Saccharomyces cerevisiae as a model system, we show that PLAST is more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. First, we identified proteins that have similar localization patterns and participate in closely-related biological processes, but do not necessarily form stable complexes with each other or localize at the same organelles. Second, we found an association between spatial and functional divergences of proteins during evolution. Surprisingly, as proteins with common ancestors evolve, they tend to develop more diverged subcellular localization patterns, but still occupy similar numbers of compartments. This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments. PLAST enables systematic and quantitative analyses of protein localization-function relationships, and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species. A public web interface of PLAST is available at http://plast.bii.a-star.edu.sg.Protein subcellular localization is a major determinant of protein function. However, this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments, and therefore it has limited applications in quantitative protein function analyses. Here, we present Protein Localization Analysis and Search Tools (PLAST), an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments. Using the budding yeast  Proteins are fundamental building blocks of cells. They perform a variety of biological functions, many of which are essential to the vitality and normal functioning of cells. Proteins have to be located at the appropriate regions inside a cell to perform their functions. Therefore, when proteins change their locations, they may acquire new or different functions. However, the relationships between the locations and functions of proteins are difficult to analyze because protein locations are often represented in distinct and manually-defined categories of subcellular regions. Many proteins have complex or subtle differences in their localization patterns that can be hardly represented by these categories. Here, we present an automated analysis tool for generating quantitative signatures of protein localization patterns without requiring manual or automated assignments of proteins into distinct categories. We show that our tool can identify proteins located at the same subcellular regions more accurately than existing categorization-based methods. Our tool allows comprehensive and more accurate studies of the relationships between protein localizations and functions. By knowing where proteins are located and how their locations were changed, we may discover their functions and better understand how they acquire these functions. Proteins have to be localized at the appropriate subcellular compartments to perform their functions. Using high-throughput protein labeling and imaging techniques, several proteome-wide studies have found that protein localization and function are strongly correlated to each other One of the main difficulties is that protein subcellular localization is often described or represented in terms of discrete, qualitative categories of subcellular compartments, such as the Gene Ontology (GO) categories To overcome these limitations, we have developed an automated analysis framework for converting raw image descriptors into quantitative signatures (or “profiles”) of protein subcellular localization patterns. We refer to this framework as Protein Localization Analysis and Search Tools (PLAST). First, we measure a large number of unbiased image descriptors that capture different spatial properties of protein localization patterns, and use a support vector machine (SVM) algorithm An important application of PLAST is to study changes in protein localization and function during evolution. Gene duplication is a main source of new genes Here, we describe the key components of PLAST, and show that PLAST is, on average, more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. We found that PLAST can 1) identify similarly-localized proteins that participate in closely-related biological processes but do not necessary form stable complexes with each other or localize at the same organelles, and 2) reveal an association between spatial and functional divergences of proteins during evolution.Supplementary Fig. S1). Other segmentation algorithms based on fluorescent whole-cell stains PLAST can be generally applied to microscopy images of proteins labeled with fluorescent protein fusion tags, fluorophore-conjugated antibodies, or other labeling techniques. PLAST has five major steps: cell segmentation, feature extraction, protein localization profile (“P-profile”) construction, P-profile dissimilarity computation, and compartment mapping Fig. 1A.Local structure features). We also extract standard intensity, texture, and moment features Second, we extract a large number of quantitative image descriptors (or “features”) from the segmented cells. We have designed several new image features based on subcellular regions and local structures of protein distribution patterns . We used the unit vector orthogonal to the hyperplane as a quantitative profile representing the spatial localization signature of the protein. As an alternative, we also construct profiles by averaging each feature value across all cells. We denote these two profile types as “P-profileSVM” and “P-profilemean”, respectively.Third, we have generalized a SVM-based drug profiling algorithm pd) between the P-profiles of all proteins into a database. Lower pd values correspond to more similar subcellular localization patterns. To compare the localization patterns of a protein to a group of proteins, such as those that constitute a subcellular organelle or compartment, we compute the mean of all the pairwise pd values between the protein and each of the group members (P-profile dissimilarity score). If the protein is part of the group, the pd to itself will be excluded from the calculation.Fourth, we measure and store the pairwise dissimilarity scores . The resulting z-scores  as a model system due to the availability of a genome-wide GFP-fusion-protein image dataset . After quality control , we obtained P-profiles for 4066 open reading frames (ORFs)  Fig. 1C.Supplementary Fig. S3) and determined the enrichments of UCSF categories and GO biological processes in each of the 20 identified clusters. We found that PLAST divides most “cytoplasmic”, “nuclear”, or “mitochondrial” proteins into ∼4 to 6 clusters that are enriched in proteins involved in different biological processes . Thus, PLAST can reveal protein subcellular localization patterns that are not obvious under manual inspections.To determine the correspondence between P-profiles and compartment categories manually assigned by UCSF via visual inspections (“UCSF categories”), we clustered P-profiles using an affinity propagation algorithm Supplementary Fig. S2C). These improvements were likely due to our better cell segmentation algorithm that does not rely on the fluorescence intensities of the GFP-tagged proteins , and our SVM-based profiling algorithm that reduces the contributions of non-informative features.Previously, two other analysis frameworks were also developed to quantify and classify the same image dataset according to UCSF categories pd values than Y2H interactors  and yeast two-hybrid (Y2H) screening Datasets). Identifying subunits of complexes that localized at multiple compartments, such as ribosome and proteasome pd to the query proteins (Subunit search based on UCSF or SGD GOSlim annotations). These two annotations are not independent from each other, because a large fraction of SGD GoSlim annotations are based on UCSF annotations. We performed paired t-tests between the maximum F1 scores obtained from different profiling/annotation methods for all the protein complexes . Overall, we found that predictions based on P-profilesSVM have significantly higher maximum F1 scores than other methods  and ubiquitin activating enzyme (Uba1) were previously found to be physically associated with proteasome To further test the ability of PLAST in searching subunits of stable protein complexes, we used a comprehensive catalog of 197 protein complexes with different subunit numbers and subcellular localization patterns . For each protein, we systematically queried the P-profile database for the pd scores between the protein and all the compartments. We assumed that the probability distribution for these pd scores could be modeled by a mixture of Gaussian distributions, in which the component distribution with the highest mean pd value was the distribution for non-specifically localized compartments. We estimated the mean and standard deviation of this “null” distribution and standardized all the pd scores based on the estimated values , indicating most proteins are localized only at small subsets of components in our catalog. The resulting z-scores , and allow us to assign compartments to different proteins using the same Bonferroni-adjusted P-value thresholds. This standardization step does not change the relative dissimilarities of different compartments to a protein.To generate a human-interpretable localization map of the yeast proteome, we used another catalog of cellular compartments as “landmarks” of the subcellular space in a yeast cell. This catalog consists of known protein components of 23 major organelles and 50 large protein complexes ( and S7). For example, we obtained two clusters of compartments that are related to cellular bud/cytoskeleton and endomembrane system, respectively. These two clusters are also closest to each other, and form a larger supercluster in the dendrogram . For example, several translation factors  and synthases (glycyl-tRNA and glutamyl-tRNA synthases) were assigned to cytosolic ribosomes; and several splicing factors , topoisomerase (Top2), and transcription initiation factor (Tfc8) were assigned to RNA polymerase II (Supplementary Dataset S2). Furthermore, we also found that the non-components assigned by PLAST to cytosolic ribosome were also significantly enriched with ORFs that were experimentally found to co-purify with cytosolic ribosome Fig. S8). Importantly, some of these non-components have uncharacterized functions or not known to be functionally associated with their corresponding compartments. Therefore, our localization map provides a repertoire of potentially novel components of the biological processes or pathways performed at major subcellular compartments in the budding yeast.To perform hard assignments of compartments to proteins, we used a Bonferroni-adjusted threshold of −4, which assigns major organelles, namely cytoplasm and nucleus, to similar numbers of proteins as UCSF annotations The second general trend is that most proteins are localized at multiple compartments. To perform a fair comparison between PLAST and UCSF annotations, we used a more relaxed Bonferroni-adjusted threshold of Introduction) have been reported in yeasts and hominoids SVM, which has the best overall benchmark performance , each of which consists of ∼10–400 duplicate pairs (Kluyveromyces lactis (K. lactis) in the phylogeny 4 to T6), and all other duplicates as “young” duplicates (T1 to T3). We found that old duplicates have significantly larger intrapair pd values, lower shared compartment ratios, and lower average protein expression levels than young duplicates , and suggest that localization divergence might be more frequently due to sublocalization. One of the limitations of our analysis is that duplicates with different divergence times may undergo different evolutionary paths. Furthermore, we could only obtain small numbers (∼10) of duplicates for some of the divergence times, and thus could not perform meaningful enrichment analyses of protein functions on them.In the first approach, we studied duplicates with different divergence times. We assumed that the localization patterns of more recently duplicated gene pairs are more similar to their ancestors. We used a phylogeny of orthologous gene groups estimated for seventeen Ascomycota fungi te pairs Fig. 5A.pd values are more similar to their ancestors. Sixty eight percent of these WGD duplicates were detected in T3 of the phylogeny that we used in the previous section. Based on UCSF annotations, a previous study estimated that ∼24–37% of these WGD duplicates now have diverged localizations and occupy significantly higher numbers of compartments than their ancestors In the second approach, we addressed these limitations by studying the large number (∼544) of duplicates generated from WGD pd for all 326 duplicate pairs that have P-profiles (Supplementary Dataset S3), and also the empirical distribution of pd for 10,000 randomly selected non-duplicate pairs from the proteome  pairs, otherwise as “dissimilarly localized” (DL) pairs. We estimated that ∼42–84% of duplicates are now DL pairs (based on 1st- and 20th-percentile thresholds), which are higher than the ∼24–37% estimated based on UCSF annotations S. cerevisiaeTo quantify the spatial divergence of WGD duplicates, we determined the proteome Fig. 6A.Next, we studied if localization and functional divergences are statistically associated events. We considered two different types of functional annotations, namely GO molecular functions and biological processes pd is significantly correlated to shared compartment ratio, but not total number of occupied compartments  that has two Homo sapiens orthologs  and three S. cerevisiae orthologs  H. sapiens IDH1 and IDH2 are specifically localized to cytoplasm and peroxisome or mitochondrion, respectively; and the S. cerevisiae IDP1, IDP2, and IDP3 are specifically localized each of these three compartments. Third, several core glycolytic enzymes were also recently found to be targeted to both cytoplasm and peroxisome via alternative splicing and stop codon read-through of the same genes in fungi How can an ancestral gene, or a gene in general, produce proteins localized in multiple compartments? Several molecular mechanisms are possible. First, the budding yeast S1 genes Fig. 6E.PLAST has several limitations. First, it only works for proteins that can be fluorescently labeled without affecting their localizations. Second, it does not work well for proteins with very low abundance levels. Third, it has poor detection of yeast two-hybrid interactions, likely due to the more transient nature of these interactions http://plast.bii.a-star.edu.sg) that allows researchers to query protein localization maps created using PLAST. These resources enable systematic and quantitative analyses of protein localization-function relationships, and will help researchers to elucidate protein functions and the causes of their changes.As more genome-wide protein localization image datasets become available for different organisms or species S. cerevisiae GFP image dataset and annotations generated by UCSF Supplementary Text S1.We used the Supplementary Fig. S9).For the UCSF yeast GFP dataset, we have developed an image-processing pipeline to estimate cell boundaries based on DIC illumination and DAPI staining. Our method consists of four major steps: background subtraction, image alignment, cell segmentation, and cell combination . In the first step, a DIC image was segmented to obtain a binary cell mask  in the UCSF yeast GFP dataset. These offsets might be due to misalignment of image acquisition instruments. To reduce these artifacts, we have developed a three-step image alignment procedure based on cross-correlation . First, to suppress high-frequency noise in the images, we smoothed the DIC and DAPI images by convolving them with a 9×9 2D Gaussian lowpass filter . We set the algorithm's window size . We selected 20 images with sparsely distributed yeast cells and 20 images with densely distributed yeast cells. These two image sets represent easy and difficult conditions for automated cell segmentation. We expect the segmentation errors of the dense images to be lower than the sparse images. To obtain a ground truth for comparing segmentation errors, we manually segmented each images using a pen-based digitizer (Toshiba M200 laptop). The cell masks for the graphical-model-based algorithm were obtained using the Matlab source code downloaded from http://murphylab.web.cmu.edu/software/2007_Bioinformatics_Yeast/ without any modification.We compared cell masks obtained from our segmentation algorithm, a previous graphical-model-based segmentation algorithm We used two different segmentation performance criteria: the boundary and Rand error indices i-th and j-th connected pixels within the respective masks. Furthermore, we denote N is the total number of pixels in the original image.We also used the Rand error index Cell Segmentation above), we defined three additional subcellular regions, namely cytoplasmic (non-DNA), cytoplasmic boundary, and inner cytoplasmic regions . The cytoplasmic region was computed by subtracting the nuclear regions from the cellular regions. The cytoplasmic boundary region was computed from the cytoplasmic region using a binary erosion operator with a circular structuring element (radius = 3 pixels). Finally, the inner cytoplasmic region was computed by subtracting the cytoplasmic boundary region from the cytoplasmic region.Based on the cellular and nuclear (DNA) regions identified from our segmentation algorithm (see Identification of subcellular regions above) of individual yeast cells to their sums. So, the total GFP intensity of each normalized cell became one. All features were extracted only after this normalization step.To eliminate the effects of protein-to-protein variations in expression levels and only consider protein spatial localization patterns, we divided the GFP intensity values of all the pixels within the detected cellular regions  to identify local structures from the GFP images. A smaller window size (w) will extract finer local structures, while a larger window size will extract coarser global structures in the protein localization patterns. For the UCSF yeast GFP dataset, we set w = ∼33 . Therefore we only used 16 different window sizes . For the UCSF yeast GFP dataset, this amounted to 90 additional features per cell. Therefore, the total number of local structure features was 96+90 = 186.In the third step, we computed the stepwise ratios (Datasets). The final number of yeast strains that we used in our study was 4066.To automatically remove badly or wrongly segmented cells, we used two quality control criteria, namely cell area and solidity (cell area/convex hull area). First, we removed segmented cells that have cell area <300 pixels or >2000 pixels. Then, we removed segmented cells that have solidity value >0.2. There were 33 strains with more than one image set, and for each strain we used only one image set. We removed three yeast strains  that have abnormal image size (58×58 pixels). After feature extraction, there were 15 cells with NaN values, and we replaced these values with the medians across all cells in each of the yeast strains. We obtained ∼20 segmented cells per yeast strain on average. Only one of the yeast strains, namely YHR011W, has less than two cells, and thus the whole strain was removed from further analysis. We also removed 55 strains that have mislocalized proteins due to the GFP tags SVM for yeast strains that have been labeled for a protein, we used a SVM Supplementary Fig. S2A), iy is the class label of the i-th training sample, W that can separate yeast cells labeled for a protein, and a fixed set of reference cells. Then, we divided W with the sum of all its elements so that it becomes a unit vector. A SVM algorithm with linear kernel function has two main parameters, the cost parameter for constraint violation (cost), and the tolerance of termination criterion (epsilon). We set epsilon to its default value, 0.01. For each yeast strain, a grid search on the values of To construct P-profilesSupervised classification of UCSF categories) to classify 2654 ORFs with single UCSF category assignments. The evaluation process was repeated five times with different random selections of reference cell sets, and we chose the set of P-profiles with the highest average accuracy in classifying the 2654 ORFs to represent protein subcellular localization .To generate the reference cell set, we first randomly sampled 9 sets of reference cells  from yeast strains that have been assigned to four of the largest UCSF categories, namely “cytoplasm”, “nucleus”, “mitochondrion”, and “endoplasmic reticulum”. Then, based on each of the reference sets, we constructed a set of P-profiles (SVM) for all the yeast strains, and trained a multi-class SVM classifier  and a group of proteins (G), we took the mean of all the pairwise dissimilarity scores:We computed the dissimilarity score between two P-profiles Supplementary Fig. S3A). We used the affinity propagation clustering algorithm implemented in “apcluster” v1.3.2 package under the R environment.Affinity propagation is an algorithm that identifies representative data points, called “exemplars”, and forms clusters of data points around these exemplars hclust function under the R environment. The P-profile dissimilarity scores and Ward agglomeration method were used.We used the standard agglomerative hierarchical clustering algorithm implemented in the The definitions of precision, recall, and F1-scores are:SVM, P-profilemean, UCSF and SGD Go Slim) were Bonferroni adjusted.For each profiling/annotation method and each protein complex, we measured the maximum F1-score of the method across all the tested query-protein sizes for the protein complex. To compare the performances of two different methods, we performed a paired t-test between the maximum F-1 scores of the methods obtained from all the protein complexes. The p-values for all pair-wise comparisons of the four methods . The original dataset has 25 categories. We removed the “cellular_component”, “microtubule organizing center”, “extracellular region”, “unknown”, and “other” categories, and included the “lipid particle”, “endosome”, and “nuclear envelope” categories. The list of components for large protein complexes is based on the manually curated SGD GO protein complex dataset (see Datasets). The original dataset has 416 protein complexes. We only considered protein complexes with at least 15 subunits, and removed the “microtubule organizing center” complex because it overlaps with the “spindle pole body” complex.To construct a localization map, we used a catalog of subcellular compartments that consists of known protein components of 23 major organelles and 50 large protein complexes. The list of protein components for major organelles is based on the manually curated SGD GoSlim cellular component dataset . Then, we removed outliers by trimming the upper and lower fifth percentiles of the obtained pd scores, and estimated the probability distribution of the retained scores using the kernel density estimation method, density, implemented in the R environment. We identified the local maximum of the distribution with the largest pd value, and used the pd value as the mean (nullμ) of the distribution of non-specifically localized compartments. We assumed the non-specific distribution is Gaussian, and estimated its standard deviation (nullσ) from all pd scores larger than nullμ. Then, we standardized all the pd scores using For each ORF, we systematically queried the P-profile database for the S. cerevisiae duplicates that could be traced to their originating ancestors without any loss events. The approximate divergence ages of the phylogenetic tree are based on past estimations We obtained a phylogeny of orthologous gene groups estimated for seventeen Ascomycota fungi To perform a non-parametric test for the statistical significance of the observed difference in means or medians between two sets of values, we randomly permutated the labels of the datasets for 10,000 times. For each permutation, we measured the mean or median difference between the two sets according to the permutated labels. The P-value was estimated to be the fraction of permutations with absolute mean or median difference larger than the observed absolute mean or median difference.http://www.cellXpress.org) to perform all the image processing and feature extraction steps http://plast.bii.a-star.edu.sg.We have developed “cellXpress” v1.10 Click here for additional data file.Dataset S2List of proteins assigned with select subcellular compartments.(CSV)Click here for additional data file.Dataset S3P-profile dissimilarity scores and numbers of subcellular compartments localized by WGD duplicate genes.(CSV)Click here for additional data file.Figure S1A DIC-based segmentation algorithm for budding yeast cells. Example images from the UCSF dataset A) sparse or (B) dense populations of budding yeast cells (white lines = detected cell boundaries). The overall boundary and Rand error indices are shown in the lower panel for these two conditions .(TIF)Click here for additional data file.Figure S2Construction of profile vectors from single-cell feature measurements. (A) Schematic showing how P-profiles are constructed to represent cells labeled for a protein. In the m-dimensional feature space, each cell is represented by a vector (red circles). “P-profilemean” is the mean or centroid (white arrow in left panel) of all feature vectors for the cells labeled for the protein. “P-profileSVM” is a unit vector (white arrow in right panel) orthogonal to a hyperplane that optimally divides the cells labeled for the protein (red circles) and a fixed set of reference cells (blue circles). The hyperplane is determined using a linear support vector machine (SVM). (B) Classification accuracies of all the randomly selected sets of reference cells. Please refer to SVM constructionP-profile for the procedures to generate these reference sets. The maximum and final selected reference cell set is circled in red. (C) Comparisons of P-profilesSVM, P-profilesmean, and quantiative features generated by two previous analysis frameworks (“Chen07” and “Huh09”) (TIF)Click here for additional data file.Figure S3Clustering of P-profiles using an affinity propagation algorithm. (A) Number of clusters (or exemplars) selected by an affinity propagation algorithm as a function of preference value. We chose to divide all the P-profiles into 20 clusters, before the number of clusters started to increase dramatically. Each cluster was named according to its most enriched UCSF category . (B) Microscopy images from the UCSF dataset (TIF)Click here for additional data file.Figure S4Automated clustering of P-profiles reveals novel localization patterns. Heatmaps showing P-values for the enrichments of (A) UCSF categories or (B) selected significantly enriched GO biological process categories (P<0.001) in the 20 identified clusters ( and S3) as determined by one-sided hypergeometric tests. Each cluster is labeled according to its most enriched UCSF category. The total number of proteins in each UCSF or GO biological process category is listed in parenthesis after the category name.(TIF)Click here for additional data file.Figure S5Normalized differences between F1-scores are weakly correlated to protein complex sizes.(TIF)Click here for additional data file.Figure S6Examples of P-profile dissimilarity score distributions for non-specifically localized compartments. .(TIF)Click here for additional data file.Figure S7Subcellular localization map for the Saccharomyces cerevisiae proteome. A subcellular localization map showing the standardized P-profile dissimilarity scores (pd) between 4066 ORFs (x-axis) and a comprehensive catalog of 73 major subcellular compartments (y-axis) in a yeast cell. The compartments (rows) were ordered using a hierarchical clustering algorithm with cosine dissimilarity scores, and labeled with color codes according to their known functions or localizations (“common” compartments = compartments assigned to large numbers of ORFs.)(TIF)Click here for additional data file.Figure S8ORFs assigned with cytosolic ribosome are enriched with ORFs co-purified with cytosolic ribosome. We obtained the list of ORFs that co-purified with cytosolic ribosome from (TIF)Click here for additional data file.Figure S9An image-processing pipeline to segment budding yeast cells.(TIF)Click here for additional data file.Figure S10Automated alignment of DIC and fluorescence images based on cross-correlation.(TIF)Click here for additional data file.Figure S11Example images showing the five subcellular regions identified using PLAST.(TIF)Click here for additional data file.Figure S12Example of binary local structures detected using different window sizes.(TIF)Click here for additional data file.Text S1Sources of the datasets that we used in our study.(DOCX)Click here for additional data file.


Computational protein short linear motif discovery can use protein interaction information to search for motifs among proteins which share a common interactor. Cytoscape provides a visual interface for protein networks but there is no streamlined way to rapidly visualize motifs in a network of proteins, or to integrate computational discovery with such visualizations.We present SLiMScape, a Cytoscape plugin, which enables both de novo motif discovery and searches for instances of known motifs. Data is presented using Cytoscape’s visualization features thus providing an intuitive interface for interpreting results. The distribution of discovered or user-defined motifs may be selectively displayed and the distribution of protein domains may be viewed simultaneously. To facilitate this SLiMScape automatically retrieves domains for each protein.SLiMScape provides a platform for performing short linear motif analyses of protein interaction networks by integrating motif discovery and search tools in a network visualization environment. This significantly aids in the discovery of novel short linear motifs and in visualizing the distribution of known motifs. High throughput experiments have greatly increased the number of known protein-protein interactions in the human interactome. However, most of these experiments do not indicate the mechanism of interaction. Many of these interactions occur between large globular domains but an estimated 15 - 40% are mediated by functional microdomains [Several predictive tools using many different methods have been developed to identify SLiMs -8. We haSLiMScape has been tested on Cytoscape version 2.8.3 and on all major platforms . It is published under the GNU Lesser General Public License version 3.0. SLiMscape depends on several websites such as the SMART  domain dSLiMscape consists of two primary analysis tools. The first, SLiMFinder , is capaSLiMScape uses SLiMFinder to locate statistically over-represented sequences within sets of proteins, defined by selecting subsets of a protein interaction network. A variety of node selection methods are provided to automatically select protein sets and different types of motif can be targeted depending on this selection process. For example, SLiM-mediated interactions can be discovered using the “Batch Interactions” selection method. This will iteratively create protein sets containing each protein (hub) and its interactors (spokes). Figure The “Batch Domains” selection method creates sets of proteins which contain a particular domain, while “Batch Domains Interactions” creates sets of proteins which interact with a particular domain. This is useful when searching for self interacting proteins and other sequences which bind to a domain. Protein sets can also be created manually, allowing for many custom strategies for finding motifs.New instances of a motif can be located using regular expression matching. This is especially useful when trying to find new instances of a SLiM found using SLiMFinder and can also be used to search for a user defined SLiM. Disorder and conservation masking are used to reduce the number of false positives. This functionality is implemented using SLiMSearch.SLiMScape presents an interactive view of results within a protein interaction network. SLiMs are first presented in a table and when activated all instances of that SLiM are highlighted, thus displaying the distribution of the SLiM. Additionally, domain annotations are provided to indicate possible SLiM-domain interactions and aid in the interpretation of results.SLiMScape integrates SLiM discovery tools with Cytoscape, a visualization program popular in the field of bioinformatics. This provides a platform for SLiM discovery which allows discovery of novel and known motifs within a protein interaction network. This increases the speed at which SLiM analyses can be performed and aids in the interpretation of results.Project name: SLiMScape• Project home page:http://sourceforge.net/projects/slimscape/• Operating system(s): Platform independent• Programming language: Java, Python• Other requirements: Cytoscape 2.8.3, Java 1.6 or higher, python 2.7 , Slimsuite , Blast , IUPred , Muscle  and Clustal .• License: GNU LGPLv3• Any restrictions to use by non-academics: see license• The authors declare that they have no competing interests.KOB proposed the idea in collaboration with NH and DS. KOB developed the application. NH and DS provided extensive testing of the application and also provided feedback. DS supervised the project. All authors wrote and approved the final manuscript.


In the phospho-PPI, phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells, the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level. Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other.Following recent advances in high-throughput mass spectrometry (MS)–based proteomics, the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role of phosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, we report unexpected, large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ∼6,000 yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein–protein interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance, proteome disorders, literature-derived signaling reactomes, and  in vivo protein pathways are supported by a combination of protein modification and molecular dynamics.To date, high-throughput proteome technologies have revealed that hundreds to thousands of proteins in each of many organisms are phosphorylated under the appropriate environmental conditions. A critical role of phosphorylation is control of protein signaling. However, only a fraction of the identified phosphoproteins participate in currently known protein signaling pathways, and the biological relevance of the remainder is unclear. This has raised the question of whether phosphorylation has other major roles. In this study, we identified new phosphoproteins in budding yeast by mass spectrometry and unified these new data with publicly available phosphoprotein data. We then performed an integrative data-mining of large-scale yeast phosphoproteins and protein–protein interactions (complex formation) by an exhaustive analysis that incorporated yeast protein information from several other sources. The phosphoproteome data integration surprisingly showed that nearly 60% of yeast genes encode phosphoproteins, and the subsequent data-mining analysis derived two models interpreting the mutual intracellular effects of large-scale protein phosphorylation and binding interaction. Biological interpretations of both large-scale intracellular phosphorylation and the topology of protein interaction networks are highly relevant to modern biology. This study sheds light on how  We unified these new phosphoproteome data with the publicly available phosphoproteome datasets of Holt et al. S. cerevisiae genomic information On the basis of liquid chromatography (LC)-MS analysis, we initially identified 1,993 The unified phosphoproteome data were superimposed onto the PPI network to generate a “phospho-PPI” network. PPI data were obtained via DIP (Database of Interacting Proteins) To explore specific characteristics of the phospho-PPI network, the number counts of interacting partners of phosphoproteins and nonphosphoproteins were analyzed . We found that, in general, phosphoproteins had more interacting partners than nonphosphoproteins. In each phospho-PPI network of the “ALL” and “Y2H” categories with enough protein nodes for the subsequent statistical analysis, the cumulative percentage distributions of node degrees (or the number count of interacting partners) of phosphoproteins and nonphosphoproteins were markedly different . For exaNode degree in PPI networks has an exponential relationship with protein expression level S. cerevisiae protein registered in UniProt, we calculated the probability of harboring intrinsic disordered regions (see P = 0.0043). Similar results were observed in the “Y2H” dataset , there was a higher node degree level for phosphoproteins than for nonphosphoproteins , then the signaling proteins may have interactions additional to the cohesive protein binding interactions in the PPI data. Indeed, some enzyme–protein substrate interactions are surprisingly stable and can be captured in protein interaction assays In the phospho-PPI network, phosphoproteins had a greater tendency than nonphosphoproteins to interact with proteins harboring phosphoprotein binding domains (PPBDs). Out of 10 known PPBDs—14-3-3, BRCT, C2, FHA, MH2, PBD, PTB, SH2, WD-40, and WW We further analyzed the phosphorylation patterns of protein pairs forming pair-wise interactions in the phospho-PPI network, and we found that both interacting proteins in each pair tended to be phosphorylated. For every category of phospho-PPI network, three types of pair-wise interactions were counted, whereby “Both,” “Either,” or “Neither” of two interacting proteins were phosphorylated. The “Both” and “Neither” types of protein interactions were significantly more common in the real phospho-PPI network than was expected from negative controls produced by RER, whereas the “Either” types of protein interactions were significantly less common than expected . NotablyPPI data contain homodimer and heterodimer information that can be captured by experimental assays such as two-hybrid assays in vitro kinase–substrate assay in vitro assay and 12 from the literature-based collection had significantly higher IKM formability than those expected from both NLS and RER (P<0.05)  . Similar(P<0.05) .in vivo signaling. This may explain why the collection harbored more kinases with high IKM formabilities (12 out of 65) than the in vitro kinase–substrate relationship data (three out of 85).Accordingly, we suggest that, when a protein complex and kinase are in close proximity within the intracellular environment, there is a high chance of simultaneous phosphorylation of member proteins participating in the complex. This is consistent with the subcellular co-localization of signaling networks recently revealed through the systematic prediction of signaling networks by using phosphoproteome data with an integrated protein network information derived from curated pathway databases, co-occurring terms in abstracts, physical protein interaction assays, mRNA expression profiles, and the genomic context It is plausible that, in living cells, the diversity of protein interactomes  is essentially influenced by the large number of phosphorylation events; many reversible phosphorylations might control condition-specific protein binding interactions related to different subcellular processes and molecular machines. On the other hand, protein phosphorylation patterns also seem to depend largely on intracellular protein interaction diversity.It is possible that many of the proteins defined as nonphosphoproteins in this study can actually be phosphorylated under appropriate cellular conditions. Even where this is true, however, the set we defined here as phosphoproteins should be enriched with proteins that are frequently phosphorylated under normal or many different cellular conditions, because the frequently phosphorylated proteins have a higher chance of being identified as phosphoproteins than do the rarely phosphorylated proteins. Accordingly, the features and models discussed in this study should reflect the overall characteristics of phosphoproteins and nonphosphoproteins among a number of different cellular conditions. This is supported by the finding that proteins that had two or more phosphosites physically interacted with more proteins than did those with only a single phosphosite  mode; AGC values of 5.00e+05 and 1.00e+04 were set for full MS and MS/MS, respectively. The normalized collision energy was set at 35.0. A lock mass function was used for the LTQ-Orbitrap to obtain constant mass accuracy during gradient analysis.An LTQ-Orbitrap XL  coupled with a Dionex Ultimate 3000  and an HTC-PAL autosampler  was used for nanoLC-MS/MS analyses. An analytical column needle with a “stone-arch” frit http://pepbase.iab.keio.ac.jp).Both Mass Navigator v1.2  and Mascot Distiller v2.2.1.0  were used to create peak lists based on the recorded fragmentation spectra. Peptides and proteins were identified by automated database searching using Mascot Server v2.2 (Matrix Science) against UniProt/SwissProt v56.0 with a precursor mass tolerance of 3 ppm, a fragment ion mass tolerance of 0.8 Da, and strict trypsin specificity, allowing for up to two missed cleavages. Carbamidomethylation of cysteine was set as a fixed modification, and oxidation of methionines and phosphorylation of serine, threonine, and tyrosine were allowed as variable modifications. Phosphopeptide identification and phosphorylated site determination were performed in accordance with a procedure reported previously Saccharomyces cerevisiae phosphoproteome data were obtained from Dataset S1 of Holt et al. http://www.uniprot.org/) scope: “PHOSPHORYLATION [LARGE SCALE ANALYSIS] AT”. Some phosphoproteins registered in UniProt had multiple synonyms of UniProt accession.For integrative analyses and comparisons of yeast multi-omics data, all identities of proteins and genes obtained from different data sources were standardized to UniProt accessions. If objects  in a data source did not have UniProt accessions, the objects were standardized to their corresponding UniProt accessions according to the cross-reference list prepared from UniProtKB/Swiss-Prot protein entries obtained from UniProt (release 15.14). In cases when an object corresponded to multiple synonyms of UniProt accessions, all accessions were used to identify its corresponding objects in other data sources.The phosphoproteome data newly identified in this study and the former phosphoproteome datasets obtained from Holt et al. and UniProt were unified according to their UniProt accessions. Positions of phosphosites and their amino acid residues in the unified phosphoproteome data were double-checked by using the proteome sequences obtained from UniProt (release 15.14).Saccharomyces Genome Database; http://yeastgenome.org) From SGD (S. cerevisiae PPI network was obtained as XML files (Scere20081014) from DIP  The Unified phosphoproteome data were mapped onto every category of PPI data prepared from DIP according to their UniProt accessions, and a phospho-PPI network was generated. Throughout this study, proteins that did not correspond to phosphoproteome data were termed “nonphosphoproteins.”To prepare negative controls for PPI and phospho-PPI networks, two different processes  was predicted by the POODLE-W program, which uses the support vector machine–based learning of amino acid sequences of structurally confirmed disordered proteins The protein disorder level of every Saccharomyces cerevisiae gene annotations belonging to “molecular function,” “biological process,” or “cellular component” of GO Slim, a higher level view of S. cerevisiae Gene Ontology (GO), were downloaded via the SGD ftp site.S. cerevisiae proteins, each of which has at least one of 10 known phosphoprotein binding domains (PPBDs), namely 14-3-3, BRCT, C2, FHA, MH2, PBD, PTB, SH2, WD-40, and WW Information on To evaluate the tendencies of phosphoproteins and nonphosphoproteins to interact with proteins that had PPBDs, the normalized probabilities of such interactions were defined. For each protein, the number of interacting protein partners that had PPBDs was divided by the number of all interacting partners.in vitro substrates recognized by most yeast protein kinases that were measured with the use of proteome chip technology .Cumulative probability distributions of node degrees of nonphosphoproteins, phosphoproteins with a single phosphosite, and phosphoproteins with two or more phosphosites in the phospho-PPI data of the “ALL” category. For each dataset, bars show proportions of proteins with more than the (0.90 MB TIF)Click here for additional data file.Figure S8Number counts of interacting protein pairs of each phosphorylation pattern shown in the phospho-PPI networks of the “IMM” and “TAP” categories. See legend to (0.60 MB TIF)Click here for additional data file.Figure S9P values estimated by comparison with negative controls. See legend to Number counts of IKMs formed in the “filtered” PPI network by each of the kinases shown in (0.90 MB TIF)Click here for additional data file.Table S1Phosphoproteome data used in this study.(1.85 MB XLS)Click here for additional data file.Table S2S. cerevisiae genes and phosphorylation annotation.List of known and predicted (0.47 MB XLS)Click here for additional data file.Table S3Phosphoproteome data used in this study.(0.09 MB XLS)Click here for additional data file.


Automated function prediction has played a central role in determining the biological functions of bacterial proteins. Typically, protein function annotation relies on homology, and function is inferred from other proteins with similar sequences. This approach has become popular in bacterial genomics because it is one of the few methods that is practical for large datasets and because it does not require additional functional genomics experiments. However, the existing solutions produce erroneous predictions in many cases, especially when query sequences have low levels of identity with the annotated source protein. This problem has created a pressing need for improvements in homology-based annotation.Lactobacillus crispatus strain ST1.We present an automated method for the functional annotation of bacterial protein sequences. Based on sequence similarity searches, BLANNOTATOR accurately annotates query sequences with one-line summary descriptions of protein function. It groups sequences identified by BLAST into subsets according to their annotation and bases its prediction on a set of sequences with consistent functional information. We show the results of BLANNOTATOR's performance in sets of bacterial proteins with known functions. We simulated the annotation process for 3090 SWISS-PROT proteins using a database in its state preceding the functional characterisation of the query protein. For this dataset, our method outperformed the five others that we tested, and the improved performance was maintained even in the absence of highly related sequence hits. We further demonstrate the value of our tool by analysing the putative proteome of http://ekhidna.biocenter.helsinki.fi/poxo/blannotator/.BLANNOTATOR is an accurate method for bacterial protein function prediction. It is practical for genome-scale data and does not require pre-existing sequence clustering; thus, this method suits the needs of bacterial genome and metagenome researchers. The method and a web-server are available at  The rapid progress in sequencing technology has enabled the generation of unimaginable amounts of bacterial genomic data. The genome sequences of thousands of bacteria have been determined, and many more are in progress . In addiMany automated protein function prediction methods describe the biological role of a gene product in terms of single-line description of protein function (DE) or gene ontology (GO). These two widely used annotation schemes are radically different. The first shows a protein function in a human-readable free text format , whereasi.e., sequences related by speciation, and less weight is given to paralogs that have evolved by gene duplication and may have acquired a new function  [The functionality of BLANNOTATOR was also tested by assigning annotations to the protein-coding sequences of N692037] . In thisExamples of selected protein-coding sequences for which our method assigned the human curator-supported function are given below.• Multiple and diverse candidate annotations. LCRIS_00064 is an example of a sequence that only BLANNOTATOR managed to characterise similarly to InterProScan  [The protein-coding sequences of the N692037]  were chaN692037] ,3. DubioN692037] .GO: Gene Ontology; DE: single-line description of protein function; COG: cluster of orthologous sequences; HAMAP: High-quality Automated and Manual Annotation of microbial Proteomes; BLAST: Basic Local Alignment Search Tool; KEGG: Kyoto Encyclopedia of Genes and Genomes; KAAS: KEGG Automatic Annotation Server; RAST: Rapid Annotation using Subsystems Technology; GOA: Gene Ontology annotation database; LD: Levenshtein distance, mLD: modified Levenshtein distance; CRISPR: clustered regularly interspaced short palindromic repeats.The authors declare that they have no competing interests.L. crispatus strain ST1 proteome annotations and participated in the drafting of the manuscript. LH supervised and financed the study and participated in the drafting of the manuscript. All authors approved the final manuscript. We thank the Bioinformatics group for helpful discussions of this project.MK invented, designed and wrote the algorithm, collected the data, performed the data analysis and drafted the manuscript. TO participated in the data analysis, performed the manual analysis of the This file contains the supporting tables and figures regarding the analysis of the SWISS-PROT data.Click here for file


Human protein complexes play crucial roles in various biological processes as the functional module. However, the expression features of human protein complexes at the transcriptome cascade are poorly understood. Here, we used the RNA-Seq data from 16 disparate tissues and four types of human cancers to explore the characteristics and dynamics of human protein complexes. We observed that many individual components of human protein complexes can be generated by multiple distinct transcripts. Similar with yeast, the human protein complex constituents are inclined to co-express in diverse tissues. The dominant isoform of the genes involved in protein complexes tend to encode the complex constituents in each tissue. Our results indicate that the protein complex dynamics not only correlate with the presence or absence of complexes, but may also be related to the major isoform switching for complex subunits. Between any two cancers of breast, colon, lung and prostate, we found that only a few of the differentially expressed transcripts associated with complexes were identical, but 5–10 times more protein complexes involved in differentially expressed transcripts were common. Collectively, our study reveals novel properties and dynamics of human protein complexes at the transcriptome cascade in diverse normal tissues and different cancers. Proteins usually exert their functions through cooperative interaction with other proteins and the protein-protein interactions are crucial for myriad biological processes Proteins are translated from their corresponding transcripts which are produced by associated genes. A number of prior studies advocated that gene/mRNA expression level could correlate well with protein abundance based on the estimation from RNA-Seq and mass spectrometry (MS) data Nevertheless, few studies have investigated the dynamic expression profiles of human protein complexes at the transcriptome cascade. Although Bossi and Lehner have inquired the human protein interactions using the microarray expression data http://mips.helmholtz-muenchen.de/genre/proj/corum/. The redundant protein complexes were removed and complexes that contain uncertain subunits (127 complexes) were also excluded. We downloaded the human transcriptome set from Ensembl database http://www.uniprot.org/). We removed the alternative haplotype/supercontig entries from the Ensembl human transcriptome. Then we mapped the human protein complex components to the Ensembl genes and transcripts based on the Uniprot ID mapping annotation. The protein complexes that all of their constituents could be found associated Ensembl genes and transcripts were kept and those ones that do not meet the criteria were eliminated  database www.illumina.com; ArrayExpress ID: E-MTAB-513) to explore the expression profile of human genes and transcripts. We also used the transcriptome sequencing data from four human cancers of breast We used the high throughput transcriptome sequencing data of 16 different human tissues from Illumina Human Body Map (HBM) 2.0 project  To carry out differential expression for the four human cancers of breast, colon, lung and prostate, we first used the R script (readmmseq.R) in the MMSEQ package to obtain the normalized (by their median deviation from the mean) aligned read counts for each Ensembl transcript. Then, we conducted the differential expression analysis of isoforms between cases and controls for four cancers using NOISeq To carry out this study, we first mapped the experimentally validated human protein complexes obtained from CORUM database To study the expression profiles of human protein complexes at the transcriptome cascade, we collected the RNA-Seq data from 16 different human normal tissues (Illumina Human Body Map 2.0 project) and 4 groups of cases and controls for the human cancers of breast To compare the differences of inquiring proteins between at the gene and isoform levels, we separately investigated the expression of those protein complexes in the 16 normal tissues using the gene and isoform expressions. If the associated transcripts of proteins expressed, these transcripts will be translated into proteins in principle. Only when all the subunits of a given protein complex expressed, can this complex have the chance to be assembled. Unless otherwise noted, we defined the possible formable protein complex (PFPC) in a certain sample as all of its complex members were detected in this sample. Using the expression of genes, average 1960 complex components and 1179 PFPCs could be detected in each sample, whereas fewer complex subunits (1871) and PFPCs (1013) were examined at the isoform level . This isTo characterize the dynamics of human protein complexes in distinct tissues, we analyzed the expression features of those protein complexes in 16 different tissues at the isoform-level resolution. We observed that many of the protein complex constituents which associated with at least two isoforms have >1 of their corresponding transcripts expressed in the same tissue. Testis had the maximum number (1113) of examined PFPCs, whereas skeletal muscle was detected the minimum number (870) of PFPCs. Each tissue was observed 83.51% of 1213 protein complexes are PFPCs on average, suggesting that the majority of these human complexes may be formed in different human tissues. Interestingly, the protein complexes that are not PFPCs mainly resulted from one of their complex members was not expressed. Moreover, 637  common PFPCs were detected across these 16 disparate human tissues. The result implies that the assembly and disassembly of human protein complexes in different tissues have dynamics and each tissue may possess their specific protein complexes. We also found that the ratio of the expressed components in all complex subunits for each tissue is higher than the ratio of PFPCs in all complexes (at least larger than 5.4% and the biggest gap is 17.6%). We noted that one main reason for this is that most of those unexpressed protein complex subunits are separately involved in multiple distinct complexes. Previous studies suggested that the protein complex constituents of yeast are to a great extent co-expressed The vast majority of those PFPCs in different tissues contain at least one complex constituent associated with the dominant isoform of expressed gene . GreaterTo investigate the expression changes related to protein complexes at the transcriptome cascade in human diseases, we further explored the expression variations of the associated transcripts of protein complexes between cases and controls for four human cancers of breast, colon, lung and prostate. In each type of these cancers, albeit the majority of the PFPCs between normal and disease samples are the same, a fraction of PFPCs are specific to normal or cancer tissues . That isWe then conducted differential expression calling for these four human cancers to further interrogate the effects of differentially expressed transcripts on human protein complexes. Using NOISeq Protein complexes are one of the functional module classes and they are crucial molecular entities in cells We found that the expression profile of human protein complexes could reveal novel insights about human diseases. In each of the four human cancers of breast, colon, lung and prostate, although the majority of PFPCs are the same between the cases and controls, a portion of PFPCs are specific to normal and cancer tissues. The absence of specific protein complexes and/or the formation of unnecessary complexes in a particular tissue may result in damages to the cells on account of the vital roles of protein complexes. In addition, because protein complexes are comprised of multiple proteins and one protein may possess several transcripts that encode this protein, therefore, albeit a few common differentially expressed transcripts that are involved in protein complexes were observed between any two of the four human cancers, much more protein complexes were found associated with differentially expressed transcripts. Each protein complex associated with a multitude of transcripts could largely increases the impacts of expression changes of their corresponding transcripts at the protein complex level. This also greatly raises the probability that two different diseases share more common properties at the protein complex level due to the expression variations of distinct transcripts would affect their common related complexes. By constructing a human disease network based on the human protein complexes, Wang et al. also found novel associations between different human diseases Currently, the number of experimentally validated human protein complexes is still small and many more human protein complexes remain to be uncovered with the improvements of corresponding identification technologies. By contrast, a great portion of yeast proteins have been found involved in the assembly of protein complexes Table S1The human protein complexes used in this study.(DOC)Click here for additional data file.Table S2Description of the RNA-Seq data used in this study.(DOC)Click here for additional data file.


Proteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard.χ2-score, GenMultiCut and FunFlow originally proposed for protein function prediction, are introduced to assign "multiplex localization" to proteins. We analyze these approaches by performing a large-scale cross validation on a Saccharomyces cerevisiae proteome compiled from BioGRID and comparing their predictions for 22 protein subcellular localizations. Furthermore, we build an ensemble classifier to associate 529 unlabeled and 137 ambiguously-annotated proteins with subcellular localizations, most of which have been verified in the previous experimental studies.We systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share at least one common subcellular localization. With the result, for the first time, four graph-based semi-supervised learning algorithms, Majority, Physical interaction of proteins has actually provided an essential clue for their co-localization. Compared to the local approaches, the global algorithms consistently achieve a superior performance. Most of the eukaryotic biological processes are carried out by the proteins in a specific compartment or organelle within the cell. Hence, the knowledge of subcellular localizations for an uncharacterized protein provides an insight into the understanding of its function, and thus a guideline for further investigations. With the advent of the high-throughput techniques, the number of newly identified proteins has been increasing explosively. However, although some experimental technologies ,2 have bRecent studies found that protein interactions in human ,11, fruiχ2-score [neighborhood counting method that relied on the local structure of the given PPI network. By contrast, GeneMultiCut (GMC) [Another drawback of the previous approaches -5,7-9 isχ2-score , belong ut (GMC)  took theut (GMC) , consideSaccharomyces cerevisiae PPI network, consisting of 3179 proteins with 12413 interactions, from BioGRID database  [The underlying rationale of our study is that physical interactions among proteins may act as an important hint for co-localization. This hypothesis has not yet been verified systematically and quantitatively except for several preliminary intuitive validations, either experimental ,10-13 oran-2011)  as a metan-2011)  databaseThe yeast PPI network contains 3179 vertices corresponding to unique proteins, and 12413 edges corresponding to the unique interactions . As a result, we can simply conclude that physical interaction is indeed an important hint for co-localization of proteins.Our study is built upon the assumption that proteins physically interacting with each other are likely to share a common subcellular localization. To verify this hypothesis systematically and quantitatively, we split the protein interaction data set into 28 groups according to different experiment systems and throughput levels (Methods). For each group, we count the fraction of interaction pairs that share at least one subcellular localization and more than two localizations, respectively. The former is used to denote the reliability of such interactions (see Methods). The results are summarized in Table χ2-score [χ2-score. In particular, MAP increased about 30% in all the "PPI-only" and "PPI-weight" cases. Consistent with previous work [We compare four graph-based semi-supervised learning algorithms (1) Majority , (2) χ2-χ2-score , 3) Genχ2-score  and (4) χ2-score  as well  Genχ2-scous work , MAP areous work .f (x) = 0 as the decision boundary. However, it is not necessarily the best choice for some localization, for example, the four localizations where the failure occurred. How to select an appropriate threshold to obtain a better performance is still a key open question in rank-based multi-label learning [χ2 − 2 algorithm with a very low AP value. We design case studies to further analyze these two unexpected phenomena in the following section.We further check the average precision (AP) and F1 micro score for each subcellular localization on both experiments. In the "PPI-only" case , 21 Partial Correct (35%), 18 Mismatch (30%) and 12 Unknown (20%). In the Correct case, for a given protein, our predictions are strictly the same as its another experimentally observed localizations [Mismatch case means that our predictions cannot be found in the experimental observation, while Unknown case denotes that a recall failure of the ensemble classifier occurred . According to their annotation situations in , the 137izations . By contCorrect, Partial Correct, Mismatch and Unknown. These types correspond to different situation of our predictions supported by annotations in UniProt and SGD database. That is, for a given protein, Correct case means that every predicted localization was previously characterized in these two database; in the Partial Correct case, at least one of but not all of the predicted localizations could be found in these two database; Mismatch case denotes that none of our predicted localizations was supported by these two database currently; in the Unknown case, the given protein has not been characterized in these two database or a failure of our ensemble classifier occurred. Similarly, we give 5 predictions for each type in Table We incorporate the second group into 529 unlabeled proteins since they are all lack of prior knowledge. The total predictions for these 606 proteins are given in Additional File There are 46 proteins annotated with more than one subcellular localization sites in the first group. To compare the performance of the ensemble classifier with the 4 basic classifiers, we summarized these predictions in Additional File Traditionally, individual proteins' physical, chemical and biological characteristics were the major features used for subcellular localization prediction. Different from this idea, we exploit another important resource, i.e., protein-protein interaction network, to address this problem. Our start point is the recent studies that observed the protein interactions in many species are related to the subcellular localization of proteins. The main contribution of this paper is the application of this concept to compare several the state-of-the-art algorithms and their uses as building block of an ensemble classifier.χ2-score, GenMultiCut and FunFlow originally proposed for function assignment, are introduced to associate "multiplex localization" to proteins. In a large-scale cross validation test on a yeast proteome complied from BioGRID database, we show that, compared to local methods, the global approaches consistently, sometimes significantly improve the predictive performance over the 22 protein subcellular localizations, except for two locations, "ER to Golgi" and "lipid particle".Firstly, we systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share a common subcellular localization. After that, for the first time, four graph-based semi-supervised learning algorithms, Major, Considering that there are both advantages and disadvantages of each method, we build an ensemble classifier to predict the subcellular localizations for 529 unlabeled and 137 "ambiguous" annotated proteins in the PPI network. Most of these predictions have been experimentally characterized in Uniprot and/or SGD database. The results further illustrate that physical interaction is indeed an important hint for co-localization of proteins.The yeast protein interaction dataset were obtained from BioGRID database  . To redui rbe the reliability of experimental source i, i.e., the fraction of interaction pairs that are from experimental source i and share at least one common subcellular localization. For an interaction between a pair of proteins u and v, we compute the reliability of that interaction usingIt is well known that the weights of the edges has a profound influence on the results,even though the networks are based on the same underlying topology . In the uv Eis the set of experimental sources in which interaction between u and v is observed, and i,uv nis the number of times which interaction between u and v is observed from experimental source i. This treats each i ras a probability and assumes independence; the product is taken over all experimental sources. We introduce two types of schemes for applying our algorithm. The first variant attempts to capture only qualitative functional links between proteins by PPI. In the second scheme, we weighted each edge by the above-mentioned procedure. In this paper, we call these variants as"PPI-only" and "PPI-weight" network, respectively.where K subcellular localizations and a protein set l proteins are labeled as {y1,..., yl} with uk y= 1 in case protein u is annotated with localization k. Our goal is to predict the labels {yl+1,..., yn} for the remaining unlabel proteins {lp+1,..., np}. The PPI network of these proteins can be represented as a graph uv wof the affinity matrix W∈ℜn × n indicates the reliability of edge between protein u and v.For a multiplex subcellular localization prediction problem, we have Here, we introduce, analyze and compare four graph-based semi-supervised learning algorithms. Although they were originally proposed for inferring protein function from PPI networks, these methods can also be applied to our problem as far as the functions are replaced with different subcellular localizations. We briefly describe the four methods in terms of our problem.It is the simplest and most straightforward algorithm that determines the subcellular localization of a protein based on the known localization of proteins lying in its immediate neighborhood. We consider all neighboring proteins and sum up the number of times each annotation occurs for each protein. As suggested by , we predσ as described in [χ2-test to determine if it is over-represented. More precisely, for a protein u, each subcellular location k is assigned a scoreFor each protein, we consider all other proteins within a radius ribed in , and thek nis the number of proteins in the σ-neighborhood of protein u that resides in the subcellular compartments k and k eis the expected number based on the overall frequency of subcellular location k within the network. Neighborhoods within radius σ = 1, 2, 3 are considered, referred to as χ2 - 1, χ2 - 2 and χ2 - 3, respectively. However, this method can not extend naturally to the case of weighted interaction graphs.where The method utilize cut-based methodology so as to maximize the number of times the same annotations are associated with neighboring proteins . Thus, iE' is the set of edges incident on two unannotated proteins, δ is a function that equals to 1 if x = y and 0 otherwise, and vh(k) denotes the number of neighbors of v previously annotated with subcellular localization k. This optimization problem, which generalizes the NP-hard problem of minimum multiway cut [where iway cut , can be iway cut . To findiway cut  applied iway cut  allows sNabieva et al.  proposedLabels are then simulated to flow from proteins with higher potential to their neighbors that have lower potentialk at time t from protein u to protein v. subsequent time step, the amount of flow is influenced by the strength of the interactions between interaction partners and satisfies the capacity constraintswhere u with subcellular localization k over d iterations is calculated as the total amount of flow that the protein receivedThe score for associating protein k is defined asWe test the performance using 5-fold cross-validation. In the "mono-localization" case, the standard evaluation criteria is the receiver operating characteristic (ROC) which plot the numbers of true positives (TPs) as a function of the number of false positives (FPs) as the scoring threshold vary. By contrast, in the "multiplex localization" scenario, we adopt the TRECVID performance metric , Averagek pand k rare the precision and recall of the subcellular localization k, respectively. And they can be calculated by using the following equationswhere ik yand ik fare the true label and predicted label, respectively.where Considering that all these methods have both advantages and disadvantages (see Methods), we build an ensemble classifier by combining the four classifiers together so as to make predictions for the 667 uncharacterized proteins. This framework can reduce the variance caused by the peculiarities of a single training dataset and hence be able to learn a more comprehensive concept than any single classifier. Figure The ensemble classifier ℂ is represented as1, ℂ2, ℂ3 and ℂ4 represent the 4 basic classifiers, Majority, χ2-score, GenMultiCut, and FunFlow respectively. The symbol ⊗ denotes the fusing operator. Thus, the process of how the ensemble classifier ℂ works can be formulatedwhere ℂuk fis the confidence score that protein u should be annotated with the k-th localization site, u annotated with subcellular localization k of the basic classifier ℂc, c = 1,..., 4, and c wis the weighting factor, which was assigned in this study with the value of the AP obtained by the basic classifier ℂc. In other words, we define the weighting factor aswhere c for subcellular localization k.where The authors declare that they have no competing interests.JQJ conceived this study, JQJ and MW processed the data and analyzed the result. The manuscript were written by JQJ, reviewed and revised by MW. All authors read and approved the final manuscript.The yeast proteome complied from the BioGRID database.Click here for fileThe subcellular localization annotations of 3165 proteins in the PPI network collected by the Yeast Gtp Fusion Localization database.Click here for fileThe subnetwork consists of 72 proteins and 204 interactions.Click here for fileThe subnetwork consists of 83 proteins and 164 interactions.Click here for filePrediction for the first group of 60 "ambiguous" proteins.Click here for filePrediction for the 606 proteins without prior knowledge.Click here for fileComparison of the four basic classifiers and the ensemble classifier for 46 "ambiguous" proteins that was annotated with more than one subcellular localization sites.Click here for file


Many computational approaches have been developed to detect protein complexes from protein-protein interaction (PPI) networks. However, these PPI networks are always built from high-throughput experiments. The presence of unreliable interactions in PPI network makes this task very challenging.In this study, we proposed a Genetic-Algorithm Fuzzy Naïve Bayes (GAFNB) filter to classify the protein complexes from candidate subgraphs. It takes unreliability into consideration and tackles the presence of unreliable interactions in protein complex. We first got candidate protein complexes through existed popular methods. Each candidate protein complex is represented by 29 graph features and 266 biological property based features. GAFNB model is then applied to classify the candidate complexes into positive or negative.Our evaluation indicates that the protein complex identification algorithms using the GAFNB model filtering outperform original ones. For evaluation of GAFNB model, we also compared the performance of GAFNB with Naïve Bayes (NB). Results show that GAFNB performed better than NB. It indicates that a fuzzy model is more suitable when unreliability is present.We conclude that filtering candidate protein complexes with GAFNB model can improve the effectiveness of protein complex identification. It is necessary to consider the unreliability in this task. A protein complex is a group of two or more associated polypeptide chains. Proteins in a protein complex are linked by non-covalent protein-protein interactions (PPIs) and together participate a certain biological process. . ProteinA number of computational methods can be used to detect protein complexes from a PPI network, a graphThe rest of the paper is organized as follows: In the method section, we present a general framework of identifying protein complexes with our GAFNB filter. The experiment is described next. Experimental results and discussion are presented at last.For a given organism, the proposed protein complex identification approach contains two steps Figure . First sWe implement two of the state-of-the-art protein complex identification algorithms here: COACH and CMC. The results of these existing computational methods provide candidate protein complexes for filtering.COACH is a corCMC discoverAfter obtaining the candidate protein complexes, we need a filter to classify the candidates into positive or negative. However, the feature values in candidates are unreliable because PPIs are generally obtained from high-throughput experiments. The traditional classifier may not be suitable for this task. Our previous work has shown GAFNB can handle unreliable information in features-11. Henc11 is the membership of Certain feature value of density (<0.5) in Fuzzy feature value (<0.5). The matrices for all features are orthogonal. We refer to such matrices as Fuzzy Certain Feature Membership (FCFM).Because PPI data has some false positives and false negatives, the features value of protein complexes based on the PPI data can be uncertain. For example, the density of candidate protein complexes is calculated by edges (PPI) in the subgraph. So the density value is not certain. We call such features as Fuzzy Features. In contrast the reliable features are Certain Features. The uncertainty about the values for a feature can be represented as a matrix, wherein an element is the membership of a Certain feature value in a Fuzzy value Table . As showP = {pα} be a set of candidate protein complexes with features A = {αi} and D = {kd} a set of classes for candidate protein complexes. Let I be a subset of S represents some candidate protein complexes. The model is trained in the following steps using positive protein complexes determined through experiments and negatives generated randomly.Let i = vij for class k dusing Laplace correction . The other elements in the same row also need to change by calculating for maintaining the orthogonality of the matrix. The number of mutations performed is determined by a parametric study. Also take the feature density and mean degree for examples, Figure c. In summary, new population members are generated after each iteration. Figure vix in Fuzzy feature value is denoted as μv ij vix[vij) for particular classes (kd), are calculated as,The membership value of Certain feature values μv ij vix,10. Condvij is,Marginal probability of fuzzy feature value kd) is calculated using,When the feature of instances (candidate protein complexes) is fuzzy I)P curve and the area under the ROC curve (AUC) values for evaluating our model . ROC curWe followed existing approaches ,18,19 top| is the number of proteins in the predicted complexes and |Vb| is the number of proteins in the real complex. If NA≥ω, a real complex and a predicted complex are considered to be matching  [In equation 2, |VAfter all real complexes and predicted clusters have their best match calculated according to their NA scores, precision, recall, and F-measure are applied to assess the methods:cp is the number of predicted complexes that match at least 1 real complex, and Ncb is the number of real complexes that match at least 1 predicted complex [N complex .For training our model, we need the negative data. However, it is rare to find a confirmed report of non-protein complexes. Hence, we randomly selected proteins in the PPI network for generating negative protein complexes. We evaluate our model on two datasets: a balance dataset, containing 493 positive and 493 negative protein complexes, and an unbalance dataset having 493 positive and 9,878 negative protein complexes. The ratio of positives to negatives is 20:1. It represents the real life scenario where positive protein complexes are very rare.Following a previous study , each prn. Hence many popular search approaches use greedy hill climbing and best first, which iteratively evaluates a candidate subset of features, then modifies the subset and evaluates if the new subset is an improvement over the old [Feature selection is the process of selecting a subset of relevant features for use in model construction. It can improve model interpretability, shorten training time and enhance generalisation by reducing overfitting. There are two common categories of feature selection algorithms: filters and wrapper. Filter methods produce a feature set which is not specific type of predictive model, such as information gain, chi-square test. It evaluates each feature individually. While Wrapper methods usually provide the best performing feature set for that particular type of model. Since the model is fixed in our study, we chose wrapper method to select features. However, it is very computationally intensive. If the number of features is n, the number of possible feature sets is 2 the old . In our  the old  to find We chose 2 different state-of-the-art methods to get candidate protein complexes for GAFNB filtering. CMC and COACH are implemented on DIP network. For COACH, the argument was set to 0.225, as mentioned in their paper as mentioned in their paper. CMC is weight edge variance with missing edge is selected for 10 times. The similar results in unbalance data as shown in Figure weight edge mean with missing edge, topological change 0.3 0.4 and degree max) are selected for 10 times. All this features that selected for ten times are graph topological features. While biological property features are different in each run. This is probably because some of them are correlated and sensitive to training data.The dataset is randomly split into 10-sets. Each set is selected in turn as the test set and the remaining sets are combined to form the training set for WEKA Wrapper algorithm. Hence, we had ten optimal features sets based on Naïve Bayes classifiers for each dataset . Figure Ten-fold cross validation was run for evaluating the performance of GAFNB model. Table The comparison results show that the precision and F-value for COACH increased from 0.3387 to 0.4852 and 0.4465 to 0.5574, respectively, when filtering its results with GAFNB model over original ones Table . HoweverEvaluation of the contribution of each type feature toward protein complex identification will be done in the future. Some of our predicted complexes do not match any complex in the benchmark complex set. We found that the predicted complexes have high biological significance, as computed using P value, and high local density, as shown in Figure In this paper, we plug a GAFNB model in the process of protein complexes detection. The candidate protein complexes from existed methods are filtered by our GAFNB model. Results show that the performance of protein complexes identification methods are improved using our GAFNB filter and the GAFNB model is more suitable when unreliability is present. In the future, we will apply our genetic algorithm fuzzy Naïve Bayes model to deal with the other task with fuzzy features.The authors declare that they have no competing interests.BX conceived of the study, participated in its design, carried out all experiments, and drafted the manuscript. HLin and ZY reviewed manuscript. KBW participated in GAFNB experiments. HLiu helped draft the manuscript. All authors read and approved the final manuscript.