Euk-mPLoc 2.0” is developed by hybridizing the gene ontology information, functional domain information, and sequential evolutionary information through three different modes of pseudo amino acid composition. It can be used to identify eukaryotic proteins among the following 22 locations: (1) acrosome, (2) cell wall, (3) centriole, (4) chloroplast, (5) cyanelle, (6) cytoplasm, (7) cytoskeleton, (8) endoplasmic reticulum, (9) endosome, (10) extracell, (11) Golgi apparatus, (12) hydrogenosome, (13) lysosome, (14) melanosome, (15) microsome (16) mitochondria, (17) nucleus, (18) peroxisome, (19) plasma membrane, (20) plastid, (21) spindle pole body, and (22) vacuole. Compared with the existing methods for predicting eukaryotic protein subcellular localization, the new predictor is much more powerful and flexible, particularly in dealing with proteins with multiple locations and proteins without available accession numbers. For a newly-constructed stringent benchmark dataset which contains both single- and multiple-location proteins and in which none of proteins has Euk-mPLoc 2.0 is more than 24% higher than those by any of the existing predictors. As a user-friendly web-server, Euk-mPLoc 2.0 is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/euk-multi-2/. For a query protein sequence of 400 amino acids, it will take about 15 seconds for the web-server to yield the predicted result; the longer the sequence is, the more time it may usually need. It is anticipated that the novel approach and the powerful predictor as presented in this paper will have a significant impact to Molecular Cell Biology, System Biology, Proteomics, Bioinformatics, and Drug Development.Information of subcellular locations of proteins is important for in-depth studies of cell biology. It is very useful for proteomics, system biology and drug development as well. However, most existing methods for predicting protein subcellular location can only cover 5 to 12 location sites. Also, they are limited to deal with single-location proteins and hence failed to work for multiplex proteins, which can simultaneously exist at, or move between, two or more location sites. Actually, multiplex proteins of this kind usually posses some important biological functions worthy of our special notice. A new predictor called “ With the avalanche of protein sequences generated in the post-genomic era, numerous efforts have been made to develop various methods for predicting protein subcellular localization based on the sequence information  In order to make the prediction engine able to use the advantage of the GO approach, the accession number for a query protein is required as a part of input; many proteins, such as synthetic and hypothetical proteins, or newly-discovered sequences without being deposited into databanks yet, do not have accession numbers, and hence cannot be treated with the GO approach. (2) Even though their accession numbers are available, it is not always certain for them to be meaningfully formulated in a GO space because the current GO database is far from complete yet. (3) Although the PseAAC approach, a complement to the GO approach in Euk-mPLoc, can take into account some partial sequence order effects, the original PseAAC However, the existing version of The present study was devoted to develop a new and more powerful predictor for predicting eukaryotic protein subcellular localization by addressing the above three problems.http://www.ebi.ac.uk/swissprot/. The detailed procedures are basically the same as described in Online Supporting Information S1.Protein sequences were collected from the Swiss-Prot database at ocations Fig. 1; Because the system investigated now contains both the single-location and the multiple-location proteins, some of the proteins in Eq.2, we haveOnline Supporting Information S1.For the current 7,766 different protein sequences, 6,687 occur in one subcellular location, 1,029 in two locations, 48 in three locations, 2 in four locations, and none in five or more locations. Substituting these data into As stated in a recent comprehensive review Euk-mPLoc predictor Euk-mPLoc to perform prediction, the accession number of a query protein would be indispensable. To avoid such a requirement, the following different procedures are proposed to derive the GO representation mode.GO database Use BLAST Those proteins which have http://www.ebi.ac.uk/GOA/ to find the corresponding GO numbers Search each of these accession numbers collected in Step 2 against the GO database at The current GO database (version 70.0 released 10 March 2008) contains 60,020 GO numbers, thus the query protein Euk-mPLocThrough the above steps, we can use the GO information derived from its representative proteins in (1) the query protein does not have significant homology to any protein in the Swiss-Prot database, i.e., (2) its representative proteins do not contain any useful GO information for statistical prediction based on a given training dataset.The above homology-based GO extraction method is particularly useful for studying those proteins which do not have UniProt accession numbers. However, it would still fail to work under any one of the following situations: Therefore, it is necessary to consider the following representation modes for those proteins which fail to be meaningfully defined in the GO space.FunD is the core of a protein that plays the major role for its function. That is why in determining the 3-D  structure of a protein by experiments  program If the significance threshold value  is The protein sample Defined this way, the protein sample becomes corresponding to a 17402-D vector Since biology is a natural science with historic dimension, all biological species have actually developed continuously starting out from a very limited number of ancestral species. It is quite typical for protein sequences A hybridization of the above three different PseAAC modes, i.e., Eq.4, Eq.6, and Eq.12, will be used to represent protein samples for establishing a new classifier for predicting eukaryotic protein subcellular localization, as described below.K nearest neighbors belongs to. However, for most benchmark datasets, when K can be confined within the range from 1 to 10. Accordingly, the ensemble classifier The prediction engine used in this study is the ensemble classifier The prediction is processed according to the following order.If the query protein K of If the query protein To make Eqs.15–16 capable to handle proteins with multiple locations as well, the ensemble classifier Euk-mPLoc 2.0”, where “2.0” refers to an updated version evolved from Euk-mPLoc Euk-mPLoc 2.0.The entire ensemble classifier thus established is called “Euk-mPLoc 2.0. Below, let us give a step-by-step guide on how to use it to get the desired results.For the convenience of experimental scientists, a user-friendly web-server was established for http://www.csbio.sjtu.edu.cn/bioinf/euk-multi-2/ and you will see the top page of the predictor on your computer screen, as shown in Read Me button to see a brief introduction about Euk-mPLoc 2.0 predictor and the caveat when using it.Open the web server at Example button right above the input box. For more information about FASTA format, visit http://en.wikipedia.org/wiki/Fasta_format.Either type or copy and paste the query protein sequence into the input box at the center of Submit button to see the predicted result. For example, if you use the sequence of query protein 1 in the Example window, the input screen should look like the illustration in Submit button, you will see “Cell membrane; Cytoplasm; Nucleus” shown on the predicted result window  test, and jackknife test Euk-mPLocEuk-mPLoc 2.0, it would be sufficient to simply compare the success rates achieved by the new predictor with those by Euk-mPLocNow the current benchmark dataset is even more stringent because, in addition to the same threshold to rigorously exclude the homologous sequences, it covers even more, i.e., 22 location sites. Besides, to the best of our knowledge, except Euk-mPLocEuk-mPLoc 2.0 on the benchmark dataset Online Supporting Information S1 but not their accession numbers were used as inputs in order to make the comparison between the two predictors under exactly the same condition. During the course of the jackknife cross-validation by Euk-mPLoc 2.0 and Euk-mPLoc, the false positives (over-predictions) and false negatives (under-predictions) were also taken into account to reduce the scores for calculating the success rate. Note that it is more complicated to count the over-predictions and under-predictions for a system containing both single-location and multiple-location proteins. For the detailed calculation process, refer to Eqs.43–48 as well as Fig. 4 in a comprehensive review Euk-mPLoc 2.0 is over 64%, which is about 25% higher than that by Euk-mPLoc.Listed in Euk-mPLoc 2.0 is more powerful than the existing predictors in identifying the subcellular locations of eukaryotic proteins, there is much room for further improvement in future studies. As shown in Euk-mPLoc 2.0 for proteins belonging to “melanosome” and “synapse” locations are very low. This is because of that, compared with the most of the other 20 location sites, the numbers of proteins in the two sites are not sufficiently large (cf. Online Supporting Information S1) to train the prediction engine in a more effective way. It is anticipated that with more experimental data available for the two sites in the future, the situation will be improved and Euk-mPLoc 2.0 will become even more powerful.Finally, it should be pointed out that although Supporting Information S1(4.45 MB PDF)Click here for additional data file.


Various regions in this space exhibit considerable difference in the local conservation of molecular function. We analyze and capture local function conservation by means of logistic curves. Based on this analysis, we propose a method for predicting molecular function of a query protein with known structure but unknown function. The prediction method is rigorously assessed and compared with a previously published function predictor. Furthermore, we apply the method to 500 functionally unannotated PDB structures and discuss selected examples. The proposed approach provides a simple yet consistent statistical model for the complex relations between protein sequence, structure, and function. The GOdot method is available online ( Proteins are an essential class of molecules playing a variety of roles within a cell. They can be described in various ways: amongst others, by sequence, structure, and function. Determining protein function by wet lab procedures is challenging and tedious. Simultaneously, sequencing and structural genomics projects turn out ever increasing numbers of protein sequences and structures, which are largely lacking functional characterization. As a consequence, there is a growing demand for computational methods that can assist human experts in the functional annotation of proteins. We present a method for protein function prediction based on a novel concept, called local function conservation. Local function conservation in sequence and structure is determined by rigorously analyzing the variability of protein function with respect to sequence and structure similarity. Our method predicts protein function even if the protein to be functionally annotated has only distant relatives. Furthermore, we estimate the reliability of the function prediction. With this approach, we advance automated function prediction and contribute to a better understanding of the complex relations between protein sequence, structure, and function. Protein structure databases are growing at a rapid rate and, in recent years, structural genomics initiatives have increased the growth rate further. Yet many protein structures remain without functional annotations. Low coverage of functional annotations substantiates the necessity of reliable automated methods for predicting the functions of proteins.A widely accepted vocabulary for characterizing gene and protein function is maintained by the Gene Ontology (GO) Consortium Some function prediction methods transfer function from similar sequences, such as GOtcha The underlying idea of similarity based function transfer is that proteins with similar sequence and structural features are likely to perform the same function We estimate the rate of errors made when inferring protein function annotations based on protein sequence and structure similarity. On a representative set of protein domains, the error rates when inferring function naively are considerable. Additionally we analyze how inference is potentially improved by combining different measures for protein similarity.Within the space spanned by the set of representative protein domains, we identify regions where function is locally conserved. The information how well a molecular function is conserved in a protein neighborhood is captured and used for predicting protein function for new proteins falling into that neighborhood. The prediction method is extensively assessed and we compare its performance with the published PHUNCTIONER method The analysis is based on a set of 7290 representative protein domains with maximal 40% sequence identity as provided by the ASTRAL Compendium Of the 7290 representative protein domains, 86% are annotated with at least one molecular function GO term and 84% are annotated with a molecular function GO term from level three, or more specific . Compared to other studies In We broaden the above analysis to all GO levels, and examine to which extent function prediction can potentially benefit from combinations of protein similarity measures. The Venn diagram in The numbers of annotations that could be inferred by one similarity measure alone range from 15499 (53% for CE) to 17216 (60% for GP). Thus, if only one of the similarity measures were used for function inference, one would miss between 2651 (9%) and 4368 (15%) correct annotations that could be inferred using all four similarity measures. The diagram demonstrates clearly that there is potential in the combination of several similarity measures for predicting GO terms.In the previous sections, we demonstrated that inferring function according to annotations attached to the nearest neighbors is useful but prone to errors. We also showed that combining different similarity measures yields a potentially better coverage of predicted GO terms. Here, we propose the GOdot method which combines the information from several similarity measures and assesses local function conservation in protein sequence and structure space in order to predict GO molecular function.The GOdot method comprises two stages: a training stage which is performed only once, and a prediction stage that is run once for each unknown query protein. The complete protocol is illustrated in The training is performed on the above-mentioned set of protein domains with no more than 40% sequence identity. Within the space of these proteins, the method looks for regions of similar domains having the same function. The degree of function conservation in such regions can vary considerably depending on the type of molecular function, the number of protein domains having that function, and the metric used to calculate protein similarity. Therefore, we estimate the degree of function conservation separately for each GO molecular function in the region around each protein domain. Analyzing the region of 200 nearest neighbor proteins and using logistic regression, we obtain one logistic curve for each GO term, capturing the extent of functional conservation in the region around the protein domain. The logistic regression is done separately for each similarity measure.http://www.geneontology.org/GO.usage.shtml#truePathRule). The combined scores provide an estimate for the reliability of the predicted GO terms.In the prediction phase, the method predicts the molecular functions for an unknown query protein using the pre-computed logistic curves. Initially, the uncharacterized query protein is compared to all protein domains in the training set. The logistic curves of the most similar proteins in the data set are used to predict the molecular functions of the query: if the logistic curve of one of these nearest neighbors indicates high function conservation for a specific GO term in the region of the query, the GO term is predicted with a corresponding raw function conservation score. Raw function conservation scores are deduced from the logistic curves based on the similarity of the query to the nearest neighbors. The method computes several raw conservation scores for the query structure, one for every GO term annotated to the nearest neighbors. Usually, there exist several raw function conservation scores for one and the same GO term, either from different nearest neighbors with the same functions, or from one protein domain selected as nearest neighbor by different similarity measures. In order to assign one score to each of the predicted GO terms, we have developed two alternative schemes of combining raw function conservation scores along the GO graph: selective combination and consensus combination. Both combination schemes ensure that a GO term is predicted together with all its ancestors in the GO hierarchy, and that parental GO terms obtain scores that are at least as high as those of their GO descendants. This approach is in compliance with the GO true path rule, which states that “the pathway from a child term all the way up to its top-level parent(s) must always be true” , d1vlma (TM score 0.74), d1vl5a (LP score 188), and d1qama (GP score 136). For each of these, the surrounding space was pre-analyzed. In Numerically, local function conservation is captured with the raw function conservation score. As depicted in −11 according to the consensus combination (which is 1−(1−0.9817)·(1−0.9955)·(1−0.9980)·(1−0.9998)). For the sake of simplicity, we have not included any GO term of the nearest neighbors more specific than GO∶0008757 into the above calculations.Using the combination schemes, the raw function conservation scores are combined selectively into 0.9998 , and into ≈1–10To assess the performance of the GOdot method for function prediction, we compare four variants of function predictors: function inference based on protein similarity alone (as discussed above), function inference based on raw function conservation scores, function inference based on selectively combined function conservation scores, and function inference based on consensus combined function conservation scores.Do raw function conservation scores improve the performance when predicting function? In How good is the quality of function inference based on combined function conservation scores compared to inference based on raw function conservation or to the naive inference based on the similarity measures alone? In Similarly, when sorting according to raw function conservation scores, as in Employing the function conservation concept clearly improves the prediction performance. The two GOdot predictors using function conservation scores significantly perform better than the reference predictors.http://www.ebi.ac.uk/GOA/goaHelp.html), such as the Ensembl Compara method −50 (http://www.geneontology.org/cgi-bin/references.cgi). The exact IEA origin is only tracked for function assignments made after May 2007.The Gene Ontology Annotation Project (GOA) keeps track of the sources of their functional annotations by use of evidence codes. An overview of the Evidence codes used by GOA is provided in On the previously studied set of 3449 representative domains there is the following evidence for function annotations: 8% are based on direct experiments, another 8% are based on literature statements, and less than 1% are found by curators based on computational evidence. The rest (83%) of the annotations is based on automatic electronic inference.When using these functional annotations for training a new computational method like GOdot, there is an obvious trade-off between quality and coverage: the higher the number of annotated proteins used, the lower is the ratio of manually curated annotations on these. The previously described set strives for maximum coverage, as this is the aim in a typical application scenario. We also tested the method on a second set based on high-quality annotation data.The high-quality data set is restricted to annotations that stem traceably form literature (evidence code TAS) or from direct experiments , leaving 945 proteins with curated experimental annotations. We repeated the analyses described in the previous section on this high-quality subset. The results are summarized in The results based on high-quality data confirm our observations made on the larger high-coverage data set. Since the higher coverage enables predictions for a broader range of GO functions and a more diverse set of query proteins, we have used it throughout this article. It is important to note that IEA annotations from GOA are based on very closely related proteins with high sequence and structure similarity. GOdot overcomes this limitation by exploiting distant relations of pairwise sequence identities below 40%, thereby going beyond current IEA approaches.Finally, we assessed the GOdot method according to the protocol published with the PHUNCTIONER method The resulting ROC curves for the GOdot method are shown in The performance comparison with PHUNCTIONER underlies the following restrictions: (i) Since the PHUNCTIONER method is not available to us we had to use the previously published results −10 . For 13 of these proteins the predictions included at least one problematic GO term. In most of these cases the problematic GO term was annotated to protein neighbors that were multidomain proteins. These GO terms corresponded to the molecular function of a particular domain outside the region of sequence or structure similarity. Four additional proteins remain uncharacterized according to public annotation databases. The corresponding GOdot predictions were not necessarily incorrect, but they only included GO terms that were not very informative. Most of the GOdot predictions (32 proteins) were consistent with additional functional information that had been made available in the UniProt GOdot was applied to 500 query proteins corresponding to PDB entries labeled with unknown function and obtained by structural genomics initiatives. We analyzed the GOdot results for the subset of these proteins having four or more GO terms with a consensus combined score >1–10Sulfolobus tokodaiiPyrococcus horikoshii (PDB ID 1ve3) is such an example. The crystal structure includes the cofactor S-adenosyl-L-methionine (SAM) bound to the protein. The protein also shows significant structural similarity to other SAM-dependent methyltransferases, and is a member of the Methyltransferase homologous family, as identified by Pfam −11. This same query was used to illustrate the GOdot function prediction process in Direct experimental evidence for the function annotation was usually not available for these proteins with consistent GOdot predictions. One case with experimental evidence is Cytochrome P450 from Thermus thermophilus provides an example of GOdot results complementing previous functional analysis. TT1426 has been identified in Pfam as a member of the Phosphoribosyl transferase domain family. The structure has been determined −11, as expected for a phosphoribosyl transferase. The hypothetical protein TT1426 from In summary, the manual inspection revealed 13 problematic cases (out of 49 proteins) for which a function was predicted falsely due to an invalid transfer of function from a multidomain protein. Four proteins could be neither confirmed nor refuted. For 32 proteins the GOdot predictions were manually confirmed with various other sequence or structure-based methods. See We propose the GOdot method for predicting molecular function of proteins. The method uses functionally conserved regions as a new concept. These functional conservations are determined by statistical learning on a representative set of protein domains. Protein sequence and structure information of an unannotated protein are used as input to GOdot, which then predicts a list of GO terms. Each of the predicted GO terms has a reliability estimate attached which is computed based on the previously determined functionally conserved regions.Both, the assessment using cross-validation on a representative dataset and the comparison with PHUNCTIONER To our knowledge, the GOdot method is the first approach that directly addresses the problem of estimating varying local function conservation in protein space with respect to different measures for protein similarity. For each similarity measure, each GO term and each protein domain in the representative training set, function conservation is captured with a logistic curve. The result is a large number of mutually intertwined and overlapping logistic curves. The set of logistic curves offers a new view on the relation between sequence and structure on the one hand and function on the other hand. We regard the analysis of functionally conserved regions as an important contribution to current function prediction efforts, and we expect forthcoming developments in this field to uncover more detailed insights into the sequence-structure-function space.Local function conservation within protein space can be determined with respect to other protein similarity measures, such as shape or surface properties of protein binding sites, for example. The GOdot method can be easily extended to include other quantitative measures of protein similarity. For any new similarity measure one would simply perform an all-against-all comparison on the training set of proteins. Local function conservation can then be determined for that similarity measure. We are working on extending the GOdot method with new similarity measures to further improve its performance.http://godot.bioinf.mpi-inf.mpg.de), to which one can submit uncharacterized PDB structures. The method performs sequence and structure comparisons of the query protein to each entry from the representative set of protein domains. GO terms are predicted and function conservation scores are computed as reliability estimates. A ranked list of predicted GO terms is the output of the web-server.The GOdot method is available online as a web-server  The GO vocabulary is structured as a directed acyclic graph (DAG). A GO term can have several parent terms. The annotation of a specific GO term to a protein then implies the annotation of all parent terms to that protein as well (this is referred to as the GO true path rule). As the GO vocabulary is organized as a DAG, a particular term can have several paths of different lengths to the root node; the term can occur on multiple levels of the ontology.Performance comparisons across GO terms from different levels of specificity are hazardous. Therefore, in our studies we focus mostly on GO terms from level three, where the GO root is defined as being at level zero and GO ‘molecular function’ as level one. We consider a GO term to belong to level three, if it has any path of length three to the root node. An example of a level three GO term is ‘transferase activity’ (GO∶015972), which has the parent ‘catalytic activity’ (GO∶052747), which is in turn a direct child of ‘molecular function’ (GO∶161526).Similarity between proteins is measured using different distance measures. We refer to observing a specific protein function consistently within a neighborhood of proteins in protein space as function conservation. We used different measures of similarity between proteins and describe a mathematical model for capturing function conservation. This model can be computed in a pre-processing step and later be used to predict protein function.p,r, we compute similarities sim using four different methods. The CE CEsim and TMsim). Global profile (GP) and local profile (LP) alignments GPsim and LPsim) of the proteins' sequences as a whole or as the best partial match, respectively.For a pair of protein domains sim, we apply the following training procedure to all protein domains in the dataset. Each protein domain p is annotated with a set gt(p) of molecular function GO terms. Let gt(p) contains all of f's parent terms.We have determined conservation of molecular function with respect to the four similarity measures mentioned above. For each similarity measure f annotated to a domain p, we determine conservation based on the occurrence of f among the nearest neighbors of domain p. The more neighbors of p have the same molecular function term f and the closer these neighbors are to p, the higher is the local conservation of f around p. We represent local function conservation using logistic regression as follows. Let r1,…kr denote the k nearest neighbors to p according to sim. In the experiments, we chose k = 200. Let X be the real-valued vector of similarities X = . Let Y denote the binary vector of observations describing for each of the nearest neighbors ir, whether f or more specific terms among its descendants are annotated to irFor each term X between proteins and their neighbors to the log-odds that the respective neighbors are annotated with the same GO term (as indicated by Y). This relation is mathematically modeled by the logistic curveβ0 and β1. Given X and Y, logistic regression yields β0 and β1. We fit such a logistic curve for each annotation of a GO term to each protein in the dataset. The multitude of logistic curves provides a view on the distribution of functional conservation.The logistic regression relates the similarities The GOdot method takes a query protein as input and predicts scores for a number of GO terms. For a query, we first predict scores representing the degree of GO function conservation. These scores are based on the local function conservation of the terms annotated to the query's nearest neighbors. The predicted scores are combined to account for multiple occurrences of related GO terms. Finally, ranking the GO terms according to the combined scores, the method produces a sorted list of GO terms.Using logistic curves to estimate local function conservation.q of unknown function. We identify q's nearest neighbor with respect to the similarity measures, for example with CEsim as mentioned above. Let x = CEsim be the similarity between q and the nearest neighbor r. The logistic curve previously computed for the neighbor r and one GO term f is used to estimate the likelihood of the GO term f occurring at similarity x to r. For a given similarity x and one GO term f, the raw function conservation scoreβ0 and β1 are the parameters representing the logistic curve for the particular GO term f attached to the particular nearest neighbor r. Thus, fψ can be interpreted as estimated probability of q having the same GO term f, given a similarity x to the neighbor r. For the other similarity measures TMsim, GPsim, LPsim, the raw function scores are defined accordingly.A typical function prediction commences with a query protein Combining raw function conservation scores along the GO graph structure.q, the different similarity measures point to potentially different nearest neighbors. These nearest neighbors are annotated with one or several GO terms. For each of these GO terms the raw function score provides an estimate of the likelihood that the transfer to the query is valid, at the given similarity. Thus, for a specific GO term f, we have four raw function scores attached to a protein, which we refer to as support For a query protein combined function conservation score per GO term. To this end, we propose the following score combination schemes which are applied to each GO term and incorporate the raw conservation scores of descendant GO terms. These combination schemes also ensure that GO terms obtain scores that are at least as high as those of their descendants.We merge several raw function conservation scores into one selective score combination scheme computes the combined function conservation f as the maximum raw function conservation score within the support of all its descendants f′ as follows:The consensus score combination scheme computes the combined function conservation The predictor. The assessment of the predictors is described in the next section.Each of the combination schemes above produces one combined score per GO term. These combined scores are estimates of the reliability of the predicted GO terms. The GO terms predicted for one query are ranked with respect to the combined scores yielding a sorted list. We refer to a combination scheme producing such a list as We assess the GOdot method's performance by cross-validation. The selective and consensus predictors are compared to a baseline predictor using precision-recall graphs.Cross-validation scheme.We perform a leave-one-out cross-validation. Predictors are trained for each protein ignoring the annotations attached to that protein. In the We assess a predictor's performance with ROC plots. All GO terms that can be inferred from the nearest neighbors are considered and scored. An imaginary threshold is shifted from top to bottom over the list of ranked GO terms, treating all terms above the threshold as predicted. At each rank the number of true positives (TP≙correct GO terms predicted), false positives (FP≙incorrect GO terms predicted), true negatives (TN≙incorrect GO terms not predicted) and false negatives (FN≙correct GO terms not predicted) is counted. These counts are combined into the performance measures true positive rate and false positive rate. At each rank, the true positive rate is the fraction of true positive predictions from all positive samples and the false positive rate is the ratio of false positive predictions divided by the number of negative samples:Predicting functions for a new query protein requires comparing the query to the set of representatives. Comparison of one protein to all 3449 protein domains in the set of representatives takes on average 4 hours for CE, 5 minutes for TM, and 2 minutes for profile alignment on a modern PC. With a compute cluster as back-end to the GOdot web-server, we provide answers typically within 20 to 60 minutes. In the training stage, establishing the protein space requires all-against-all comparisons, which is quite expensive (300 CPU days). When the distances are available, computing the logistic curves for 28774 annotations  takes 9 minutes and is negligible in comparison.Table S1Details on Application to Structural Genomics Proteins. The table provides details on the 49 cases described in the paper, including PDB identifiers.(0.06 MB PDF)Click here for additional data file.Text S1Significance Analysis. Additional evaluation of the significance of the findings, based on an extended ROC-analysis / cross-validation(0.06 MB PDF)Click here for additional data file.Figure S1Performance assessment of consensus combination vs. selective combination. We use precision-recall graphs to compare the different predictors resulting from consensus score combination and selective score combination with predictors employing mere protein similarity measures and a background predictor. The plot is based on the cross-validation results, each curve describing the median performance of one distinct predictor. The boxes indicate 25% and 75% quantiles, the whiskers represent the maximum deviation from the median. The predictors employing protein similarity measures only, have a performance worse than the background predictor for very low recall rates. For very similar proteins, GO terms are predicted as likely, regardless of their level within the GO hierarchy. This leads to false terms predicted as very likely and thus to a precision of below 1 for recall 0.(0.28 MB PDF)Click here for additional data file.Figure S2Assessing similarity based inference on the high-quality data set. We entirely repeated the estimates and calculations performed for the high-coverage data set in the main manuscript on a high-quality data set. This high-quality data set is restricted to annotations that stem traceably from literature (evidence code TAS) or from direct experiments , leaving 945 proteins with curated experimental annotations. This figure corresponds to (0.17 MB PDF)Click here for additional data file.


The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage.Protein-protein interaction data can be integrated with the functional knowledge existing in the Gene Ontology (GO) database. We apply similarity measures to assess the functional similarity between interacting proteins. We present a probabilistic framework for predicting functions of unknown proteins based on the functional similarity. We use the leave-one-out cross validation to compare the performance. The experimental results demonstrate that our algorithm performs better than other competing methods in terms of prediction accuracy. In particular, it handles the high false positive rates of current interaction data well.The experimentally determined protein-protein interactions are erroneous to uncover the functional associations among proteins. The performance of function prediction for uncharacterized proteins can be enhanced by the integration of multiple data sources available. Since the completion of sequencing the human genome , discoveThe availability of complete genomes in a wide variety of organisms has shifted the single-gene-based function prediction problem to a genome-level. Several approaches have been introduced on the basis of correlated evolution mechanisms of genes. The conserved gene neighborhood across different, distantly related genomes reveals the potential functional linkages . The domIn recent years, the data generated by high-throughput techniques have facilitated the functional classification. For example, microarrays monitor the expression levels of thousands of genes, and the correlated expression profiles of the genes can be interpreted as their functional relatedness . ProteinIn this study, we explore an effective methodology for predicting functions of unknown proteins using the connectivity of protein interaction networks. Previously, a number of approaches have been proposed to predict protein function from protein interaction networks. The neighbor counting method , which iSeveral graph theoretic approaches took into account the global topology of protein interaction networks. Vazquez et al.  and KaraMost of the previous approaches to predict protein function from protein interaction networks are based on the assumption that two interacting proteins have a similar function or share functions. However, these tasks overestimate the tendency of functional links between the proteins that interact with each other. In addition, it is recognized that current interaction data include a substantial number of false positives and false negatives although they have been curated using various computational techniques. It signifies that the high-throughput experimental methods frequently generate spurious data, and the set of interactions accumulated in a large scale is still incomplete. Because of the inappropriateness of direct use of current interaction data for function prediction, the integration of functional knowledge elicited from other biological resources is necessary.In this article, we propose a novel probabilistic approach for function prediction from protein interaction networks. We integrate the connectivity of protein interaction networks with the semantic knowledge in the Gene Ontology (GO) database for the purpose of improving the accuracy of function prediction. First, we measure the functional similarity between interacting proteins using GO. Next, we present a probabilistic framework to compute the confidence in function prediction for each unknown protein. Our experimental results show that this approach is robust to current erroneous data and thus predicts function more accurately than previous methods.Semantic knowledge, which is also called ontology, is the representation in which concepts are described by their meanings and their relationships to each other . It is tRecently, various measurements of functional similarity between proteins using GO have been proposed -28. SuchT, then it is also annotated on more general terms on the paths from T to the root in the GO structure. The set of proteins annotated on the root term becomes transitive. Under this property, the number of annotated proteins on a term T is capable of quantifying the specificity of T. The functional similarity between interacting proteins is then measured based on the commonality of the annotations of two terms on which they are annotated. The more annotations two terms share, the more similar they are. The measured similarity can be also normalized by the number of annotated proteins on the most specific individual terms whose annotations include each interacting protein. (See Methods for details of annotation-based approaches.) Annotation-based approaches generally perform better than structure-based approaches when the annotations in GO are fairly complete.Annotation-based approaches focus on the number of proteins annotated on GO terms. They suppose the GO annotations follow the transitivity property, i.e., if a protein is annotated on a term n distinct proteins, p1,...,pn}. In p1,...,pk (k <n) are functionally annotated and pk+1,...,pn are unannotated. We predict functions of an unannotated protein pi where k <i ≤ n. Let f, and pi and j ≤ m. If there is no interaction evidence between pi and pi has the function f given Our approach predicts multiple functions for each protein, which is functionally uncharacterized but has the evidence of interactions. It is based on the Bayesian formula using the functional similarity measured by the metrics described above. Suppose protein-protein interaction data contain a set of P(f = 1) is the prior probability that pi has the function f, P (pi interacts with P(f = 1) is the conditional probability that pi interacts with pihas the function f. Based on the assumption that the events of the interactions between pi and where Equation 1 is then transformed intoP(f = 0) is the probability that pi does not have the function f.where Mf be the maximum functional similarity. The allowable amount in functional similarity for pi and f can be Rf = Mf - P(f = 1) follows a binomial distribution.Let Pf is the probability that two proteins have the same function f. We can approximate the binomial distribution to a normal distribution with the mean μ and variance σ2.where In the same way,Then, Equation 3 can be re-written aswhereμf+ and pi and the proteins annotated on f. Similarly, μf- and pi and the proteins that are not in the annotation of f. P(f = 1) becomes the ratio of the number of proteins having f to the total number of known proteins, and P(f = 0) is 1 - P(f = 1). As an alternative to Formula 7, we compute log(λf), which can be the prediction conifdence for pi to the function f.We assessed the tendency of functional linkage between interacting proteins in current interaction databases. We extracted the interaction data of Saccharomyces cerevisiae from three databases: 12352 interactions from MIPS , 17186 fFirst, we measured the functional similarity of each interacting protein pair using the structure-based method in Formula 13. Figure Next, we measured the functional similarity of each interacting protein pair using the annotation-based method in Formula 14. Figure Finally, we observed the functional consistency of each interacting protein pair using the Jaccard index. According to the transitivity property of functional annotations, if a protein is annotated on a function, then it also has more general functions on the paths towards the root in the hierarchical structure. We calculated the number of common functions among the number of all distinct functions of an interacting protein pair. Figure We evaluated the effectiveness of the functional similarity measurements to choose the best one for our function prediction approach. We used the full version of protein-protein interaction data of Saccharomyces cerevisiae from DIP , which iWe first evaluated interacting pairs by functional co-occurrence in the categories from FunCat  in MIPS.Next, we evaluated the ordered interacting pairs by functional consistency. Similar to the calculation in the previous section, the functional consistency was computed by the ratio of the number of common functions to the number of all distinct functions of an interacting protein pair. Figure We additionally investigated the appropriateness of functional similarity, which is scored as a real number between 0 and 1. We measured the functional similarity using Formula 13 and 14, and divided the interacting pairs into ten groups by the range of 0.1 of similarity. We then calculated the average functional co-occurrence and functional consistency for each group. The results are shown in Figure Mi be the set of functions from the actual annotation in MIPS for a protein pi, Ni be the set of functions predicted by our algorithm for pi, and Ki be the set of common functions of Mi and Ni. Precision and recall are then described as:We assessed the performance of our function prediction approach by the leave-one-out cross-validation method. For each distinct protein in the actual annotations from FunCat in MIPS, we assumed it was un-annotated and predicted its functions. The prediction performance was evaluated using precision and recall . We transformed the format of the actual annotations, from the set of proteins annotated on each functional category to the set of functions for each protein. Let andKi| is the size of the set Ki and n is the total number of distinct proteins that are annotated on at least one functional category and have the interaction evidence.where |Figure p has a function using the functional similarity weights between p and level-1 or level-2 neighbors. The functional similarity weight of two proteins is calculated by the commonality of their neighbors in the protein interaction network. Since we used the same interaction data from DIP as inputs for all the methods, we did not consider the reliability of the data source. We used a threshold of the likelihood to generate the output set of predicted functions for each protein. We then obtained different output sets by various thresholds. The sets of predicted functions become larger as the threshold lowers.We compared the prediction performance with two previous methods, which are currently the most competing. One is the approach weighting the functional similarity of direct and indirect neighbors (level-2 neighborhood) , and thed = 1 and did not consider the edge weights, i.e., assigned 1 to each edge weight. We used the similarity of annotation neighborhood patterns as a threshold.The second method constructs the set of annotation neighborhood patterns for each function, and computes the similarity between the annotation neighborhood pattern of an unknown protein and the set of annotation neighborhood patterns of each function. We used the annotations from the functional categories in MIPS since they are used as a ground truth in our experiment. We set the parameter Figure δ. That is, for each protein, δ best predicted functions are generated as an output. As a set of functions to be predicted, we used the ones on the same level in a hierarchy. The previous experiment used all the functions in the hierarchical structure. However, predicting very general functions is meaningless when a small number of functions are predicted for each protein. We thus selected only the functional categories and their annotations on the third level from the top in the functional hierarchy. We then evaluated the prediction accuracy using precision, but if a protein had the functions less than δ in the actual annotations from MIPS, the number of annotated functions, Mi, was used instead of the number of predicted functions, Ni , in Formula 9. Table δ value up to 6. It means our approach predicts the specific functions of any protein with higher accuracy than the previous methods.In the experiment above, we implemented the function prediction algorithms using a threshold of prediction confidence. If a protein has low rates of prediction confidence for any function, the prediction result for the protein is not generated. For the comprehensive comparison of the prediction accuracy for all proteins, we implemented the algorithms again using a threshold of the number of predicted functions, λf) where λf is calculated by Formula 8. A protein can thus correspond to more than one predicted function with different confidence rates. Table According to the most recent version of functional annotations in MIPS, a significant number of uncharacterized proteins in Saccharomyces cerevisiae still exist. We implemented our algorithm to predict their functions. Among the unknown proteins in MIPS, we selected only the proteins that have more than three interacting partners in DIP to additionally avoid the effect of false positive interactions. For each selected protein, our algorithm generated a list of functions with prediction confidence, which is the value of log. Various measurements for the functional similarity were evaluated in terms of functional co-occurrence and consistency of interacting pairs. The experimental results in Figure Functional characterization of proteome is a central goal in the field of Bioinformatics. The experimentally determined protein-protein interactions are crucial data sources to uncover the functional knowledge of uncharacterized proteins. However, a pre-process to assess the functional linkage of interacting proteins from current interaction data is required for predicting protein function successfully.In this article, we presented a novel concept for integrating the connectivity of protein interaction networks with already published annotation data in Gene Ontology (GO). Our results imply that function prediction from protein interaction networks has been calibrated by integrating with the functional knowledge from GO. Clearly, the prediction accuracy can be more improved by the integration of multiple data sources available, which are relevant to functional linkage. Furthermore, developing effective integration models for the explosive amount of heterogeneous biological data sources is promising in future research for functional knowledge discovery.To measure the functional similarity of an interacting protein pair, we can use the path length between two GO terms in the GO structure. We first find the most specific GO terms on which each interacting protein is annotated, and then selects the term pair that has the shortest path between the terms. The shortest path length can be scaled down by the maximum depth of GO and applied log smoothing.Ti and Tj are the GO terms whose annotations include the interacting proteins p1 and p2, respectively, and depth denotes the maximum path length from the root term to a leaf.where Ti represents all the terms on the paths towards the root from Tiin the DAG structure. Ti is exclusive and the root term is inclusive. The similarity is then calculated by the common parent GO terms over all distinct parent GO terms.As an alternative way, we can consider the proportion of common parent GO terms. A set of parent terms of a term Ti and Tj, respectively.where As a combination of the two factors, path length and common parent terms, we can use the maximum path length from the root to the most specific common term on which the interacting proteins are annotated together. It is then normalized by the path lengths from the root to the most specific terms which have the annotation of each interacting protein.Tr is the root term and Tc is the most specific common parent term of Ti and Tj. Figure where c depends on the probability P(c) of the event. Specifically, the smaller the probability of the event, the larger the self-information associated with receiving information when the event indeed occurs. The information content of a concept C is defined as the negative log likelihood of C, -log P(C). The similarity between two concepts is measured by their commonality, i.e., the information content of the most specific common concept [In Information Theory, self-information is a measure of the information content associated with the outcome of a random variable. The amount of self-information contained in a probabilistic event  concept .T can be represented as -log P(T) where P(T) is the proportion of the proteins annotated on T, and the similarity of two terms Ti and Tj is calculated by -log P(Tk) where Tk is the most specific common term of Ti and Tj. To measure the functional similarity of interacting proteins p1 and p2, we list all pair-wise terms that have the annotations of p1 and p2 respectively and select the maximum similarity of the pairs. Suppose Sk is a set of proteins annotated on the GO term Tk that includes both p1 and p2 in the annotation. The functional similarity between p1 and p2 is defined as:In the same manner, the information content of a GO term Ti and Tj.Figure YRC designed and implemented the data integration and prediction algorithm, analyzed the results and drafted the manuscript. LS partly analyzed the results. MR and AZ coordinated the project and revised the manuscript. All authors read and approved the final manuscript.Function prediction results for unknown proteins. For each unknown protein, our probabilistic approach predicted a set of functions with prediction confidence of greater than 10. The predicted functions are described as the IDs used in MIPS.Click here for file


In this respect, UniProt/Swiss-Prot is currently the most comprehensive collection and it is routinely cross-examined when trying to unravel the biological role of hypothetical proteins. Bioscientists frequently extract single entries and further evaluate those on a subjective basis. In lieu of a standardized procedure for scoring the existing knowledge regarding individual proteins, we here report about a computer-assisted method, which we applied to score the present knowledge about any given Swiss-Prot entry. Applying this quantitative score allows the comparison of proteins with respect to their sequence yet highlights the comprehension of functional data.  Life scientists seek to accumulate knowledge about thedistinct functions of biomolecules. Currently, approximately 20000 gene lociwith a total of up to 35000 transcripts have been reported for the human genomeand transcriptome, respectively , 2. GenoCOMMENTS field. Firm data such as those derived from biochemical analyses are assignedclear-cut attributes. For instance, the various properties of an enzyme arerecorded by a CATALYTIC ACTIVITY aswell as by a FUNCTION attribute. Yet,in cases other than enzymes, any other biological function can also be foundwithin the FUNCTION attribute. Biologically, relevant information is also reflected in the protein name (DESCRIPTION line). Other categories such as ENZYMEREGULATION, PATHWAY, and TISSUE provide only little further informationconcerning the protein functional properties than already defined by FUNCTION OR CATALYTIC ACTIVITY.UniProtKB/Swiss-Prot and UniProtKB/TrEMBL store proteinprimary data and associated biological and biochemical information. These twoare often used as the prime source for data mining. In this study, we focusedon the knowledge regarding functional aspects of proteins archived inSwiss-Prot because this repository is generally believed to be a high-quality,manually curated protein knowledge base . Moreovepfs).When using computer-assisted methods, the primary structureof proteins can be easily searched for likely regions of resemblance. Annotatedknowledge can be adopted by applying automated bioinformatic routines for thosesequences, which are not explicitly listed in Swiss-Prot, but however, shareremarkable similarity with experimentally well-studied proteins or proteinsequence motifs that have previously been characterized in detail , 9, 10. pfs procedure, we performed apeer survey. A representative group of trained biologists was asked to manuallyassess the functional knowledge for 30 randomly selected Swiss-Prot entries. Theoutcome and feedback guided us when specifically moulding the computer-assistedprocedure for the evaluation of currently available information concerning the individualfunctional properties of proteins. The resulting computational measure was subsequently used for benchmarking of the current knowledge regarding entries in data repositories andlibraries. This relation allowed us to incorporate pfs into the result list of protein-protein sequence comparisons. This particular featureprovides a reliable indication of how well a particular query hit has beenpreviously annotated regarding functional assignments. Precomputed protein's function scores (pfs) and online BLASTP searches against pfs-annotated Swiss-Prot database are accessible at http://biwww.che.sbg.ac.at/PFS/.In parallel to the developmentof the Database entries were randomly selected. 13 peers comprising of oneundergraduate student who has been trained in molecular biology, five PhDstudents, five post doctoral fellows, and three principal investigators workingas scientists in the field were asked to validate the information provided withthese particular entries and to regard the displayed existing knowledge aboutthe protein as high, medium, or low.pfs, which is the sum of factors computed for the respectivesections of a Swiss-Prot entry, where LITERATURE REFERENCES is denoted f1,DESCRIPTION = fd, FUNCTION,and CATALYTIC ACTIVITY = ff  sequencingdata. The accompanying publications rarely address functional questions nor dothey experimentally verify bioinformatic analyses via firm biochemicalinvestigations prior to submission to Swiss-Prot. Therefore, Swiss-Prot added aspecial attribute for large scale experiments, such as genome sequencing orfunctional genomic approaches. These publication formats were rated zero. Apparently, a large number of publications is often a good reflection of accumulating knowledge. However, f1 (pfs increment for literature) should not overwhelm the value offunctionally-related terms as defined before. As a matter of fact, entries forproteins with largely unknown molecular function typically have fd and ffvalues that are low or close to zero. In cases ofentries that contain numerous publications, the mere addition of an average rawliterature factor to the aforementioned low fdand ff would yield adeceptively high pfs value. Therefore,the average raw literature factor was compared to the assessment calculatedfrom the sections DESCRIPTION, FUNCTION, and CATALYTIC ACTIVITY, respectively. The average literature factor was factorized bymultiplication with the maximum of fd and ff. Theproposed scheme has been built and refined iteratively by reading the contentof several hundred Swiss-Prot entries and checking the coherence with thecalculated pfs. The source code forcomputing pfs for Swiss-Prot is available upon request.A simple rule was generated inorder to yield a solid estimate for the information content of Swiss-Prot: forevery single entry, a score was compiled named TY = ff . At the ertainty . SeveralScientists, who routinely useSwiss-Prot or have been trained to extract information about protein from avariety of data repositories, were asked to evaluate 30 randomly pickedSwiss-Prot entries. Eventually, the quality of all data as provided within theSwiss-Prot entry for this particular protein should be regarded as being high,medium, or low . In caseLITERATURE REFERENCES,DESCRIPTION and FUNCTION, and CATALYTIC ACTIVITY. Thesefields were also highly appreciated by the peers for measuring biologicalfunction. Therefore, the text included in these sections was carefully evaluatedfor particular specifications of down-weighing characterizations as shown inpfs with a maximum value of 3 (pfs. When plotting log (number of words) against pfs,only a weak correlation of r = 0.66 after applying Pearson's formula could beobserved . Inaddition to this, we observed a rather broad distribution with respect to wordcount, which makes it hard to believe that pfs may be deduced by merely counting words. We furthermore analyzed the degree ofconsent between peers scoring and pfs. Taking the entire data set into account, only a week correlation of r = 0.58could be revealed. When selecting only those cases that resulted in aconcordance score of >62%, which was half of the entries, a correlation of r = 0.78 was obtained.Quality assessment of allSwiss-Prot entries was done by putting weight on to what is currently knownabout the biological function(s) of an individual protein. The information is abstractedin the respective sections of a Swiss-Prot entry lue of 3 . The evapfs was used to classify all Swiss-Protentries by sorting them into three categories, representing a “low” (0 < 1),“medium” (1-2), and “high” degree of knowledge (>2) presently available withrespect to function and/or biological role  read and survey data collections without applying astandardized validation method. Since knowledge regarding thebiological function of proteins is not evenly distributed in databases, and standardized bioinformatic procedures, which would allowindividual researchers to specifically qualify informationprovided in knowledge databases, have not been established yet, which allow individual researchers to specifically qualify informationprovided in knowledge databases, we developed a novel method. Assessment andcross validation of the current knowledge regarding proteins presented in Swiss-Protby this linguistic analysis yielded results, which can be hardly achieved byany sort of peer evaluation procedure. In line with this, it is also notsurprising that only in selected cases a strong correlation between results ofthe presented peer data and pfs isappended to protein identifiers within a sequence comparison result list. Inthis way, nonexperts in a particular field quickly obtain a ranked listing ofknowledge distribution when examining the hit list. In addition, taggingprotein identifiers with pfs are mosthelpful for identifying links, which guide one to the most informative databaseentry. Hence, we regard pfs as animportant improvement for accelerating data mining strategies and to providingnew avenues for evaluating data derived from genomic and proteomic projects. Inthe near future, this tool may become an invaluable tool for curators ofknowledge databases, primarily to earmark entries which are of minor quality,in order to either eliminate the entry or to improve it during a subsequentupdate.Considering the impact on dailyresearch, we propose that


Fusarium graminearum (telomorph Gibberella zeae) is the causal agent of several destructive crop diseases, where a set of genes usually work in concert to cause diseases to crops. To function appropriately, the F. graminearum proteins inside one cell should be assigned to different compartments, i.e. subcellular localizations. Therefore, the subcellular localizations of F. graminearum proteins can provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. Unfortunately, there are no subcellular localization information for F. graminearum proteins available now. Computational approaches provide an alternative way to predicting F. graminearum protein subcellular localizations due to the expensive and time-consuming biological experiments in lab.The fungal pathogen F. graminearum protein subcellular localizations from the primary structures. First, a non-redundant fungi data set with subcellular localization annotation is collected from UniProtKB database and used as training set, where the subcellular locations are classified into 10 groups. Subsequently, Support Vector Machine (SVM) is trained on the training set and used to predict F. graminearum protein subcellular localizations for those proteins that do not have significant sequence similarity to those in training set. The performance of SVMs on training set with 10-fold cross-validation demonstrates the efficiency and effectiveness of the proposed method. In addition, for F. graminearum proteins that have significant sequence similarity to those in training set, BLAST is utilized to transfer annotations of homologous proteins to uncharacterized F. graminearum proteins so that the F. graminearum proteins are annotated more comprehensively.In this paper, we developed a novel predictor, namely FGsub, to predict F. graminearum protein subcellular localizations in a comprehensive manner. We make four fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can solve imbalance problem and avoid false positive results. Second, we design an ensemble classifier which employs feature selection to further improve prediction accuracy. Third, we use BLAST to complement machine learning based methods, which enlarges our prediction coverage. Last and most important, we predict the subcellular localizations of 12786 F. graminearum proteins, which provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus.In this work, we present FGsub to predict  Fusarium graminearum (telomorph Gibberella zeae) is the causal agent of several destructive crop diseases. For example, F. graminearum causes destructive Fusarium head blight (FHB) on wheat and barley, and is a leading cause of economical loss for these crops [F. graminearum causes economical losses of $3 billion in the United States of America between 1991 and 1996 [The fungal pathogen se crops . It is eand 1996 . In addiF. graminearum proteins can provide insights into biological processes in which proteins are involved, and give hints to pathogen-host interactions. Generally, proteins are transported to specific compartments in a cell so that they can function properly. That is, the subcellular localizations can provide insights into protein functions and help understand the pathogenic process of this destructive fungus. Although the whole genome of F. graminearum has been sequenced and partly annotated [F. graminearum right now. Since it is expensive and time-consuming to determine protein subcellular localizations by biological experiments in lab, computational approaches provide alternative ways to predicting F. graminearum protein subcellular localizations.Therefore, it is necessary to investigate the mechanism underlying the pathogenic process of this destructive fungus, which can help to find an efficient way to control it. The annotation of nnotated , there ak-nearest neighbor classifiers [In literature, a number of machine learning methods have bee developed for protein subcellular localization prediction, such as ssifiers -7, artifssifiers -10, suppssifiers -13, Bayessifiers -16, and ssifiers -20 inclussifiers -23.For example, SubLoc  utilizedF. graminearum protein subcellular localizations from the primary structures, i.e. protein sequences. First, a non-redundant fungi data set with subcellular localization annotation is collected from UniProtKB [F. graminearum protein subcellular localizations for those proteins that do not have significant sequence similarity to those in training set. The performance of SVMs on training set with 10-fold cross-validation demonstrates the efficiency and effectiveness of the proposed method. In addition, for F. graminearum proteins that have significant sequence similarity to those in training set, BLAST is utilized to transfer annotations of homologous proteins to uncharacterized F. graminearum proteins so that the F. graminearum proteins are annotated more comprehensively.In all methods described above, subcellular localization prediction is actually formulated as a classification problem, where the proteins belonging to the target compartment are usually treated as positive samples while the rest are negative samples. Therefore, the number of negative samples is generally much larger than that of positive samples, which leads to imbalance problem and degrade the performance of the classifier . Under tniProtKB  databaseF. graminearum proteins, which can provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. The data sets used in this work and the prediction results are available at http://csb.shu.edu.cn/fgsub/.Consequently, we predict the subcellular locations for 12786 F. graminearum proteins, a set of fungi proteins with subcellular localization annotations were extracted from UniprotKB and used as training set in this work. As shown in Table To predict subcellular localizations of To evaluate the performance of our method, AUC (area under ROC curve) score was employed in this work. First, different features were evaluated for selecting the most informative features. Table Next, we investigated the effects of balancing and feature selection on performance of classifier. In this work, the feature threAA was taken as an example. Figure XdZ with gap d (0 ≤ d ≤ 13) between any two amino acids X and Z. For fair comparison, SVM classifiers were used for all methods. Table F. graminearum.Since there are a number of methods have been developed for protein subcellular localization prediction, to see the performance of our method, we compared our method with several existing methods. In this part, our method was compared with PLOC  and PSLD0 ≤ d ≤ 1 between F. graminearum. First, we predicted F. graminearum protein subcellular localizations based on trained classifiers, and assigned 12532 proteins to 10 subcellular localizations. Second, BLAST was employed to predict F. graminearum protein subcellular localizations based on protein sequence similarity among F. graminearum proteins and fungi proteins with known subcellular localizations so that this can complement SVMs based methods. Moreover, other subcellular localizations with few known proteins were also considered except the 10 localizations used to train classifiers. As a result, 4897 more proteins were assigned to 24 subcellular localizations. Finally, there are 12786 proteins in total that were assigned to at least one of 24 protein subcellular localizations. In summary, our predictions cover about 95.98% (12786 out of 13321 proteins) of F. graminearum proteins. Table F. graminearum proteins among 24 cellular compartments. Figure F. graminearum invades plant host, receives signal, and gets nutrients from host with membrane proteins. The third largest population of proteins are found to belong to Mitochondrion which is also found to be the third largest compartment in Saccharomyces cerevisiae.After getting the training data set and trained classifier, we aim to predict protein subcellular localizations for F. graminearum protein subcellular localizations can guide future experiments and provide insights into protein function and pathogenic process underlying F. graminearum-host interactions. The prediction results are available at http://csb.shu.edu.cn/fgsub/.Although the predictions need to be verified in lab and there are possible false positives, we believe that the predicted F. graminearum proteins with the assumption that proteins in the same cellular compartment should have similar functions. Right now, there are 4321 proteins that have been annotated in MIPS Fusarium graminearum Genome DataBase (FGDB) [In order to validate the predicted protein subcellular locations, we investigated the functions of e (FGDB)  was adopi, j) is the functional similarity score for a protein pair , f (i) represents the set of functional terms from FGDB for protein i, ∩ is the intersection of two sets, U is the union of two sets, and |·| means the number of elements in the set, i.e. cardinality of the set. The higher s is, the higher confidence that the protein pair  have similar functions.where s. It can be seen from Figure In this work, all possible protein pairs predicted to the same subcellular location were compared against all possible pairs predicted to different subcellular locations. Figure F. graminearum in our previous work [in vivo should be co-located in the same cellular compartment. In literature, Shin et al [−15 was obtained based on the hypergeometric distribution defined as following.Furthermore, to verify our predicted results, we use the core protein-protein interaction (PPI) data set predicted for ous work . In ordein et al  have useN = 12786 × 12785/2 = 81734505, n = 19659 + 4197 = 23892, M = 40067528, m = 19659. The p-value shows a statistical significant difference, which demonstrates that a majority of our predictions are correct.Where F. graminearum protein subcellular localizations from the primary structures in a comprehensive manner. We make four fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can solve imbalance problem and minimize false positive results. Second, we design an ensemble classifier which employs feature selection to further improve prediction accuracy. The 10-fold cross-validations and comparison with other methods demonstrate that our proposed methods are indeed effective for predicting protein subcellular localizations. Third, we use BLAST to complement machine learning based methods, which enlarges our prediction coverage. The two methods complement each other and therefore make the predictions more effective. Last and most important, we predict the subcellular locations of 12786 F. graminearum proteins, which provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. Although these predictions are not verified in lab, we believe that the predictions can provide guidelines for future experiments and help to understand this destructive fungus F. graminearum.In this work, a novel predictor, namely FGsub, is proposed to predict F. graminearum protein subcellular localizations. The number of proteins in the data set is 23228, of which 17769 is annotated. After discarding those subcellular location annotations followed ‘By similarity', ‘Potential’ and ‘Probable’, there are 10554 proteins left and were used as reference data set, and these proteins belong to 24 subcellular localizations. Since there are some compartments with few annotated proteins (less than 30), it is not suitable to build classifiers for these compartments due to the small number of samples. Finally, in this paper, 10 subcellular localizations were kept for machine learning based methods, including Extracellular, Cytoplasm, Nucleus, Mitochondria, Endoplasmic reticulum, Golgi apparatus, Peroxisome, Endosome, Vacuole and Cell membrane. Furthermore, proteins located in more than one subcellular compartment were removed, those with less than 50 amino acids in length were removed, and those with ambiguous amino acids  were also removed from the data set. In addition, CD-HIT program [In this work, the annotation of protein subcellular localization for fungi were downloaded from the UniProtKB  database program  was usedIn machine learning, each protein should be represented as a feature vector. In this work, the amino acid triplet (threAA), gapped amino acid composition (GapAA)  , N-term3) features. For gapped amino acid composition, GapAA XdY (d ≥ 1) denotes the peptide of length d + 2, where amino acids X and Y are separated by d amino acids. Given an upper bound of gapped distance I, i.e. 1 ≤ d ≤ l, each protein can be represented as a vector consists of all possible combinations of GapAA XdY (1 ≤ d ≤ l). Therefore, each protein can be represented as a l × 20 × 20-dimensionality vector, where each feature represents the frequency of GapAA appearing in the sequence. For N-terminal and C-terminal features, the first 20 and 40 residues are used for N-terminal composition, and the first 20 and 50 residues are used for C-terminal composition. Furthermore, amino composition (AA), paired amino acids (pAA), one-gapped PAA (Gap1AA), two-gapped PAA (Gap2AA) and three-gapped PAA (Gap3AA) are used as features for N-terminal and C-terminal compositions, respectively. Therefore, each protein is a vector consist of 3240 features for N-terminal and C-terminal compositions respectively.For threAA information, each protein vector is generated consisting of frequency of all possible combinations of three amino acids from 20-amino acid alphabet. Therefore, each protein contains 8000 (20In addition, to reduce effect of protein sequence length, each feature value is normalized as following:ijV is the value for feature j in vector i, m is the dimensionality of the vector i, where j ∊ {1, …, m}. The representations of each protein described above have thousands of features, which leads to high computation cost and the noise in the data generally degrade the performance of classifiers. To find out informative features and reduce computation cost, we first utilize t-test to rank the features and then employ sequential forward feature selection to select the most informative features starting from the top ranked features by t-test. The obtained feature set is used in sequel.where m subsets, where each subset has similar size as the positive data set. After the sampling procedure, we get m training sets, where each training set consists of one subset from the negative data and the positive data, i.e. {positive set, negative subset 1}, ..., {positive set, negative subset m}. With the newly generated data sets, we train m classifiers with one for each training set. Given a new test example, the prediction results are obtained by fusing the outputs from the m classifiers, where the results are combined by a voting scheme.After getting the feature vectors for protein sequences, one classifier can be designed for each localization, and the new protein sequence can be classified into one or more subcellular localizations or none of the ten localizations. However, as described previously, the imbalanced problem will arise in this case. To overcome this problem, a bagging-like re-balanced classifier is presented in this section. Figure n classifiers can be constructed if there are n different ways to describe the protein sequences. For a new test example, the combination of outputs from the n classifiers is the final decision. In this paper, the weighted majority voting method is adopted here, where the weight for each classifier is the AUC score obtained by each classifier on the training set using 10-fold cross-validation.With different feature extraction methods described above, each protein is described in a different way. It has been shown that different descriptions for proteins can lead to different results. Generally, there is no guarantee that one single method can always outperform other methods in any cases. On the other hand, these methods may complement each other, and the combination of these methods may lead to better results. In this work, we combine classifiers trained in different feature spaces introduced by different feature extraction methods. Figure F. graminearum protein subcellular localizations especially for those proteins with low sequence similarity with known proteins. Furthermore, subcellular localization tends to be evolutionarily conserved, and the homologous with localization annotation appears to be a good indicator of the target protein. We therefore use a homology method, in which a BLAST search of a submitted protein is carried out against our database of 10554 proteins with known localization using an E-value cutoff of 1e-10. The localization information of its homologous proteins is then transferred to the target protein.After getting the ensemble classifiers trained on the fungi data set with localization annotations, these classifiers can be used to predict F. graminearum annotations were downloaded from MIPS FGDB [in vivo should be generally co-located in the same cellular compartment. In other words, proteins in the same subcellular localization are more likely to interact with each other.To validate the predicted protein subcellular localizations, we investigate the functions of proteins with the assumption that proteins in the same cellular compartment generally have similar functions. IPS FGDB , where aIPS FGDB . It is fThe authors declare that they have no competing interests.XMZ conceived the basic idea. CLS conducted the experiments and wrote the manuscript. XMZ, WHT and LNC participated in system design, provided valuable comments, and helped to draft the manuscript.