expressed.We evaluated the data to see if essential proteins tend to display more co-expressed partners in the PINs (assumption a). Figure According to , centraldata partition strategy works as follows. First, it computes the PCC value for each pair of directly interacting proteins in the original PIN. Second, a series of thresholds are determined according to the distribution of PCC values of all the PPIs in the original PIN where maxPCC (minPCC) is the maximal  PCC value among all PPIs’ in the original PIN. There are two ways to generate the thresholds. One is the absolute thresholding strategy, in which we have each threshold thri∈[0, maxPCC), i = 1,2,…,m. The other is so-called uniform thresholding strategy, in which we have each threshold thri∈, i = 1,2,…,m. Third, according to each thresholding strategy, m PINs are generated from the original PIN. In the absolute thresholding strategy, for each threshold thri, the PPIs, whose absolute PCC values are below the threshold, are deleted from the original PIN, and a new PIN, say PINi, in which the absolute PCC values between all directly interacting protein pairs are larger than the threshold thri, is generated. Then m PINs are generated from the original PPI data. Similarly, m PINs can be generated by using the uniform thresholding strategy. For each threshold thri, PINi is generated by deleting all PPIs whose PCC values are less than thri from the original PIN.The The data partition process, which is guided by the correlation, is different from the random sampling strategy for training data generation in bagging. Instead, it is similar with that of boosting since both of them select training data under the guidance of some knowledge. On the other hand, the PINs are all generated before applying learners , which is similar with that of bagging, and so the following training and predicting  processes can be executed in parallel. Therefore, the data partition strategy integrates the advantages of both bagging and boosting.In this paper, five commonly used centrality measures are employed, and their performance are compared with and without the ensemble framework. These five centrality measures are degree centrality (DC) , betweenm scores are calculated for each protein according to each centrality measure on each PIN (m generated PINs in data partition stage). For example, if we take DC as the base centrality measure, then for each protein, there are m DC scores for it calculated from the m PINs respectively. Note that, according to the data partition strategy, the magnitudes of the m scores for each protein generally decreases with the increase of thresholds for generating the PINs, that is, the score of a protein calculated on PINi is generally larger than that computed on PINi+1 for the same protein according to the same centrality measure. The scores for all proteins computed from the same PIN using the same base centrality measure are normalized by dividing their maximal value. The normalization aims to make scores calculated from different PINs comparable and to make it convenient for the integrating stage.In the grading stage, one set of m PINs for each centrality measure.In ensemble learning, multiple predictions by ensemble models are often combined in some way  to classify new examples. In this paper, we use a weighted voting strategy to combine the scores computed on the wi be the weight assigned to PINi, then wi ≤ wi+1, i = 1,…,m-1. Let s be the score calculated on PINi for protein j according to a centrality measure, then the final score, fs(j), for protein j in the ensemble framework is calculated byAccording to the basic ideas behind the data partition strategy, larger weights should be given to the scores computed from the PINs generated with larger thresholds. Formally, let The ensemble framework aims not only to emphasize PPIs which have more contributions to protein essentiality, but also to reserve useful information of the original PIN to the greatest extent. With the increase of thresholds, more and more PPIs are deleted from the original PIN, and some useful information may also be lost. Therefore, we would set the weight of the original PIN to 1 to alleviate such information lost.n ranked proteins are taken as the candidates of essential proteins, out of which the number of true essential proteins are determined according to the list of known essential proteins. Therefore, the more essential proteins that are contained in the top n candidates, the better the prediction or ranking method is.In the ensemble framework, proteins are ranked according to their final scores in descending order. Top For convenience, the ensemble framework using DC  as the base model is referred to as EnDC . EnDC-a denotes the ensemble method using absolute thresholding strategy and EnDC-u denotes the ensemble method using uniform thresholding strategy. The same goes for other four ensemble methods: EnBC, EnCC, EnEC and EnSC.thri = {0,0.1,0.2,…,0.8,0.9,0.91,0.92,…,0.95} for the absolute thresholding strategy, and thri = {-0.7,-0.6,…,0,0.1,0.2,…,0.8,0.9,0.91,0.92} for the uniform thresholding strategy. Therefore, in the ensemble framework, there are m = 15 PINs generated using absolute thresholding strategy while m = 19 PINs using uniform thresholding strategy.In the following experiments, we set We first conducted experiments to evaluate the performance of these two thresholding strategies. The weight vector w = {1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987} for ensemble methods with absolute thresholding strategy, and w = {1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233} for ensemble methods with uniform thresholding strategy.n ranked proteins, the ensemble methods, using either thresholding strategy, considerably outperform their corresponding centrality measures. This validates the usefulness of the ensemble framework for identifying essential proteins from PINs. For example, in top 100 ranked proteins, more than 100 % improvement is obtained by four ensemble methods  on PIN76K and by two ensemble methods (EnCC-a and EnEC-a) on PIN24K, as compared with their corresponding centrality measures. EnDC-a and EnSC-a achieve an 85 % improvement on PIN24K as compared with DC and SC, respectively. EnBC-a compared with BC achieves the least improvement, but there are still 68 % improvement on PIN24K and 97 % improvement on PIN76K, respectively.Figure n in top n candidates.We also compared our ensemble methods with two PCC based methods: applying centrality measures in PCC-threshold PIN, and in PCC-weighted PIN. For PCC-threshold method, we set threshold to 0.75, which is based on the distribution of co-expression weights distribution and IBEP weights distribution  and three proteins  among the top 100 ranked proteins by BC and by EnBC-a respectively are not contained in standard-1122 but are contained in essential protein collection of SGD. YFL023W is a low-degree protein since its degree  is less than the minimal degree  of the top 100 proteins ranked by BC, but its node strength is high (3.17) which means that it is highly co-expressed with some of its interacting proteins , YDL060W (0.95), YNL207W (0.94), YNL277W (0.65), the value is Pearson correlation). It’s interesting that all the four highly co-expressed partners are also essential proteins according to standard-1122. On PIN76K, there are more such proteins among the top 100 ranked proteins by five centrality measures than by the corresponding ensemble methods. For example, there are 9, 5, 6, 2, and 6 such proteins among the top 100 ranked proteins by BC, CC, DC, EC, and SC respectively, while there is only one such protein (YGR159C) among the top 100 ranked proteins by EnBC-a and zero for other four ensemble methods.Saccharomyces cerevisiae indicate that the overlap rate among the top ranked proteins by these centrality measures is very low, in addition to the low accuracy of these centrality measures.The discovery of essential proteins at the network level is an important research field. Many network topology-based centrality measures for the discovery of essential proteins have been proposed. However, most of them are based on the topological properties of PINs and only have limited prediction accuracy. One reason might be the incomplete and noisy PINs on which the centrality measures depend. The other reason might be the fact that proteins’ essentiality are expected to relate to multiple biological features rather than one or few features captured by an individual centrality measure. Effective integration of multiple centrality measures as well as multiple biological data sources would be very useful for the task of identifying essential proteins. However, our experimental results for five commonly used centrality measures  on two yeast PINs (PIN76K and PIN24K) for n ranked proteins with smaller n . Furthermore, the overlap rate of essential proteins increases notably among the top proteins ranked by different ensemble methods. Essential proteins with low degree but highly co-expressed with some of their neighbors are more easily identified by ensemble methods than by centrality measures. It’s also interesting to note that the higher overlap rate of essential proteins between different ensemble methods does not translate to increased correlation but rather decreased correlation compared with the centrality measures. Therefore, the ensemble framework is valuable for predicting essential proteins from PINs. For organisms which have no available protein interaction data, computational methods for the construction of PINs [in silico network topology for predicting essential proteins.To tackle the above problem, we propose an ensemble framework for the discovery of essential proteins, which is based on two ideas that essential proteins tend to be co-expressed with some of their interacting neighbors and that PPIs between two highly co-expressed proteins are expected to be more important to the proteins’ essentiality. Five commonly used centrality measures  are used as base models. The performance of the ensemble framework has been evaluated on two yeast PINs (PIN24K and PIN76K). Experimental results indicate that the ensemble framework can greatly improve the prediction ability of five commonly used centrality measures, especially for top  of PINs  could gin. The reason may be the fact that although essential proteins tend to be co-expressed with their interacting proteins, there are some essential proteins that have low co-expression levels with their interacting proteins. With the improvement in accuracy and completeness for both protein interaction data and gene expression data, we envisage that the effectiveness of the proposed ensemble framework might be enhanced accordingly.The proposed ensemble framework can considerably improve the prediction accuracy of five centrality measures as well as the overlap rate between them for predicting essential proteins. However, the prediction accuracy decreases with the increase of In future research, more sophisticated ensemble frameworks will be explored for the discovery of essential proteins, especially the ensemble framework to integrate the predictive power from different topological properties as well as different types of biological information. Feature selection is often of a very important role in classification models. As reported in , featureBC, betweenness centrality; BC-thr, PCC-threshold method using BC as the base model; CC, closeness centrality; CC-thr, PCC-threshold method using CC as the base model; DC, degree centrality; DC-thr, PCC-threshold method using DC as the base model; EC, eigenvector centrality; EC-thr, PCC-threshold method using EC as the base model; EnBC-a, the ensemble method using absolute thresholding strategy and BC as the base model; EnBC-u, the ensemble method using uniform thresholding strategy and BC as the base model; EnCC-a, the ensemble method using absolute thresholding strategy and CC as the base model; EnCC-u, the ensemble method using uniform thresholding strategy and CC as the base model; EnDC-a, the ensemble method using absolute thresholding strategy and DC as the base model; EnDC-u, the ensemble method using uniform thresholding strategy and DC as the base model; EnEC-a, the ensemble method using absolute thresholding strategy and EC as the base model; EnEC-u, the ensemble method using uniform thresholding strategy and EC as the base model; EnSC-a, the ensemble method using absolute thresholding strategy and SC as the base model; EnSC-u, the ensemble method using uniform thresholding strategy and SC as the base model; maxPCC, maximal PCC; minPCC, minimal PCC; PCC, Pearson correlation coefficient; PIN24K, protein-protein interaction network from DIP database; PIN76K, protein-protein interaction network from BioGRID database; PINs, protein-protein interaction networks; PPI, protein-protein interaction; PPIs, protein-protein interactions; SC, subgraph centrality; SC-thr, PCC-threshold method using SC as the base model; wBC, PCC-weighted method using BC as the base model; wCC, PCC-weighted method using CC as the base model; wDC, PCC-weighted method using DC as the base model; wEC, PCC-weighted method using EC as the base model; wSC, PCC-weighted method using SC as the base model


There have been many tools proposed to predict those complexes. Among them, PPSampler, a protein complex prediction algorithm based on the Metropolis-Hastings algorithm, is reported to outperform other tools. In this work, we improve PPSampler by refining scoring functions and a proposal distribution used inside the algorithm so that predicted clusters are more accurate as well as the resulting algorithm runs faster. The new version is called PPSampler2. In computational experiments, PPSampler2 is shown to outperform other tools including PPSampler. The F-measure score of PPSampler2 is 0.67, which is at least 26% higher than those of the other tools. In addition, about 82% of the predicted clusters that are unmatched with any known complexes are statistically significant on the biological process aspect of Gene Ontology. Furthermore, the running time is reduced to twenty minutes, which is 1 Protein complexes are essential molecular entities in the cell because intrinsic functions of an individual protein are often performed in the form of a protein complex. Thus, it is helpful to identify all protein complexes of an organism for elucidation of the molecular mechanisms underlying biological processes. However, reliable protein complex purification experiments are rather laborious and time-consuming. Thus it has been expected to provide reliable candidates for true protein complexes by computational prediction methods.Most computational approaches to predict the components of protein complexes are designed based on the observation that densely connected subgraphs in a protein-protein interaction (PPI) network are often overlapped with some known protein complexes. One of the differences among those methods is the search strategies to find good clusters of proteins. For example, MCL  is consif1, f2, and f3. The main part of f1 is equivalent to the total sum of the PPI weights within predicted clusters of size two or more. The second scoring function of f2 is designed based on the constraint that the frequency of sizes of predicted clusters obeys a power-law distribution. This constraint is derived from the observation that the frequency of sizes of known complexes obeys a power-law distribution in CYC2008 [f2 evaluates the difference between a given power-law distribution and the distribution of sizes of clusters in a partition. The third scoring function of f3 is the gap between the number of proteins within predicted clusters of size two or more and a target value of that number. It should be noted here that f2 and f3 can be considered to be regularization terms to encourage sparse structures  is a scoring function to be minimized, where C is a state in a domain of states, D. A target probability distribution is determined from f(C) by the following way:PPSampler2 is designed based on the Metropolis-Hastings algorithm  see Fig, which iT is a temperature parameter. Note that there exists the relationship that the higher the probability, the lower the score. Thus, by sampling, a minimized score and the corresponding state can be found. To exploit the Metropolis-Hastings algorithm, in addition to D and f(C), a proposal distribution, denoted by Q(C′|C), which is a probability distribution of C′ ∈ D given C ∈ D, should be also specified. The formulations of them for PPSampler2 are given in the subsequent sections after that of a PPI dataset.where G = , where V is a set of proteins under consideration, and E is a subset of V × V \ {{u, u}|u ∈ V}, representing a set of PPIs. Notice that any self-interactions, {u, u}, are excluded in E. Suppose that each PPI, e, has a weight, w(e) ∈ ℝ+, representing the reliability of the interaction of e. Note that the higher the weight of an interaction, the more reliable the interaction. For a pair of proteins, e, not in E, the weight of e is defined as w(e) = 0.A PPI network is often used as an input to protein complex prediction tools. It can be defined as an undirected graph, C be a partition of V . Namely, C can be represented asThe domain of states we use here is the same as in , which cC is called a cluster of proteins. All partitions of V are states in D. In the subsequent section, the formulation of the score of C is given explicitly.An element of f ′(C) in this paper, for a partition C is formulated as the negative of the product of three different scoring functions, f1(C), f2(C), and f3(C), i.e., f ′(C) = −f1(C)·f2(C)·f3(C). These three scoring functions are formulated as follows. The first scoring function of f1 is designed to return the total sum of the PPI weights within predicted clusters of size two or more in C. The second scoring function of f2 evaluates the difference between a given power-law distribution and the distribution of sizes of clusters in C. The third scoring function of f3 is the gap between the number of proteins within predicted clusters of size two or more in C and a target value of that number.In our previous work , the entf(C), proposed in this work is given as the negative of the sum of three new scoring functions, g1(C), g2(C), and g3(C), i.e.,The new entire scoring function, f is changed from the product of three terms in the previous work. The motivation is to increase the acceptance rate of proposed states. For current and candidate states, C and C′, the term of −(f(C′) − f(C)), which is calculated in the Metropolis-Hastings algorithm, can be expected to be higher than −(f ′(C′) − f ′(C)) due to the difference between the forms of f and f ′.As can be seen, g1(C), g2(C) and g3(C), use the same source data as f1(C), f2(C) and f3(C), respectively, but are refined in the following way.The three new scoring functions, Scoring function g1(C)We define N is a parameter specifying the upper bound on the size of a cluster in C. The above function, g1(c), can be interpreted as follows. If c is of size one, g1(c) is set to be zero. This means that c has no influence to g1(C). Next, g1(c) is negative infinity if the size of c is greater than N, or if c includes a protein which has no interactions with the other proteins in c. In this case, P(C) goes to zero. Otherwise, g1(c) is equal to the total sum of the weights of all interactions within c divided by the positive square root of the size of c.where g1(c) is f1(c). The difference between g1(c) and f1(c) appears only in the last case of the three cases, in which the score of a cluster, c, is formulated as Note that the scoring function of the previous work  correspog1(c), can be considered to be a density measure. Actually, density measures are used in many previous works to infer protein complexes. For example, Wu et al. [x is the number of interactions within c. Namely, its denominator, c| nodes. Note that because the PPI network used in their work is supposed to be unweighted, the numerator is just the number of edges inThe new scoring function, u et al.  uses a tc. However, it can be observed that the larger a cluster, the relatively lower the value of the above measure. Namely, the larger a cluster, the severer evaluation it suffers. Then, Feng et al. [c| as the denominator instead of c|, more gradual functions, 2 |c|, have also been evaluated. Then, it turns out that 2 |c| give similar F-measure scores which are higher than those of the others. Thus, g et al.  eased thScoring function g2(C)g2(C), evaluates how much closer the relative frequency of clusters of a size in C and a predefined target relative frequency of the size, which is given as a parameter. We denote by Cψ(i) the relative frequency of clusters of size i in C for size i = . In addition, we denote by ψ(i) a predefined target value of the relative frequency of clusters of size i in C. Note that ψ(i) is set to be a power-law functionThe second scoring function, γ is the power-law parameter and its default value is set to be 2. This default value is an approximation of the value, 2.02, of the regression curve obtained from the relative frequency of CYC2008 complexes of size i =  by minimizing the sum of squared errors at sizes i. The sum of the squared errors is small (0.0014).where Ci, ψ(i) is evaluated by the normal distribution of mean ψ(i) and standard deviation σ,i2, which defined by the formula:For a cluster size, Cψ(2)C, ψ(3)C, . . . , ψ(N ) given ψ(2), ψ(3), . . . , ψ(N ) and σ2 =  is formulated as the product of the above N − 1 formulas:The joint probability of whereg2(C) is derived g3(C), is also derived from a normal distribution. The number of proteins within the clusters of size two or more in C is denoted by s(C), i.e.,The third scoring function, λ be a target value of s(C). The value of s(C) is evaluated by the normal distribution of mean λ and standard deviation σ2,Let where6.In this work, Q(C′|C), provides the transition probability to a candidate state C′∈ D given a current state C ∈ D. The proposal distribution we use here is obtained by improving that of PPSampler1. The differences between them will be pointed out in the following explanation of our new proposal distribution.A proposal distribution, u ∈ V, is chosen uniformly at random. Thus, the probability of choosing u is u is removed from the cluster including u in C, and then the destination of u is determined by the following probabilistic procedure. As a result, a conditional probability is associated with the resulting state.At first, a protein, β, the chosen protein, u, forms a new singleton cluster. The resulting state C′ has the following conditional probability(i) With a constant probability u, is added to an existing cluster in C, which is chosen probabilistically by the following way. All the proteins v ∈ V \ {u} that share interactions with u are sorted according to the weights w in decreasing order. The ith protein in the resulting list is denoted by iv. Namely, we have the following relationship:(ii) Suppose that the chosen protein, u dis the degree of u in the PPI network. Note that in the proposal distribution of PPSampler1, all proteins v ∈ V \ {u} are sorted and used in the subsequent procedure. In the next step, the probability of choosing a cluster, c ∈ C, is set to be the probability proportional towhere Note that the term corresponding to β is set to be β = 1/100 as in [The value of 00 as in .f, is changed from the product of three terms to the sum of them. Another is that the new proposal distribution proposes states which are likely to have higher probabilities.Notice that the reduction of the running time of PPSampler2 is realized by the combination of the two factors. A factor is that the scoring function, u and v be the pair of proteins with the highest PPI weight among all of the given PPI weights. Then the cluster consisting only of u and v is created. In addition to it, each of the remaining proteins forms a singleton cluster. It is trivial that the probability of this state is not zero.The initial state is the same as that of PPSampler1, which is the following partition. Let C, with the highest probability among all the states sampled. After removing all the clusters of size one in C, the remaining clusters are all treated as predicted complexes.PPSampler2 returns as output the state, s and t, the overlap ratio between s and t, denoted by ov, is formulated as follows:To evaluate predicted clusters by known complexes, three measures, precision, recall, and F-measure are used. To use the three measures, a matching criterion for two sets of proteins is required to determine whether a predicted cluster is matched with a known complex. For two sets of proteins, s and t are identical to each other. We say that s and t are matched if ov ≥ η, where η is a predefined threshold. Notice that, in the case where s and t share only one protein, the overlap ratio turns to be zero. Otherwise, the overlap ratio is equal to the ratio of the number of common proteins between s and t to the geometric mean of the sizes of s and t.Thus, it is one if s and t share less than two proteins, the overlap ratio is defined as zero. The reason to do that can be explained as follows. The typical value of η in the literature is s and t are both of size two and share only one protein, they are determined to be matched because η is set to be On the other hand, if example, ). HoweveC is a set of predicted clusters of proteins by a protein complex prediction algorithm and K is a set of known complexes. The precision of C to K with η is defined asHere we suppose that whereC matched with at least one known complex in K with an overlap ratio threshold η. In a similar way, the recall of C to K with η is defined aswhich is the number of predicted clusters in whereK matched with at least one predicted cluster in C with an overlap ratio threshold η. The F-measure of C to K with η is defined as the harmonic mean of the corresponding precision and recall. Namely, we havewhich is the number of known complexes in Notice that all clusters of size one are completely not counted in this matching statistics. Hereafter, a predicted cluster of any tool means a set of two or more proteins predicted as a protein complex.The Gene Ontology (GO) provides a unified representation of gene and gene product attributes across all species . Thus, Gc, by a GO term, t, can be calculated by the hypergeometric distribution in the following equation:Such a term can be identified by comparing the statistical significances of a cluster by GO terms. The statistical significance of a predicted cluster, c contains b proteins in the set, M, of proteins annotated by t, and V is the set of all proteins in the whole PPI network [http://go.princeton.edu/cgi-bin/GOTermFinder), whose implementation depends on GO::TermFinder [where  network . In thisrmFinder . The p-vIn this section, we report the results of performance comparison, carried out in a similar way as , of PPSaThe set of all PPIs with their weights in WI-PHI  is givenS. cerevisiae. It is pointed out in our previous work [The gold standard dataset of known complexes used here is the complexes of the CYC2008 database . Recall ous work  that amoη, of the overlap ratio is set to be The default values of the parameters of PPSampler2 is given in Table pc Nand kcN, respectively. Those numbers lead to precision, recall, and F-measure, which are graphically represented in Figure At first, all the predicted clusters of size two or more are evaluated with all the known protein complexes. The matching results of the above algorithms are shown in Table The (average) precision score of PPSampler2 is 0.618, which is 15% higher than the second best, given by PPSampler1 (0.537). In addition, the third best is 0.307, achieved by COACH, which is only 50% of the best. Thus, PPSampler2 outperforms the other algorithms in precision.In recall, PPSampler2 outperforms the others, too. The recall score is 0.742, followed by 0.620 and 0.603 given by COACH and MCL, respectively. Thus, the best score is 20% and 23% higher than them, respectively. Note that the recall score of PPSampler2 is 39% higher than that of PPSampler1, 0.534.In F-measure, PPSampler2 achieves the highest score, 0.674. It is 26% higher than the second highest, 0.536, given by PPSampler1. Note that PPSampler1 needs about eight hours to achieve that F-measure score. On the other hand, PPSampler2 can obtain its F-measure score in twenty minutes. Namely, PPSampler2 runs 24 times faster than PPSampler1. Thus, PPSampler2 is superior to PPSampler1 in prediction accuracy as well as running-time. Furthermore, the third highest F-measure score, achieved by COACH, is 0.411. Thus the F-measure of PPSampler2 is 64% higher than it. This indicates how high PPSampler2 outperforms the others.It would be interesting to see which known complexes are successfully detected by PPSampler2. All of the known protein complexes perfectly detected by PPSampler2 and not by the other tools are extracted. For each of those known protein complexes, the best overlap ratio obtained by each algorithm is given in Additional file i.e., those of size four or more is also considered, because many prediction algorithms have been evaluated by known complexes of size four (or three possibly) or more  of the 408 curated heteromeric protein complexes in the CYC2008 database are heterodimeric protein complexes, and 87 21%) of them are heterotrimeric protein complexes. Totally, 259 63%) of the 408 complexes are complexes of size two or three. They can be said to be the majority of the known protein complexes. Thus, the performances on those hetero-dimeric and trimeric complexes will be dominant in the performance on the set of the 408 complexes. Then the performances on those small-sized complexes are evaluated. In addition, the performance on the remaining predicted clusters, % of the % of themC, of all clusters predicted by an algorithm, we denote by C|i the subset of C whose elements are of size i and by C|i ≥the subset of C whose elements are of size i or more. For the set, K, of all known protein complexes, we denote by K|i the subset of K whose elements are of size i and by K|i ≥the subset of K whose elements are of size i or more. For each of the sizes of i = 2 and 3, the precision and recall for size i are defined as precision and recall precision and recall respectively. Note that K is again set to be the set of all protein complexes in CYC2008.The performance measures specialized for this purpose are almost the same as ones formulated in . For theThe matching results on size two is shown in Table pcN. This result is reflected directly to precision, shown in Figure The matching results on size three is shown in Table The matching results on size four or more is shown in Table It is reasonable to suppose that the list of protein complexes recorded in databases are still incomplete. This assumption indicates potential protein complexes. Under the assumption, statistically significant clusters by GO which are unmatched with any known complexes are good candidates for potential protein complexes.The number of statistically significant clusters unmatched with any known complexes is shown in Table Figure The evaluation of randomly generated clusters of proteins is also carried out to see how meaningful the evaluation of clusters predicted from the original PPIs is. A random partition of all proteins is generated by shuffing the input PPIs in the following way. The text file of WI-PHI has three columns, corresponding to an interactor, the other interactor, and their weight. Each column is permutated randomly. PPSampler2 is applied to the resulting random PPIs with the default parameter set. This process is repeated three times and their performance scores are almost the same. Thus, one of them is picked up and is summarized as follows.The number of proteins within predicted clusters of size two or more is 2012. The number of those predicted clusters is 731, and among them only one cluster is matched with a known complex. Thus, the precision score is 0.001. The number of complexes matched with some predicted clusters is also one. Then the recall score is 0.002, and the resulting F-measure score is 0.002. The numbers of statistically significant clusters on the GO aspects, biological process, cellular component, and molecular function are 53, 25, and 34, respectively, and their fractions to the number of predicted clusters are 7.3, 3.4, and 4.7%, respectively. Thus, these results imply that the predicted clusters from the original PPIs of WI-PHI are very meaningful.In this section, we assess the robustness of PPSampler2 to some important parameters of it.λ, representing a target number of proteins within clusters of size two or more, used in the scoring function, g3. The values of λ applied here are ranged from 100 to 6000 with 100 increments in between. For each of them, a single execution of PPSampler2 is carried out. The resulting performance is shown in Figure λ. On the other hand, the recall line increases up to about 0.8 when the value of λ moves up to 3000 from 100, and is then saturated at the same level thereafter. This observation on recall would imply the incompleteness of known complexes of CYC2008. If more true complexes were available, the recall line would continue to increase more and the precision line would decline more gradually. Recall that an F-measure score is calculated from those of precision and recall. As can be seen, the F-measure line has a unique peak and the F-measure scores at λ = 1100, . . . , 2800 are more than 0.6. Thus, the following implication can be obtained. When candidates for yet-to-be-discovered protein complexes are needed to be obtained, the value of λ should be set to be larger to some extent.The first parameter is L, the number of iterations of PPSampler2. Recall that the default value of L is set to be 2 × 106. The following values are used as L: 10i for i = 2, . . . , 6, j × 106 for j = 2, 4, . . . , 20, and k × 107 for k = 3, 4, . . . , 10. The result is shown in Figure L = 2 × 106. Thus, the default value of L is set to be that value.The second parameter to be considered here is g2(C), regulates the frequency of sizes of predicted clusters in C so that it obeys a power-law distribution. As we mentioned in the Background section, even without g2, PPSampler2 can generate almost the same outputs as with g2. Performance comparison of PPSampler2 with and without g2 can be found in Figure g2 is slightly lower than that with g2.Recall that the scoring function, g2 obeys a power-law distribution. Among the ten outputs of PPSampler2 without g2, the most typical one, whose F-measure score is the closest to the mean of the ten F-measure scores, is picked up. The regression curve to the frequency of sizes of the predicted clusters of the output is shown in Figure i−1.352 with a small root-mean-square error (RMSE) 5.999 where i is a cluster size. Thus, we can say that even without g2, PPSampler2 can generate clusters whose size distribution obeys a power-law distribution. Probably, this outcome is realized due to the scoring function of a cluster c,It is interesting to see how much the frequency of sizes of clusters predicted by PPSampler2 without c. Under this assumption, g1(c) is equivalent to the mean of the weights of all pairs of proteins within c. This fact implies that clusters of size two with an interaction whose weight is higher than those of the neighboring interactions are likely to be formed because for such a size-two cluster, if a neighboring protein is added to the cluster, the averaged weight is lower than that of the size-two cluster. Thus, if the denominator is g1 cannot make the frequency of sizes of predicted clusters obey a power-law distribution. On the other hand, if the denominator of g1 is g1 can become larger by adding another proteins. This would be the mechanism of finding a set of clusters whose size distribution is a power-law distribution.Suppose that instead of We have proposed a new protein complex prediction method, PPSampler2, by improving the scoring functions and proposal distribution of PPSampler1. The performance of PPSampler2 is superior to other methods. Especially, 92% of the predicted clusters are either matched with known complexes or statistically significant on the biological process aspect of GO. Namely, most of the predicted clusters by PPSampler2 are biologically reliable. Thus, PPSampler2 is useful to find good candidates for potential protein complexes.There is no other financial or non-financial competing interests.CKW analyzed outputs of PPSampler2 and drafted part of the Result section. OM designed the computational methods, implemented the computer programs, performed most of the computational experiments, analyzed the results, and drafted the paper. Both authors read and approved the final manuscript.Example of known protein complexes perfectly detected by PPSampler2. All of the known protein complexes perfectly detected by PPSampler2 and not by the other tools are extracted. For each of those known protein complexes, the best overlap ratio obtained by each tool is given.Click here for file


N-linked glycosylation is one of the most important, chemically complex, and ubiquitous post-translational modifications in all eukaryotes. The N-glycans that are covalently linked to proteins are involved in numerous biological processes. There is considerable interest in developments of general approaches to predict the structural consequences of site-specific glycosylation and to understand how these effects can be exploited in protein design with advantageous properties. In this study, the impacts of N-glycans on protein structure and dynamics are systematically investigated using an integrated computational approach of the Protein Data Bank structure analysis and atomistic molecular dynamics simulations of glycosylated and deglycosylated proteins. Our study reveals that N-glycosylation does not induce significant changes in protein structure, but decreases protein dynamics, likely leading to an increase in protein stability. Overall, these results suggest not only a common role of glycosylation in proteins, but also a need for certain proteins to be properly glycosylated to gain their intrinsic dynamic properties. N-linked glycans)O-linked glycans)Glycans are present in cells as covalent attachments to other molecules such as proteins (glycoproteins) or lipids (glycolipids), although isolated glycans can also bind to proteins as ligands12N-linked glycosylation is a chemical process in which oligosaccharyltransferase catalyzes the enbloc transfer of the oligosaccharide portion of a lipid-linked oligosaccharide (LLO) onto the acceptor asparagine of nascent proteins, defined by the consensus sequon Asn-X-Thr/Ser (X ≠ Pro)1011O-linked glycosylation begins with the addition of a single monosaccharide GalNAc  to serine/threonine at a site which does not have a well defined sequence motif, and this GalNAc can be further elongated or modifiedProtein glycosylation is one of the most important post-translational modifications in the cell, and more than half of all proteins in nature are expected to be glycosylatedN- or O-glycans modulate protein's biophysical properties and consequently regulate the function of the native protein encoded by the genomeN-linked glycosylation is perhaps the most chemically complex and ubiquitous protein modification in all eukaryotes15Protein modifications by N-glycans on the folded glycoproteins are investigated in terms of protein structure and dynamics in their glycosylated and deglycosylated forms using an integrated computational approach of the Protein Data Bank (PDB) structure analysis and atomistic molecular dynamics (MD) simulations. Our study reveals that N-glycosylation does not induce significant global/local changes in protein structure, but decreases protein dynamics, likely leading to an increase in protein stability.There is considerable interest in developments of general approaches to predict the structural consequences of site-specific glycosylation and to understand how these effects can be exploited in protein design with advantageous properties. This knowledge is essential to develop glycoprotein therapeutics in modern medicineN-glycosylation on proteins' global and local structures by measuring structural similarity between the glycosylated (GP) and deglycosylated (P) forms of identical proteins in the PDB (see Methods for detailed descriptions on the preparation of the PDB structure sets and the similarity measurement). The same analysis was also performed between deglycosylated forms (P/P pairs) for a comparison with the results from the GP/P pair.We first investigated the impact of N-glycosylation on the global protein structure is insignificant (at least in the protein structures currently available in the PDB). The impact of N-glycans on protein structure was also evaluated in terms of local structure RMSD using a radius of 15 Å  and TM-score. The RMSD distribution in the GP/P set is comparable to that in the P/P set . 91% of  of 15 Å  from theN-glycosylation. However, the PDB glycoprotein structures are “static” and thus they cannot be directly used to extract the extent of changes in protein dynamics upon glycosylation. To better understand the effect of N-glycosylation on protein structure and dynamics, six representative glycoproteins were elaborately selected from the PDB, and three independent 200-ns MD simulations were performed for both glycosylated and deglycosylated forms of each protein , which is in agreement with the results from the PDB structure analysis. 1cxpC is the system showing the largest mean RMSD difference between the glycosylated and deglycosylated protein, but the magnitude is still around 1 Å.The RMSD with respect to the initial structure is plotted as a function of simulation time in N-glycosylation does not cause large conformational changes in proteins. To further investigate the effect of N-glycosylation on protein dynamics, the fluctuations of each residue are analyzed by root-mean-square fluctuations (RMSFs) (see Methods for detailed descriptions).Our PDB analysis and MD simulations demonstrate that P-value < 0.05). The RMSF is increased by deglycosylation at most of the glycosylation sites in the benchmark systems  is observed upon glycosylation . There iN-glycans could act like molecular glue, holding the residues around the glycosylation sites together through the favorable interactions, resulting in stabilizing the protein structures. We have characterized hydrogen bonds in the crystal structures  includes covalently linked Glycan ReaderGlycan Reader detected 5,248 carbohydrate-containing PDB files (~7%) among all the downloaded files. A total of 9,728 protein chains that include covalently linked glycans or interact with glycan ligands were extracted. The protein chains with the covalently attached glycans were subsequently divided into N-linked and O-linked glycoproteins. A total of 4,802 protein chains were designated to N-linked glycoprotein chains . The N-glycoproteins were then filtered to remove redundancy in each PDB file with a 90% sequence identity cutoff. The N-glycoproteins with less than 50 amino acids were also discarded because short proteins often do not have well-defined tertiary structures. A total of 2,384 N-glycoproteins were retained after the filtering process. Protein chains were also individually extracted from the downloaded PDB files that do not contain any carbohydrate molecules, followed by the same filtering process to remove redundant protein chains and small proteins. Finally, there were a total of 79,058 protein chains without associated carbohydrates.N-glycosylated proteins and the same, but deglycosylated proteins in the PDB, the identical sequence of each N-glycoprotein chain  was searched against the sequences of the deglycosylated protein chains using the stand-alone BLAST (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/LATEST-BLAST/). Hereafter, we refer to this protein pairs as the GP/P set. The number of pairs in this set was 12,619, which is bigger than the number of N-glycoproteins in the final data set  because there are multiple glycosylated and deglycosylated protein structures (from different PDB files) and we include them to consider the effect of differences in X-ray crystallographic conditions. Similarly, we also prepared a list of protein pairs that do not have any carbohydrate molecules. This set is called the P/P (deglycosylated/deglycosylated) set. In this case, the sequences of deglycosylated proteins in the GP/P set were used as query for the BLAST search against the sequences of protein chains that do not contain carbohydrate molecules, aiming at using only P/P pairs whose proteins belong to the GP/P set. There were a total of 18,292 P/P pairs.To prepare a list of pairs between To measure the global structural similarities, each protein structure pair in both GP/P and P/P sets were superposed using a structure alignment tool, TM-alignN-glycosylation site was also measured for the GP/P and P/P sets. These local RMSDs were calculated separately for every glycosylation site in a protein; N-glycoprotein local structures were defined as concentric shells by a radius of 15 Å from the Cα atom of a glycosylated Asn residue. The glycosylated residues in each N-glycoprotein were obtained from Glycan Reader. We identified the equivalent residues in the corresponding deglycosylated protein by performing a global sequence alignment between the sequences of glycosylated and deglycosylated proteins; although two protein chains have 100% sequence identity, calculating their local RMSD using equivalent residue pairs is often tricky due to mismatches in the sequence length and residue number. Local structure pairs were superposed by a least squares fitting of the equivalent residue pairs to calculate the RMSD. Similar to the global structure comparison, the GP/P and P/P pairs were clustered in terms of 100% sequence identity and glycosylation site, and the average RMSDs for each cluster were used for the histogram analysis.The Cα-RMSD around an N-glycoproteins for MD simulations, all the PDB N-glycoprotein chains of identical sequences were first clustered and then the protein chains with the largest number of PDB files were chosen from each cluster. The protein chains with less than 10 PDB files were discarded from the cluster list. The number of residues in each glycan was counted for each N-glycoprotein. Six protein chains were finally selected as the representative N-glycoprotein systems based on the number of the PDB files, the maximum number of glycan residues, the number of amino acid residues, and the simulation system size (In order to select the representative tem size . For preQuick MD Simulator integrated with Glycan Readerhttp://www.charmm-gui.org)N-glycosylated protein, glycans, and structurally important ions were removed. The N- and C-termini were capped with acetyl (ACE) and N-methyl (CT3) groups, respectively. The TIP3P model was used for explicit water molecules. The cubic system size was determined to have at least 10 Å from the protein in each XYZ direction, and 150 mM KCl was added. The system information is given in To simulate the glycoproteins and deglycosylated forms, we have followed the general procedure of system building and equilibration in −1 and a Nosé-Hoover Langevin-piston46The CHARMM36 force field37383940Each system was further simulated for 200 ns on AntonTwo quantities were measured from the MD simulation trajectories and compared between glycosylated and deglycosylated proteins: global RMSD and root-mean-square fluctuation (RMSF) for each residue. Only Cα atoms were used for these calculations. The RMSD was calculated with respect to the initial PDB structure for each simulation trajectory. For the RMSF calculation, the average structure of the last 50 ns was used as the reference structure. To quantitatively compare dynamic properties between glycosylated and deglycosylated proteins, the mean RMSDs and RMSFs with the standard errors during the last 50 ns were calculated over the three independent replicates.t-test to compare the mean RMSD (or RMSF) with the null hypothesis that the RMSDs (or RMSFs) of glycosylated and deglycosylated proteins are identical and the alternative hypothesis that the RMSD (or RMSF) of deglycosylated proteins is larger than that of glycosylated ones. The hypothesis tests for the average RMSD and RMSF were evaluated with the significance level of 0.05.We have performed one-tailed paired 1l6xA and 3gmlA systems  showed aH.S.L. and W.I. designed the study and experiments. H.S.L., Y.Q. and W.I. prepared manuscript. H.S.L. and Y.Q. performed the experiments.Supplementary info


Protein microarrays are used to identify targets of drugs, small molecules, and enzyme substrates that may be modified in numerous ways such as by phosphorylation or methylation, to detect protein binding properties, investigate protein-protein interactions, and to define protein-based biomarkers in a high-throughput manner. They are based on the development of DNA microarray techniques and have thousands of purified proteins immobilized on a solid surface. The technique requires a blocking step to be applied to remove any non-specific binding. After incubation of the protein probe (e.g. patient serum) with a suitable tag, visible detection reagents are employed to quantify the corresponding signals. Proteins microarray are classified into three types: analytical, functional and reverse-phase protein microarrays . FunctioA new, emerging protein array that utilizes molecular technology for the detection of protein abundance in serum, other body fluids and cell lysates. This technology is represented by SomaLogic's SOMAmer® (Slow Off-rate Modified Aptamer) proteomic assay. This technology combines the properties of antibodies and traditional aptamers with extremely large specificity assembly. The selected target specific aptamers with specific three-dimensional structure are modified into a “SOMAmer” that provides protein-binding specificity as well as the primary nucleic acid sequence backbones with streptavidin linker attached to beads bind to proteins in the sample mix. The proteins that are bound to their specific SOMAmer reagents are then biotinylated followed by photocleaving of the linker to release the SOMAmer-protein complexes. Via subsequent capture, all biotinylated proteins are bound to a second streptavidin bead followed by denature of SOMAmer-protein targets, protein targeted SOMAmer are collected, labeled, denatured and hybridized to DNA array fabricated by the complementary strand of the SOMAmers. This technology can efficiently, accurately and rapidly identify and quantify over 1000 proteins across approximately eight logs of concentration in small sample volumes. Other protein microarrays are under research level development and not yet widely applied for immune monitoring.® technology for the following discussion regarding samples and data in detail. The workflow of an example protein microarray is illustrated in Fig. Here, we concentrate on the ‘seromics’ ProtoArraySimilar to DNA microarrays, a potentially large number of fluorescence signals may be obtained from each array depending on the amount and diversity of antibodies or proteins in each sample that may bind to the thousands of proteins coated on each slide. The results of positive controls are used to determine the quality of the assay. A microarray scanner is required to read and quantitate the fluorescence units from each slide, and results can be exported into spreadsheet format. Bioinformatics is essential to handling and processing the large amount of data obtained through multiple steps including data acquisition, pre-processing, visualization, differential analysis, result verification and computational feature annotation and network analysis. Several software and computational tools have been developed for signal detection, data preprocessing, quality control and data normalization –6.Different proteins may be coated at different amounts on the slides resulting in potentially different levels of fluorescence signal; therefore the assay is not meant to be quantitative when comparing results between the various proteins. As may be expected from high content analyses, there are also potential non-specific, false positive events that can be found from protein microarray analysis. Therefore, it is critical that the identified antibodies/antigens should be validated by other assays such as western blot, ELISA, Luminex or mass spectrometry. Given the large scale of protein synthesis required for manufacture of the arrays and the ability to run only 1 sample per slide, the assay is relatively expensive which needs to be overcome for more widespread application and use in larger scale clinical trials. The somewhat limited number of proteins coated on the slides , and lack of availability of antibodies in other complimentary platforms to verify specificity of each of the proteins on the slides also currently restricts the broader application of the analytical protein microarray platform.Proper collection and storage of samples and careful standardized sample processing procedure are required to minimize inter- and intra-assay variation and improve the data reproducibility. Frozen serum and plasma samples collected from blood are usually kept in −20 or −80 °C freezer for long-term storage to preserve sample stability. Batched pre- and post-therapy sample analyses performed in a single run are recommended to avoid possible differences arising from the inter-assay variation. Samples should not be subjected to multiple freeze-thaw cycles in order to minimize potential degradation of proteins in the sample as well. Special attention should be given to therapeutic agents that may be present in samples as a result of patient drug therapy which may interfere with interpretation of results due to interactions with proteins in the arrays.® platform in last ten years. Further research is warranted to validate this novel technology and these potential biomarkers sufficiently in the clinical setting for routine clinical application.The sensitivity and specificity of a protein microarray were compared to the standard ELISA with a 94 % concordance , 3. Spec