Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution.Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species  Proteins are active in complexes and therefore it is of utmost importance to understand their protein-protein interaction patterns. Over the last decade, various large-scale screening methods have been developed and implemented providing insight into interactomes for a number of model species. Despite all these efforts, our knowledge about molecular mechanisms determining interaction specificity and underlying protein network formation is limited. Here, we applied an integrated computational and experimental approach to identify short sequence motifs that determine interaction specificity for members of the large and important MADS domain transcription factor family. We show that the method is able to predict MADS domain transcription factor interaction networks for non-model plant species with a reasonable accuracy. Subsequently performed experiments revealed the possibility to rewire protein-protein interaction networks in a desired manner by generating small dedicated mutations in predicted interaction motifs. Additional bioinformatics analyses led to interesting hypotheses about protein-protein interaction network evolution. All together, these results provide insight in the way protein-protein interaction specificity is laid down in primary protein sequences and open the road towards a better understanding of protein complex formation and the possibility to control this important process. Our ability to derive structural information from primary protein sequences has matured Most proteins are member of protein families, which are groups of evolutionarily related proteins that are characterized by the presence of specific domains. When proteins from a given family interact with each other these interactions are often highly specific The ability to form various homo- and heterodimers is in particular relevant for eukaryotic transcription factors such as the MADS domain transcription factors For the MADS domain transcription factor family as well as for other protein families, duplications occurred during evolution Here we present a combined computational and experimental approach towards understanding how protein-protein interaction specificity is encoded in the MADS domain protein sequences. We started with our recently developed interaction prediction method The basic premise of our computational approach, IMSS (Interaction Motif Search and Selection) Arabidopsis MIKC MADS domain transcription factor family, using the available interaction data We applied IMSS to all members of the Arabidopsis data as described above were used as input data with the original IMSS algorithm as presented before Arabidopsis data together with data from additional species as input . Leave-one-out cross-validation on the Arabidopsis proteins was used to select the best performing model We also tested leave-family-out cross-validation with similar results (not shown). To assess the quality of our predictions, both in the cross-validation setting discussed here and in the experimental validation described below, we use the F-score, which is the harmonic mean of precision and recall.We applied IMSS with three different settings: (1) the With 0.44+/−0.3 (average +/− standard deviation) the F-score for “ara_new” was slightly higher than the F-scores for the other two settings . However, these differences are clearly very small, and indeed the overlap between the predicted interaction motifs is relatively large, especially between “ara_orig” and “ara_new”. Around 90% of the sequence positions are simultaneously either covered or not covered by a motif in each of these two models. This number is somewhat lower between these two models and the “all_species” model (around 75%). By comparing with randomly generated motif-hits with the same distribution over the proteins but randomized positions, we found that the overlaps in motifs between our three IMSS settings are statistically significant with p<0.001 (data not shown).For each protein the resulting predicted protein-protein interaction motifs from “ara_new” are given in Remarkably, no interaction motifs were predicted in the first helix in the MADS domain. This helix contacts both the equivalent helix in the partner MADS domain and the DNA to which the MADS domain binds In addition to the above-mentioned hotspot region, several motifs are found in the K-domain. This region is indeed known to be involved in dimerization, and in fact for a couple of proteins experimental data is available showing that mutations in the K-domain change interaction specificity. In particular, for AP3 and PI, several mutations are known that influence their partner formation Arabidopsis MIKC MADS domain proteins based on leave-one-out cross-validation and performed best in the experimental validation (see below), it turned out to have only a very low performance on data from non-Arabidopsis species, for which it obtained a recall of only 0.16. We cannot calculate precision or F-score for these additional species, because in most cases only interactions are known and no information is available about non-interactions. However, based on leave-one-out cross-validation the “all_species” model obtained a recall of 0.60+/−0.44 for the data from other species. The reason for this strong difference in performance is that despite a quite similar position of motif hits in Arabidopsis between the models (see above), their exact definition is somewhat different. Some of the motifs obtained upon training with Arabidopsis information only, appeared to be too “strict” and hence, occurring only in few cases in non-Arabidopsis sequences (data not shown). As mentioned above, the “all_species” model obtained a F-score of 0.40+/−0.3 for the Arabidopsis interactions. Hence, the performance of this model for Arabidopsis is only slightly lower than what was obtained without adding these additional data (“ara_new”), and the recall on these additional data is comparable to what is obtained for Arabidopsis. The model obtained after training with this augmented dataset provided insight into interaction patterns among family members across plant species  and 0.63 (SOC1). To put these values in perspective, we used as a null model the interaction pattern obtained by the wild type proteins. Using this null model would give a substantially lower F-score of 0.56 and 0.38, respectively, which means that our predictor performs much better in this case. F-scores for all the experimental test-cases are shown in Mutations were designed based on the predicted interaction motifs  and the All mutations that we introduced led to changes in interactions. Overall, in eight out of 15 cases (∼53%) the mutations we introduced led specifically to loss of interactions, in three cases (∼20%) specifically to gain of interactions, and in the remaining ∼27% of cases, both loss and gain were obtained . The numThe changing interaction patterns were predicted with a reasonable accuracy for most of the mutated proteins ; Table 1In SVP we introduced two single mutations and a double mutation aiming to change its interaction pattern towards that of the closely related AGL24 proteins. Alternative splicing has been reported for SVP and the protein named SVP1, which was used in this particular experiment, is encoded by the fully spliced SVP transcript As indicated above, our predictor performs worse than the null model in five out of 15 cases. This seemingly moderate performance can be understood as caused by side effects of the mutations for at least some of the cases see . This inThe putative effect of mutations on protein-protein interaction specificity must have played an important role in the evolution of the MADS protein interaction network. Our interaction motifs provide means to investigate this. Of importance here is the balance between motif conservation, which leads to a static interaction network, and variability of motifs, which could lead to interaction loss or gain and potentially to neo- or sub-functionalization of particular proteins. The latter is especially relevant after gene or  genome duplications, which has played an important role in expansion of the plant MADS domain transcription factor family Arabidopsis accessions. This includes for instance a Q->E SNP in ANR1 that occurs in several ecotypes  data ecotypes .−7 the distribution of fraction conservation of characters of motifs was significantly different from that of non-motif characters. Focusing on motif-characters that are non-wildcards (i.e. not “*”), the difference was even somewhat higher, 0.73+/−0.09 (motifs) vs. 0.58+/−0.07 (non-motifs) . To obtain further insight into the conservation of predicted interaction motifs, we analyzed the extent to which motifs are conserved in putative orthologous protein sequences from various sequenced plant genomes see . This co-motifs) . In addiArabidopsis cases we found have clearly different interaction patterns. For the proteins in the SEP clade for instance, some evidence is present that they are not fully functional redundant The observed conservation of predicted interaction motifs provides supporting evidence that the IMSS motifs are under functional constraints. Such functional constraints would be relaxed after a gene duplication in one of the two copies, allowing sub- or neo-functionalization to occur via modification of interaction motifs. To analyze this, we first focused on indels coinciding with IMSS motifs see . SpecifiSVP splicing variant . In contrast to methods such as InSite Traditional experimental techniques to identify and test the role of specific amino acids or combinations of amino acid residues in specifying protein-protein interaction capacity, such as “alanine scanning” e.g. , are bascis-regulatory changes Studies of gene regulatory network evolution often focus on The basic idea behind our bioinformatics method to predict protein interactions based on their sequences was recently published IMSS-prediction identifies regions in the protein, but does not directly tell which amino acid residue to change in the short motif sequence, and into which amino acid it has to be changed. In order to decide on this, we aligned sequences of related proteins and chose residues to be mutated based on conservation See . The resThe point mutations were generated by PCR-based site-directed mutagenesis using the original Gateway entry clones Yeast transformation was performed according to the Quick and Easy transformation protocol Performance of our predictions of changes in interaction specificity was measured via the F-score, which equals 2*precision*recall/. Here precision  =  TP/(TP+FP) and recall  =  TP/(TP+FN), where TP indicates the number of correctly predicted interactions, FP indicates the number of predicted interactions that are not correct, and FN indicates the number of experimental interactions that are incorrectly not predicted as interactions.The F-score when using the original interaction data as a predictor for the interactions of the mutated proteins was calculated in a similar manner. In addition, a series of models were trained with interaction data to which increasing amounts of noise were added, from 10% to 40%. These numbers refer to the percentages of interactions that were removed from the data, and converted into interactions between randomly chosen pairs of proteins that do not interact according to the experimental data.www.1001genomes.org (data for 80 ecotypes). Intron/exon structures were defined using the software tool Scipio Protein structures were modeled using Modeller 8.2 www.maizesequence.org), Medicago truncatula (www.medicago.org), papaya Arabidopsis protein sequences were identified by aligning each sequence to each Arabidopsis sequence using MUSCLE Arabidopsis protein, its conservation was calculated as the fraction of characters in the motif which were identical in the homologous regions in its orthologs; the same was calculated for all Arabidopsis proteins which did not have a motif occurrence at that particular location in the sequence.Conservation of motif occurrences was assessed as follows. First, MIKC MADS sequences were obtained from the genomes of rice Arabidopsis . From these, we obtained pairs of putative duplicates, which we defined simply as two proteins from the same species both having their highest sequence similarity with members of the same clade in d with high sequence identity, and one insertion/deletion occurring. d was set to 6, and the cutoff for identity was set to 5 . Subsequently, the overlap between those indels and the predicted interaction motifs was assessed.We focused on indels because occurrence of an insertion or deletion could be interpreted as a signature of disruption of the interaction motif. For each pair of protein sequences, indel positions in their sequence alignments were probed by looking for stretches of length To perform intramolecular correlated mutation analysis of AP1, sequences of MADS proteins were obtained using blastp on the NR database, filtering with hmmsearch Figure S1Relation between position of intron-exon borders and predicted interaction motifs. Histogram of distances  between motif occurrences and exon borders, for IMSS motifs (red) and random motif occurrences (green).(9.45 MB TIF)Click here for additional data file.Figure S2Effects of alternative splicing of SHORT VEGETATIVE PHASE (SVP) and SEPALLATA4 (SEP4) on predicted interaction motifs. Top, in alternatively spliced SVP3 , a predicted motif at an exon border is spliced out, resulting in loss of interactions (bold indicates motif occurrence in SVP1). Bottom, splicing removes predicted interaction motifs from the SEP4-II splice variant (SEPALLATA4-II) that are present in the SEP4-I variant (bold indicates motif occurrences in SEP4-I).(0.31 MB TIF)Click here for additional data file.Table S1IMSS motif positions within the Arabidopsis MIKC MADS domain transcription factor proteins.(0.55 MB DOC)Click here for additional data file.Table S2Interaction data from additional species.(0.20 MB DOC)Click here for additional data file.Table S3Prediction of large-scale MADS interaction data.(0.04 MB DOC)Click here for additional data file.Table S4Interaction prediction for SEP-homologs.(0.05 MB DOC)Click here for additional data file.Table S5Mutagenesis positions.(0.06 MB DOC)Click here for additional data file.Table S6Results yeast two-hybrid assays.(0.48 MB DOC)Click here for additional data file.Table S7Analysis of known mutations in MADS domain proteins.(0.08 MB DOC)Click here for additional data file.Table S8Previous computational studies that revealed residues that are under functional constraint.(0.05 MB DOC)Click here for additional data file.Table S9SEP equivalents grouped according to expression in whorl one.(0.05 MB DOC)Click here for additional data file.Table S10SNPs overlapping IMSS motifs.(0.05 MB DOC)Click here for additional data file.Table S11Pairs of sequences with indel overlapping predicted interaction motif.(0.07 MB DOC)Click here for additional data file.Text S1Additional text van Dijk et al.(0.11 MB DOC)Click here for additional data file.


The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected.We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided.We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments. Although the genomes of several organisms have been sequenced, the functions of the genes' products are often unknown. The most important gene products are probably proteins. They are virtually involved in performing every kind of biological function – for instance, catalysis, cell motility and signal transduction. Furthermore, proteins are the most abundant class of macromolecules in living cells. Hence, the analysis of an organism's complete set of proteins, which is referred to as the proteome, is a crucial aim of biological sciences. One tool for achieving this goal is provided by location proteomics; i.e. the subcellular localisation of the complete set of proteins. Depending on the cell compartments that a protein occurs in, conclusions about its function can be drawn. Moreover, proteomic changes, for example those caused by the cell cycle or chemical agents, can be exploited for the diagnosis of diseases or the development of innovative therapies. Since the amino acid sequences of numerous proteins are known or can be derived from sequenced genomes, these amino acid sequences have been exploited to predict the proteins' locations. In order to accomplish this task, features such as targeting signals, which control the transportation, specific structural elements as well as information on homologous proteins are analysed. A comprehensive summary of prediction methods has been published by Chou and Shen .Unfortunately, dynamic changes and differences between cell types are very difficult to predict based on a protein's amino acid sequence . TherefoIn order to visualise the proteins, they are fused with fluorescent proteins. Afterwards they can be observed by means of a fluorescence microscope . The proThe classification of protein distribution patterns into a set of pre-defined locations is the most common approach to determine protein locations. Here, the research has been dominated by Murphy and his group. They have experimented with a multitude of different methods – in particular, microscopy techniques , numericIn recent years, the interest in developing automated methods for the subcellular localisation of proteins has increased. Conrad and his colleagues proposed a feature-based machine learning approach to the analysis of twelve protein locations in single-cell images of live human cells . Here, tOther researchers have employed techniques tailored to specific locations: Kasson and his co-researchers proposed a technique for the classification of proteins localised at the plasma membrane . SchiffmIn contrast to subcellular location prediction based on amino acid sequences (cf. ), the clIn addition to assigning protein distribution patterns to a fixed set of locations, the interest in automatically identifying and grouping distinct location patterns has risen. It has been particularly pushed forward by Murphy's group ,8,23-25.In order to find relevant location patterns, Murphy's group proposed a method, which distinguishes between all proteins under analysis, even if they share a common location ,23. TheyThe analysis of tagged proteins in live cells is a difficult task. A fluorescence image typically contains multiple or even numerous cells. These cells may be in various states resulting in different locations of the proteins. Furthermore, the cells themselves are not necessarily visible. Thus, a corresponding fluorescence micrograph contains bright spots corresponding to accumulations of tagged proteins. These spots vary in size and shape. But they cannot be associated with specific cells and locations therein. A trained biologist might be able to estimate the position of the surrounding cells. However, in an automatic context, additional information is necessitated. This knowledge is usually acquired by considering additional images, e.g. fluorescence images of stained nuclei, cell membranes or cytoplasms . UnfortuA combination of exploiting available biological knowledge and enabling the incorporation of new information would be more suitable with respect to large-scale analyses than the approaches discussed above. In such large-scale experiments unknown locations are likely to show up and must be processed. Otherwise, they result in errors. Therefore, in this article, a technique is introduced, which does not distinguish between the classification of known protein locations and the identification and grouping of new ones; for example, if it is trained to recognise proteins situated in the lysosomes, it will reject images of proteins located in the mitochondria as unknown. These unknown distribution patterns can be grouped according to their similarities or labelled by an expert and incorporated into the running system. In order to ensure that the created classes have a biological meaning, we favour the approach based on manual labelling. Therefore, we do not have to drop a large fraction of the available data as proposed by Murphy's group ,23. In aBesides fusing two different approaches to protein localisation, the proposed protein localisation technique has been adapted to the automated cell recognition method we have published in -28. So, Spodoptera frugiperda – a moth inhabiting the northern hemisphere. In 1977 a cell line called IPLB-SF-21 was extracted from immature ovaries of Spodoptera frugiperda pupae [The cells, the microscope images of which are analysed within the scope of this paper, stem from the fall army worm da pupae . It servda pupae .Insect cells have been proven to be beneficial for the high-level expression of foreign proteins -33. Hereμm and 20 μm, which is relatively large. Cells from the budding yeast Saccharomyces cerevisiae, for example, reach only diameters of about 8 μm [Sf9 cells have several beneficial features, which make them amenable to high-throughput investigations. First of all, they are robust and not very demanding; for example, they grow at room temperature and in serum-free medium without any added growth factors . In addiout 8 μm . So the As known protein localisation approaches have been applied to alternative cell types  and yellow fluorescent proteins (YFPs) were only available for a subset of the considered locations, conventional organelle probes had to be applied so as to simulate further locations. Otherwise, a reasonable variety of protein distribution patterns could not have been collected. But as the resulting fluorescence micrographs are assumed to be very similar to the corresponding protein distribution patterns, the negative influence of this procedure is limited. Table GFP-SKL as well as the GFPs themselves were amplified from pEGFP-Tub (Clontech) with the primers GGATCCATGGTGAGCAAGGGCGA, CTCGAGTTACTTGTACAGCTCGTCCATGC and CTCGAGTTAAAGCTTGCTCTTGTACAGCTCGTCCATGC. After amplification the constructs were cloned into pGEM-T Easy. The genes were then cut by Nco1 and Xho1 and ligated into the insect expression vector pIEx4 (Novagen). The nucleoli were visualised with a YFP fusion to the Medicago truncatula homologue of the Nop56 protein. Nop56 is a nucleolar protein required for ribosomal subunit synthesis [Medicago truncatula RNA by RT-PCR with the primers ATTCTCGAGTAATGGCACTCTTTCTCCC and ACTAGGATCCTTATTCAGCATCCTTCTTTT and cloned via Xho1 and BamH1 in frame with the YFP open reading frame into pEYFP-C1 (Clonetech). The fusion construct was then cut with Nco1 and BamH1 and ligated into pIEx4.All organelle stains were obtained from Molecular Probes. Before staining or transfection, the cells were plated in 24-well glass-bottom plates. For all stains except for the plasma membrane stain the cells were incubated for 30 minutes with the appropriate dye concentration and washed three times with medium. For the FM dye the images were taken 5–15 minutes after the addition of the dye without washing steps. In order to visualise the cytoplasm including the nucleus, a GFP was used directly, without creating a fusion protein. The cytoplasm excluding the nucleus was depicted by means of expressing a GFP fused to cytoplasmic proteins. The peroxisomes were marked by a GFP with a peroxisomal targeting sequence consisting of the carboxy-terminal-fused amino acids serine, lysine and leucine . The resynthesis . It was ® Insect Cell Medium at 25°C. Their transfection was performed using the constructed expression vectors and Insect Gene Juice (Novagen) in 24-well glass-bottom plates according to the manufacturer's protocol. The fluorescence patterns were visualised directly after staining or 36 hours after transfection with an Olympus IX81 microscope equipped with appropriate filters. In addition to the fluorescence images showing protein distribution patterns, bright-field images were taken in parallel. These bright-field images can be applied to find the surrounding cells. All images were captured with a 60 × objective  by the scanR Software (Olympus Soft Imaging Solutions GmbH).The Sf9 cells were obtained from Novagen and maintained in serum-free BacVectorObserved protein distribution patterns can be represented in several ways. In comparison to the direct application of pixel intensities, the usage of numerical features has proven particularly advantageous for the classification of fluorescence images showing tagged proteins ,10. FromIn the literature, subcellular location features are either computed from images of single cells or multi-cell images. Single-cell images usually have been cropped manually ,13,39 orSince the applied cell line as well as the proposed mask images have not been applied within the frame of an automated protein localisation technique before, the known feature sets are only of limited use, although individual features are transferable. Consequently, we have chosen an own feature set. We have selected the corresponding features in such a way as to enable a comprehensive description of all protein distribution patterns which might appear in Sf9 cells: Firstly, features enabling a consideration of the positions of tagged proteins relative to the surrounding cells are employed. They comprise the well-known Zernike moments  and regiSecondly, morphological features are employed, in particular pattern spectra , which aFinally, general properties of the protein distributions are examined by means of fractal features  and histAll of these features have been applied in such a way that an incorporation of the respective cell mask is enabled. Using Zernike moments and region-dependent texture features, each cell is mapped to a circle, which resembles the shape of the Sf9 cells. Using the other types of features, pixels not contained in the respective mask are explicitly neglected.As a basis for all further investigations, basic datasets containing feature vectors of images showing ten major protein locations were generated. These locations correspond to specific cell compartments or combinations thereof. Exemplary micrographs are shown in Figure In order to characterise the protein distribution patterns, we compiled two different feature sets. They are referred to as feature set  moments ,44 with The 1, 326 manually extracted cell masks may differ from cell masks, which are automatically determined using a cell recognition approach. In particular, the cell boundaries are likely to vary slightly, which might influence the outcome of the protein localisation. But in an automated context, proteins have to be localised in such automatically acquired cell masks. In order to analyse and solve this problem, we propose the application of a procedure enabling the automatic generation of additional training data. It was successfully applied within the scope of a cell recognition approach before (see ) and worSuitable features were selected by means of the stepwise discriminant analysis (SDA). It chooses a set of features depending on statistical properties of the data. The used classifier is not taken into account. However, from the literature it is known that the stepwise discriminant analysis is very well-suited for selecting features in the context of protein localisation ,15. In oThe actual protein localisation is performed by classifying observed protein distribution patterns in classes corresponding to protein locations; that is, observed protein distribution patterns are assigned to one of the ten locations depicted in Figure F0 performs an encoding of the input vector x(t) called complement coding. The resulting vector xF1(t) constitutes the input vector of the subsequent layer F1. The nodes of the output layer F2 are linked to all nodes of the F1 layer. The corresponding weights ρ. Moreover, each F2 neuron is associated with a class label. As the SFAM is an incremental network, there are neurons, which are not in use but required for an extension of the network – the uncommitted nodes. During the training process, they are incorporated if the available nodes are not able to represent an input vector.The SFAM has a three-layered architecture  is set to its class label. The other nodes remain unchanged. Thus, a classification result c(t) can be determined by:In order to classify a presented feature vector, all i represented by As the presented approach is not only intended to localise a fixed set of proteins, a measure specifying whether an input vector is known or unknown is required. So, previously unknown locations can be recognised and incorporated into the system. Furthermore, it would be beneficial to have information on the degree of knowledge so as to find related or similar protein locations. Unfortunately, the original activation arpenter  cannot f1 denotes the city block norm and and ∧ symbolises an element-wise minimum operation. The choice parameter α is usually set slightly higher than zero, which increases the influence of the category size even more: For α> 0, small categories are preferred to large ones.Here, |·|F2 neurons, which is independent of the categories' sizes, we decided to employ the alternative measure i according to the city block norm.In order to determine an activation of the assified . z˜iF2 yields a class label for known input vectors and -1 otherwise (cf. Equation 1).The minimum value τ equals zero, our extended SFAM is identical to the original SFAM. In principle, τ constitutes an additional degree of freedom, which is to be optimised in conjunction with the vigilance parameter ρ in order to reach high classification accuracies. But in addition, τ is essential to parametrise the rejection of unknown inputs.If In principle, the SFAM is able to incorporate new data (from known as well as hitherto unknown classes). Provided that the distribution patterns are presented in random order, there is no decrease in accuracy. But as the proposed approach aims at creating a method which is amenable to high-throughput processing, user interactions cannot be performed when a new input occurs; only post-processing would be possible. Thus, unknown protein distribution patterns have to be sorted out for a future inspection by an expert. Without such a manually assigned class label, these patterns might not have any biological meaning. Dissimilarity in the feature space is a necessary but not sufficient criterion to create new classes, since it depends on the employed features and similarity measures rather than biological knowledge. Furthermore, a manual association of clusters with specific protein locations would necessarily be biased by the applied protein localisation technique and the expectations of its users. Only pre-labelled data, which are used to analyse the proposed technique, ensure an objective evaluation.τ. The corresponding feature vector has a distance larger than τ to the closest category. However, if τ is chosen in such a way as to maximise the classification accuracy, the vast majority of fluorescence images showing the protein locations used for training are considered as known and classified accordingly. So, if a new location resembles a known one, the corresponding feature vectors are likely to be regarded as known and classified incorrectly. In order to circumvent this problem, we introduced a second threshold τ2 with respect to τ and defines the minimum distance to the closest category that a pattern must have in order to be recognised as potentially unknown, or rather as a possibly new protein location. In this case, an expert could be asked for advice.Using the SFAM, an unknown location is characterised by a minimum activation τ2 must be selected in such a way that a compromise between correctly classifying the known locations and detecting potentially unknown locations is reached. In particular, the amount of required manual assignments should be minimal, although the user interventions occur after the actual experiment. With respect to the unknown locations, the fraction of inputs recognised as potentially unknown (fu) should be high. So, the new locations can be detected. On the other hand, images of already known classes should not be regarded as potentially unknown. The corresponding results are symbolised by fk. Due to these considerations, an optimal value τ2 can be determined by maximising the difference between fu and fk:τ is still usable for the classification. So, even if τ2, a suggestion for a likely protein location can be made unless τ as well.In addition, This section introduces all of the methods that were applied so as to evaluate our approach. Firstly, several accuracy measures are discussed, concerning the quantitative evaluation. Afterwards, we describe the methods for analysing our protein localisation approach with respect to both tasks which we aim to solve: the classification into a pre-trained set of locations as well as the identification and incorporation of new protein locations.i, it can be written as follows:In order to contrast the classification results, the total accuracy ACC, is utilised. It has been applied by numerous researchers to this task before and constitutes a standard measure  can be incorrectly classified without a significant impact on ACC. This is impressively shown in [Here, shown in  where a i = 1...9 = 100%), the minimum of i = 10 = 0%). So, if the arithmetic mean was used for parameter optimisation, for example for the parameters of the employed classifier, an equally correct recognition of all locations could not be guaranteed. In order to circumvent this problem, here we propose to employ the mean accuracy i.However, e to ACC ,10, doesi, the more it decreases in comparison to the arithmetic mean. In principle, other measures, such as the geometric mean, would have been possible as well. But, we decided to use the harmonic mean, as it punishes unbalanced results more strongly. This is particularly important, since we perform the optimisation of the SFAM's parameters based on i more precisely. In order to simplify the mathematical notation, The harmonic mean is always smaller than or equal to the arithmetic mean: The larger the difference between individual accuracies ACCThe training was performed without distinguishing between manually and automatically acquired protein distribution patterns. But the results concerning both types of sample were computed independently, so as to enable an evaluation of the cooperation of our protein localisation technique with the applied cell recognition method.ρ and threshold τ) and five-fold cross-validation to test the localisation results.In order to assess the proposed protein localisation approach with respect to the classification of the protein distribution patterns in the ten regarded locations, the basic datasets were randomly mixed and split into ten subsets. No dataset contains masks of cells which are part of another dataset. Using these subsets, five groups of eight training and two test datasets each were created. Here, the selection of the test datasets occurred in such a way that they were disjoint. So, it became possible to apply eight-fold cross-validation to the optimisation of the classifiers' parameters  and the new unknown protein locations (fu) were measured.After finishing the training procedure, the networks' abilities to recognise unknown protein locations were evaluated. Here, the threshold ρ and τ had not been necessary here, as their values were already known. The whole process was repeated for every test dataset possible. So, it constitutes five-fold cross-validation.Once unknown protein locations have been detected, they can be incorporated in the SFAM. But, since the proposed approach is intended to be applied as a high-throughput system, no user interactions are possible during the protein localisation. However, samples recognised as an unknown location could be automatically sorted out for further assessment. After they have been associated with a protein location by a biological expert, the SFAM can be retrained. The evaluation of the retraining capabilities was performed similar to the analysis of the classification accuracies with respect to the eight previously known protein locations; i.e., four of five datasets were utilised. The remaining one was used as a test dataset. An optimisation of Besides the features themselves, the size of the feature set might affect the retraining process. Therefore, experiments based on reduced feature sets were conducted as well. However, it is virtually impossible to select a set of features, which accounts for a number of classes that are not known in advance. Therefore, this kind of feature reduction would only be of limited use with respect to the detection and learning of new cell compartments.Table A comparison of the accuracies regarding the manually and automatically obtained samples reveals that our protein localisation technique can be successfully applied based on cell masks yielded by the automatic cell recognition approach. The results regarding the automatically generated patterns are even slightly better, which might be a consequence of their noticeably higher number.The feature reduction led to a significant improvement of the classification results. Feature set ℬ, which employs the region-dependent texture features, seems to be more appropriate than feature set In order to demonstrate the classifiers' abilities to discriminate between the ten chosen protein locations, Table Besides the experiments concerning a fixed set of protein locations, we analysed the ability of the proposed protein localisation technique to detect and incorporate new protein locations after training. Since feature set ℬ produced the best results with respect to the classification task, feature set fu). So, a sufficient number of training samples was available for the following retraining step. On the other hand, 32.5% of the images showing known protein locations were considered as potentially unknown as well (fk). This means that a biological expert would have to label about one third of all images depicting known location patterns, which might be too high a number for some experiments.First, the fractions of patterns classified as potentially unknown were analysed , are classified with acceptable mean accuracies fk = 32.5% (cf. Figures The values of Since the number of available features has a strong impact on the classification accuracy of the SFAM, we assumed that it might affect the retraining process as well. Therefore, we arranged a reduced feature set based on the systems using a fixed set of protein locations. Such a reduced feature set was expected to reveal important information regarding the retraining process, even though it is of limited use for real applications, since no information about the unknown classes is available. Unfortunately, all results regarding the fixed set of protein locations were computed by means of five-fold cross-validation; that is, five runs using different classifiers were performed to determine mean values for the classification results. Consequently, five reduced feature sets resulted from the complete feature set. So, a single reduced feature set needed to be compiled first. Here, we exploited the fact that the reduced feature sets for a specific parameter setting are very similar, in particular, if the SDA is employed. The final features were therefore selected by a set union of the five sets yielded by the cross-validation runs. The size of these feature sets was chosen so as to maximise the mean accuracy. Due to the strong overlap of the five sets provided by the SDA, which comprise 15 features each, the resulting set encompasses only 22 features in total.fk decreased from 32.5% to 25.7% and the mean accuracies rose by approximately 4%, which confirms the results concerning the application of feature reduction methods known from the classification task. Only fk.Again, the feature reduction proved beneficial. fk could be employed for training the classifier to recognise the old classes. So, it constitutes no drawback if fk does not equal zero.The preference of the old, known cell protein locations is caused by the training procedure: The networks were trained with the old cell compartments until their weights did not change between two subsequent epochs. So, conflicts resulting from overlapping categories belonging to different classes could be solved. Afterwards, the input vectors of the new locations were presented once. Therefore, the new locations are not as well integrated into the classifier as the old ones. This could be circumvented if samples for the known cell compartments were presented during retraining. Then, there would be no difference between batch and on-line learning. In principle, the fraction of samples measured by The influence of the procedure used for generating additional training data was measured by repeating the experiments introduced in the section concerning the classification task. But here only manually acquired protein distribution patterns were used for the training. Table Although the impact of the automatic data generation on the mean accuracies is not significant, the results from Table The given results demonstrate that our approach is able to classify protein distribution patterns with a sufficient accuracy. The distribution patterns may either be obtained manually or yielded by a cell recognition approach. Although the total accuracy seems to be slightly lower than the one of related protein localisation techniques developed by Murphy's group which reach values up to 95.3% (see [Furthermore, here the optimisation of relevant parameters is performed based on a different measure – the mean accuracy Besides analysing the classification of protein distribution patterns in a set of fixed locations, we have demonstrated that using our approach an incremental learning of new protein locations can be performed: At first, patterns from these new locations are detected as unknown and sorted out for future inspection. After the biological experiment has finished, an expert is able to examine these patterns and associate them with appropriate locations. Then, they can be incorporated into our system and used in further experiments. Even if a protein distribution pattern is sorted out, the classifier can provide information about its similarity to the known locations, which might support potential users. As an alternative to the proposed semi-automated procedure, a clustering of the unknown inputs as suggested by Murphy's group  could beWe have introduced a technique, which unifies several approaches that are usually investigated separately: Besides performing a feature-based protein localisation, it is adapted to a cell recognition method and enables the detection and incorporation of new protein locations, if required. Although almost the complete system can be applied in a fully-automated way, a semi-automated approach is preferred regarding the retraining with new locations in order to ensure the biological relevance of created classes. But even then, no user interaction is required during the actual analysis.Our approach can be considered as being incremental due to two different facts: Firstly, an increasing number of cells that are visible in a fluorescence microscope image increases the accuracy of the protein localisation. Secondly, additional locations can be incorporated into the pre-trained system. Both aspects constitute an innovative contribution to the ongoing research into the subcellular localisation of proteins in living cells.The proposed technique does not necessitate demanding microscopy or image-enhancing methods such as digital deconvolution, which facilitates its application in high-throughput experiments. Furthermore, since no fluorescence channels are employed for cell recognition or to acquire auxiliary images, they are free for co-localisation experiments that could refine the set of recognisable protein locations in close analogy to manual protein localisation procedures .2D Sf9 Dataset, which is available from .The microscope images as well as the 1,326 cell masks that were used for evaluating the proposed protein localisation technique have been summarised to the MT drafted the manuscript and played the major role in developing the proposed protein localisation approach. NJ conducted the underlying biological experiments, provided the required microscope images and cell masks, and was involved in writing the manuscript. FK conceived of the study and edited the manuscript.


During the last years, high throughput experimental methods have been developed which generate large datasets of protein – protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information.Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings.In this paper, a clustering tool, called GIBA (named by the first characters of its developers' nicknames), is presented. GIBA implements a two step procedure to a given dataset of protein-protein interaction data. First, a clustering algorithm is applied to the interaction data, which is then followed by a filtering step to generate the final candidate list of predicted complexes.The efficiency of GIBA is demonstrated through the analysis of 6 different yeast protein interaction datasets in comparison to four other available algorithms. We compared the results of the different methods by applying five different performance measurement metrices.Moreover, the parameters of the methods that constitute the filter have been checked on how they affect the final results.GIBA is an effective and easy to use tool for the detection of protein complexes out of experimentally measured protein – protein interaction networks. The results show that GIBA has superior prediction accuracy than previously published methods. Proteomic data and more specifically PPIs data are of great scientific interest through their connection with important cellular functions such as extra and intra cellular signaling, cell communication etc . MoreoveEach method has its strengths and weaknesses; however every method has a certain error rate for the detection of a protein-protein interaction. The main basic errors are under-prediction and over-prediction  of protein interactions . BesidesG = -, where nodes (V) represent the proteins and edges (E) the pairwise PPIs. The graph model makes it easy for many computational methods derived from the graph theory to be applied on these noisy datasets to extract functional modules such as protein complexes. The goal of those approaches is to detect highly connected subgraphs which are protein complex candidates.Usually, the aggregation of the PPIs of an organism is modeled as an undirected graph, symbolized as et al suggested the RNSC algorithm  [.• for the cutting edge parameter: Choosing a parameter value out of the proposed range would be meaningless because the parameter method would become either too rigorous and it would produce very few clusters (if it was higher than the proposed maximum) or would add noise to the final data (if it was lower than the proposed minimum).We have examined all possible combinations, using a parameter step of 0.5, in three datasets with different properties: 2 online database dataset (MIPS and DIP) and a dataset from individual experiment (Gavin_2006). So we have run 192 different filter combinations for each dataset.From our experiments it was shown that the most important method affecting most the final results is the density method. In figure However, the increase of density parameter improves the quality of the identification of protein complexes. As it can be seen in figure Although, the increase of density causes positive effect on mean score metric, we could not claim the same thing for the geometrical accuracy metric. In figure In this paper, we have introduced the GIBA tool in order to identify protein complexes from pairwise protein – protein interaction datasets. GIBA workflow splits in a two step process: initially, it clusters the whole input protein network and afterwards it applies a filtering process to obtain the final clusters. In addition, GIBA is user friendly and can also provide intermediate results for every step that can be useful for further use. With our experiments we proved the efficiency of GIBA comparing to 4 other methods.The main issue of our future work will be the appliance of machine learning methods to detect how the properties of the initial protein – protein interaction dataset can take advantage of the filtering process in order to achieve better results. This could lead to the development of a new algorithmic approach with adaptive behavior relative to the initial protein network.1. Project name: GIBA: A clustering tool for detecting protein complexes2. Project home page: 3. Operating system(s): Windows.4. Programming language: Java and C.5. License: GNU GPL.6. Any restrictions to use by non-academics: No.The authors declare that they have no competing interests.CNM conceived and developed the idea of the GIBA tool to perform clustering on PPI graphs. CNM also wrote the code for the filtering methods, performed the experiments and wrote the manuscript. GAP implemented the GIBA tool as java application, wrote part of the manuscript and revised the manuscript. RS made contribution to the experiments and revised the manuscript. SDL was responsible for the experiments performed; the metrics used in their evaluation and revised the manuscript. SK directed the whole project, wrote and revised the manuscript.Guide to use GIBA tool.Click here for fileSummary of experimental results. The percentage of successful predictions is shown in the first column; the absolute number of valid predicted complexes is shown in the second column as well as the total number of predicted complexes. The mean score of the valid predicted complexes is shown in the third column. The last three columns present the Sensitivity (Sn), the Positive Predictive Value (PPV) and the geometric Accuracy (Acc_g) respectively.Click here for file


We expect reliable PPI annotations to show that proteins interacting in vivo are co-located in the same cellular compartment. Our goal here is to evaluate the potential of using PPI annotation in determining SCL of proteins in human, mouse, fly and yeast, and to identify and quantify the factors that contribute to this complementarity.Many biological processes are mediated by dynamic interactions between and among proteins. In order to interact, two proteins must co-occur spatially and temporally. As protein-protein interactions (PPIs) and subcellular location (SCL) are discovered Using publicly available data, we evaluate the hypothesis that interacting proteins must be co-located within the same subcellular compartment. Based on a large, manually curated PPI dataset, we demonstrate that a substantial proportion of interacting proteins are in fact co-located. We develop an approach to predict the SCL of a protein based on the SCL of its interaction partners, given sufficient confidence in the interaction itself. The frequency of false positive PPIs can be reduced by use of six lines of supporting evidence, three based on type of recorded evidence  and three based on type of biological evidence , with biological evidence more-effective than recorded evidence. Our approach performs better than four existing prediction methods in identifying the SCL of membrane proteins, and as well as or better for soluble proteins.Understanding cellular systems requires knowledge of the SCL of interacting proteins. We show how PPI data can be used more effectively to yield reliable SCL predictions for both soluble and membrane proteins. Scope exists for further improvement in our understanding of cellular function through consideration of the biological context of molecular interactions. Proteins are fundamentally involved in all cellular processes, with many of their functions transduced through pairwise or multivalent interactions with other proteins. Discovering and modelling this network of protein-protein interactions (PPIs) has long been a goal of functional biology. Eukaryotic cells contain a number of physical compartments, across and within which their PPI networks are organised and structured. Each type of compartment provides a unique physiological environment within which specialized functions are carried out. To interact, proteins (or any other molecules) must necessarily share a common subcellular location (SCL), or an interface between physically adjacent SCLs, at least transiently or conditionally. Thus identifying the compartment(s) within which each protein is located,Both experimental and computational approaches have been applied to identify the SCL of proteins. Each has its own advantages and limitations. Experimental approaches are direct but typically time-consuming, expensive, potentially subject to artefact, and do not project the full range of biological complexity , whereaset al. report that 76% of interactions in their yeast PPI set are between proteins located in the same SCL [Recent studies based on high-throughput technologies have confirmed that interacting proteins tend to be located within the same compartment, or in physically adjacent compartments, in human -5, fly  and yeassame SCL . These sFor decades, molecular biologists have discovered individual protein interactions using low-throughput techniques, accumulating low-coverage but high-quality PPI data. More recently these methods have been adapted, and new methods devised, to survey PPI on a more global, proteome-wide scale. However, these high-throughput methods have been reported to have high rates of false positives (FPs), and concerns exist that experimental conditions in these assays may not accurately reflect cellular physiology ,10-13. ATo improve the quality of PPI data, additional criteria have been examined including discovery by different analytical technologies, e.g. yeast two-hybrid (Y2H) assays and co-immunoprecipitation (co-IP)  or diffeHere we examine the contribution of six such factors to the inference of SCL from public PPI data in human, mouse, fly and yeast. We generate a high-confidence dataset for each species, and use it to test whether PPIs are in fact co-located: six publicly available PPI datasets were evaluated and integrated, while SCL information was obtained from Gene Ontology (GO) annotations in UniProt. To reduce the effect of false positive PPIs we generate six subsets of our data, each supported by a different line of evidence: three related to the evidence type itself , and three based on inferred biology . We develop four alternative strategies and algorithms for using these PPI data to predict SCL, drawing explicitly on these six lines of evidence singly and in combination. Considering membrane and non-membrane proteins separately, we test these strategies against a set of newly released, manually curated SCL data not previously available to us, and compare the performance of our method to four previously published SCL prediction methods.Inconsistency of annotation systems for newly discovered proteins at the different protein-sequence databases often results in different identifiers being assigned to the same protein . To faciAfter standardization, a number of identifiers in each database remained unmapped to UniProt AC; we omitted these proteins from our integrated dataset. Redundant, divergent and convergent entries were also removed. With the human and mouse data, this process resulted in a substantial loss of proteins from some sources , while other resources showed much smaller reductions in protein number (4–6%). In the fly and yeast data, fewer proteins were redundant, or failed to map to UniProt ACs; only 4–8% of fly proteins and 0.2–7% of yeast proteins were removed across databases. Many of these difficulties in mapping Genbank Identifiers, NCBI Reference Sequence (RefSeq) ACs, and Protein Sequence Database (PSD) identifiers unambiguously to UniProt ACs were due to changes in annotation convention at the public databases, and/or from intrinsic complexity of the data, and highlight a continuing issue with integration among protein sequence databases Fig. .Removal of unmapped proteins and resolution of redundant proteins resulted in the removal of 12% of PPIs from BIND, 5% from IntAct, 11% from DIP, 6% from MINT, 12% from MPPI and 12% from HPRD. As expected from the results of protein mapping described above, PPI numbers for human and mouse were substantially reduced from the BIND, DIP and HPRD data. For example, in BIND, 42% of human PPIs and 37% of mouse PPIs were removed, while in DIP, 34% of human PPIs and 65% of mouse PPIs were removed Fig. ., whereas if they had been identified from different papers they would represent independent observations. For this and other measures of confidence, analysis of overlapping PPIs and literature provides an important measure of reliability. To inspect this overlap, we interrogated the intersections of PPIs and PMIDs collected among databases, and compared the types of experimental methods used to discover PPIs.We compared several properties of PPI datasets across databases to search for substantial biases in these data. Parameters considered include the number and overlap of proteins and PPIs recorded in databases, literature citations attributed to PPIs, and experimental methods for PPI detection. We consider these factors important to establish the independence of the data contained in each resource, hence the quality of our confidence measures. For example, our measure of the multiplicity of records in databases would be undermined if the multiple entries actually represent a single observation from one paper that had been shared among databases that frequently exchange data et al. [et al. [Despite similar objectives and approaches among PPI databases , each reet al.  identifi [et al.  detected [et al. -31.To examine the literature shared in common by these databases, we used PubMed identifiers (PMIDs), which are unique identifiers for biomedical literature, as a proxy for journal articles. We found little overlap of PMIDs among these six PPI databases. Articles which are cited in only one database covered 87% of human, 89% of mouse, 96% of fly and 73% of yeast PPIs Fig. . Across e.g. Experimental detection, MI:0045) into an Others class. The supporting method most frequently identified in these data was protein complementation, within which yeast two-hybrid (Y2H) methods [We categorized PPI-detection methods into classes based on the terminology provided by the Protein Standards Initiative (PSI) Molecular Interaction (MI) vocabulary  Fig. 1e1e. The f methods  were the methods .e.g. a large proportion of human and mouse PPIs in the BIND database identified by biophysical methods, where x-ray crystallography (MI:0114) alone represents around 30% of all PPIs; this reflects the inclusion in BIND of data from structural studies of protein complexes. Given the doubts that currently exist about the rate of FP predictions in high-throughput data [Most PPIs in these databases have been identified using complementation assays, reflecting the continuing dominance of Y2H techniques for high-throughput PPIs determination. Individual exceptions exist, put data , the subGiven the low degree of overlap among PPI sets from different databases especially for human and mouse, it appears worthwhile to integrate data from multiple resources to maximize coverage of the interactome. Although for fly and yeast more PPIs are common to more databases Fig. , the actWith the latter species especially, approaches that base confidence in a PPI on its co-occurrence in separate databases are misdirected, as presence in multiple databases reflects the accession of a few papers reporting large-scale studies, rather than truly independent observations of the interaction captured from unrelated literature.To investigate subcellular location, we extracted from UniProt the GO Cellular Component (CC) annotations for proteins in our dataset. To optimize reliability of the annotation, GO terms with evidence codes IC (Inferred by Curator), IEA (Inferred from Electronic Annotation), ISS , NAS (Non-traceable Author Statement), ND , and NR (Not Recorded) were not included due to the uncertain provenance of these annotations . GO is an ongoing project, and the assignment of terms to proteins can be subjective: for example, some proteins are annotated with a more-general term higher in the GO hierarchy, whereas others are annotated with lower-level, more-specific terms. For consistency, we collapsed all CCs for each protein to the parent terms in our definition of cellular compartments. In this way we associated proteins to one or more of 15 compartments .After sorting GO terms according to evidence codes and collapsing terms, 32% of human proteins, 43% of mouse proteins, 9% of fly proteins and 73% of yeast proteins in our combined dataset are associated with one or more GO CC terms. For these four species respectively there are 17%, 27%, 2% and 71% heterodimeric interactions in which each partner has at least one GO CC term Fig. . In abouTo assess the potential for PPI-based inference to uncover new subcellular location information, we identified heterodimeric PPIs in which only one interacting partner (but not both) is annotated with at least one GO CC term. In these four species we found 8830, 1239, 5775 and 5727 heterodimeric PPIs that satisfy this criterion. These are our potential prediction sets, and contain 3280, 783, 2998 and 1406 unique proteins that lack SCL annotation. The missing SCL annotation could potentially be inferred from the known location of the interaction partner(s)  were predicted as secreted or membrane proteins Fig. , likely We were interested in locations enriched for pairs of interacting proteins where each protein within a pair is annotated with that location . For PPIs with proteins co-located in two or more compartments, we scored a co-PPI for each shared compartment. Most (48–58% across species) co-PPIs were located in the nucleus Fig. . The secWe examined the degree to which interacting proteins are likely to share the same compartment, and compared this incidence of co-location to that computed for the same PPI set with randomized GO CC annotations. We first filtered GO CC terms based on evidence codes, then collapsed to higher-level GO CC terms representing fifteen subcellular locations .Our combined dataset contains 1860 proteins and 3298 PPIs in human, 780 proteins and 740 PPIs in mouse, 461 proteins and 540 PPIs in fly, and 3837 proteins and 16110 PPIs in yeast. This reference dataset for each species is composed of PPIs that are non-redundant, heterodimeric interactions in which both interacting partners have at least one annotated GO CC term. The proportion of co-PPIs was calculated in the reference set in each species. Across the four species, in 57–69% of PPIs both interacting proteins share at least one GO CC term in common (Table To test whether these observed frequencies of co-PPIs could occur by chance (given the data), we randomly shuffled GO CC terms for proteins in the reference set while keeping interacting pairs intact, and calculated co-PPI frequencies in these random sets. The latter proportions , we investigated strategies for extracting subsets of the data that have higher proportions of co-PPIs. To this end we established a number of high-quality PPI datasets, and compared co-PPI frequencies in these sets to those observed in the reference and random sets described immediately above (Section 5). This allowed us to test the proposition that many PPIs in which the protein interactors are annotated as occurring in different SCLs are in fact false positive interactions.To determine the likely efficacy of using PPI networks to improve the inference of SCL  terms, as represented by the BP score ;ii. presence of domains known to participate in domain-domain interactions (DDIs) ;iii. knowledge of equivalent interactions between orthologs (interologs) in alternative species ,43;iv. accession into multiple databases;v. support by more than one literature reference; andvi. discovery using multiple experimental detection methods.The final three types of supporting evidence (iv-vi) are different approaches to estimating whether multiple independent observations underlie a given PPI, but are not necessarily independent of each other. Entries in multiple databases might reference the same paper; interactions supported by multiple citations might represent a single original observation referred to in an additional context, or replicate a result using the same experimental method and therefore potentially suffer the same likelihood of FP prediction as the original discovery; and multiple experimental methods might be used within a single laboratory or paper but nonetheless be based on the same construct re-used in different methodological contexts. Keeping these caveats in mind, we examined the usefulness of these evidence types as proxies for multiple independent observations.[supported by evidence of shared biological process.A biological process can be considered to be a series of functions or events. Many of these events involve PPIs, and it can be imagined that two interacting proteins are likely to participate in the same biological process (BP). Ewing and colleagues  found th to calcu. Lowest  and Lin  measuressupported by DDI evidence.A protein interaction requires physical contact and/or chemical reaction between subsets of amino acid residues in two or more protein molecules. These contacts or reactions are thought to be largely mediated by structural domains: domain-domain interactions (DDIs) underpin PPIs. We used iPfam, a subset of the Pfam database of protein domains and families, as our source of interactions between Pfam domains that have a representative structure deposited in Protein Data Bank (PDB) . We obtae.g. human), if orthologs are present in another species (e.g. mouse) and form an interaction pair there, then that second interaction pair  is referred to as the interolog of the interaction pair in human [supported by interolog evidence if an interolog was identified in at least one other of these species.It has been proposed that pairs of interacting proteins are often conserved across species . For a gin human . Lehner in human  construcin human  and idensupported by presence in multiple databases.Each PPI database has its own literature retrieval system, its own criteria for identifying and accepting PPIs, and different human experts who extract and curate PPI information from the literature. It is thus reasonable to suppose that PPIs accepted into multiple databases are those supported by broader or less-equivocal evidence. We consider that PPIs accessioned into more than one PPI database to be et al. [e.g. journal article). PubMed assigns each item a unique PubMed identifier (PMID). We asked whether, over all databases, a PPI is associated with only one, or more than one, PMID and in the latter case consider an interaction to be supported by literature. No special consideration was given to possible overlap of authors or institutions among multiple literature sources.Several assessment systems for true positive interactions employ literature as supporting evidence. Rual et al.  extracteet al. [supported by experimental detection methods.PPIs observed under different experimental methods show low FP frequencies. Several interaction networks have been described based on the assumption that PPIs supported by different methods may be assigned as true positives ,13,50. Fet al.  report tUsing each of these lines of evidence in turn, we created six subsets from each of the four species-specific reference sets. Each of these 24 subsets was constituted from only those PPIs supported by one of the lines of evidence described above. We then observed the proportion of co-PPIs (number of co-PPIs/number of PPIs) in each subset. The number of proteins, and observed and expected proportions of co-PPIs, in each subset are presented in Table For 22 of the subsets, the proportion of co-PPIs vis-à-vis the parent reference set was increased; the exceptions were multiple database evidence in fly, and for multiple experimental methods in mouse. A higher proportion of co-PPIs in a subset than in the parent reference set implies that use of that evidence type can improve the inference of SCL. Note that this proportion places an upper bound on the degree of improvement that can be expected in similarly supported PPI sets where the location of one protein is unknown. An obvious initial question is which types of evidence are most efficacious in improving the inference of SCL.We found that PPIs in which the interacting proteins are annotated participants in similar biological processes have higher rates of co-PPI than in the corresponding reference sets Table . These sRequiring each member of an interacting pair of proteins to contain a known interaction domain increased the proportion of co-PPIs by 8 to 37 percentage points across species, compared with the corresponding reference sets.Requiring each PPI to have an interolog in at least one of the other three species in this analysis increased the proportion of co-PPIs by 8 to 34 percentage points compared to the corresponding reference set. We did not require supporting interologs to be present in our reference set. Fewer than 10% of reference PPIs were supported by interologs, rendering this line of evidence the least generally applicable.Where annotation is available, subcellular location of interologs is highly conserved across species. We examined co-PPIs in human for which at least one interolog has been identified in one of the other three species in this study, and SCL of both members of that interolog has been annotated: 85 such interolog pairs were found. Of these interologs, 79 93%) are co-PPIs in the same SCL as their human counterpart, 4 share at least one SCL annotation term with their human counterpart but are not co-PPIs in the second species , and 1 is a co-PPI in mouse but in a different SCL than in human  was observed. This indicates that the presence of a PPI interaction in multiple databases is a reliable indicator of quality only where the curated PPI data are not dominated by large-scale studies. In time, as more high-throughput studies are released for more organisms, this line of supporting evidence may become increasingly less appropriate as a means of establishing data quality.e.g. article) reports fewer than 10 PPIs (counts based on our in-house database). For this subset the proportion of co-PPIs is dramatically increased in fly and yeast, although the coverage of PPIs is greatly reduced. For human and mouse, however, restricting the analysis to small-scale studies did not significantly change the co-PPI proportion  except for human, where the increase is only 60% as great. Coverage, however was higher than observed for interolog-supported subsets.PPIs identified using at least two high-level experimental detection methods showed a high proportion of co-PPIs (68–89%) in comparison with the corresponding reference sets. Biochemical assay methods (MI:0401) remain more widely applied than those based on protein complementation (MI:0090) such as yeast-two-hybrid (Y2H), although Y2H has been used in many studies and large-scale analyses  to 23 (mouse) percentage point improvement in coverage vis-à-vis the best single consistent approach. The union of recorded evidence type approaches likewise gave co-PPI proportions intermediate among those of the corresponding constituent approaches, and a 14 (human) to 50 (fly) percentage point improvement in coverage. Quality or coverage (not both simultaneously) could be further increased by optimising the combination of biological and recorded evidence type.To evaluate more formally the potential benefit of using PPI data to predict SCL, we formalized several variants of our approach, then evaluated these variants on the species-specific reference sets, the six subsets described above, and selected unions of subsets Table . Recall D consisting of five proteins (A-E) and five PPIs, with an interaction denoted by the colon (:) and "true" SCLs (in parentheses) based on UniProt:Consider dataset A (nuc) : B (nuc)A (nuc) : D (nuc+cyto)B (nuc) : C (nuc)B (nuc) : D (nuc+cyto)D (nuc+cyto) : E (nuc)D and temporarily mask its annotated location. We denote these proteins using the prime ('), and the resulting dataset as D':Note that X : Y is equivalent to Y : X. First, we select at random one protein from each PPI in A' (--) : B (nuc)A' (--) : D (nuc+cyto)B' (--) : C (nuc)D' (--) : B (nuc)E' (--) : D (nuc+cyto)We then proceed as follows for the four variants . In the DISCRETE variant, we simply replace the masked GO CC term with the known location of the interaction partner:A' = nuc from B (nuc)A' = nuc+cyto from D (nuc+cyto)B' = nuc from C (nuc)D' = nuc from B (nuc)E' = nuc+cyto from D (nuc+cyto)D.To evaluate the performance of the method, we then compare these newly assigned term(s) with the true GO CC term(s) from dataset D and the randomized dataset D' as above), for each masked protein (not PPI) in turn, the GO CC term(s) are assigned based on the union of SCLs annotated for all its interaction partner(s) in dataset D':By contrast, in the MERGED variant  and D (nuc+cyto), A' = nuc+nuc+cyto = nuc+cytoB' : C; from C (nuc), B' = nucD' : B and D' : E; from B (nuc) and E (nuc), D'= nuc+nuc = nucE' : D; from D (nuc+cyto), E' = nuc+cytoD.As above, we then compare these newly assigned term(s) with the true GO CC term(s) from dataset In the COMMON variant, when a protein interacts with more than one other protein only those SCLs common to all its interaction partners are used to replace the masked terms. There are two possible cases:1- if a protein interacts only with one other protein, the GO CC terms assigned to it come from the SCL annotated for that partner, as for the DISCRETE variant above; or2- if a protein interacts with multiple partners, we assign the intersection of the partners' GO terms:A' : B and A' : D; from B (nuc) and D (nuc+cyto), A' = nucThe latter decision comes about because nuc is the only term common to the SCL annotations of both B and D.MAJORITY differs in using only those GO CC terms present in annotations of at least half of the interacting partners of a protein. Using this method, assignments for B', D' and E' are identical to those in COMMON, butA' : B and A' : D; from B (nuc) and D (nuc+cyto), A' = nuc+cytoAlthough this outcome happens to be the same as for MERGED in this simple case, the reason is different  been present, we would infer A' = nuc under MAJORITY and A' = nuc+cyto under MERGED).Obviously, further variants of arbitrary complexity could be developed, for example incorporating weighting of terms and/or based on machine-learning approaches or network analysis.To evaluate these variants, we calculate accuracy from the counts of true (T) and false (F) predictions:To penalise variants for potential over-prediction, we calculate both a permissive and a strict accuracy. For permissive accuracy (PA), a true prediction is scored for a protein if-and-only-if (iff) the true and predicted termsets have at least one term in common. A false prediction is scored iff no term from the prediction termset is found in the true termset. For strict accuracy (SA), a true prediction is scored iff the true and predicted termsets are identical; a false prediction is scored in all other cases. Our permissive calculations of true and false predictions are equivalent to the scoring scheme used previously to evaluate overall SCL prediction .We evaluated the above four variants of our approach for each of four species using the reference datasets and subsets. Values for PA and SA are presented in Additional file With human PPIs, PA values ranged from 0.64–0.85 for the DISCRETE variant of our approach, 0.72–0.86 for MERGED, 0.65–0.85 for COMMON, and 0.68–0.86 for MAJORITY  but possess one or more domains that extend into the adjacent compartment (e.g. the cytoplasm) and interact with a protein there. Our reference dataset contains 61 pairwise interactions between a Type I membrane protein and a soluble protein  physically adjacent to the membrane in question (e.g. the nuclear membrane). These may (at least in principle) be true interactions, but according to the rules above are not identified as true positives. In the absence of a dependable, automated way to account for physical adjacency within the cell, the permissive and strict accuracy values reported for our approach may be too conservative.Returning to the issue of false predictions: it is important to appreciate that in some cases a pair of proteins that actually interact The efficacy of our PPI-based approach depends on the quality of SCL annotation. The above results make it clear that PPI-based approaches have substantial potential as a predictor of SCL. Nonetheless significant issues remain, notably the tradeoff between quality and coverage.We compared the performance of our SCL prediction methods  to four publicly available SCL prediction methods, Proteome Analyst , WolfPSOWhen applied to the PPI data involving only membrane proteins, all three variants were more accurate than any other SCL prediction methods Table . OverallIn addition to the above sets, we constructed a dataset of interactions between transmembrane and soluble proteins. With these data, the accuracy of our methods was lower than the four public SCL prediction methods Table  and loweWhen all PPIs are considered, our prediction methods performed better than three of the four public methods; Proteome Analyst showed somewhat higher PA and SA values Table . ProteomThe existing methods were better at predicting the SCL of soluble proteins than of membrane proteins, but our methods showed markedly better performance when applied to interactions between membrane proteins. The methods that we evaluated in this study are based on the detection of features that determine SCL, either in protein sequences  or in protein knowledgebase records . WolfPSORT, CELLO and pTARGET have largely been trained on soluble proteins, and this is reflected in their lower PA and SA values with membrane proteins. Proteome Analyst collects features from the SWISS-PROT KEYWORDS and SUBCELLULAR LOCATION fields of entries for proteins with the strongest BLAST matches to the query sequences . If no pAs described above (Section 3.1), we applied our four variant approaches to infer GO CC term(s) for proteins of unknown SCL that interact with proteins of known SCL . Under this assumption, we showed that the co-PPI frequency in our reference set for each of the four species is 16–33 percentage points greater than random, i.e. substantial true-positive signal exists in our base data. We then demonstrated that the proportion of co-PPIs could be increased by applying, one at a time, six lines of additional supporting evidence describing the PPIs: co-PPI proportions were increased by 8–40 percentage points in 21 of the 24 subsets, and by up to 36 percentage points with the few combinations of evidence (union sets) that we tested. The greatest proportion of co-PPIs was always found in union sets supported by biological evidence type  and description-centric features  on the quality of prediction as judged by measures of permissive and strict accuracy. We evaluated our approach on human data Table  and on aWe compared the SCL prediction capacity of the COMMON, MAJORITY and MERGED methods to that of Proteome Analyst, WolfPSORT, CELLO and pTARGET; PA and SA values were examined for membrane-only, soluble-only, and mixed PPI subsets. Our methods always performed substantially better on the membrane-only subset, and usually better on the soluble-only subset. This demonstrates that considering membrane organization yields substantially better results in predicting SCL based on PPI data.We also applied our approach to the mouse potential prediction set, inferring SCL for 783 proteins of unknown SCL. It transpired that SCL had recently been experimentally determined (independently of our work) and accessioned into a later version of the LOCATE database  for 72 pAdditional directions remain to be explored. Multiple (non-pairwise) interactions and larger PPI networks may offer the possibility of inferring SCL based on secondary and tertiary as well as primary relationships. Challenges include those related to the reliability of PPI data, the quality of ontological annotation, tradeoffs between inference quality and coverage, adjacency of compartments and multi-molecular complexes within the cell, contingency of interaction, post-translational modification, and temporality of co-location. Our demonstration in this work that reliable PPI data can complement conventional experimental and computational approaches in identifying the SCL of proteins, provides a foundation on which these additional directions can be developed.We obtained protein-protein interaction (PPI) data for human, mouse, fly and yeast from six publicly available databases: Biomolecular Interaction Network Database (BIND: 25/06/2006) , DatabasPPIs from different data sources were standardized by matching identifiers to UniProt accession numbers (ACs). GI numbers were mapped to UniProt ACs through PIR . The preFour of these databases  contain protein from all four species. MPPI deals with only mammalian protein interactions, and HPRD only human protein interactions. In this study, we attempt to consider only pairwise PPIs; interactions within complexes were excluded, except (in the case of BIND) where further experimental evidence for direct pairwise physical interaction is cited. Standardization yielded 21121 non-redundant (NR) human PPIs (19686 heterodimers), 3032 NR mouse PPIs (2753 heterodimers), 31126 NR fly PPIs (30878 heterodimers) and 23586 NR yeast PPIs (22635 heterodimers). These constitute our in-house datasets.To obtain more-reliable annotation of the subcellular location (SCL) of these proteins, their associated GO terms (cellular component: CC) were filtered according to evidence codes provided by the Gene Ontology Consortium . SCL annAfter these consecutive processes, we obtained evidence-filtered, CC term-collapsed proteins: 2584 in human, 1062 in mouse, 784 in fly, and 3951 in yeast. These proteins take part in 3298 PPIs in human, 740 PPIs in mouse, 540 PPIs in fly and 16110 PPIs in yeast, in which both interacting proteins are distinct (i.e. the pairwise interaction is heterodimeric). We refer to these as our protein and PPI reference sets for each species.For each PPI in each of the four species-specific reference sets described immediately above, we looked for annotation in regard to six lines of supporting evidence, and on this basis constituted a total of 24 (4 species × 6 data types) subsets, each containing only those PPIs supported by one of these lines of evidence. The six lines of supporting evidence are:i. interaction supported by similarity of GO Biological Process (BP) terms, as represented by the GO BP score ;ii. interaction supported by the presence of one or more domains known to participate in domain-domain interactions (DDIs) ;iii. interaction supported by presence of an equivalent interaction between a pair of orthologs (interolog) in another of these four species ,43;iv. interaction supported by presence in multiple databases;v. interaction supported by literature; andvi. interaction supported by experimental detection methods.These lines of evidence are described more precisely in Results and Discussion, Section 6.1.e.g. nucleocytoplasmic shuttling complex) match to two different compartments and were collapsed into both high-level locations .The Gene Ontology (GO) Cellular Component (CC) annotation terms and evidence codes for each protein were obtained from UniProt Knowledgebase (version 12.1) to provide its SCL. To improve reliability, GO terms with less-reliable evidence were removed from each protein, after which GO terms remaining for each protein were collapsed into one of 15 defined locations . A few terms  from UniProt (version 12.1) and recorded the evidence code for each BP term. BP terms were excluded according to the criteria applied to the CC terms . Both proteins in a PPI were required to have at least one BP term each. Using these criteria, 6462 human PPIs (2950 proteins), 1136 mouse PPIs (1086 proteins), 1819 fly PPIs (1104 proteins), and 17087 yeast PPIs (3801 proteins) were available for analysis.et al. [For each PPI, we used FSST to compute the average BPscore describing the similarity of GO BP terms and accepted only scores that equal or exceed the threshold 0.70  each of the interacting proteins has an ortholog in another species (e.g. mouse) and that pair of orthologs interacts, then that second interacting pair  is referred to as the interolog of the interacting pair in human [Orthologs were obtained from the Inparanoid database . If for in human . In the For comparative evaluation of SCL prediction, union set of all human PPIs where a PPI has at least one line of supporting evidence (ALL) was used. PPIs from ALL were categorized into three groups according to the membrane organization (MO) of interacting proteins in each PPI, resulting in three groups: 121 membrane-only PPI, involving 162 proteins, 938 soluble-only PPI, involving 807 proteins and 305 mixed PPI involving 206 proteins.Prediction of SCL by three variants  was carried out as illustrated in Section 8 of Results and Discussion. SCL of proteins in the membrane-only, soluble-only, and mixed PPI groups was assigned from interacting partners.Proteins in each group were utilized as an input sequence to four publicly available and scalable SCL prediction methods: Proteome Analyst (PA-SUB 2.5) , WolfPSOIn Proteome Analyst, proteins which were included in the training set of that method were not included. Because Proteome Analyst does not stipulate a threshold for positive prediction, we considered positive predictions to be those for which the associated probability was ≥ 90%. With WolfPSORT, we selected the prediction with the highest score. For CELLO, predictions with the highest probability were collected. If a method generated equal best predictions , both predictions were accepted.Permissive and strict accuracy were examined for all predicted SCLs from three variants and four SCL prediction methods against SCLs of corresponding proteins in the ALL set.e.g. membrane-like) SCLs. After matching identifiers in LOCATE to UniProt ACs and removing LOCATE-specific annotation terms, 1713  proteins and their locations (LOCSCL) remained. Of these, 83 proteins are found in the reference set, and 72 in the potential prediction set (Section 3.1). As LOCATE is not cited as literature for any protein in UniProt, we expect the SCL information in LOCATE to be independent of UniProt annotation.We obtained SCL information for mouse proteins from the LOCATE database , a rigore.g. Bacterial display, MI:0009) which mapped to two or more high-level categories was placed into the "Others" category. These data were used to examine the distribution of experimental methods for detecting PPIs across PPI databases  and using detection method as a line of supporting evidence .PPI datasets from the resources used here  are frequently annotated with a description of the method used to detect the interaction. These terms are defined in the Protein Standards Initiative (PSI) Molecular Interaction (MI) ontology. It is known that PPI database curators sometimes annotate a single detection method recorded in a given paper using different terms , so we c (under Tools and Data/Databases and Datasets), from the link "PPI – Shin et al. (2009)". Perl scripts implementing the four variants of our prediction method are linked from the same page.Our protein and PPI datasets for the four species are available at BIO: subset constituted by union of biological evidence types; BPscore: Biological Process score; CC: Cellular Component (hierarchy within Gene Ontology); DDI: domain-domain interaction; EVI: subset constituted by union of recorded evidence types; GO: Gene Ontology; PA: permissive accuracy; PPI: protein-protein interaction; SA: strict accuracy; SCL: subcellular location.CJS conducted all experiments and analysed the data. CJS and SW designed the experiments. MJD and CJS designed and evaluated the SCL inference methods which were implemented by CJS. MJD assisted with biological interpretation. MAR supervised the project. All authors contributed to writing the manuscript.The number of PPIs according to presence of GO CC for protein and PPI. In heterodimeric PPIs, the two interacting proteins are different. The reference set for this study consists of all PPIs in which a GO CC term is available for each of the two interacting proteins. We identify a further set of PPIs for which one protein in each interacting pair has a GO CC term and the other one does not; this constitutes our potential prediction set. The remaining PPIs have no associated GO CC terms and are not informative for this study.Click here for fileCoverage and proportion of PPIs. These data are drawn from the subsets supported by the BPscore line of evidence.Click here for fileComparison of protein SCL between interologs. The SCL of proteins in human PPIs was compared with location of their orthologs from mouse, fly and yeast interologs.Click here for filePPI subsets based on low-throughput experimental data. DB, Method and PMID subsets were generated as described in Methods section 1.4, based on a reference set consisting only of those PPIs detected by low-throughput experimental methods.Click here for fileNumbers of PPIs annotated with PSI-MI experimental detection methods. These data are drawn from the subsets supported by the experimental detection methods line of evidence, for the four species under consideration.Click here for fileEvaluation of prediction method variants on reference and supported subsets for human, mouse, fly and yeast. The permissive accuracy (PA) and strict accuracy (SA) were calculated for all variants  for all interactions, and for interactions involving an integral membrane protein (M).Click here for fileInteractions between Type I integral membrane proteins and soluble intracellular proteins in human. The PPIs in adjacent compartments  are listed for human; only domains present as interacting pairs in the iPfam subset are included.Click here for fileEvaluation of SCL prediction methods using human reference set and subsets. SCL prediction methods, including three variants of our approach  and four existing methods were compared using the human reference set (REF) and two different unions of subsets (BIO and EVI).Click here for fileEvaluation of prediction method variants using LOCSCL. These 146 PPIs come from the mouse reference set and have additional SCL annotation data available from LOCATE [m LOCATE . They arClick here for file