Due to rapid sequencing of genomes, there are now millions of deposited protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a protein of interest to transfer functional information from the homologs to the given protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of protein structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the protein structure space.Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of proteins, we propose higher-order LDA-obtained topic-based representations of protein structures to provide an alternative route for remote homology detection and organization of the protein structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the protein structure domain.We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of protein structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership.This work opens exciting venues in designing novel representations to extract information about protein structures, as well as organizing and mining protein structure space with mature text mining tools. The fIt is often the case that two proteins with similar function cannot be inferred based on sequence information alone. Sequence-based function inference may miss detecting similar proteins where either early branching points (in such case the proteins are referred to as remote homologs) or convergent evolution has resulted in high sequence divergence while largely preserving structure and function. Many sequence-based methods have been offered to extend the applicability of sequence alignment tools for the detection of remote homologs -10. The The presence of remote homologs was identified as early as 1960, when Perutz and colleagues showed through structural alignment that myoglobin and hemoglobin have similar structures but different sequences . BecauseWork on effective structure comparison methods has been spurred due to the Structural Genomics Initiative  aiming tThe majority of structure-comparison methods obtain a structure similarity score after aligning the two protein structures provided for comparison. While this is desirable, particularly in cases when the structures need to be analyzed in detail for the locations of high similarity regions, most structure alignment methods tend to be computationally expensive. As such, they are not suitable to be applied at a large scale over structural databases of proteins for the purpose of detecting structural neighbors of a protein of interest. To address this issue, filter approaches have been proposed, where the objective is to rapidly rule out some structures and employ more expensive structure alignment tools on the remaining set of structures.Most filter approaches for structure comparison rely on finding suitable representations of protein structure so that fast distance measurements can be employed over the representations to rapidly score the similarity of two protein structures without the computationally-intensive step of aligning two structures under comparison ,29-41. Tfragbag representation has been shown efficient and effective at identifying structural neighbors of a given protein, including close and remote homologs . The α Ccoordinates of the particular segment under consideration are compared to each fragment f in the library (1 ≤ i ≤ F), and the fragment with the lowest least-root-mean-squared-deviation (lRMSD) is reported as the fragment matching the particular segment  [The presence of a fragment  points) . The entGiven this representation, any distance or similarity measurements can be used over the fragbag vectors of two protein structures to measure their structural distance or similarity. In , variousGiven fragbag representations of protein structures, the newly defined (fragbag) vector space, which has dimensionality 400, can be reduced to a few dimensions through various dimensionality reduction techniques. In , PCA hasfragbag method. The key idea, first introduced in [We propose an alternative representation of protein structure in this paper based on topics obtained through a popular technique in text mining, LDA. LDA was introduced in  as a genduced in  but limitϕ, is assigned to each topic 1 <= t <= T. Each of these distributions represents the probability of each fragment in F participating in topic t. For each protein i Pthat is constructed, we obtain a mixture of topics by assigning another multinomial distribution, iθ. Each fragment in protein i Pis generated by first selecting a topic t according to iθ, and then using that topic's distribution t ϕto select the actual fragment. Each fragment within each protein represents a latent variable, iz, that is assigned to a specific topic. The assignment of multinomial distributions is obtained from a Dirichlet distribution, which is the conjugate prior for the multinomial distribution. As such, each sample from a Dirichlet yields a multinomial distribution. Separate Dirichlet distributions are used for sampling the distribution of topics within a protein (iθ) and for the distribution of fragments within a topic (ϕt) and are parameterized by α and β respectively.We briefly describe the concepts of LDA and how they map to our investigation of proteins. The graphic model for LDA is shown in Figure iz. This is accomplished using the LDA algorithm from [i zto a random topic and then utilizes many iterations of Gibbs sampling to approximate the ϕ and θ distributions. We direct the reader to [The goal in LDA is to maximize the likelihood of the posterior through the refinement of the topic assignments thm from . This meeader to  for a moIn this context, topics make for general representations of proteins, under which a protein is treated as a mixture of many topics, albeit with different probabilities. As we relate in Results, one can employ these topic-based representations to identify structural neighbors of a protein. We additionally show how topics categorize the protein structure space, revealing interesting insight into what it is that each topic captures about protein structure and function.T. Tuning T can be accomplished by measuring the information gain provided in each topic compared to a baseline [0 pand 1p, One of the parameters in LDA is the number of topics baseline . The disbaseline  to measuT. Let M represent all the parameters, including T, for the LDA model. Equation 1 shows the likelihood of M generating the set of proteins P. Taking the log of both sides yields Equation 2. Equation 3 shows the calculation for computing each protein iP, and taking the log of both sides yields Equation 4. F is the total number of fragments used to describe the ensemble and v appears in protein i. PP(vf|kt) is the probability of the fragment v fbeing in topic kt, which is provided by the multinomial distribution k. Pϕ(kt|iP) is the probability of topic k tbeing in protein iP, which is provided by iθ. These measurements are shown in the Results section to demonstrate that the log likelihood decreases as the number of topics increases.In addition, log likelihood evaluates how well the data (the fragments defining protein domains) fits the model, which in this case is the topic space model produced by LDA. When performing parameter estimation, a common strategy is to maximize the log likelihood as proposed in . We emplN x T matrix, where N is the number of proteins and T is the number of topics. The row vector for protein i Precords the number of times a fragment is classified to be within a given topic jT. Additionally, each protein is assigned a label according to some classification standard; a label corresponds to a class. For instance, a label may be the fold of the protein, as obtained from the top level of the SCOP hierarchy. Alternatively, the label can track the superfamily membership of a protein in SCOP.Each topic may capture "signatures" associated with different classifications . To test for these signatures, we propose using heatmaps constructed over the LDA-computed topic space. LDA presents the topic space as a iL. We sum fragment counts for topic j Ton each protein assigned the same label iL. This provides us with a fragment count for topic j Tin label iL. Normalizing over all labels provides us with probability P(iL|jT). This produces an L x T matrix, where each column in the matrix sums to one. Results in this paper visualize this matrix as a heatmap, with colors following the low-to-high probabilities in a blue-to-red colors scheme.Many protein domains are assigned the same label P(iL|jT) may be assigned to a class with label i Lsimply because of the high number of domains in the class with label iL. This situation arises when analyzing topic signatures over the superfamily classification in SCOP. In this case, we take a different approach to obtaining a heatmap that elucidates topic signatures for protein classes. We employ the ChiSquare significance test [jT. For each protein with label iL, we compute the number of fragments found within topic j T for the binary classifier .The set of 9,852 protein domains in these superfamilies is extracted, and LDA is applied to this set. When using the topic-based representation, each protein's multinomial distribution across the topic space returned by LDA serves as its coordinates in the 10-dimensional space . The resulting 10-dimensional vectors are treated as a training dataset, and 7 classifiers are built (SVM is a binary classifier) in order to predict superfamily membership with binary classifiers. When using the fragbag representation, the training vectors are 400-dimensional as opposed to topic vectors which are 10-dimensional.i (1 ≤ i ≤ 7), the set of training vectors corresponding to domains in that superfamily are treated as the positive training dataset. The rest of the vectors, corresponding to domains in other superfamilies are treated as the negative training dataset. We note that for some of the superfamilies, there are many more negative instances than positive ones, as expected. In such cases, re-balancing of data is performed by undersampling the negative class in order to achieve an equal count of positive and negative instances.When building an SVM classifier for superfamily C = 1.0. Ten-fold cross-validation is used to measure the classification performance, as related in the Results section. For each protein domain, the prediction among the 7 classifiers that has the highest confidence is chosen as the final prediction for that domain. In this way, superfamily membership is predicted for each family, and standard TPR, FPR, and accuracy measurements can be used to evaluate performance.Each SVM classifier is trained independently (on each superfamily), using a polynomial kernel and a soft margin parameter α = 50/(number of topics) and β = 200/(fragment library size). Extracting the fragbag representation for each protein domain in a dataset of 31,155 domains (datasets are detailed below) takes 10 hours. LDA runtimes depend on the number of topics requested and vary from 2 hours for 10 topics to 24 hours for 200 topics. The following analysis conducted here is organized in four sets of experiments. The WEKA data mining package [We use a MATLAB implementation for LDA . All our package  is emploWe first tune LDA varying the number of topics to show that most information can be obtained with a relatively small number of topics. The topics that allow obtaining comparable results in this context are then analyzed in detail in terms of what fragments they capture. This allows associating "semantic" meaning to topics in terms of the over-represented fragments they contain.Second, we demonstrate that the representation of a protein domain through LDA-obtained topics, as described in Methods, is just as useful as the fragbag representation to capture structural similarity and report structural neighbors with comparable accuracy. We do so over a database of 2,930 sequence-nonredundant structures, extracted from CATH, as in ,54. EachOur third set of experiments concerns how topics can be used to organize protein structure space as compared to the fragbag representation. This analysis is placed in context by first demonstrating the usefulness of the fragbag representation in obtaining a low-dimensional map of the protein structure space through PCA. We restrict our analysis and visualization to two levels in the SCOP hierarchy, class and superfamily. The dataset we employ to demonstrate the co-localization of structurally- and functionally-similar proteins (according to classes in a SCOP hierarchy) consists of 31,155 protein domains extracted from SCOP 1.71 . This daOur fourth and final set of experiments demonstrates that the topic-based representation captures important information about a protein structure that allows predicting superfamily membership. Binary classifiers are used for this purpose to predict one of the 7 most-populated superfamilies for given protein structures. Our results show that both representations allow standard classifiers to achieve high prediction accuracy, which we believe opens the way towards using simple representations for automated and reliable hierarchic classification of proteins in databases such as SCOP and CATH.T, starting with T = 10 through T = 200. Figure T . To formulate a quantitative comparison, we compute the mean and variance of each set of KL distances for each of the 11 settings of T, which is shown in the bottom right panel of Figure T [We show that increasing the number of topics results in topics of low information gain, demonstrating that the chosen number of 10 topics is appropriate. We compute the symmetric KL distance, as described in Methods, to measure the information gain of each topic over the baseline distribution of fragments over all SCOP domains. We do so for 11 different settings of Figure T .T in Figure Additionally, we show the log likelihood, measured as detailed in the Methods section, for various settings of Before relating results into how the topic-based representation compares to fragbag and other methods in detecting remote homologs and organizing protein structure space, we provide further insight into what the topics capture. In text mining, peeking into the top populated word(s) readily provides semantic meaning into what a topic captures. It is not possible to directly do so in the protein structure space. However, inspecting the top fragment(s)  and correlating this information with analysis on classes most likely to be associated with certain topics provides information into the meaning of a topic in the protein structure space. The top-populated fragments in each topic are shown in Figure We first compare the ability of the topic-based representation vs. fragbag to identify structural neighbors of a protein. We recall that the dataset employed for this analysis is the sequence-nonredundant dataset of 2,930 protein structures extracted from CATH. Each protein in this dataset is treated as a query. The gold standard on which proteins in the dataset are determined to be structural neighbors of a query protein is obtained by a best-of-six structural alignment protocol, courtesy of R. Kolodny. Three different structural alignment scores (SAS) of 5, 3.5, and 2.0Å are employed. A SAS threshold of 2.0Å allows identifying close homologs of a protein, whereas a threshold of 5Å identifies remote homologs. Given a particular SAS threshold and the gold standard of structural neighbors obtained with that threshold, the following experiment is conducted.Employing the fragbag or topic-based representation and the cosine distance over the particular representation under investigation and continuously varying the decision threshold , a receiver operating curve (ROC) can be constructed, and the average area under the curve (AUC) score can be reported. The ROC curve plots the true positive rate (TPR = TP/(TP+FN)) vs. the false positive rate (FPR = FP/(FP+TN)) over the decision threshold. Summarizing the ROC with AUC allows associating a score with each query protein. Averaging over all proteins in the dataset, essentially treating each of them in turn as a query protein, allows obtaining an average AUC and thus measuring the effectiveness of a particular representation at capturing structural neighbors. Performing this analysis at the three different SAS thresholds further allows judging the effectiveness at capturing close to remote homologs.Figure In particular, the average AUCs on each SAS threshold obtained with the fragbag and topic-based representations are listed in Table We now proceed to demonstrate how the fragbag and topic-based representations can be used to provide low-dimensional maps or categorizations of the known protein structure space.We conduct a PCA analysis on the SCOP dataset described above. The accumulation of variance on the ordered eigenvalues, plotted in Figure α, β, α + β, and α/β proteins. The PCA map in Figure α proteins, whereas the second PC captures most of the all-β proteins. There is more variation in the proteins assigned to the all-β class, but a closer inspection reveals some of these proteins contain one or a few α-helices (data not shown). As expected, the other two folds, which combine a-helices and β-sheets, span the space. The layout of protein folds in this low-dimensional map is in agreement with other studies [Figure  studies ,34.Figure It is interesting to note that the fragbag representation essentially unravels the non-linearity in the protein structure space. In other studies, most notably by Kim and colleagues , MDS hasβ class. This is reversed for topics 5-10. In contrast, the other classes either have a high mixture or a low mixture of each topic. Correlating these results with those shown in Figure α-helical topologies, as evidenced by the top fragment shown. Topics 5-10 are related instead to β-sheet topologies. Put together, these results demonstrate that classes at the fold level of the SCOP hierarchy have unique topic signatures. It is worth emphasizing that this result is made even stronger when considering that, often, domains assigned to the β class may contain a few α-helices (data not shown). The analysis suggests that topics capture structural categorization.Taken together, the above analysis suggests that topic space is an informative low-dimensional embedding of the protein structure space that allows capturing structural similarity. To complete the analysis, we elucidate topic signatures per SCOP class at different levels of the SCOP hierarchy. The heatmap shown in Figure β-sheets, and immunoglobulin domains are all-β proteins. On the other hand, the P-loop Binding domain is rich in α-helices. Encouragingly, the topics that are over-represented in this superfamily are topics 1-4, which capture α-helical fragments, as shown in Figure α-helical fragments. This agrees with the SCOP classification of this domain as all α. Similarly, EF-hand is only significantly represented in topic 1, which is dominated by α-helical fragments. This is in agreement with the all α SCOP classification. The topic signatures capture the other superfamilies, as well, suggesting that topics additionally capture functional categorization.The heatmap in Figure Finally, a set of 7 classifiers is built as described in the Methods section. This experiment is repeated twice, once using the fragbag and the other using the topic-based representation. The distribution of the protein domains employed as training data in each case across the 7 superfamilies is shown in Figure In this work we have investigated a novel low-dimensional categorization of protein structure space combining mature and popular tools in text mining with work in structural bioinformatics. The LDA-obtained topic representation of protein structure is analyzed in detail for its ability to summarize a protein structure with multinomial distributions. Our investigation reveals that indeed meaningful topics can be discovered in protein structures, and that these topics can in turn be used to reveal similar protein structures and organize protein structure space.In particular, results presented in this work suggest that topic-based categorization of protein structures preserves structural and functional co-localization. Specifically, topics obtained through LDA are shown to capture structural similarity with sufficient accuracy on both close and remote homologs and additionally yield a low-dimensional organization of the protein structure space that preserves groupings by structure and function. Topics are also shown to provide sufficient discriminative power to standard supervised learning classifiers like SVMs for predicting superfamily membership. Taken together, the results suggest that the LDA-obtained topic representation of protein structure can be used to aid classification in structural databases.The work presented in this paper opens exciting new venues in extracting and organizing information about protein structures and protein structure space through mature tools in text mining. We additionally hope that this work can inspire further investigation of higher-order representations of protein structures both for structure comparison and for investigating the relationship between protein sequence, structure, and function. Specifically, future work may choose to further mine and refine the topic-based representation in a way that provides visually-friendly categorizations of protein structure to potentially assist hierarchic organizations in current structural databases, such as SCOP and CATH. Additional future work can explore employment of LDA over structure components others than backbone fragments.The authors declare that they have no competing interests.KM suggested the methods and the performance study in this manuscript and drafted the manuscript. JV helped design and implement the techniques, carried out some of the analysis, and investigated the results. AS and DB guided the study, provided comments and suggestions on the presented methodology and performance evaluation, and improved the manuscript writing.


High-throughput bio-techniques accumulate ever-increasing amount of genomic and proteomic data. These data are far from being functionally characterized, despite the advances in gene (or gene’s product proteins) functional annotations. Due to experimental techniques and to the research bias in biology, the regularly updated functional annotation databases, i.e., the Gene Ontology (GO), are far from being complete. Given the importance of protein functions for biological studies and drug design, proteins should be more comprehensively and precisely annotated.downward Random Walks (dRW) to predict missing (or new) functions of partially annotated proteins. Particularly, we apply downward random walks with restart on the GO directed acyclic graph, along with the available functions of a protein, to estimate the probability of missing functions. To further boost the prediction accuracy, we extend dRW to dRW-kNN. dRW-kNN computes the semantic similarity between proteins based on the functional annotations of proteins; it then predicts functions based on the functions estimated by dRW, together with the functions associated with the k nearest proteins. Our proposed models can predict two kinds of missing functions: (i) the ones that are missing for a protein but associated with other proteins of interest; (ii) the ones that are not available for any protein of interest, but exist in the GO hierarchy. Experimental results on the proteins of Yeast and Human show that dRW and dRW-kNN can replenish functions more accurately than other related approaches, especially for sparse functions associated with no more than 10 proteins.We proposed kNN can serve as tools for replenishing functions of partially annotated proteins.The empirical study shows that the semantic similarity between GO terms and the ontology hierarchy play important roles in predicting protein function. The proposed dRW and dRW-The online version of this article (doi:10.1186/s12859-015-0713-y) contains supplementary material, which is available to authorized users. The walker performs direct random walks with restart on the GO DAG with limited number of direct edges, since the maximum depth of GO terms in the whole GO hierarchy (as of July 2014) is 15, t will reach a steady state after several iterations. In our empirical study, we set the number of iterations to 10.where t can move to its descendant GO nodes (if any) and can also stay at t. As the walker walks along the GO DAG, since the transition probability between two terms is filtered by the GO structure and the probability for a walker stays at the starting GO node is not zero, the probability the walker starts from t to its direct child GO nodes (if any) is larger than that to t’s other descendant nodes (if any). Similarly, the walker will not end in one leaf of the ontology, and the probability a walker moves to a leaf node is smaller than that to the leaf node’s ancestors. If the probability for a walker stays at the starting point is 0 , then ti-th protein, for a term t to v, and it can be viewed as the estimated likelihood that the i-th protein is annotated with v also. Taking into account all the available terms in To this end, we make use of the steady transition probability θ is an adaptive threshold parameter and is equal to the mean value of all nonzero elements in θ is adopted to filter out estimations that are too small, since the number of candidate GO terms is rather large and only a few are associated with a given protein. In other words, θ can help removing some false positive estimations, since the missing GO terms of a protein are often located at deeper levels of the GO hierarchy, and the closest ancestors  can provide more accurate estimations than the farthest ancestors [v for the i-th protein.where ncestors . \docume) in Eq.  gives thv currently is not associated with any of the N proteins and it should be associated with the i-th protein, if its ancestor terms  are associated with the i-th protein, since R>0, then Suppose GO term entclass1pt{minimaThe semantic similarity between proteins derived from functional annotations of proteins is correlated with the similarity derived from different types of proteomic data and genomic data , 40, 43,i and j is defined as follows:We use the GO annotations of proteins and Lin’s structure similarity to compute the semantic similarity between two proteins. We then use this semantic similarity to determine the neighborhood relationship among proteins and to predict missing functional annotations of partially annotated proteins. Similarly to Tao et al. , the semi and j. Each pair in where kNN style classifier to predict functions for a partially annotated protein. In particular, we make use of the missing functions pre-estimated by dRW and the functions of the nearest neighbors. The probability that a protein i is annotated with term v  as input. If two entirely different proteins with the same initial functional annotations, they will be predicted with the same functions by dRW and dRW-kNN. In fact, the proposed dRW-kNN can incorporate other kinds of proteomic data  to determine the neighborhood proteins of a protein. In this way, two entirely different proteins with the same initial functional annotations can have different neighborhood proteins and be predicted with different functions. To focus on the main idea of random walks on the GO hierarchy and to keep consistency with other related methods, we do not include other kinds of proteomic data in the experiments. It is an interesting future work to integrate the protein-specific information with dRW-kNN and to study the difference between using semantic similarity and protein-specific information.The proposed dRW and dRW-kNN. Naive is solely based on the frequency of a function, and it can not replenish the second kind of missing functions. In contrast, dRW and dRW-kNN depend on the functional annotations of proteins and the GO hierarchy, and they can predict the second kind of missing functions, and they are not so dependent on the frequency of function as Naive. In addition, dRW-kNN utilizes the semantic similarity between proteins to determine the functional annotations of proteins, and the semantic similarity is correlated with different types of proteomic data, thus dRW-kNN has the potential to incorporate other proteomic data, whereas Naive does not. Our following experiments will show that dRW and dRW-kNN produce quite different results from Naive in the same experiments.The Naive method suggested by Clark and Radivojac  is a bas1 that contains hierarchical relationships between GO terms organized in three orthogonal axes of biological concepts. The Gene Ontology Annotation (GOA) files of Yeast and Human were obtained from the European Bioinformatics Institute2. Each GOA file contains annotations relating the genes to the three sub-ontologies, namely BP, MF, and CC. We processed the GO file to exclude the GO terms annotated as ‘obsolete’. We processed the GOA file to exclude the annotations with evidence code ‘IEA’ (Inferred from Electronic Annotation), ‘NR’ (Not Recorded), ‘ND’ , or ‘IC’ (Inferred by Curator).We downloaded the GO file3. We apply the true path rule to append all the ancestor GO terms of the detailed terms for a protein. The summary of processed GO annotations of proteins are listed in Table The missing functions of a protein are often located at deep levels of the GO hierarchy. These functions are typically associated with no more than 10 proteins and thus are viewed as sparse functions (or GO terms). Many methods are shown to work well for the terms associated with 10 (or more) proteins , 15, 23.completely masked in the experiments, thus they end up not being associated with any of the N proteins, though they exist in the GO hierarchy. These completely masked terms are viewed as the second kind of missing functions.There are no off-the-shelf datasets that can be directly used to check the performance of missing functions prediction, since the GO and GOA files are updated regularly. Similarly to the experimental protocol in Yu et al. , we assum to denote the number of missing functions of a protein, Nm to represent the number of masked functions, and N proteins for a given setting of m. For example, m=3 means three functions are masked for a protein, N3=1000 means 1000 functions are masked for the N proteins, and N proteins. For the protein that is annotated with no more than m functions, we do not mask all the functions, and ensure it has one GO term (if any). A portion of the proteins has no function annotations, and we do not apply the mask operation on these proteins.In the experiments we use kNN against PILL [We compare our proposed dRW and dRW-nst PILL , ITSS [2nst PILL  and Naivnst PILL . PILL annst PILL . The parMacroF1, AvgROC, RankingLoss, RAccuracy, Fmax, and Coverage. These evaluation metrics measure the accuracy of protein function prediction according to different aspects, and they have been applied to evaluate the results of multi-label learning and protein function prediction [1-RankLoss instead of RankingLoss. In this way, the higher the value of all the evaluation metrics (excluding Coverage), the better the performance is. Since the various metrics capture different aspects of the methods, it is difficult for a single approach to consistently outperform the others across all the evaluation metrics.The accuracy of protein function prediction can be assessed by different evaluation criteria or metrics, and different prediction models are affected by different metrics. To do a fair and comprehensive comparison, we use six evaluation metrics, namely ediction , 31, 35.kNN on predicting missing functions of partially annotated proteins. For each setting value of m, we randomly mask m functions of a ‘completely’ annotated protein. The masked functions are considered as missing functions of the protein. We then apply the competing methods to predict the missing functions and evaluate the performance by using the evaluation metrics introduced above. For each setting of m, we repeat the mask and evaluation operation in each round for 10 times. In Table m=1,3,5) on proteins of Yeast annotated with BP functions. Other results on Yeast and Human are provided in Tables S1-S5 of Additional file bold font are the best (or comparable best) statistically significant results, according to a pairwise t-test at 95 % significance level.In this section, we conduct experiments to investigate the performance of dRW and dRW-kNN and dRW achieve the best results in most cases, and dRW-kNN often gets better results than dRW. In summary, out of 108 configurations , dRW-kNN outperforms dRW in 65.74 % of the cases, outperforms ITSS in 94.44 % of the cases, and outperforms PILL in 83.33 % of the cases; ties with them in 7.41 %, 4.63 %, and 5.56 % of the cases; and loses to them in 26.85 %, 0.93 %, and 11.11 % of the cases, respectively. dRW-kNN also outperforms the baseline approach Naive in almost all the cases. For example, on RAccuracy which evaluates how many missing functions of N proteins correctly replenished, dRW-kNN achieves 12.23 %, 44.11 %, 35.69 % and 105.37 % improvements over dRW, ITSS, PILL and Naive, respectively. The superior results achieved by dRW-kNN confirm its effectiveness in predicting missing functions of partially annotated proteins, and also further support the integration of downward random walks with the semantic similarity between proteins for missing function prediction.From these tables, we can observe that dRW-kNN. From the tables, we can observe that both dRW and dRW-kNN statistical significantly outperform ITSS in most cases, the reason is that the former two methods can pre-estimate the likelihood of both kinds of missing functions: the ones that exist in the neighborhood of a protein, and the ones that are not associated with any proteins but are semantically similar to some of the existing ones. In contrast, ITSS can only predict the first kind of missing functions. PILL utilizes Lin’s corpus similarity, which computes the information content of a term based on the term’s frequency in the corpus. For a GO term that does not exist in the corpus, its similarity with respect to other GO terms is set to 0. Therefore, PILL can only predict the first kind of missing functions and it loses to dRW in most configurations.dRW directly predicts functions of a protein by performing random walks on the GO DAG, and sometimes achieves comparable results with dRW-N proteins. It often achieves the lowest performance, and sometimes stable results with respect to some evaluation metrics . This fact shows the need of designing tools to effectively predict protein functions. Naive sometimes achieves higher 1-RankLoss than other methods; this is because 1-RankLoss favors the predictor that produces correctly ranked pairs of functions, and it is in tune with the Naive method, which ranks the functions based on their frequencies.The Naive method predicts functions based on the frequency of the GO terms of the kNN and ITSS is that dRW-kNN takes advantage of dRW to pre-estimate the missing functions, whereas ITSS does not. The performance margin between dRW-kNN and ITSS is much larger than the margin between dRW-kNN and dRW. This fact shows the downward random walks contribute much more than ITSS (or kNN) on predicting missing functions. This observation also demonstrates that downward random walks are of paramount importance to enhance the performance of missing function prediction.The main difference between dRW-kNN and ITSS, we measure the Area Under the ROC Curve (AUC) of each GO term, and use ΔAUC to represent the AUC difference between dRW-kRW and ITSS. To investigate the performance of dRW-kNN and ITSS for different levels of sparsity, terms are divided into three groups: (i) terms associated with at least 3 but less than 10 proteins ), (ii) terms associated with at least 10 but less than 30 proteins ), (iii) and terms associated with at least 30 proteins (≥30). The ΔAUC on Yeast annotated with BP terms for each group are reported in Fig. To further study the difference between dRW-kNN achieves a larger AUC than ITSS with respect to most GO terms. The terms in the group [ 3,10) have the largest ΔAUC, followed by the group [ 10,30) and then by the group ≥30. This observation shows the proposed dRW-kNN can achieve better results than ITSS on the sparse terms. There are two reasons: (i) dRW-kNN applies dRW to pre-estimate the likelihoods of missing functions of a protein, and then makes the prediction based on the pre-estimated likelihoods and on the available functions associated with the neighborhood proteins, while ITSS makes predictions only based on the functions associated with the neighborhood proteins; (ii) dRW-kNN can predict the second kind of missing functions, while ITSS can not. For the terms associated with at least 30 proteins, dRW-kNN still outperforms ITSS in some cases. These results also support the effectiveness of dRW in estimating missing functions of a partially annotated protein.From these figures, we can observe that dRW-IC(t) by the frequency of term t in the corpus. dRW-Disjoint does dRW on the GO hierarchy based on a recently proposed disjointness axioms similarity [bold font are the best statistically significant results, according to a pairwise t-test at 95 % significance level.We conduct additional experiments to study the influence of dRW based on different semantic similarities between GO terms. Here, we introduce three variants of dRW: dRW-Corpus, dRW-Disjoint and dRW-E. dRW-Corpus performs dRW on the GO hierarchy based on Lin’s corpus similarity, which computes milarity . dRW-E aFrom these tables we can see that dRW achieves better results than the three variants in most cases. Our proposed dRW almost always significantly outperforms dRW-Corpus. The reason is that the GO annotations of proteins are far from complete, dRW-Corpus makes twice use of the annotations, whereas our proposed dRW makes one use of the annotations. Therefore, the latter one is less suffered from the incomplete annotations. There is no explicit disjoint relationship between terms in the GO hierarchy. For the experiments, two terms never co-associated with the same protein are viewed as disjoint terms. For this reason, there are some false disjoint terms. Thus, dRW often performs better than dRW-Disjoint.η than by W . In fackNN and other comparing methods in situations that reflect real life scenarios, we predict missing BP functions of partially annotated proteins using an older version GOA file (date: 2010-01-20) of Yeast and Human, and then validate the predicted missing functions using a recent GOA file (date: 2014-06-09). The parameters settings of these comparing methods are kept the same as in the previous experiments. We process the older GOA file in the same way as the recent GOA file used in the previous experiments. We keep the root BP GO term (‘GO:0008150’) in the historical rollback experiments, since some new annotations of proteins correspond to the direct child nodes of the root node. After preprocessing, there are 4,338 terms associated with at least one protein in the recent Yeast GOA file. Among these 4,338 terms, 3,581 terms are associated with at least one protein in the older Yeast GOA file. Thus, 757 terms are not associated with any proteins in the older Yeast GOA file. As to Human proteins, there are 11,212 GO terms associated with at least one protein in the recent GOA file. Among these 11,212 terms, only 6,823 terms are associated with at least one protein in the older Human GOA file. Therefore, 4,389 terms are not associated with any protein in the older Human GOA file. Obviously, the historical rollback experiment is more challenging than our previous experiments. ITSS, PILL and Naive can not predict the GO terms that are not associated with any proteins in the older GOA files.To further study dRW, dRW-We choose the top 100 largest likelihoods predicted by each of the comparing methods and report the number of true positive predictions in Table kNN take advantage of downward random walks, but they do not share any true positive predictions. The reason is that dRW directly uses the available annotations of a partially annotated protein and GO hierarchy to replenish the missing functions, and dRW-kNN replenishes the missing functions of a protein based on the GO annotations associated with its neighbors. dRW and PILL make different true positive predictions. The cause is that PILL utilizes Lin’s corpus similarity and it does not utilize the GO structure in replenishing the missing functions. Both dRW-kNN and ITSS predict the missing functions of a protein by the GO annotations associated with its neighbors, they share 2 true positive predictions on Yeast and 14 true positive predictions on Human. For example, they correctly predict that SAC7 (a protein of Yeast) is annotated with GO:0006259 and GO:0044260.The true positive predictions made by dRW are different from those of other comparing methods. Both dRW and dRW-kNN wrongly predicts that ENO2 is annotated with GO:0000027 and GO:0042273. That is because dRW gives high priority to a GO term that has large semantic similarity with the terms associated with a protein, and dRW-kNN prefers terms associated with the neighbors of the protein. dRW and PILL do not share any false positive predictions. For example, dRW assigns GO: 0071507 to Yeast proteins ATP6 and CHO2, and PILL assigns GO:0071507 to Yeast protein MDL1. They also wrongly assign different GO terms to the same protein. dRW assigns GO:0043001 to Yeast protein CPA1, whereas PILL assigns GO:0030476 and GO:0070591 to CPA1. dRW-kNN and ITSS share 30 false positive predictions on Human and they share 10 false positive predictions on Yeast proteins PTP1, SIP18, SNU13 and YSF13, but they assign different terms to protein PTP1. These facts can be attributed to that dRW-kNN replenishes the functions by the functions associated with the neighbors of a protein, and the ones pre-estimated by dRW, whereas ITSS only uses the functions associated with the neighbors of the protein.The false positive predictions made by dRW and those of other comparing methods are also quite different. For example, dRW wrongly predicts that Yeast protein ENO2 is annotated with GO:0034462, whereas dRW-kNN, ITSS and Naive remain the same. By applying the true path rule, the number of positive predictions produced by dRW and PILL increases sharply, and dRW achieves the largest number of true positive predictions. dRW can predict the first and second kind of missing functions, and thus for dRW-kNN. In contrast, the other comparing methods can only predict the first kind of missing functions. The missing functions predicted by dRW are not only sparse (associated with no more than 10 proteins), but also locating at relatively deeper levels (≥4) in the GO hierarchy. These results indicate that dRW can more accurately predict functions that locate in deep levels of the GO hierarchy than others, and also support our motivation to apply dRW along with the GO hierarchy for missing function prediction.Regardless of the application of the true path rule, the positive predictions produced by dRW-As to the true positive predictions made by Naive, they are always associated with the same GO term (‘GO:0009987’ for Yeast proteins and ‘GO:0044699’ for Human proteins). In the older GOA files, these two terms, locating at the 2nd level of the GO hierarchy, are associated with 3768 Yeast proteins and 5517 Human proteins, respectively. Thus, we can say that the functions predicted by Naive produces are rather shallow.By applying the true path rule, the number of true positive predictions made by PILL increases from 0 to 11 (on Yeast) and from 19 to 80 (on Human). PILL directly uses the available functions of a protein and the semantic similarity between GO terms to predict the missing functions of proteins, but it only uses the GO hierarchy to compute the semantic similarity between terms, and does not use the hierarchy in the process of missing function prediction. In addition, the semantic similarity between terms is calculated based on Lin’s corpus similarity. Therefore, PILL loses to dRW and it can not predict the second kind of missing functions.kNN gets a larger number of true positive predictions than ITSS, although they both utilize the same semantic similarity between proteins and a kNN style classifier to predict missing functions of proteins. The cause is that dRW-kNN takes advantage of dRW to pre-estimate the missing functions. The difference between dRW-kNN and ITSS supports the benefit of using dRW for missing functions prediction. However, after applying the true path rule on the top 100 positive predictions, these two methods do not produce any new true positive predictions, whereas dRW makes more true positive predictions. That is because (i) dRW-kNN and ITSS predict missing functions of a protein based on the functions associated to its neighborhood proteins, and the larger the frequency of a function is, the more likely it is considered as missing for the protein; (ii) the probability of functions associated with neighbors is set to 1, and the probability of missing functions of a protein pre-estimated by dRW is smaller than 1. Since we only choose the top 100 predictions, the functions associated with neighbors are favored against the pre-estimated ones. From these examples, we can see that dRW is inclined to predicting the second kind of missing functions and dRW-kNN is biased towards the prediction of the first kind of missing functions.dRW-kNN are lower than that produced by Naive. However, we should not simply conclude that Naive performs better than the former two methods. From the list of true positive predictions in the four additional excel files, the missing functions of proteins predicted by Naive are shallow and bring little biological knowledge, these functions are associated with a large number of proteins and locates at the 2nd level of GO hierarchy. In contrast, the missing functions of proteins predicted by dRW and dRW-kNN are not only locating at deeper levels than the ones made by Naive, but also associated with much fewer proteins in the older GOA files.Even if applied the true path rule, the true positive rates achieved by dRW on Yeast and Human, and that made by dRW-kNN achieve lower accuracy in the rollback experiments than in the masked GO terms experiments. The reasons are three fold: (i) both the GO structure and the terms are updated from 2010 to 2014. For example, the number of direct GO annotations (without appending the ancestor terms via true path rule) provided in the Human GOA file increases from 29,407 to 74,109 by 2014-06-09, the number of terms in GO also increases from 34,427 to 41,239. (ii) The number of considered GO terms in the historical rollback are 4,338 for Yeast and 11,212 for Human, whereas the number of considered terms in the previous experiments are 2,979 and 7,294, respectively. In addition, the number of second kind of missing functions in the masked GO terms experiments are about 209 for Yeast and 135 for Human, and the number of second kind of missing functions in the rollback experiments are 757 for Yeast and 4,389 for Human. (iii) The masked GO terms experiments randomly mask some leaf terms and treat these masked terms as missing functions of proteins, but the appended missing functions of a protein do not always follow the same random pattern. In fact, in the recent GOA file, we found that the appended missing functions of a protein are the descendants of one or several terms associated with the protein, instead of all the terms associated with the protein. For example, the appended annotations of Human protein ALG6 are the descendants of GO:0006464, and the appended annotations of Human protein CLDN16 are descendants of GO:0008150.dRW and dRW-conservative, since a positive prediction without a corresponding validated annotation might simply indicate a lack of study of the protein, rather than an incorrect prediction. The proteins in the recent GOA file are still partially annotated. Over time, more true positive predictions will be validated and also some false negative predictions may be resulted in. We also have to notice that dRW, dRW-kNN and the comparing methods can bring over-annotated functions of proteins, and they are not the best approach for every protein. These over-annotated functions of a protein are descendants of the available functions of the protein, they are often corresponding to specific ones. In addition, these over-annotated functions do not have biological evidence support. The reason is that dRW and dRW-kNN only use the GO structure and available annotations of proteins to predict the missing functions. How to address these limitations is an open problem and interesting to pursue in future work. One possible way is to filter out the over-annotated functions by referring to other data sources  and softwares . We stikNN, that perform downward random walks with restart on the Gene Ontology directed acyclic graph, and the available functions of proteins to predict missing ones. The proposed models are able to predict two kinds of missing functions: the functions that are associated with some proteins but are missing for others; and the ones missed for all the proteins of interest but that exist in the GO hierarchy. Our empirical study on the proteins of Yeast and Human shows that the proposed models outperform several competitive related methods. This paper will drive more research on missing function prediction of partially annotated proteins. As part of our future work, we are interested in investigating other semantic similarities between GO terms and incorporating protein specific information to accurately predict missing functions.In this paper we study the problem of predicting new functions for partially annotated proteins. We propose two methods, dRW and dRW-1http://geneontology.org/page/download-ontology. 2ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/. 3http://geneontology.org/page/go-annotation-conventions.


Sequence–function relationship in a protein is commonly determined by the three-dimensional protein structure followed by various biochemical experiments. However, with the explosive increase in the number of genome sequences, facilitated by recent advances in sequencing technology, the gap between protein sequences available and three-dimensional structures is rapidly widening. A recently developed method termed deep mutational scanning explores the functional phenotype of thousands of mutants via massive sequencing. Coupled with a highly efficient screening system, this approach assesses the phenotypic changes made by the substitution of each amino acid sequence that constitutes a protein. Such an informational resource provides the functional role of each amino acid sequence, thereby providing sufficient rationale for selecting target residues for protein engineering. Here, we discuss the current applications of deep mutational scanning and consider experimental design. Protein engineering has extensively been applied to several fields of biotechnology, including the medical sciences and for industrial applications. Since the first report of protein engineering by site-directed mutagenesis in 1982, numerous proteins have been engineered using various techniques developed by technological advances ,4. Such Integration of high-throughput sequencing technology with protein engineering involves the coupling of protein variant generation with a high-throughput screening system. Instead of selecting a few selected protein variants, high-throughput sequencing allows the sequencing of millions of protein variants, termed as deep mutational scanning . DependiDeep mutational scanning as a concept is quite exciting. It identifies the effect of all possible amino acid changes for each position in a protein and compares the enrichment of mutation to appropriate selection pressure depending on the characteristics of the protein. This is enabled by the delivery of the information between thousands or millions of mutations to protein variants’ phenotype by high-throughput sequencing. Compared to the initial protein variant library, the mutations quantified after selection enable identification of the effect of mutations in certain residues to the functional change of the protein variant. Overall, the elucidation of such vast information on protein sequence space in relation to selection pressure aims to determine the protein sequence and function relationship . In addiBecause deep mutational scanning is a relatively new concept that differs from conventional protein engineering strategies and experimental designs, this review introduces the basic overview of the methodologies that help determine the experimental design as well as the computational analysis using recent studies. Precautions for data interpretation are also discussed along with perspectives on the current limitations and future developments.To design a deep mutational scanning experiment for a target protein, three conceptual steps that involve construction of the protein variant library, screening, or selection for functions, and high-throughput sequencing must be considered. First, a library containing the mutated sequence of the target protein is generated, which is used as the input variant library a; SecondThe desired sequence space and the size of the protein must be considered for the initial mutant library generation. For instance, a hypothetical protein “hyp-A” constituted by 100 amino acids would require the generation of 1900 different variants for single site mutagenesis of all residues that constitute the protein. To obtain an unbiased mutant library that constitutes all possible sequence space requires synthesis of 1900 oligonucleotides or 3800 primers. The complete coverage of initial library over the sequence space and unbiased concentration of each mutant is important because the initial library is also sequenced as a control . To valiOligonucleotide-directed random mutagenesis is another mutagenesis method frequently used in deep mutational scanning ,34,35,36et al. used PCR-based random mutagenesis, which showed that the mutation frequency of AT-to-NN was similar to GC-to-NN [Another method for generating a protein variant library is random mutagenesis by PCR ,39,40,41GC-to-NN . OverallThe expression system for deep mutational scanning is not too different from the conventional protein engineering methods that require a selection system. Similar to any directed evolution strategy in protein engineering, the expression system requires a link between the mutated DNA and the protein variant that enables determination of the corresponding mutation responsible for the changed phenotype . On the et al. used the phage display system to investigate the affinity of binding of the WW domain (named by the two conserved tryptophan residues in the domain) to its peptide ligand [Recent studies have shown that three types of expression systems are frequently used in deep mutational scanning—the plasmid system, phage or yeast display system, and bacterial or yeast two-hybrid system . Among te ligand . Using be ligand . Thus, tin vivo. McLaughlin et al. have used the bacterial two-hybrid system to quantitatively link the expression of fluorescent proteins, which enabled FACS to be used as the selection system [pdz3, which were tested by repeated experiments with different peptide ligands. This study is exemplary in that the discovery-based approach was used to gain information and the gained information was used to successfully apply hypothesis-driven engineering. The two-hybrid system is very effective and efficient in that the reporter system allows rapid quantitative analysis. Although this system requires the preparation of the two-hybrid system for either bacteria or yeast, this system would be a very good candidate for deep mutational scanning in proteins with binding affinity functions.The “two-hybrid” system is a screening system used in protein–protein and protein–DNA interactions to activate a downstream gene by binding it to a transcription factor ,50. One n system . Using tin vivo cell-based screening assays [The plasmid expression system is one of the oldest protein expression systems that is relatively easy to handle . For appg assays ,26,28,29et al. developed a circle sequencing method that involves denaturing DNA into single-stranded DNA (ssDNA) followed by circularization [High-throughput sequencing has generated a large amount of sequence data in different applications. In particular, the sequencing of genomes, transcriptomes, ribosome-interacting RNAs, and protein-interacting DNAs has been widely used in countless studies, regardless of the organism or the application ,53,54. Trization . By usinrization . Anotherrization ,59,60. Frization ,62. To det al. used a plasmid system that had an ampicillin resistance gene [E. coli would not survive in the presence of ampicillin without the resistance gene, mutations that had a lower frequency than the sequencing error rate found in the ampicillin-resistance gene were removed from the gene of interest (GoI)  d. In thiOther aspects of the sequencing process that must be considered are the sequencing depth and library diversity. The sequencing depth can be calculated by considering the length of the target protein and the number of possible variants. For example, hyp-A is 300 bp long and the number of possible variants is 1900. By multiplying the protein length with the number of possible variants, we find that 570,000 is the number of base pairs required for the sequencing depth to be 1×. Thus, sequencing of two variant libraries with 100× depth requires 114 Mb, which is not a problem considering that the current benchtop sequencing platforms can produce gigabytes of data . HoweverAs is the case for all applications of high-throughput sequencing data, the data analysis requires to be coupled to a logically and statistically acceptable workflow to answer the biological question or determine its relevance. For deep mutational scanning, the protein’s sequence and function relationship is the main question towards which the data analysis should be directed. Conceptually, this can be divided into two parts: analysis of the data to generate a mutational map or table and data interpretation of the mutational map. While data analysis involves bioinformatics to determine the abundance of each mutation at all positions, data interpretation is directed at determining the biological implication by calculating the abundance of mutations to different amino acids at different sites.et al., respectively [The nature of deep mutational scanning data is that it contains many mutations. Thus, data analysis is directed at identifying the mutations that result in a codon change in genes coding for different amino acids and determining the abundance of such mutations at all positions for each amino acid. The first part of deep mutational scanning data analysis does not differ from the conventional treatment of sequencing data. After removal of low-quality reads, the data should be mapped to the vector system sequence, the organism genome, and PhiX and only the unmapped reads should be collected. This step essentially removes any possible contaminations. If the sequencing library is constructed with the sequencing error precautions described in ectively ,66. Afteectively ,67. The ectively .i.e., these sequences have a structurally or functionally important characteristic. Sites with mutations indicated in red color are bombarded with every amino acid change, suggesting that these sites are tolerant to mutations, i.e., these residues do not affect protein function or that their effects are negligible. This information is important because it identifies structurally important residues that are sequentially and structurally far from the active sites and difficult to predict. Sites with enriched mutations after selection are also quantified by different equations in different studies to show the importance of these residues. The characteristics of the residues can also be identified by investigating the type of amino acid changes. For example, the heatmap in Interpretation of deep mutational sequencing data starts from the investigation of sites that have enriched mutations. With this perspective, it is advantageous to generate a colored heatmap from the data in the mutational table because it contains information that is easier to interpret by visualization a. First,The advance of high-throughput sequencing technology and screening assays has led to the development a new strategy of protein engineering called deep mutational scanning. This strategy has succeeded in showing the mutational consequence of different proteins by using different combinations of methods for variant protein generation, screening, and sequencing. However, mutational scanning is not applicable to all proteins because not all proteins are coupled with high-throughput screening assays to investigate their function. Furthermore, analysis of multi-site mutations and their epistasis is difficult because it requires a method that can screen tens of millions of mutants and be applicable only to small-sized proteins because of the sequencing read length.in vivo. Nevertheless, fashioning a screening system is a challenging task but is essential to deep mutational scanning, which is its biggest limitation. A means to overcome this problem would greatly enhance the future prospects of deep mutational scanning.The availability of a high-throughput screening system is a difficult concern to be addressed. For deep mutational scanning, the screening system requires the DNA to be linked to the phenotype and the screening system needs to be a high-throughput system. Designing such a screening system is difficult for enzymes and proteins for which the interacting substrate is not known, especially if the protein only functions pdz3 [Analysis of multi-site mutations and their epistasis has been performed in a few studies ,33,48,70pdz3 . Interespdz3 . For larpdz3 ,72,73,74Overall, deep mutational scanning strategies provide mutational data of a protein by linking mutation to the phenotype. Such mutational data serve as valuable information to understand the proteins’ functional mechanism, which can then be applied to clinical application of antibodies, studies on human genetic diseases, structural protein sciences, and protein engineering ,23,27,53


The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins , organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure–activity relationships.Database URL:http://drumpid.bioapps.biozentrum.uni-wuerzburg.de New analysis technologies have contributed to huge volumes of molecular data. Numerous databases have been developed to explore these  with comThe drug-minded protein interaction database (DrumPID) has been designed for researchers to quickly obtain custom, tailored information on drugs and protein interactions with the idea to rapidly understand and screen related compounds for their effects in protein interaction networks considering related organisms. It fills here a niche between the current databases, quite useful to explore potential antibiotic lead structures, optimizing predictions from animal tests and better explore the chemical space around a compound together with the protein interaction networks affected. For each capability DrumPID makes direct calculations based on the chemical properties of the drug, collating and comparing information from several databases, as well as its own stored data.A broad user interface is displayed on multiple windows, allowing the user to compare drug-centered and protein-centered queries at the same time. Multiple windows also allow the user to study and compare targets and interactions between different drugs. Moreover, the acquired information can be further analyzed with biological software systems, such as cytoscape and embedded plugins. Besides the drug name, chemical structures (SMILES notation) and affected proteins  can also be queried. Furthermore, a combination of querying options allows the user to derive information as well as screening for drugs and drug families, their chemical properties, involved protein networks, organism-specific protein interactions and general protein families. SMILES strings help in posing queries. They are easily placed in large windows. There is an intuitive auto-completion function as well as automatic removal of blanks. Additional search options cover information on indications and pathway maps. Moreover, an implemented similarity search also enables the identification of similar drug molecules for SMILES notations and allows further analyses, e.g. potential targets, especially for new synthesized compounds.http://www.drugs.com/) by warehousing existing information and drug links.FDA-approved drugs from the DrugBank database , 2 were For each drug, we downloaded protein targets and corresponding pathways from the DrugBank , 2 and Khttp://lifescience.opensource.epam.com/indigo/). Moreover, we implemented an additional perl script which converts SMILES strings into a PDB structure file for download (opening in a new popup window).Based on the SMILES notation we calculated the corresponding drug structure (as SVG output file) using the command-line utility indigo-depict from the cheminformatics indigo toolkit  and calculated data  were internally stored and warehoused into a MySQL database (in which each drug is given a unique identifier) to benefit from the advantages of a relational database. These include efficient data management, easy data-updates and rapid accessibility for our search engine. There is an inbuilt logic of database preference and information provision , which is implemented mainly in PHP . In addition, we implemented a similarity search for SMILES notations using the fingerprint function  from the cheminformatics R package rcdk . For detailed information and workflow, see DrumPID offers multiple search categories with multiple queries and keywords (including data mining applications) and query assistance  and demonstration queries guide the user.Queries place the drug into its protein interaction context considering indication/associated pathogen, generic drug names, compounds (SMILES notations) as well as affected proteins and networks . Each dridentifiers ,biological properties (targeted protein interactions and pathways including source scheme),protein binding affinity and orthologous groups  of targeted proteins,chemical properties (e.g. Lipinski’s rules), pharmacological information and indications see .DrumPID contains 1383 FDA-approved drugs, 4951 proteins, 4078 ortholog groups  and over 1 million different protein interactions (various organisms); in addition, stored data from >5000 FDA and non-FDA approved drugs are made available (upon acceptance of the manuscript). Up to 50 results are shown in one page, indexed entry numbers allow to browse all results. The result page is divided into different sections:In addition, pathway cards from KEGG are directly shown (zoom out by mouseover). Moreover, for protein interactions we provide crosslinks to organism-specific interactome databases [HPRD , iHop 1, STRING http://www.ccdc.cam.ac.uk/pages/Home.aspx; over 800 000) and ChemSpider (http://www.drugs.com/) are combined; regarding structure, function and interaction, predictions from AnDom (http://www.selleckchem.com/) offer users the option to get further detailed information on a drug depending on the scientific interest.Compared with other databases, the DrumPID interface is easy to navigate and handle, and results are all quickly accessible from within one page. Alternative databases excel in complementary aspects: ChEMBL  regardinemSpider , 2 linksemSpider , 2 and KemSpider  are combemSpider , 2 and KemSpider  as well om AnDom  and GoSyom AnDom  are combom AnDom  and diseom AnDom , 15. ThuMoreover, for each drug target the corresponding orthologous group (COG/KOG) is calculated. The COG/KOGs are calculated comparing over all complete genomes all proteins belonging to the same gene family and labeling them with the same number to indicate that they belong to the same cluster of COG/KOG sequences. The COG/KOGs help to identify all proteins of this family occurring in the same organism. For a given drug, this indicates how easy the drug will reach other proteins also belonging to the same family as the target protein, which gives a good first estimate for potential side-effects . Furthermore, comparing the same COG/KOG over a range of organisms allows the prediction of drug effects for a whole clade or even larger groups. This is, for instance, useful when estimating how broad acting an antibiotic drug will be. Furthermore, the COG/KOG also allows the user to see the complete variation of the target protein family over all organisms. Together with some pharmacological data (of course only if available) this allows a first estimate of quantitative structure–activity relationship for the target protein COG/KOG family .In addition, our substructure similarity search option helps users to find a corresponding substring for a SMILES input and allows further analysis, e.g. drug target analysis. Thus, the search option helps to obtain information of the drug functionality which is of interest especially for previously unknown and new synthesized compounds. Moreover, due to the fact that substrings can influence various targets, the similarity search also allows for an input drug the SMILES-based identification of additional side-targets based on the substrings, which might be helpful for drug development, e.g. off label use or new treatment options by adding new side chains.in silico search modes of different types and are specific predictions generated from DrumPID for each use case. They were specifically generated by us to show the potential of DrumPID based on typical scientific questions and challenges in drug research. Our DrumPID allows an integrated drug-minded view at the same time, this makes investigation of drug–protein interactions more specific, e.g. functional COG/KOGs information in different organism, large-scale and tissue specific interaction screens such as in platelets (PlateletWeb) and other tissues (e.g. GoSynthetic and STRING database) as well as experimental (e.g. IMEx and STRING database) and predicted protein interactions (STRING database) . A natural limitation of DrumPID is the amount and type of data stored, in particular searches will only work according to the key words given and suitable matches to data stored. For a good search, a detailed analysis combining several steps and key words is best . A further option besides querying with key words  are searches with SMILES strings according to chemical similarity.The following application examples illustrate how DrumPID can be used to find drugs for a target and rapidly explore its target interaction context. The examples illustrate The standard use case is to get information on a specific drug or to screen a database to find available drugs for a target protein. For example, we are interested in a drug to activate the AMP kinase (AMPK) in cancer cells. For this, users can screen DrumPID for tyrosine-protein kinases . We will focus on one of these, Metformin see , which cThis connection, as well as other such interactions, alludes to Metformin’s recently discovered life prolonging abilities in diabetic-patients with still open potential in healthy individuals . MoreoveThis example shows the identification of a drug to target a specific protein, for example the platelet-derived growth factor receptor beta (PDGFRB). For this search, DrumPID (search category ‘affected protein: platelet-derived growth factor receptor’) identifies four drugs. Simply by browsing DrumPID users can see that these drugs not only target PDGFRB but also have several other targets. It is well known that the PDGFR signaling (PDGFRB-PDGFRA dimer) is activated by binding of PDGF ligands and involved, e.g. in wound healing and post-infarction cell repair. However, for users it is important to study exactly the network connectivity of such multi-target drugs in a cell-type-specific context, e.g. in platelets. DrumPID can support such specific analysis: following the target link to our in-house PlateletWeb database , users can find out that PDGFRB has 46 platelet interacting proteins. Furthermore, there are 20 non-platelet interacting proteins in other human tissues . Trypanosoma brucei (T. brucei) AAZ12612; Trypanosoma cruzi (T. cruzi) DHFR-TS and XP_819618.1; here not shown). The identified protein can be further analyzed, e.g. regarding the protein structure and/or the interaction network (not shown here). Thus, users are not only able to explore the specific target but also design new experiments and therapeutic agents for trypanosomiasis, e.g. which are highly selective against T. brucei, but show no effect in human cells.DrumPID is also useful for more specialized scientific questions such as organism-comparative queries, for instance, identification of potential targets and drugs which specific inhibit the parasitic DNA replication but do not affect humans. Aspergillus fumigatus infection. DrumPID readily identifies here drugs for promising protein targets. We illustrate this for the two metabolic enzymes ergosterol (easier task: no ergosterol metabolism in humans) and riboflavin synthase . Searching DrumPID for ergosterol identified six drugs including Amphotericin B and Natamycin well known in the treatment of Aspergillus infection. This validates the approach, as these drugs are even FDA approved. Furthermore for riboflavin synthetase, the suggestion by DrumPID concerns riboflavin  and hence fungal-specific antimetabolites (modifying riboflavin) that bind exclusively A.fumigatus riboflavin synthetase. For this, detailed analyses are necessary: differences to the human version in the A.fumigatus riboflavin synthetase are made visible from COG/KOGs in DrumPID. Moreover, users get further information, e.g. regarding the pathway and orthologous groups, help in analyzing differences between organisms which then enables to develop a drug specifically targeting only fungal metabolic processes but no human processes .A drug design challenge we study intensively is antifungal treatment in The analysis of protein targets for which currently no drug is available. Using our COGMaster function, users get the functional COG/KOG classification and corresponding drugs for the COG/KOGs from DrumPID, which allow, e.g. functional pathway analysis to find differences/similarities in human and model organisms. Predicted targets are then available for further experimental tests.For new synthesized compounds with unknown functions, users can use the similarity search function, which rapidly calculates the corresponding substring for a SMILES input. Thus, users get first potential functional information and can explore potential antibiotic lead structures, optimize predictions from animal tests or explore the chemical space around a compound together with the affected protein interaction networks.Advanced use cases include:The DrumPID allows the user to rapidly understand and screen compounds for their effects in protein interaction networks, considering a bundle of interactome databases and algorithms, related organisms and searches for disease indications. It is useful for exploring potential antibiotic lead structures, optimizing predictions from animal tests and exploring the chemical space around a compound together with protein interaction networks. Users may study individual pathways or protein interactions, as well as potential targets in various organisms. Protein structures are rapidly analyzed, including catalytic domains and SCOP classification as well as structure prediction (AnDom). DrumPID will be updated every month.Supplementary data are available at Database Online.


Epstein-Barr virus (EBV) plays important roles in the origin and the progression of human carcinomas, e.g. diffuse large B cell tumors, T cell lymphomas, etc. Discovering EBV targeted human genes and signaling pathways is vital to understand EBV tumorigenesis. In this study we propose a noise-tolerant homolog knowledge transfer method to reconstruct functional protein-protein interactions (PPI) networks between Epstein-Barr virus and Homo sapiens. The training set is augmented via homolog instances and the homolog noise is counteracted by support vector machine (SVM). Additionally we propose two methods to define subcellular co-localization (i.e. stringent and relaxed), based on which to further derive physical PPI networks. Computational results show that the proposed method achieves sound performance of cross validation and independent test. In the space of 648,672 EBV-human protein pairs, we obtain 51,485 functional interactions (7.94%), 869 stringent physical PPIs and 46,050 relaxed physical PPIs. Fifty-eight evidences are found from the latest database and recent literature to validate the model. This study reveals that Epstein-Barr virus interferes with normal human cell life, such as cholesterol homeostasis, blood coagulation, EGFR binding, p53 binding, Notch signaling, Hedgehog signaling, etc. The proteome-wide predictions are provided in the  Nevertheless, the data size is still much smaller than intra-species PPI network size1011Virus-host interaction helps virus to hijack host cellular processes for survival and replication within its host. Through interactions with host proteins, virus perturbs and interrupts host signalling pathways to alter key cellular functions1234567823456782+ ion levels, etc. This experimental PPI networks can be treated as a reliable training data for computational modeling.Epstein-Barr virus (EBV) is the first known human tumor virus that acts as the causative agent of infectious mononucleosis, and plays important roles in the origin or progression of B cell malignancies, e.g. Hodgkinlymphoma, diverse AIDS-associated lymphomas. Nowadays Epstein-Barr virus is also viewed as epithelial tumor virus as well as lymphotropic virusk-mer, gene co-expression, protein structural information, etc.) to improve predictive performance23456783To our knowledge, no computational method has to date been proposed for EBV-human PPI networks reconstruction. The existing computational methods for HIV-human PPI prediction generally focus on integrating multiple feature information ; Virhostomehttp://interactome.dfci.harvard.edu/V_hostome/index.php); VirusMenthahttp://virusmentha.uniroma2.it/). We remove those obsolete and uncurated proteins by checking against the Uniprot database (http://www.uniprot.org/uniprot/). Those proteins that have no gene names are also removed. As a result, VirusMINT contains 266 PPIs, Virhostome contains 128 PPIs and VirusMentha contains 189 PPIs. The data distribution and intersection between the three data sets are illustrated in The experimental PPI networks between Epstein-Barr virus and Homo sapiens are collected from three virus-host PPI databases: VirusMINTThe above data are viewed as positive examples. To train a two-class predictive model, we randomly sample the negative examples in the EBV-human protein pair space exclusive of the positive examples. To date how to determine the sampling ratio of negative examples is a controversial issue in computational biology234773U to denote the training data, we obtain two sets of GO terms for each protein i. One set contains the GO terms of the homologs (denoted as U (denoted as S) is defined as follows.Gene ontology (GO)237810i1, i2) as follows.Based on these notations, we formally define the two feature vectors for a protein pair  indicates that if the protein pair  shares the same GO term g, the corresponding component value in the feature vector GO term g, the value is 0; otherwise, the value is 1. The above definition is symmetrical, i.e., the protein pair  and the protein pair  have identical feature representation.For each GO term xi ∈ Rn, i = 1, 2, …, l and class labels y ∈ Rl, yi ∈ {−1, 1}, C-SVM solves the following primal optimization problem:Homolog knowledge transfer is conducted via homolog instance to serve the purposes of (1) enrichment of the feature information of target instance; (2) substitution for the target instance when the gene/protein is not annotated; (3) augmentation of the training data to reduce the risk of model overfitting. However, the homolog instances may carry noise that results from evolutionary divergence, hence we need to choose a noise-tolerant machine learning framework to implement homolog knowledge transfer. To our knowledge, support vector machine (SVM) is a theoretically well-established machine learning algorithmω represents weight vector, ϕ(xi) is mapping function and C denotes penalty parameter. Here the slack variables ξi are introduced to tolerate a certain level of noise, without which, i.e. ξi = 0, i = 1, …, l, C-SVM formulated in formula (3) would be degenerated to a hard-margin SVM. In formula (3), the adverse effect of noise is counteracted by the penalty parameter C.where ϕ(x) and ϕ(y), i.e. k = (ϕ(x)•ϕ(y)). In the kernel function k, there is no need of explicit definition and computation of the mapping function ϕ(x). Here we adopt Gaussian kernel.In addition, SVM uses well-known kernel trick to define the inner product between mapping function γ controls the flexibility of Gaussian kernel.where ||Δ|| denotes the 2-norm of vector Δ, and the hyperparameter i1, i2) is represented with the target instance f (x) accordingly yields two outputs, i.e. Each test protein-protein pair  as follows.Where |Δ| denotes the absolute value of Δ. Based on the final decision function, we can further determine the final class label for the test protein-protein pair , and then SPl, SEl and MCCl for each class label can be calculated by formula (8). Overall accuracy and MCC can be calculated by formula (9),Here we use cross validation and independent test to evaluate the model performance. To reduce the risk of evaluation bias, we simultaneously adopt multiple performance metrics including ROC-AUC (Receiver Operating Characteristic AUC), SE (sensitivity), SP (specificity), MCC (Matthews correlation coefficient), F1 score and Accuracy. Except AUC score, all the other metrics can be derived from confusion matrix. Given confusion matrix Mi,j records the counts that class i are classified as class j, and L denotes the number of class labels. AUC is calculated based on the decision values as defined by formula (5), and F1 score is calculated by formula (10).where the element of confusion matrix Multi-instance SVM achieves the best performance , slightly outperforming Multi-instance SVM Novel  and Single-instance SVM . The results of Multi-instance SVM Novel indicate that the proposed model still works well when the GO knowledge of the gene/protein concerned is not available. Comparing the SP, SE and MCC scores on the positive class and the negative class  that contains 350 examples. To train a two-class SVM model, we also randomly sample 350 negative data see .The resuftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/taxonomic_divisions/uniprot_sprot_human.dat.gz). For each EBV gene/protein, we derive its prediction space by excluding those EBV-human protein pairs that already exist in the training data. Averagely over 20,000 human candidate genes are derived for each EBV gene. The results of the proteome-wide predictions are provided in the δ = 0.01 (see formula (6)) to reduce the risk of false positive predictions. Among the 648,672 EBV-human protein pairs, there are 51,485 protein pairs predicted to be interacting (positive), accounting for 7.94% positive rate. Jansen et al.There are 32 EBV genes/proteins to study in the training data (VirusMINT + Virhostome). The potential human target genes are retrieved from Uniprot  and 25 experimental evidences from recent literature. Take the evidences from recent literature as examples. The interactions {BGLF4, SUMO1} and {BGLF4, SUMO2} have been experimentally verified27We further validate the proteome-wide predictions against the latest virus-host database and recent literature. It is not easy to gather supporting evidences in that new evidences are scarce and scattered among thousands of literature. Nevertheless, we still find 58 evidences to support our predictions see , includiThe above-described performance estimation of cross validation and independent test has demonstrated the reliability of the proposed method, and the validation against the latest database and recent literature further demonstrates the practical feasibility of the proposed method, we still need to apply the proposed method to other pathogen-host PPI data. Different from the existing methods that reconstruct PPI networks between HIV-1 and Homo sapiens2345678Salmonella and Homo sapiens is smaller than or approximate to that of the PPI networks between Epstein-Barr virus and Homo sapiens. In ref. Salmonella and Homo sapiens. This method also augments the training data via homolog instances, but it differently implements noise control under the framework of AdaBoost. We conduct the performance comparison on the same Salmonella training data asSalmonella data in terms of noise tolerance and generalization ability.The size of the PPI networks between HIV-1 is a well-studied virus with the largest experimental virus-host PPI networks, and accordingly computational modeling on the networks has aroused much attention from researchers2345678et al.In recent years, pathogen-host PPI networks reconstruction as a research field of microbial informatics has drawn much attention from computational biologists, e.g. HIV-12345678In this work we propose a noise-tolerant homolog knowledge transfer method to discover novel human target genes and signaling pathways, where homolog knowledge is used as independent homolog instances to augment the training data. The homolog instances serve three major purposes: (1) reducing the risk of model overfitting that results from small training data; (2) enriching the feature information of the target instances; (3) substituting the target instances when the knowledge of gene ontology of the gene/protein concerned is not available. The homolog noise that results from evolutionary divergence is counteracted by the regularization technique of support vector machine (SVM).δ defined in formula (6) is increased, the two ratios will be decreased to achieve lower risk of false positive predictions.False positive rate is an important concern of computational reconstruction of protein-protein interaction networks. At present we cannot eliminate false positive predictions completely because the data quality and the computational method are far imperfect. What we are concerned about is how large false positive rate is acceptable. Unfortunately, we do not know the true ratio of positive (interactions) to negative (non-interactions) in the real world, thus we cannot rationally determine the acceptable false positive rate. Nevertheless, we still attempt to evaluate the risk of false positive predictions from the two aspects. The first aspect is the ratio of the predicted positives to the whole space of protein pairs. The proposed method predicts 51,485 functional interactions in the space of 648,672 EBV-human protein pairs (7.94%). If we put the constraint of subcellular co-localization on the predictions, we obtain 869 stringent physical PPIs (EBV gene and human gene are annotated with the same GO term of cellular compartment) and 46,050 relaxed physical PPIs . Low ratio of positive predictions surely reduces the risk of false positive predictions. The other aspect is the ratio of EBV targeted human genes. In this work, the computational results show that most of the EBV genes/proteins are predicted to individually interact with less than 5% human genes/proteins. Low ratio of EBV targeted human genes also implies low risk of false positive predictions. If the threshold To reduce the risk of false positive predictions and make the predictions reliable, we need to take into account several major factors for computational modeling, e.g. data size, data quality, data representativeness, computational algorithm, etc. In this work, the data size is increases via homolog instances; the representativeness of negative data is implemented via random sampling in the huge space of protein pairs; the data quality is guaranteed by adopting literature-curated experimental PPI data; and SVM is adopted as the computational framework to reduce the risk of negative homolog knowledge transfer.The Computational results show that the proposed method achieves satisfactory cross validation and independent test performance. Using the trained model, we have reconstructed the proteome-wide protein-protein interaction networks between Epstein-Barr virus and Homo sapiens, where 33 predictions have been validated against recent VirusMentha database and 25 predictions have been validated against recent literature. To gain more insights, we further conduct GO enrichment and pathway enrichment analysis of predicted proteome-wide EBV-human PPI networks.To cluster the EBV-targeted human proteins that fulfil identical molecular functions, participate in the same biological processes or reside in the same cellular compartments, we use gene ontology term (GO term) as the distance metric for clustering, i.e., the interacting human partners that are annotated with the same GO term are assigned to the same cluster. All the GO terms of human genes/proteins are classified into three major classes, biological processes (P), molecular functions (F) and cellular compartments (C). For each major class, we further consider two scenarios to study the common attack patterns of the 32 EBV proteins: (1) all the 32 EBV proteins are involved in the PPI subnetwork; (2) NOT all the 32 EBV proteins are involved in the PPI subnetwork. The predicted PPI subnetworks are given in the http://www.uniprot.org/uniprot/Q9NRY6), PLSCR3 is claimed to mediate ATP-independent bidirectional transbilayer migration of phospholipids upon binding calcium ions. PLSCR3 also plays a central role in the initiation of fibrin clot formation, the activation of mast cells, the recognition of apoptotic cells and the translocation of cardiolipin from the inner to the outer mitochondrial membrane. From the predicted interactions, we can infer that EBV proteins may interfere with the cholesterol homeostasis and the fibrin clot formation of the host cell. Besides PLSCR3, the other three human proteins {NPC1L1, EHD1, LDLR} are also predicted to be targeted by most of the EBV proteins. NPC1L1 plays important roles in cholesterol biosynthetic process, cholesterol transport and intestinal cholesterol absorption (http://www.uniprot.org/uniprot/Q9UHC9). EHD1 plays roles in cholesterol homeostasis and positive regulation of cholesterol storage and blood coagulation (http://www.uniprot.org/uniprot/Q9H4M9). LDLR plays roles in phospholipid transport, lipoprotein metabolic process and regulation of cholesterol homeostasis (http://www.uniprot.org/uniprot/P01130). In addition, it has been reported the activity of EBV protein LMP2A depends on cholesterol and cholesterol depletion from plasma membrane blocks LMP2A endocytosis, LMP2A phosphorylation and LMP2A ubiquitination, resulting in the accumulation of LMP2A on plasma membraneThe predicted PPI subnetwork GO:0042632 extracted from the http://www.uniprot.org/uniprot/P09486), SPARC appears to regulate cell growth through interactions with the extracellular matrix and cytokines, and is involved in the biological processes of blood coagulation, platelet activation/degranulation, heart development, extracellular matrix organization. In ref. The predicted PPI subnetwork GO:0007596 extracted from the http://www.uniprot.org/uniprot/Q12805), EFEMP1 binds the EGF receptor (EGFR) to induce EGFR autophosphorylation and activation of downstream signaling pathways. In ref. The predicted PPI subnetwork GO:0005154 extracted from the http://www.uniprot.org/uniprot/Q9NQR1). In ref. The predicted PPI subnetwork GO:0002039 extracted from the Pathogens communicate with the host via chains of interactions  to subvert the host cellular machinery for its purposes. In ref. There are 335 predicted interactions between EBV proteins and the known Notch signaling components. As illustrated in http://www.uniprot.org/uniprot/O43323), DHH acts as intercellular signal essential for a variety of patterning events during development, e.g. male sex determination, spermatid development, Leydig cell differentiation, etc., and may function as a spermatocyte survival factor in the testes. Among the EBV proteins, {EBNA-LP, EBNA1, EBNA3, BGLF4, BMLF1, BZLF1} are predicted to target a majority of Hedgehog signaling components. In ref. There are 175 predicted interactions between EBV proteins and the known Hedgehog signaling components. As illustrated in How to cite this article: Mei, S. and Zhang, K. Computational discovery of Epstein-Barr virus targeted human genes and signalling pathways. Sci. Rep.6, 30612; doi: 10.1038/srep30612 (2016).