Efficient prediction of human protein-protein interactions at a global scale
- Andrew Schoenrock1,
- Bahram Samanfar2,
- Sylvain Pitre1,
- Mohsen Hooshyar2,
- Ke Jin3,
- Charles A Phillips4,
- Hui Wang5, 6,
- Sadhna Phanse3,
- Katayoun Omidi2,
- Yuan Gui2,
- Md Alamgir2,
- Alex Wong2,
- Fredrik Barrenäs5, 6,
- Mohan Babu7,
- Mikael Benson5, 6,
- Michael A Langston4,
- James R Green8,
- Frank Dehne1 and
- Ashkan Golshani2Email author
© Schoenrock et al.; licensee BioMed Central Ltd. 2014
Received: 6 March 2014
Accepted: 12 November 2014
Published: 10 December 2014
Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.
On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.
The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.
Protein-protein interactions (PPIs) are essential molecular interactions that define the biology of a cell, its development and responses to various stimuli. Physical interactions between proteins can form the basis for protein functions, communications, and regulation and controls within a cell. Such interactions can result in the formation of protein complexes that perform specific tasks. Similarly, internal and external signals are often realized and communicated through the formation of stable or transient PPIs. Due to their central importance to the integrity of communication networks within a cell, PPIs are thought to involve important targets for drug discovery  and are linked to a number of cellular conditions and diseases .
Our current knowledge of global PPI networks in different organisms is hindered by the constraints and limitations of existing experimental techniques amenable to high throughput PPI studies, such as yeast-two-hybrid (Y2H) and affinity purification combined with mass spectrometry (APMS). While both of these techniques have been successfully applied to global PPI detection in the yeast, Saccharomyces cerevisiae -, they suffer from significant shortcomings highlighted by the lack of overlap observed between the PPI data in different reports. The two benchmark large-scale yeast APMS investigations have less than 25% overlap and this overlap is even less for the two classic Y2H projects . Only 24 PPIs are shared between all four studies, further highlighting the gap in our understanding of global PPI networks. Although recent technical improvements are expected to increase the confidence of the detected PPIs and hence fill some of the current gap of knowledge, increasing the coverage and quality of PPI networks remains an important challenge ,-.
Computational tools offer time and cost effective alternatives to traditional wet-lab PPI detection tools. They may also be used as “filters” to increase confidence in data derived from wet-lab experiments ,. Like other techniques, most computational tools also suffer from notable deficiencies. For example, most computational methods rely heavily on previously reported data. Assuming that there are inherent discrepancies in the training data, the accuracies of such tools to detect new interactions are often questionable. Moreover, novel interaction domains or motifs are likely to be missed by methods that rely heavily on the structures or other high-level features of protein pairs known to interact. Another major shortcoming of computational tools is that they are often too computationally intensive, making them impossible to use for proteome-wide analysis. To date, no comprehensive all-against-all analysis of the entire human PPI network has been possible.
A small number of large-scale computational PPI prediction methods have recently been published (e.g. -). Although these methods have provided important contributions to the field, they are not applicable to the entire human proteome due to computational complexity, availability of input protein features, or unacceptably high false positive rates. For example, a recent study by Elefsinioti et al. examined five million protein pairs and predicted 94,009 “high confidence” interactions . Given a conservative estimate of 22,000 human proteins, leading to 242 million possible pairs, Elefsinioti et al. have examined only 2% of the potential interactome while others have examined just over 7%  and 12.4%  of the total interactome. Presumably these methods were limited to examining only small subsets of protein pairs due to computational complexity (i.e. runtime) or the availability of input protein features. For example, the method of Elefsinioti et al.  requires 18 complex features for each protein relating to annotated function, sequence-derived attributes, and network structure. Likewise, the method of Zhang et al.  requires structural information for both proteins in the putative interaction and is therefore only applicable to 13,000 human proteins (even with homology-based models). When considering protein pairs rather than individual proteins, approximately 50% sequence coverage results in an examination of at most 25% of the possible PPIs. In fact, Zhang et al. report that they were able to develop models for 36 million interactions, representing 12.4% of the 242 million possible interactions. Even if these methods could be applied to all human protein pairs, typical false positive rates will render existing methods unusable on larger data sets. For example, considering that the method of Elefsinioti et al.  predicts 94,009 “high confidence” interactions among only 1.6% of protein pairs, then we can reasonably expect nearly 6 million “high confidence” predicted interactions if their method were to be applied to the entire human proteome. This is an order of magnitude higher than the largest current estimate of the true size of the human interactome , leaving the experimenter to weed through a multitude of false positive predictions to find the few true interactions. Likewise, using a previously published computational method , Zhang et al. recently reported  a false positive rate implying 41.2% precision, and their recall over an independent test set of 24,000 newly reported PPIs is less than 7%. Consequently, there is a need for the development of efficient tools that are readily amenable to proteome-scale PPI prediction. This is especially important as the field of personalized medicine will benefit tremendously from a fast and accurate method that can predict the global PPI maps of different individuals from their genomic sequences alone.
A subset of cellular PPIs is mediated by defined short, linear polypeptide sequences -. Leveraging this fact, a number of computational tools have been developed to detect PPIs solely on the basis of primary sequence ,,. Such approaches do not rely on known structures or other protein features that are not easily deduced from primary protein sequences, and are thus, in principle, able to interrogate portions of the proteome that are inaccessible to other methods. Some of their predictions have been confirmed by tandem affinity purification , in vitro binding assays , and in vivo functional analysis . An added benefit of sequence-based PPI prediction is that short polypeptide sequences in one organism can be used to predict PPIs in another . We note that, while the wide applicability of sequence-based PPI prediction methods is clearly a strength, in not using structural predictions, such techniques may be unable to account for structural features such as binding site accessibility or widespread contacts between non-contiguous residues.
We have developed a computational tool termed the Protein Interaction Prediction Engine (PIPE) that uses co-occurrence of short polypeptide regions to detect novel PPIs in S. cerevisiae . Although PIPE was able to analyze potential PPIs within certain proteomes, applying this tool to more complex proteomes remained infeasible due to computational complexity. Analyzing the ~242 million protein pairs in the human proteome was estimated to require approximately 6.3 million CPU-hours of computation. In order to study the human PPI network, we developed a new Massively Parallel (MP) version of PIPE, which we call MP-PIPE. MP-PIPE overcomes some of the limitations of existing methods through computational acceleration of the algorithm (speed) and improved precision. We present a comprehensive all-against-all (pair-wise) analysis of the human proteome and study its biological properties. We then demonstrate the accuracy and utility of the MP-PIPE inferred interactome using a range of functional assays.
Results and discussion
MP-PIPE performance and scalability enables computational scan of entire human proteome
In the following, we discuss the runtime performance of MP-PIPE on different hardware architectures which eventually enabled us to perform global PPI analysis of a human cell within three months. More precisely, we tested our MP-PIPE solution on three different compute clusters. These clusters included a six node cluster with 24 total compute cores (small cluster), a 32 node cluster with 128 total compute cores (medium cluster), and a 50 node cluster with 6,400 total hardware supported threads (large cluster). The performance of MP-PIPE was initially tested on a single large cluster node with varying numbers of threads, and then in a second test we increased the number of nodes (see details in Methods section). The test data set consisted of 50,000 random protein pairs. However, this data set proved to be too large to compute using a small number of threads, so a subset containing 5,000 random pairs was used to examine the runtime performance of the code with 1–16 threads and then the full 50,000 pair data set was used for tests with 16 or more threads. For those test cases that were performed over the smaller 5,000 pair subset, runtimes were extrapolated to estimate the runtime over the full 50,000 pair dataset. The results are shown in Figure 1. The speedup curve shown in Figure 1B shows a dramatic performance improvement using up to 128 threads and then a slight improvement from there up to 512 threads. We found that using more than 512 threads creates memory problems. For the second test, we increased the number of large cluster nodes used, where each node ran one MP-PIPE worker with 512 worker threads. The results are shown in Figure 1C. The performance of MP-PIPE scales almost linearly as the number of compute nodes increases. This scalability property of MP-PIPE enabled us to perform global PPI analysis of the human proteome within three months.
Verification of MP-PIPE against experimental data
Confusion matrix for the leave-one-out cross-validation tests used to determine the prediction accuracy of MP-PIPE
Known interacting pairs
Assumed non-interacting pairs
Predicted to Interact
Predicted not to Interact
The ratio-adjusted confusion matrix the leave-one-out cross-validation tests used to determine the prediction accuracy of MP-PIPE
Known interacting pairs
Assumed non-interacting pairs
Predicted to Interact
Predicted not to Interact
Statistical performance metrics for MP-PIPE based on true negatives (TN), true positives (TP), false negatives (FN), and false positives (FP) seen in the leave-one-out cross-validation tests, corrected to use a positive: negative ratio of 1:100
Specificity (True Negative Rate)
Sensitivity/Recall (True Positive Rate)
Matthews correlation coefficient
This leave-one-out test was repeated with all homologs removed from our human dataset as in . This reduced our protein sequence set from 22,513 to 14,867 and our experimentally verified human PPI set from 41,678 to 19,588 pairs. Removing homologs at the 40% identity level effectively removes all protein isoforms from our LOO performance assessment. This leads to a conservative estimate of performance as not removing the homologs could potentially inflate the reported statistical performance figures ,. As can be seen in the figure in Additional file 1 (pink line), the recall of our method is slightly reduced when homologous proteins are removed from our dataset, however if we adjust our decision threshold to maintain a recall of 23%, we still achieve a precision of 69.1%.
All-against-all (pair-wise) scan of the human proteome
After three months of 24/7 computation on the 50 fully dedicated nodes of the large cluster (plus additional computation on the medium cluster), MP-PIPE completed the scan of the human proteome. With the chosen operating point as described in the previous section, MP-PIPE predicted 172,132 protein interactions. Of these high confidence predictions, 132,710 protein interactions have never been reported previously. Given that 41,678 human protein interactions are known (previously reported) and were included in the MP-PIPE database and would therefore be predicted to interact, MP-PIPE has potentially more than quadrupled our knowledge of the human interaction network. At the chosen operating point, MP-PIPE data covers more than one fifth of the estimated human PPI landscape. In comparison, Elefsinioti et al.  have examined 2% of the interactome while others have examined just over 7%  and 12.4%  of the total interactome. The list of the reported interactions is found in the table in Additional file 2. The list is ordered according to PIPE score, where higher values represent higher confidence levels for an interaction. Distribution of run time for different human protein pairs is illustrated in Figure 1A. The length of the query proteins does not appear to correlate with runtime (data not shown). The analysis performed on MP-PIPE’s predicted 172,132 interactions throughout the rest of this study will cover both the known 41,678 and novel 132,710 interactions, unless stated otherwise.
Percentages of Homo sapiens pairs in which both partners share the same GO SLIM annotation as well as third party interactions
Derived from GO annotation
Third party interaction
Cellular component (CC)
Molecular function (MF)
Biological process (BP)
CC & MF & BP
(a) Random H. sapiens pairs
(b) Previously reported H. sapiens interactions
(c) Predicted H. sapiens interactions identified in this study
Overlap of co-purifying proteins identified through LGTS-MS with previously reported (known) interactions and MP-PIPE predictions
# of prey
Network-wide analysis of hubs and betweenness centrality
Enrichment of biological process for proteins with highest Hub Degree (Hubs) or Betweeness Centrality (B.C.) measurements (Top 10, Top 25, and Top 50)
Regulation of gene expression
Protein kinase activity
Regulation of cell death
The usefulness of the predicted interactions in biological investigations
Our computationally predicted interactome represents a comprehensive all-to-all interaction network in humans. This network generates a wide range of testable hypotheses concerning biological processes, and informs our understanding of the overall architecture of cellular function. Here, we demonstrate the usefulness of this new predicted interactome through prediction of gene functions, experimental verifications and analysis of putative protein complexes.
Using the predicted human protein interaction network to assign breast cancer proteins
CDK3 is a cyclin-dependent kinase that functions in cell cycle progression and mitosis, and plays an essential role in G1/S transition through its activation of the E2F transcription factor family and G0/G1 transition by Rb phosphorylation . E2F and Rb play a regulatory role in the transcription of BRCA1 , connecting CDK3 to BRCA1. In further agreement with the observed interactions, CDK3 has high expression levels in cancer cells and participates in cell proliferation and transformation by enhancement of ATF1 activity, a gene that physically interacts with BRCA1 (,). Furthermore, both BRCA1 and CDK3 are involved in cell cycle transition, further supporting a potential role for CDK3 in breast cancer.
AURKB has several functions during mitosis, including spindle assembly, chromosome segregation, and cytokinesis . AURKB has high sequence similarity with AURKA, another protein of the Aurora kinase family, however they are reported to differ functionally from each other during mitosis . It is shown that BRCA1 may be phosphorylated by AURKA, resulting in impaired function of BRCA1 in G2/M transition . AURKB has a single reported interaction within breast cancer pathway through BRCC complex . The interactions identified here add credibility to the involvement of this protein in breast cancer pathway.
SMC1B is a meiosis-specific protein involved in chromosome segregation during anaphase, synapsis, and recombination . SMC1B is also part of the cohesin complex, which includes SMC1, SMC3, RAD21 and several other proteins . The cohesin complex plays a role in several cellular processes such as DNA repair, gene expression regulation and chromosome segregation (,). Recent studies showed that several subunits of the cohesin complex are also important in DNA damage response . In addition, SMC1b has been linked to neck and head cancer , further supporting a role for this protein in cancer.
We also examined the PPI network for mutations associated with resistance to breast cancer therapeutics doxorubicin and Trastuzumab. Individual mutant proteins were analyzed against the human proteome (one-against-all) for their PPIs at a recall of 23% at a precision of 82.1%. In this way, 5 personalized human PPI networks were predicted, each differing by a mutation in one gene only. The 5 PPI profiles were compared to that of their corresponding control networks. The list of these mutants is found in the table in Additional file 8. Four mutations, P04637a, P04637b, P04637c and P04637d, in p53 (P04637) protein have been linked to resistance to the chemotherapeutic breast cancer drug, doxorubicin . The PPI profile for P04637b and P04637d was identical to that of the wild type. However, the other two mutants showed some differences. For example, P04626a and P04626c lost their interactions with the nuclear transcription factor Y, NFYC (Q13952) and the ubiquitin conjugating enzyme E2 L3 (P68036) involved in nuclear hormone receptors transcriptional activity, among others. Similarly, a truncated form of HER2 (P04626) is responsible for resistance against HER2-targeted breast cancer therapeutics such as Trastuzumab . We observed that truncated-HER2 lost several PPIs including an interaction with a G-protein signaling RGS8 (P57771) which functions as an inhibitor of signal transduction, and an interaction with an early growth response protein ERG1 (P18146) involved in cell differentiation. Of interest, the truncated HER2 formed a new interaction with the tumor suppressor p53 protein. A possible explanation for this novel interaction for the truncated HER2 could be that segments of the deleted region might have physically hindered the availability of the region responsible for an interaction with p53 in the wild type form.
Identification of novel molecular markers for seasonal allergic rhinitis
Glucocorticoids (GCs) have a key role in the treatment of patients with seasonal allergic rhinitis (SAR) and other allergic disorders . Because of this and difficulties in evaluating treatment response based on clinical signs and symptoms, there is a need for protein markers to monitor that response. The identification of such markers is complicated by the involvement of a large number of inflammatory proteins in SAR . We hypothesized that novel biomarkers could be identified among proteins predicted to interact with proteins belonging to known inflammatory pathways in SAR including the acute phase response pathway, complement signaling pathway and glucocorticoids receptor pathway ,.
Proteins from the acute phase response pathway, complement signaling pathway and glucocorticoids receptor pathway were extracted from the Ingenuity pathway analysis (IPA) software. Interactors of these proteins were selected from our predicted human PPI network. We included secreted, membrane and cytoplasmic proteins, but excluded nuclear proteins. We prioritized candidate biomarkers based on their number of known and predicted interactions with proteins known to be involved in SAR-associated responses. Next, we focused on proteins with a high number of predicted interactions.
From the literature we extracted 191 proteins that belong to the acute phase response pathway, complement pathway, and glucocorticoids receptor pathway (Additional file 9). These proteins formed the known set of SAR-associated proteins (SARp). From our predicted human PPIs, the proteins that interacted with SARp were determined. A total of 3334 proteins were found to interact with one or more SARp. We prioritized five new proteins with a high number of total and predicted interactions to SARp as candidate biomarkers, namely PRB1, PRB2, SFN, LYN and Akt2. Using ELISA, we analyzed these candidates in nasal fluid from 40 patients with SAR before and after GC treatment. This study represented protein expression analysis for 400 samples (5 proteins, before and after GC treatment, in 40 patients).
The proteome-wide PPI network can identify translation genes
We then further investigated the involvement of HAP2, ERG28 and LYS12 in translation by subjecting their deletion mutants to drugs that affect translation. hap2∆, erg28∆ and lys12∆ showed altered levels of sensitivity to streptomycin and cycloheximide (Figure 8C). Next, translation efficiency (rate) was measured using an inducible LacZ gene cassette on a p416 plasmid . Deletion mutants for ERG28 and LYS12 had a drastic reduction in the rate of induced LacZ synthesis further linking ERG28 and LYS12 to translation (Figure 8D). Interestingly ERG28 is a well-characterized protein involved in ergosterol biosynthetic pathway, the relation of which to translation is not readily expected. However, in agreement with a link to translation, ERG28 was previously shown to physically interact with a polysome associated mRNA binding protein SLF1 , and a putative RNA helicase SPB4 that sediments with 66S pre-ribosomes . Further, ERG28 is localized to ER membranes and a general link between sterol biosynthesis and translation has previously been proposed .
Identification of protein complexes within the human interaction network
Protein complexes can be defined as a group of proteins that interact with each other to form a functional unit. Paracliques - can be computationally identified as a sub group of proteins within the interaction network with high degree of interconnectivity and may define putative complexes. Given the size of the human PPI network, prediction of paracliques requires advanced computational approaches to complete a thorough analysis within a reasonable timeframe. We have applied a novel graph theoretic approach to automatically identify paracliques within the network (see Methods for details). Our analysis led to a number of interesting predictions. For each paraclique, a statistical analysis of gene ontology (GO) term enrichment was performed. The table in Additional file 10 lists the top GO term for each paraclique along with a P-value for the observed enrichment. For example, Paraclique 1359 is a complex of six proteins with 13 interactions (Additional file 11: Figure A). O00151 (PDLIM1) is a cytoskeletal protein that acts as an adapter to bridge other proteins (like kinases) to the cytoskeleton. P20929 (NEB) is a muscle protein involved in maintaining the structural integrity of sarcomeres and membranes associated with the myofibrils (F-actin stabilization). The rest of the members (P08670 (VIM), P14136 (GFAP), P17661 (DES) and P41219 (PRPH)) are intermediated filament proteins. On the basis of GO enrichment (P-value 6.5E-07), one may conclude that the activity of this complex is associated with cytoskeleton and structural integrity of the cell.
Paraclique 1409 is a complex of six proteins with 14 interactions (Additional file 11: Figure B). Q02246 (CNTN2) is involved in cell adhesion and the remaining proteins (O94779 (CNTN5), Q02246 (CNTN2), Q12860 (CNTN1), Q8IWV2 (CNTN4), Q9P232 (CNTN3), and Q9UQ52 (CNTN6)) are involved in cell surface interaction during nervous system development. On the basis of GO enrichment, we can assign this complex to cell adhesion (P-value 2.2E-10).
Paraclique 2164 is a complex of five proteins with 10 interactions (Additional file 11: Figure C). Three of its members (P32298 (GRK4), P34947 (GRK5) and P43250 (GRK6)) are G protein-coupled receptor kinase and the remaining two (Q9NP86 (CABP5) and Q9NZU8 (CABP1)) are calcium-binding proteins. Considering the fact that biological interaction between G-protein coupled receptor and calcium-binding proteins has been widely reported and seems essential in signaling pathways, one may conclude that this complex plays a role in G-protein coupled signaling pathway, a claim which is supported by enriched Gene Ontology term (P-value 3.75E-08).
Limitations and future work
While MP-PIPE represents a significant step forward towards computing a complete human interactome, there remain a number of limitations which lead us to future work. In order to operate at a reasonable precision rate, we have tuned our decision thresholds to be extremely conservative, resulting in a limited sensitivity of 23%. Future work will examine ways to continue to increase sensitivity/recall without sacrificing our false positive rate. Where MP-PIPE has advantage over structure-based methods is in coverage: MP-PIPE requires only sequence as input and is therefore applicable to all protein pairs. However, in future work we will examine ways to capitalize on the rich information encoded in protein structure when such inputs are available. At present, this represents only a small fraction of protein pairs, however, this proportion is expected to grow with ongoing large-scale protein structure determination initiatives. As with all computational methods, another potential limitation in prediction accuracy is the quality of input data used to train MP-PIPE. As more experimental data of higher quality becomes available, we expect MP-PIPE to also become more accurate. Lastly, we are continuing to apply parallelization and algorithmic optimizations to MP-PIPE to further reduce runtimes for whole-proteome scans. This will be critical if we are to investigate large numbers of organisms for comparative studies, or if we wish to compute personalized interactomes, accounting for the multitude of genetic variations that make each person’s interactome unique.
In this study, we present a comprehensive pair wise analysis and prediction of the entire human PPI network using the principles of short co-occurring polypeptide regions as mediators of PPIs. Through this massive computational analysis, we predict approximately 170,000 PPIs, of which 140,000 have not been reported previously. The distribution of the novel PPIs on the basis of sub-cellular localization, molecular function and biological process are very similar to those of previously reported interactions, highlighting the reliability of our predictions. Moreover, we demonstrate that MP-PIPE predictions can effectively explain experimentally observed LGTS-MS interaction data (recall 29.31%, precision 55.33%). Our predictions are useful for understanding cellular biology as a whole, with approximately 8,000 protein complexes in our inferred interaction network. Furthermore, specific processes can be successfully interrogated using our new predictions: on the basis of inferred interactions we predict and experimentally confirm novel functions for proteins involved in translation, and identify new molecular markers for seasonal allergic rhinitis. Our analysis highlights the usefulness of the predicted PPIs for functional analysis of the human proteome. The speed associated with this approach sets the path for investigating the PPI map for individual humans in a timely fashion. Personal (specific to an individual) PPI maps may improve our knowledge of network and personalized medicine.
Sequential PIPE algorithm
For a given organism (e.g. S. cerevisiae, C. elegans, or human), the PIPE algorithm relies on a database of known and experimentally verified protein interactions. For example, for the 22,513 human potential open reading frames included in the current study, only 41,678 high confidence interactions are known (out of 253,406,328 possible protein pairs). Since experimental verification can have large numbers of false positives (up to 40%, see e.g. ), the PIPE database is carefully constructed to avoid false data and stores only protein interactions that have been independently verified by multiple experiments. The database represents an interaction graph G where every protein corresponds to a vertex in G and every interaction between two proteins X and Y is represented as an edge between X and Y in G. The remainder of this section outlines how, for a given pair (A, B) of query proteins, our PIPE method predicts whether or not A and B interact.
In the first step of the PIPE algorithm, protein A is split up into overlapping fragments of size w. This can be thought of using a sliding window of size w across protein A. For each fragment a i of A, where 0 < = i < = |A| - w +1, we search for fragments "similar" to a i in every protein in graph G. A sliding window of size w is again used on each protein in G, and each of the resulting protein fragments is compared to a i . For each protein that contains a fragment similar to a i , all of that protein's neighbors in G are added to a list R. To determine whether two protein fragments are similar, a score is generated with the use of the PAM120 substitution matrix. If the similarity score is above a tuneable threshold then the fragments are said to be similar or to “match” (see pseudocode below). In the next step of the PIPE algorithm, protein B is split into overlapping fragments b j of size w (0 < = j < = |B| - w +1) and these fragment are compared to all (size w) fragments of all proteins in the list R produced in the previous step. We then create a result matrix H of size n x m, where n = |A| and m = |B| and initialize it to contain zeroes. For a given fragment a i of A, every time a protein fragment b j of B is similar to a fragment of a protein Y in R, the cell value at position (i, j) in the result matrix is incremented. The result matrix indicates how many times a pair (a i , b j ) of fragments co-occurs in protein pairs that are known to interact. It is based on this matrix that the query proteins are predicted to interact or not. The following explains the basics of the algorithm in pseudocode:
A modified median filter, which simply sets a cell’s value to 1 if most of the neighbouring cells are greater than zero and zero otherwise, is applied and the two query proteins were predicted to interact if the average cell value was above a set threshold. By varying this threshold, a range of precision-recall values may be obtained (see Additional file 1). Note that throughout this paper, for our analysis a prevalence of 1 PPI per 100 protein pairs is consistently assumed for our results, as well as for comparison to other results as was done in . Recall measures the proportion of true interactions that will be detected. Precision measures the proportion of predicted interactions that correspond to true interactions. For our leave-one-out cross-validation experiments (as described in the ‘Verification of MP-PIPE Against Experimental Data’ section), our 41,678 high confidence positive PPIs are taken from BioGrid . Random protein pairs not previously reported to interact were used for our negative interaction data. This is considered to be a conservative approach when assessing prediction accuracy .
The MP-PIPE (massively parallel PIPE) system is a massively parallel, high throughput protein-protein interaction prediction engine and is the first system that is capable of scanning the entire protein interaction network of complex organisms such as human. In order to achieve that goal, large numbers of concurrent PIPE instances need to be executed on a large-scale parallel compute cluster. This created two major challenges.
The first problem was the lack of scalability that made it difficult for large numbers of PIPE instances to effectively take advantage of all available computational resources without massive load imbalances. This load-balancing problem was not as significant in simpler organisms, such as S. cerevisiae and C. elegans, but lead to a large amount of unused resources when making predictions on more complex organisms such as human. Interestingly, the number of human proteins and protein pairs is not exceptional and simpler organisms such a C. elegans actually have more proteins and protein pairs than human. However, the human protein interaction network has more known interactions and a more complex structure. In particular, the calculation/prediction of these interactions is considerably more time consuming. Previous PIPE experiments for S. cerevisiae , and experiments for C. elegans reported in  showed that PIPE can process each individual protein pair within seconds. However, for human proteins, the picture changes dramatically. The running time for one individual protein pair can fluctuate between less than a second and more than 12 hours. Human proteins have a much more complex structure that appears to lead, in some cases, to a very large number of fragment similarities found by PIPE. When trying to run earlier versions of PIPE on human protein pairs, individual PIPE instances would simply be given static lists of protein pairs to make predictions on. Due to the wide variance of processing time for human protein pairs, some PIPE instances would finish very quickly while, by the end, there may be a single PIPE instance working for hours on a single protein pair while all of the other instances are idle. The imbalance when processing human protein pairs was so great that it resulted in more wasted resources than utilized resources when processing batches of protein pairs. To process a global scan of all human protein pairs, this issue had to be overcome.
The second major issue facing concurrent PIPE instances on a processor is inefficient usage of memory. Typically the number of PIPE processes running on a single machine is set to the number of compute cores on that machine. For example, on a quad-core machine there would typically be four PIPE processes running to utilize the chip fully. If different PIPE processes were left to work completely independently of each other, each process would have to load its own copy of the interaction graph along with all the other PIPE data. For less complex organisms this was not a major issue since the amount of data loaded was relatively small but the complexity of the human proteome translates into significantly more data needed by PIPE. The memory needs for a single PIPE instance for the human proteome increased to such a degree that running as many PIPE instances as compute cores can easily lead to program crashes due to a lack of memory. This would imply that processor cores would be left unused due to memory limitations. To process a global scan of all human protein pairs, this issue had to be overcome.
The basic structure of MP-PIPE is a two-level master/slave and all-slaves model. A single MP-PIPE scheduler process is in charge of managing the main list of protein pairs to be processed as well as reporting the results. The MP-PIPE scheduler distributes work to several MP-PIPE worker processes in packets. Each packet contains a relatively small number of protein pairs. Each MP-PIPE worker executes the PIPE algorithm on protein pairs received from the MP-PIPE scheduler. By giving each worker only a relatively small amount of work at a time we ensure that if a worker does get stuck with abnormally time consuming protein pairs, the other workers will continue to work on their packets and, when they finish, they will request more work from the scheduler process and continue to work. This aspect of the MP-PIPE’s architecture deals with the load imbalance problem by ensuring that all PIPE processes are working as long as there is still work to be done. It should be noted however that if the packet size is too small then the amount of communication between the scheduler and worker processes will negatively impact the running time of the system. It is therefore important to balance the packet size between being too small (too much communication overhead) and too large (too much work imbalance).
To improve the memory efficiency, the second level of MP-PIPES’s architecture uses an “all-slaves” model. Each PIPE worker process consists of a number of parallel threads, called worker threads, among which it distributes the protein pairs to be processed. The worker threads of an MP-PIPE worker are to be executed on a shared memory multi-core processor. The PIPE interaction graph and other necessary PIPE data require considerable amounts of memory. For MP-PIPE, the data stored at an MP-PIPE worker process was re-designed to become a parallel data structure on which all worker threads for that worker can operate concurrently. Much care was taken to implement this as memory efficient as possible so that a single shared copy fits into the main memory of a processor node executing an MP-PIPE worker. This allowed more threads to run simultaneously on a given processor node by reducing the overall memory usage and solved the memory issues discussed. The scheduler/worker part of MP-PIPE was implemented using MPI (Message Passing Interface) and the worker threads within each MP-PIPE worker were implemented in OpenMP (http://openmp.org/).
Complete scan of the human proteome
MP-PIPE evaluated all possible protein pairs in the human proteome. Most of this work was performed on the large Victoria Falls cluster, using the medium cluster to offload some of the harder pairs (i.e. those pairs that took longer than 12 hours). The following represents the human proteome at the time that the MP-PIPE scan was performed.
Total number of human proteins: 22,513
Total number of protein pairs to examine: 253,406,328
Total number of known protein interactions: 41,678
Total number of proteins with at least one known interacting partner: 9,459
Total number of proteins with no known interacting partners: 13,054
Largest number of known interactions partners for a single protein: 265
Average number of known interactions per protein: 3.70
Average number of known interactions per protein with at least one interaction: 8.81
The 22,513 human proteins used in this study are made up of the union of Uniprot “confirmed” proteins and proteins involved in physical interactions reported in BioGRID, with no isoforms removed. The 41,678 previously reported interactions were obtained from BioGRID, which is an amalgamation of several experimental studies. The human proteome has almost seven times more known interactions than the C. elegans proteome, and the average human protein has more than double the known interactions than a C. elegans protein. Coupled with the fact that the human proteins are, on average, longer than the C. elegans proteins, this significantly increases the complexity of scanning the entire human proteome. Furthermore, as outlined above, the running time for one individual protein pair can fluctuate between less than a second and more than 12 hours. This creates an additional load-balancing problem as discussed above. In fact, some individual protein pairs required six days of computation.
On the Victoria Falls cluster, 50 nodes were used, each with their own MP-PIPE worker process running 256 threads. This implies 12,800 parallel computational threads running on 6,400 hardware-supported threads. The number of threads per node was scaled down from 512 threads due to the fact that each individual thread needed significantly more memory than in our tests. The Victoria Falls cluster was used to process the vast majority of protein pairs. The results presented in this paper were obtained through three months of exclusive 24/7 use of the large Victoria Falls cluster. If one of its worker threads got stuck with a protein pair that was running more than 12 hours, that protein pair was off-loaded to the medium cluster since its individual cores are more powerful than a single Victoria Falls thread.
Hubs and betweenness centrality
Protein interactions can be represented as an interaction network, where the proteins are interactors (nodes) and connections (interactions) are shown as edges. The number of edges incident to one node is called the degree of that node. Hubs are high degree nodes that interact with many other proteins (nodes) through various pathways . To find the hubs in the human predicted interaction graph, the proteins were sorted by their degree in the network. Betweenness centrality is an important global property of networks. The betweenness centrality of a node v is the ratio of the number of shortest paths between a pair of nodes a and b on which v lies and the total number of shortest paths between a and b, summed over all possible pairs of nodes. These measures were used to identify proteins of interest. The betweenness centrality of a protein v is defined as , where σ ab (v) = # shortest paths from a to b through v, σ ab = # shortest paths between a and b.
Versatile affinity-tagging, purification, and protein identification
The full length, sequence verified, non-mutated, Gateway-compatible cDNA entry clones for the human chromatin-related proteins (CBX1, RNF2, H2AFX, and RBBP4) were obtained from Harvard PlasmidID. The HEK293 and HEK293T cells were cultured in Dulbecco’s modified Eagle’s medium with 10% fetal bovine serum and antibiotics essentially as previously described ,. The sequence-verified clones were cloned into the lentiviral expression vector essentially as previously described ,. The lentivirus-encoded tagged ORFs were transduced into HEK293 cells, and the stably expressed cells were subsequently selected with puromycin at a concentration of 2 μg/ml for a minimum of 48 hours and expanded, essentially as described previously ,. The expression of the tag in stable cells was subsequently confirmed by Western blotting using anti-FLAG antibody against the 3X FLAG epitope. Affinity purification and sample processing for protein identification by mass spectrometry was performed essentially as previously described ,. The high-confidence matches of the resulting MS/MS spectra was mapped to the reference human protein sequences using the SEQUEST database search engine with match quality evaluated using the STATQUEST algorithm . The identified co-purifying proteins were filtered out at a confidence threshold at 90% with two or more peptides. Since each tagged samples were independently affinity purified two times to rule out the non-specific binding proteins, we averaged the peptide counts over the replicates. Moreover, the co-purifying proteins that were identified at 90% cut-off with one single peptide and the most common background contaminants or proteins that bound to the unrelated VA-tagged GFP samples in our replicate purifications were filtered out to eliminate the noise from the dataset.
The comparison of LGTS-MS results with the MP-PIPE predicted and previously reported (known) interactions was done by calculating the precision and recall on the basis of LGTS-MS results representing real interactions. To do this we filtered the MP-PIPE predictions and known interactions to contain detectable proteins (the set of all proteins seen in any of the LGTS-MS experiments performed here). Reachable proteins were defined as those proteins that interact directly with the bait or indirectly through one or two intermediaries within the sets of filtered interactions.
Yeast growth condition
Standard rich (YPD) and synthetic complete (SC) media were used as a growth media . To investigate translation inhibitory drugs (antibiotics) on growth rate of yeast deletion mutants, streptomycin (40 mg/ml) was added to SC media and cycloheximide (60 ng/ml) was added to YPD media .
Drug sensitivity test
Yeast cells were grown in liquid YPD or SC media to mid-log phase (48 hours) and diluted to a concentration of 10−2 to 10−5 cells/20 μl. From each dilution, 20 μl was spotted onto solid media containing translation inhibitory drugs. Media with no antibiotics was used as a control. All yeast cells were incubated at 30°C for 1–2 days .
Protein expression assay
Translation fidelity was measured using plasmids pUKC817, pUKC818 and pUKC819, which carry premature stop codons UAA, UGA and UAG in a β -galactosidase expression cassette ,. Translation efficiency was assessed using plasmid p416 (containing Gal-inducable LacZ expression cassette) as described by ,. β-galactosidase assay was performed using ONPG, O-nitrophenil-α-D-galactopyranoside, as descibed by -.
Total RNA was extracted using RNeasy Mini Kit (QIAGEN) according to manufacturer's instruction. To synthesize cDNA, 33 μl of RNA in combination with 3 μl poly T primer (random hexamer) was incubated for five min at 70°C, and then cooled on ice for five min. 6 μl RT-buffer, 15 μl dNTPs, and 3 μl RNaseI were added to the mixture and incubated for five min at 37°C. 3 μl RT enzyme was added to the mixture and incubated for an additional 1 hour at 42°C followed by aa 10 min incubation at 70°C. qPCR amplification and detection was performed on a Rotor Gene 3000 from Corbett Research. A final mixture of 2.4 μl H2O, 2.4 μl 10X PCR buffer, 1,25 μl dNTPs(4 mM), 1.25 μl MgCl2, 2 μl SYBR Green (1/4000), 7.6 μl primer mix (1 mM) and 3 μl Taq was used in addition to 5 μl template (cDNA). Thermocycler conditions were set to the following: 50°C for two min, 95°C for 10 min, 40 cycles of 95°C for 30s-60°C for 30s-72°C for 30s and a final 72°C for ten min ,. The primers used in the qRT-PCR were designed based on the sequences for LacZ (F: TTGAAAATGGTCTGCT GCTG, R: TATTGGCTTCATCCACCACA) and PGK-1 (F: CAGACCATTCTTGGCCATT, R: CGAAGATGGAGTCACCGATT). PGK-1 (phosphoglycerate kinase) was selected as the positive control due to being one of the most highly expressed genes in yeast, producing up to 5% of total mRNA content .
Prediction and analysis of candidate biomarkers for seasonal allergic rhinitis
40 patients with SAR were included in the study. SAR and symptom scores were defined as previously described ,. Their median (range) age was 28 (18–58) and 19 were women. The mean ± SEM symptom score of the 40 patients after treatment decreased from 15.7 ± 1.0 to 4.3 ± 0.6 (P < 0.0000001). The study was approved by the Ethics Committee of the Medical Faculty of the University of Gothenburg. Written informed consent and questionnaire data sheets were obtained from all patients.
Proteins from the acute phase response pathway, complement signaling pathway, and glucocorticoids receptor pathway were extracted from the Ingenuity pathway analysis (IPA) software. Interactors to these proteins were predicted using PIPE. We included secreted, membrane and cytoplasmic proteins, but excluded nuclear proteins. We prioritized candidate biomarkers based on their number of known and predicted interactions. Next, we focused on proteins with a high number of predicted interactions.
Proteins were examined by ELISA in nasal fluids from 40 patients with SAR before and after GC treatment. V-akt murine thymoma viral oncogene homolog 2 (Akt2) was with an ELISA kit from R&D Systems Inc. (Minneapolis, MN, USA). Proline-rich protein BstNI subfamily 1 (PRB1), proline-rich protein BstNI subfamily 2 (PRB2), v-yes-1 Yamaguchi sarcoma viral related oncogene homolog (LYN) and stratifin (SFN) were analyzed with ELISA kits from Uscnlife Life Sciences and Technology (Wuhan, China). All experiments were performed according to the manufacturer’s protocol. The Wilcoxon matched pairs signed ranks test was performed to compare two paired groups. A P-value less than 0.05 was considered significant.
Graph algorithms for assigning protein complexes
To decompose the predicted protein pairs into putative complexes, we applied a novel algorithm that combines pre-existing graph-theoretic tools with hierarchical clustering concepts. The algorithm has three independent stages: the initialization stage, which consists of generating an initial set of clusters, the merge stage, which determines which two clusters to merge next, if any, and the glom stage, which evaluates vertices for inclusion into a cluster. The initialization stage is run once, after which the merge and glom stages run alternately until either the desired number of clusters is reached or until neither stage results in a change to any cluster.
Since initialization is an independent step, any initial clustering may be used. It is not required that the initial clustering be overlapping, although stages two and three may grow the clusters so that the end result is overlapping. We chose to use the set of all maximal cliques as the initial clustering. A clique is a set of vertices with all edges present; a clique is maximal if no vertex can be added to it to form a larger clique. The set of maximal cliques forms a natural overlapping clustering of a graph with the most rigid requirements, namely that all edges be present within each cluster. Real-world graphs often have many small and medium sized maximal cliques, and the protein prediction graph is no exception. These clusters are then allowed to merge and grow in stages two and three, gradually relaxing the stringency until the desired number of clusters is reached. To enumerate all maximal cliques, we used the well-known algorithm of Bron and Kerbosch described in  with bitwise improvements from .
In the merge stage, the overlap of all clusters is evaluated and the two clusters with the highest overlap proportion are merged. If no two clusters overlap by a proportion greater than a parameter m, then no clusters are merged.
In the glom stage, every vertex not already belonging to a particular cluster is considered for inclusion into a cluster in similar fashion to the paraclique algorithm described in . Those vertices with connectivity proportion greater than g, the glom factor, are added to the cluster. The first time through the glom stage, every cluster is considered. Subsequent glom stages only consider the cluster newly created by the merge stage, as all other clusters having already been considered.
In practice, calculating all pairwise overlaps to find the highest degree of overlap can make the merge stage computationally prohibitive. A small change, however, yields a good approximation version that can be run until the number of clusters is reduced to the point where the exact version can take over. Rather than merging the clusters with the highest overlap, the approximation version merges the first two clusters encountered with overlap at least a, the approximation parameter. For the protein prediction graph, which was initialized with more than 100,000 maximal cliques, we ran the approximation version until the number of clusters reached 20,000, at which point we switched to the exact version. Ultimately, a list of 8,739 paracliques were identified and characterized through a statistical analysis of the GO annotations of each member protein.
The authors would like to thank Sandy Kassir for technical assistance. This work is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). It is also supported by the Canadian Institutes of Health Research and Saskatchewan Health Research Foundation (RSN-124512), European Community Seventh Framework Programme (grant 223367), the Swedish Research Council, Linköping University, Sahlgrenska Academy, NIH (awards R01-AA-018776 and 3P20MD000516-07S1), DOE (contract DE-FG02-07ER46363), NSF (grant DGE-0801540), Australian Research Council (project DP120102576) and the National Energy Research Scientific Computing Center (supported by DOE Office of Science contract DE-AC02-05CH11231). The funding agencies for this study had no involvement in the study design nor the collection, analysis and interpretation of data.
This work is dedicated to the loving memory of Minoo Golshani (Rajabian) who dedicated her life to helping her community and touched everyone's heart on the way.
- Khan SH, Ahmad F, Ahmad N, Flynn DC, Kumar R: Protein-protein interactions: principles, techniques, and their potential role in new drug development. J Biomol Struct Dyn. 2011, 28: 929-938. 10.1080/07391102.2011.10508619.View ArticlePubMedGoogle Scholar
- Nibbe RK, Chowdhury SA, Koyuturk M, Ewing R, Chance MR: Protein-protein interaction networks and subnetworks in the biology of disease. Wiley Interdiscip Rev Syst Biol Med. 2011, 3: 357-367. 10.1002/wsbm.121.View ArticlePubMedGoogle Scholar
- Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci U S A. 2001, 98: 4569-4574. 10.1073/pnas.061034498.View ArticlePubMed CentralPubMedGoogle Scholar
- Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang M, Johnston M, Fields S, Rothberg JM: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature. 2000, 403: 623-627. 10.1038/35001009.View ArticlePubMedGoogle Scholar
- Gavin AC, Aloy P, Grandi P, Krause R, Boesche M, Marzioch M, Rau C, Jensen LJ, Bastuck S, Dumpelfeld B, Edelmann A, Heurtier MA, Hoffman V, Hoefert C, Klein K, Hudak M, Michon AM, Schelder M, Schirle M, Remor M, Rudi T, Hooper S, Bauer A, Bouwmeester T, Casari G, Drewes G, Neubauer G, Rick JM, Kuster B, Bork P, et al: Proteome survey reveals modularity of the yeast cell machinery. Nature. 2006, 440: 631-636. 10.1038/nature04532.View ArticlePubMedGoogle Scholar
- Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, Ignatchenko A, Li J, Pu S, Datta N, Tikuisis AP, Punna T, Peregrín-Alvarez JM, Shales M, Zhang X, Davey M, Robinson MD, Paccanaro A, Bray JE, Sheung A, Beattie B, Richards DP, Canadien V, Lalev A, Mena F, Wong P, Starostine A, Canete MM, Vlasblom J, Wu S, Orsi C, et al: Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature. 2006, 440: 637-643. 10.1038/nature04670.View ArticlePubMedGoogle Scholar
- Jessulat M, Pitre S, Gui Y, Hooshyar M, Omidi O, Samanfar B, Tan LH, Alamgir M, Green JR, Dehne F, Golshani A: Recent Advances in Protein-Protein Interaction Prediction: Experimental and Computational Methods. Expert Opinion on Drug Discovery. 2011, 6: 921-935. 10.1517/17460441.2011.603722.View ArticlePubMedGoogle Scholar
- Lievens S, Lemmens I, Tavernier J: Mammalian two-hybrids come of age. Trends Biochem Sci. 2009, 34: 579-588. 10.1016/j.tibs.2009.06.009.View ArticlePubMed CentralPubMedGoogle Scholar
- Qi Y, Bar-Joseph Z, Klein-Seetharaman J: Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins. 2006, 63: 490-500. 10.1002/prot.20865.View ArticlePubMed CentralPubMedGoogle Scholar
- von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P: Comparative assessment of large-scale data sets of protein-protein interactions. Nature. 2002, 417: 399-403. 10.1038/nature750.View ArticlePubMedGoogle Scholar
- Pitre S, Alamgir M, Green JR, Dumontier M, Dehne F, Golshani A: Computational methods for predicting protein-protein interactions. Adv Biochem Eng Biotechnol. 2008, 110: 247-267.PubMedGoogle Scholar
- McDowall MD, Scott MS, Barton GJ: PIPs: human protein-protein interaction prediction database. Nucleic Acids Res. 2009, 37: D651-656. 10.1093/nar/gkn870.View ArticlePubMed CentralPubMedGoogle Scholar
- Elefsinioti A, Sarac OS, Hegele A, Plake C, Hubner NC, Poser I, Sarov M, Hyman A, Mann M, Schroeder M, Stelzl U, Beyer A: Large-scale de novo prediction of physical protein-protein association. Mol Cell Proteomics. 2011, 10: M111 010629-10.1074/mcp.M111.010629.View ArticlePubMed CentralPubMedGoogle Scholar
- Zhang QC, Petrey D, Deng L, Qiang L, Shi Y, Thu CA, Bisikirska B, Lefebvre C, Accili D, Hunter T, Maniatis T, Califano A, Honig B: Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature. 2012, 490: 556-560. 10.1038/nature11503.View ArticlePubMed CentralPubMedGoogle Scholar
- Zhang QC, Petrey D, Norel R, Honig BH: Protein interface conservation across structure space. Proc Natl Acad Sci U S A. 2010, 107: 10896-10901. 10.1073/pnas.1005894107.View ArticlePubMed CentralPubMedGoogle Scholar
- Neduva V, Russell RB: Peptides mediating interaction networks: new leads at last. Curr Opin Biotechnol. 2006, 17: 465-471. 10.1016/j.copbio.2006.08.002.View ArticlePubMedGoogle Scholar
- Chica C, Diella F, Gibson TJ: Evidence for the concerted evolution between short linear protein motifs and their flanking regions. PLoS One. 2009, 4: e6052-10.1371/journal.pone.0006052.View ArticlePubMed CentralPubMedGoogle Scholar
- Stein A, Aloy P: Novel peptide-mediated interactions derived from high-resolution 3-dimensional structures. PLoS Comput Biol. 2010, 6: e1000789-10.1371/journal.pcbi.1000789.View ArticlePubMed CentralPubMedGoogle Scholar
- Pitre S, Dehne F, Chan A, Cheetham J, Duong A, Emili A, Gebbia M, Greenblatt J, Jessulat M, Krogan N, Luo X, Golshani A: PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs. BMC bioinformatics. 2006, 7: 365-10.1186/1471-2105-7-365.View ArticlePubMed CentralPubMedGoogle Scholar
- Petsalaki E, Stark A, Garcia-Urdiales E, Russell RB: Accurate prediction of peptide binding sites on protein surfaces. PLoS Comput Biol. 2009, 5: e1000335-10.1371/journal.pcbi.1000335.View ArticlePubMed CentralPubMedGoogle Scholar
- Neduva V, Linding R, Su-Angrand I, Stark A, de Masi F, Gibson TJ, Lewis J, Serrano L, Russell RB: Systematic discovery of new recognition peptides mediating protein interaction networks. PLoS Biol. 2005, 3: e405-10.1371/journal.pbio.0030405.View ArticlePubMed CentralPubMedGoogle Scholar
- Pitre S, North C, Alamgir M, Jessulat M, Chan A, Luo X, Green JR, Dumontier M, Dehne F, Golshani A: Global investigation of protein-protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences. Nucleic Acids Res. 2008, 36: 4286-4294. 10.1093/nar/gkn390.View ArticlePubMed CentralPubMedGoogle Scholar
- Pitre S, Hooshyar M, Schoenrock A, Samanfar B, Jessulat M, Green JR, Dehne F, Golshani A: Short Co-occurring Polypeptide Regions Can Predict Global Protein Interaction Maps. Sci Rep. 2012, 2: 239-10.1038/srep00239.View ArticlePubMed CentralPubMedGoogle Scholar
- Park Y: Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences. BMC bioinformatics. 2009, 10: 419-10.1186/1471-2105-10-419.View ArticlePubMed CentralPubMedGoogle Scholar
- Ben-Hur A, Noble WS: Choosing negative examples for the prediction of protein-protein interactions. BMC bioinformatics. 2006, 7 (Suppl 1): S2-10.1186/1471-2105-7-S1-S2.View ArticlePubMed CentralPubMedGoogle Scholar
- Park Y, Marcotte EM: Revisiting the negative example sampling problem for predicting protein-protein interactions. Bioinformatics. 2011, 27: 3024-3028. 10.1093/bioinformatics/btr514.View ArticlePubMed CentralPubMedGoogle Scholar
- Yu CY, Chou LC, Chang DT: Predicting protein-protein interactions in unbalanced data using the primary structure of proteins. BMC bioinformatics. 2010, 11: 167-10.1186/1471-2105-11-167.View ArticlePubMed CentralPubMedGoogle Scholar
- Mak AB, Ni Z, Hewel JA, Chen GI, Zhong G, Karamboulas K, Blakely K, Smiley S, Marcon E, Roudeva D, Li J, Olsen JB, Wan C, Punna T, Isserlin R, Chetyrkin S, Gingras AC, Emili A, Greenblatt J, Moffat J: A lentiviral functional proteomics approach identifies chromatin remodeling complexes important for the induction of pluripotency. Mol Cell Proteomics. 2010, 9: 811-823. 10.1074/mcp.M000002-MCP201.View ArticlePubMed CentralPubMedGoogle Scholar
- Vogel MJ, Guelen L, de Wit E, Peric-Hupkes D, Loden M, Talhout W, Feenstra M, Abbas B, Classen AK, van Steensel B: Human heterochromatin proteins form large domains containing KRAB-ZNF genes. Genome Res. 2006, 16: 1493-1504. 10.1101/gr.5391806.View ArticlePubMed CentralPubMedGoogle Scholar
- Sanchez C, Sanchez I, Demmers JA, Rodriguez P, Strouboulis J, Vidal M: Proteomics analysis of Ring1B/Rnf2 interactors identifies a novel complex with the Fbxl10/Jhdm1B histone demethylase and the Bcl6 interacting corepressor. Mol Cell Proteomics. 2007, 6: 820-834. 10.1074/mcp.M600275-MCP200.View ArticlePubMedGoogle Scholar
- Rao PS, Satelli A, Zhang S, Srivastava SK, Srivenugopal KS, Rao US: RNF2 is the target for phosphorylation by the p38 MAPK and ERK signaling pathways. Proteomics. 2009, 9: 2776-2787. 10.1002/pmic.200800847.View ArticlePubMed CentralPubMedGoogle Scholar
- Margueron R, Reinberg D: Chromatin structure and the inheritance of epigenetic information. Nat Rev Genet. 2010, 11: 285-296. 10.1038/nrg2752.View ArticlePubMed CentralPubMedGoogle Scholar
- Kim JA, Haber JE: Chromatin assembly factors Asf1 and CAF-1 have overlapping roles in deactivating the DNA damage checkpoint when DNA repair is complete. Proc Natl Acad Sci U S A. 2009, 106: 1151-1156. 10.1073/pnas.0812578106.View ArticlePubMed CentralPubMedGoogle Scholar
- Jeong H, Mason SP, Barabasi AL, Oltvai ZN: Lethality and centrality in protein networks. Nature. 2001, 411: 41-42. 10.1038/35075138.View ArticlePubMedGoogle Scholar
- Yu H, Greenbaum D, Xin Lu H, Zhu X, Gerstein M: Genomic analysis of essentiality within protein networks. Trends Genet. 2004, 20: 227-231. 10.1016/j.tig.2004.04.008.View ArticlePubMedGoogle Scholar
- Albert R, Jeong H, Barabasi AL: Error and attack tolerance of complex networks. Nature. 2000, 406: 378-382. 10.1038/35019019.View ArticlePubMedGoogle Scholar
- Young RA: Control of the embryonic stem cell state. Cell. 2011, 144: 940-954. 10.1016/j.cell.2011.01.032.View ArticlePubMed CentralPubMedGoogle Scholar
- Yosef N, Regev A: Impulse control: temporal dynamics in gene transcription. Cell. 2011, 144: 886-896. 10.1016/j.cell.2011.02.015.View ArticlePubMed CentralPubMedGoogle Scholar
- Bithell A, Johnson R, Buckley NJ: Transcriptional dysregulation of coding and non-coding genes in cellular models of Huntington's disease. Biochem Soc Trans. 2009, 37: 1270-1275. 10.1042/BST0371270.View ArticlePubMedGoogle Scholar
- Girvan M, Newman ME: Community structure in social and biological networks. Proc Natl Acad Sci U S A. 2002, 99: 7821-7826. 10.1073/pnas.122653799.View ArticlePubMed CentralPubMedGoogle Scholar
- Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M: The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol. 2007, 3: e59-10.1371/journal.pcbi.0030059.View ArticlePubMed CentralPubMedGoogle Scholar
- Ozgur A, Vu T, Erkan G, Radev DR: Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics. 2008, 24: i277-285. 10.1093/bioinformatics/btn182.View ArticlePubMed CentralPubMedGoogle Scholar
- Chen J, Aronow BJ, Jegga AG: Disease candidate gene identification and prioritization using protein interaction networks. BMC bioinformatics. 2009, 10: 73-10.1186/1471-2105-10-73.View ArticlePubMed CentralPubMedGoogle Scholar
- Dezso Z, Nikolsky Y, Nikolskaya T, Miller J, Cherba D, Webb C, Bugrim A: Identifying disease-specific genes based on their topological significance in protein networks. BMC Syst Biol. 2009, 3: 36-10.1186/1752-0509-3-36.View ArticlePubMed CentralPubMedGoogle Scholar
- Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL: The human disease network. Proc Natl Acad Sci U S A. 2007, 104: 8685-8690. 10.1073/pnas.0701361104.View ArticlePubMed CentralPubMedGoogle Scholar
- Feldman I, Rzhetsky A, Vitkup D: Network properties of genes harboring inherited disease mutations. Proc Natl Acad Sci U S A. 2008, 105: 4323-4328. 10.1073/pnas.0701722105.View ArticlePubMed CentralPubMedGoogle Scholar
- Yarden RI, Papa MZ: BRCA1 at the crossroad of multiple cellular pathways: approaches for therapeutic interventions. Mol Cancer Ther. 2006, 5: 1396-1404. 10.1158/1535-7163.MCT-05-0471.View ArticlePubMedGoogle Scholar
- Hall JM, Lee MK, Newman B, Morrow JE, Anderson LA, Huey B, King MC: Linkage of early-onset familial breast cancer to chromosome 17q21. Science. 1990, 250: 1684-1689. 10.1126/science.2270482.View ArticlePubMedGoogle Scholar
- Easton DF, Ford D, Bishop DT: Breast and ovarian cancer incidence in BRCA1-mutation carriers. Breast Cancer Linkage Consortium. Am J Hum Genet. 1995, 56: 265-271. 10.1002/ajmg.1320560305.View ArticlePubMed CentralPubMedGoogle Scholar
- Wu J, Lu LY, Yu X: The role of BRCA1 in DNA damage response. Protein Cell. 2010, 1: 117-123. 10.1007/s13238-010-0010-5.View ArticlePubMed CentralPubMedGoogle Scholar
- Fabbro M, Savage K, Hobson K, Deans AJ, Powell SN, McArthur GA, Khanna KK: BRCA1-BARD1 complexes are required for p53Ser-15 phosphorylation and a G1/S arrest following ionizing radiation-induced DNA damage. J Biol Chem. 2004, 279: 31251-31258. 10.1074/jbc.M405372200.View ArticlePubMedGoogle Scholar
- Yu X, Chen J: DNA damage-induced cell cycle checkpoint control requires CtIP, a phosphorylation-dependent binding partner of BRCA1 C-terminal domains. Mol Cell Biol. 2004, 24: 9478-9486. 10.1128/MCB.24.21.9478-9486.2004.View ArticlePubMed CentralPubMedGoogle Scholar
- Shrivastav M, De Haro LP, Nickoloff JA: Regulation of DNA double-strand break repair pathway choice. Cell Res. 2008, 18: 134-147. 10.1038/cr.2007.111.View ArticlePubMedGoogle Scholar
- Ren S, Rollins BJ: Cyclin C/cdk3 promotes Rb-dependent G0 exit. Cell. 2004, 117: 239-251. 10.1016/S0092-8674(04)00300-9.View ArticlePubMedGoogle Scholar
- De Siervi A, De Luca P, Byun JS, Di LJ, Fufa T, Haggerty CM, Vazquez E, Moiola C, Longo DL, Gardner K: Transcriptional autoregulation by BRCA1. Cancer Res. 2010, 70: 532-542. 10.1158/0008-5472.CAN-09-1477.View ArticlePubMed CentralPubMedGoogle Scholar
- Houvras Y, Benezra M, Zhang H, Manfredi JJ, Weber BL, Licht JD: BRCA1 physically and functionally interacts with ATF1. J Biol Chem. 2000, 275: 36230-36237. 10.1074/jbc.M002539200.View ArticlePubMedGoogle Scholar
- Zheng D, Cho YY, Lau AT, Zhang J, Ma WY, Bode AM, Dong Z: Cyclin-dependent kinase 3-mediated activating transcription factor 1 phosphorylation enhances cell transformation. Cancer Res. 2008, 68: 7650-7660. 10.1158/0008-5472.CAN-08-1137.View ArticlePubMed CentralPubMedGoogle Scholar
- Carmena M, Ruchaud S, Earnshaw WC: Making the Auroras glow: regulation of Aurora A and B kinase function by interacting proteins. Curr Opin Cell Biol. 2009, 21: 796-805. 10.1016/j.ceb.2009.09.008.View ArticlePubMed CentralPubMedGoogle Scholar
- Hans F, Skoufias DA, Dimitrov S, Margolis RL: Molecular distinctions between Aurora A and B: a single residue change transforms Aurora A into correctly localized and functional Aurora B. Mol Biol Cell. 2009, 20: 3491-3502. 10.1091/mbc.E09-05-0370.View ArticlePubMed CentralPubMedGoogle Scholar
- Ouchi M, Fujiuchi N, Sasai K, Katayama H, Minamishima YA, Ongusaha PP, Deng C, Sen S, Lee SW, Ouchi T: BRCA1 phosphorylation by Aurora-A in the regulation of G2 to M transition. J Biol Chem. 2004, 279: 19643-19648. 10.1074/jbc.M311780200.View ArticlePubMedGoogle Scholar
- Ryser S, Dizin E, Jefford CE, Delaval B, Gagos S, Christodoulidou A, Krause KH, Birnbaum D, Irminger-Finger I: Distinct roles of BARD1 isoforms in mitosis: full-length BARD1 mediates Aurora B degradation, cancer-associated BARD1beta scaffolds Aurora B and BRCA2. Cancer Res. 2009, 69: 1125-1134. 10.1158/0008-5472.CAN-08-2134.View ArticlePubMedGoogle Scholar
- Revenkova E, Eijpe M, Heyting C, Hodges CA, Hunt PA, Liebe B, Scherthan H, Jessberger R: Cohesin SMC1 beta is required for meiotic chromosome dynamics, sister chromatid cohesion and DNA recombination. Nat Cell Biol. 2004, 6: 555-562. 10.1038/ncb1135.View ArticlePubMedGoogle Scholar
- Peters JM, Tedeschi A, Schmitz J: The cohesin complex and its roles in chromosome biology. Genes Dev. 2008, 22: 3089-3114. 10.1101/gad.1724308.View ArticlePubMedGoogle Scholar
- Dorsett D: Cohesin: genomic insights into controlling gene transcription and development. Curr Opin Genet Dev. 2011, 21: 199-206. 10.1016/j.gde.2011.01.018.View ArticlePubMed CentralPubMedGoogle Scholar
- Zhang X, Yang H, Lee JJ, Kim E, Lippman SM, Khuri FR, Spitz MR, Lotan R, Hong WK, Wu X: MicroRNA-related genetic variations as predictors for risk of second primary tumor and/or recurrence in patients with early-stage head and neck cancer. Carcinogenesis. 2010, 31: 2118-2123. 10.1093/carcin/bgq177.View ArticlePubMed CentralPubMedGoogle Scholar
- Aas T, Borresen AL, Geisler S, Smith-Sorensen B, Johnsen H, Varhaug JE, Akslen LA, Lonning PE: Specific P53 mutations are associated with de novo resistance to doxorubicin in breast cancer patients. Nat Med. 1996, 2: 811-814. 10.1038/nm0796-811.View ArticlePubMedGoogle Scholar
- Zagozdzon R, Gallagher WM, Crown J: Truncated HER2: implications for HER2-targeted therapeutics. Drug Discov Today. 2011, 16: 810-816. 10.1016/j.drudis.2011.06.003.View ArticlePubMedGoogle Scholar
- Bousquet J, Schünemann H, Zuberbier T, Bachert C: Baena‐Cagnani C, Bousquet P, Brozek J, Canonica G, Casale T, Demoly P: Development and implementation of guidelines in allergic rhinitis–an ARIA‐GA2LEN paper. Allergy. 2010, 65: 1212-1221. 10.1111/j.1398-9995.2010.02439.x.View ArticlePubMedGoogle Scholar
- Bousquet J, Khaltaev N, Cruz AA, Denburg J, Fokkens WJ, Togias A, Zuberbier T, Baena-Cagnani CE, Canonica GW, van Weel C, Agache I, Aït-Khaled N, Bachert C, Blaiss MS, Bonini S, Boulet LP, Bousquet PJ, Camargos P, Carlsen KH, Chen Y, Custovic A, Dahl R, Demoly P, Douagui H, Durham SR, van Wijk RG, Kalayci O, Kaliner MA, Kim YY, Kowalski ML, et al: Allergic Rhinitis and its Impact on Asthma (ARIA) 2008 update (in collaboration with the World Health Organization, GA(2)LEN and AllerGen). Allergy. 2008, 63 (Suppl 86): 8-160. 10.1111/j.1398-9995.2007.01620.x.View ArticlePubMedGoogle Scholar
- Wang H, Chavali S, Mobini R, Muraro A, Barbon F, Boldrin D, Aberg N, Benson M: A pathway-based approach to find novel markers of local glucocorticoid treatment in intermittent allergic rhinitis. Allergy. 2011, 66: 132-140. 10.1111/j.1398-9995.2010.02444.x.View ArticlePubMedGoogle Scholar
- Wang H, Gottfries J, Barrenäs F, Benson M: Identification of Novel Biomarkers in Seasonal Allergic Rhinitis by Combining Proteomic, Multivariate and Pathway Analysis. PLoS One. 2011, 6: e23563-10.1371/journal.pone.0023563.View ArticlePubMed CentralPubMedGoogle Scholar
- Alamgir M, Eroukova V, Jessulat M, Xu J, Golshani A: Chemical-genetic profile analysis in yeast suggests that a previously uncharacterized open reading frame, YBR261C, affects protein synthesis. BMC Genomics. 2008, 9: 583-10.1186/1471-2164-9-583.View ArticlePubMed CentralPubMedGoogle Scholar
- Schenk L, Meinel DM, Strasser K, Gerber AP: La-motif-dependent mRNA association with Slf1 promotes copper detoxification in yeast. RNA. 2012, 18: 449-461. 10.1261/rna.028506.111.View ArticlePubMed CentralPubMedGoogle Scholar
- Garcia-Gomez JJ, Lebaron S, Froment C, Monsarrat B, Henry Y, de la Cruz J: Dynamics of the putative RNA helicase Spb4 during ribosome assembly in Saccharomyces cerevisiae. Mol Cell Biol. 2011, 31: 4156-4164. 10.1128/MCB.05436-11.View ArticlePubMed CentralPubMedGoogle Scholar
- Benko AL, Vaduva G, Martin NC, Hopper AK: Competition between a sterol biosynthetic enzyme and tRNA modification in addition to changes in the protein synthesis machinery causes altered nonsense suppression. Proc Natl Acad Sci U S A. 2000, 97: 61-66. 10.1073/pnas.97.1.61.View ArticlePubMed CentralPubMedGoogle Scholar
- Chesler EJ, Langston MA: Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data. RECOMB Systems Biology and Regulatory Genomics. 2006, ᅟ, San Diego, 150-165. 10.1007/978-3-540-48540-7_13. 4023View ArticleGoogle Scholar
- Eblen JD, Gerling IC, Saxton AM, Wu J, Snoddy JR, Langston MA: Graph Algorithms for Integrated Biological Analysis, with Applications to Type 1 Diabetes Data. Clustering Challenges in Biological Networks. World Scientific. 2009, 10: 207-222.Google Scholar
- Langston MA, Perkins AD, Saxton AM, Scharff JA, Voy BH: Innovative Computational Methods for Transcriptomic Data Analysis: A Case Study in the Use of FPT for Practical Algorithm Design and Implementation. The Computer Journal. 2008, 51: 26-38. 10.1093/comjnl/bxm003.View ArticleGoogle Scholar
- Gursoy A, Keskin O, Nussinov R: Topological properties of protein interaction networks from a structural perspective. Biochem Soc Trans. 2008, 36: 1398-1403. 10.1042/BST0361398.View ArticlePubMedGoogle Scholar
- Ni Z, Olsen JB, Emili A, Greenblatt JF: Identification of mammalian protein complexes by lentiviral-based affinity purification and mass spectrometry. Methods Mol Biol. 2011, 781: 31-45. 10.1007/978-1-61779-276-2_2.View ArticlePubMedGoogle Scholar
- Kislinger T, Cox B, Kannan A, Chung C, Hu P, Ignatchenko A, Scott MS, Gramolini AO, Morris Q, Hallett MT, Ignatchenko A, Scott MS, Gramolini AO, Morris Q, Hallett MT: Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell. 2006, 125: 173-186. 10.1016/j.cell.2006.01.044.View ArticlePubMedGoogle Scholar
- Jessulat M, Alamgir M, Salsali H, Greenblatt J, Xu J, Golshani A: Interacting proteins Rtt109 and Vps75 affect the efficiency of non-homologous end-joining in Saccharomyces cerevisiae. Arch Biochem Biophys. 2008, 469: 157-164. 10.1016/j.abb.2007.11.001.View ArticlePubMedGoogle Scholar
- Lucchini G, Hinnebusch AG, Chen C, Fink GR: Positive regulatory interactions of the HIS4 gene of Saccharomyces cerevisiae. Mol Cell Biol. 1984, 4: 1326-1333.View ArticlePubMed CentralPubMedGoogle Scholar
- Krogan NJ, Kim M, Tong A, Golshani A, Cagney G, Canadien V, Richards DP, Beattie BK, Emili A, Boone C, Shilatifard A, Buratowski S, Greenblatt J: Methylation of histone H3 by Set2 in Saccharomyces cerevisiae is linked to transcriptional elongation by RNA polymerase II. Mol Cell Biol. 2003, 23: 4207-4218. 10.1128/MCB.23.12.4207-4218.2003.View ArticlePubMed CentralPubMedGoogle Scholar
- Stansfield I, Akhmaloka , Tuite MF: A mutant allele of the SUP45 (SAL4) gene of Saccharomyces cerevisiae shows temperature-dependent allosuppressor and omnipotent suppressor phenotypes. Curr Genet. 1995, 27: 417-426. 10.1007/BF00311210.View ArticlePubMedGoogle Scholar
- Shenton D, Smirnova JB, Selley JN, Carroll K, Hubbard SJ, Pavitt GD, Ashe MP, Grant CM: Global translational responses to oxidative stress impact upon multiple levels of protein synthesis. J Biol Chem. 2006, 281: 29011-29021. 10.1074/jbc.M601545200.View ArticlePubMedGoogle Scholar
- Pfaffl MW, Lange IG, Daxenberger A, Meyer HH: Tissue-specific expression pattern of estrogen receptors (ER): quantification of ER alpha and ER beta mRNA with real-time RT-PCR. APMIS. 2001, 109: 345-355. 10.1034/j.1600-0463.2001.090503.x.View ArticlePubMedGoogle Scholar
- Yu S, Vincent A, Opriessnig T, Carpenter S, Kitikoon P, Halbur PG, Thacker E: Quantification of PCV2 capsid transcript in peripheral blood mononuclear cells (PBMCs) in vitro. Vet Microbiol. 2007, 123: 34-42. 10.1016/j.vetmic.2007.02.021.View ArticlePubMedGoogle Scholar
- Chambers A, Tsang JS, Stanway C, Kingsman AJ, Kingsman SM: Transcriptional control of the Saccharomyces cerevisiae PGK gene by RAP1. Mol Cell Biol. 1989, 9: 5516-5524.View ArticlePubMed CentralPubMedGoogle Scholar
- Benson M, Strannegard IL, Strannegard O, Wennergren G: Topical steroid treatment of allergic rhinitis decreases nasal fluid TH2 cytokines, eosinophils, eosinophil cationic protein, and IgE but has no significant effect on IFN-gamma, IL-1beta, TNF-alpha, or neutrophils. J Allergy Clin Immunol. 2000, 106: 307-312. 10.1067/mai.2000.108111.View ArticlePubMedGoogle Scholar
- Bron C, Kerbosch J: Algorithm 457: finding all cliques of an undirected graph. Comm. ACM New York. 1973, 16 (9): 575-577. 10.1145/362342.362367.View ArticleGoogle Scholar
- Zhang Y, Abu-Khzam FN, Baldwin NE, Chesler EJ, Langston MA, Samatova NF: Genome-scale computational approaches to memory-intensive applications in systems biology. In Proc. 18th SC 2005, IEEE Computer Society Washington. 12.Google Scholar
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