With advancements in synthetic biology, the cost and the time needed for designing and synthesizing customized gene products have been steadily decreasing. Many research laboratories in academia as well as industry routinely create genetically engineered proteins as a part of their research activities. However, manipulation of protein sequences could result in unintentional production of toxic proteins. Therefore, being able to identify the toxicity of a protein before the synthesis would reduce the risk of potential hazards. Existing methods are too specific, which limits their application. Here, we extended general function prediction methods for predicting the toxicity of proteins. Protein function prediction methods have been actively studied in the bioinformatics community and have shown significant improvement over the last decade. We have previously developed successful function prediction methods, which were shown to be among top-performing methods in the community-wide functional annotation experiment, CAFA. Based on our function prediction method, we developed a neural network model, named NNTox, which uses predicted GO terms for a target protein to further predict the possibility of the protein being toxic. We have also developed a multi-label model, which can predict the specific toxicity type of the query sequence. Together, this work analyses the relationship between GO terms and protein toxicity and builds predictor models of protein toxicity. Advancements in synthetic biology2 as well as protein design3 have made it now possible to construct artificial proteins that fold and assemble into desired structures and achieve specific tasks in a cell. Artificial protein synthesis has also revolutionized the biotechnology industry, where the technique has been used to program microbes to produce drugs at reduced production cost, to create disease-resistant crops that improve the yield, or to design new vaccines and therapeutic antibodies to cure diseases6.Proteins carry out various functions in a cell, forming functional networks and signaling pathways that are essential to sustain life. Understanding the function of component proteins in the networks is a fundamental step to obtain critical insights into complex cellular mechanisms. As a means to elucidate the function of a protein and the relationship between the function and the sequence or the structure of the protein, experimentally, it is common to construct mutants of the protein and test their function 7. To prevent release of toxic proteins, there are ongoing efforts to build systems and devices that collect unknown proteins or organisms together that identify proteins with potential harm11. There is a strong demand for such systems for lab facilities of gene synthesis, places where many people gather, e.g. airports, and war zones where biological attack might occur.While there are many applications of constructing desired artificial peptides and proteins, a potential problem is the production of harmful or toxic proteins. There are two scenarios where toxic proteins may be constructed: One situation would be that a newly designed protein happens to have an unexpected harmful function. There are many aspects of cell function that are still unclear, thus, foreseeing such side effects when designing a new protein may be very difficult. The second possible case would be an intentional design or release of toxic proteins for biological attack12. ToxinPred13 and other series of methods developed by the Raghava group target detection of toxic bacterial peptides using machine learning methods based on sequence information15. ClanTox uses a machine learning method that was trained on known peptide ion-channel inhibitors16. These methods are similar in approach in that they use sequence information. Moreover, the methods except for ThreatSEQ have a limited application to peptide toxins.A computational algorithm for detecting toxic proteins should take a protein or DNA sequence as input and alerts if the protein can be harmful. ThreatSEQ developed by Battelle Memorial Institute identifies sequences of concern by comparing them with a curated database of known toxic proteins17. GO is a controlled vocabulary of function of proteins and has been widely used for function annotation and prediction. Previously, our lab has developed a series of function prediction methods19 including PFP22 and Phylo-PFP19, which have been shown to be among the top-performing function prediction methods in the community-wide automatic function prediction experiment, Critical Assessment of protein Function Annotation (CAFA)24. Here, we show that the toxicity of proteins can be well predicted from GO terms that are predicted by PFP. First, we examined the distribution of GO terms in annotations of toxic proteins and showed that GO terms are promising features for predicting toxicity. Next, we developed a neural network for predicting protein’s toxicity from their GO term annotations. Finally, we further extended the method to predict the mode of action of toxicity of a protein.In this paper, we present a new method, NNTox , which can predict the toxicity of a query protein sequence based on the protein’s Gene Ontology (GO) annotationFirst, we will describe the datasets used in this study. Then, we explain the neural network model of NNTox.25 using the keyword “Toxin” (UniProtKB KW-0800). A total of 6,497 toxin proteins were obtained. From the 6,497 toxin proteins, we collected a set of 1,506 unique GO terms that were included in their GO annotations. The GO term of “toxin activity” (GO:0090729) was removed from the collection because this term obviously related to toxicity and can bias prediction if it is included in the annotation of proteins in the training and testing set for the toxicity prediction. From this toxin protein set, we removed proteins that were redundant to other proteins in terms of their GO term annotations. We did not use sequence similarity for the redundancy criterion because the input to our model is GO terms. The non-redundant dataset contained 488 toxin proteins.Toxin proteins were collected from the UniProtKB-SwissProt databaseNon-toxin proteins were also collected from UniProtKB SwissProt using the following two conditions: (1), they are not tagged with the keyword “Toxin”. (2), 95% of GO terms annotating the protein belong to the toxin GO term set. The second criterion makes most of the GO term annotation of toxin and non-toxin proteins very similar. Using this approach 82,583 non-toxin proteins were obtained. Then, as was done for the toxin protein dataset, proteins with redundant GO annotations were removed, which resulted in 6,594 non-toxin GO proteins.The Toxin keyword had 11 sub-classes, which were cardiotoxin (134/8), enterotoxin (94/12), neurotoxin (2744/100), ion channel impairing toxin (2429/74), myotoxin (121/22), dermonecrotic toxin (148/4), hemostasis impairing toxin (865/95), G-protein coupled receptor impairing toxin (186/33), complement system impairing toxin (160/6), cell adhesion impairing toxin (207/18), and viral exotoxin (9/4). The first number in the parentheses is the total number of proteins in the sub-class downloaded from UniProtKB-SwissProt while the second number is those in the non-redundant toxin proteins. Using this information, we compiled a dataset of the mode of action of the toxin proteins. Out of the 488 non-redundant toxin proteins, 270 proteins had information of the mode of action. A protein is assigned to multiple classes if it belongs to more than one sub-class keywords. Out of the 270 proteins, 173 proteins belong only to one sub-class, 88 proteins have two assigned sub-classes, and 9 have three sub-classes.A protein in the dataset is represented by a vector of 2,596 binary (1 or 0) values (except for the last position), which indicates existence of the particular GO term in its GO annotation. 2595 GO terms represents all the GO terms found in toxin proteins as well as general GO terms that frequently appear in UniProtKB database . The last position of the vector represents the number of GO terms that are associated with the protein but are not present among the above 2,595 GO terms. Using only toxin GO terms in the feature vector limits the scope of GO terms that the network can see and using all  will lead to spare features. As a middle ground, we added top background GO term in the feature vector as well.http://www.github.com/kiharalab/NNTox.We used a five-layer fully connected feedforward neural network for the toxin/non-toxin prediction Fig. . The inpPredicting the mode of action of toxin proteins is a multi-label classification problem, where one toxin could have more than one mode of action. For example, conotoxin, a snail toxin, is both a neuro-toxin and an ion channel inhibitor toxin. Thus, classes are not mutually exclusive. We modified the neural network described above to perform multi-label prediction, by replacing softmax in the last layer with computing the sigmoid cross-entropy loss. In the sigmoid cross-entropy loss, the loss calculated for every label is independent of the loss in other labels, and thus allows for multiple labels to be predicted.1, v2, … v11] is the label vector, where vi = 1 represents that the protein has the mode of action i. For each mode of action i, we calculated the positive count (i), i.e., the number of times vi = 1 and the negative count (i), i.e., the number of times vi = 0 in the training dataset. The weight wi given to a mode of action/class i is wi = (negative count (i))/(positive count (i)). Thus, the weight is 1 if the number of positive and negative counts is equal while giving more weight as the positive count decreases.The sub-classes of toxins are imbalanced, e.g. neurotoxin and ion channel inhibiting toxin have more proteins than other sub-classes. This can cause bias in the network while training towards highly represented classes. To overcome this problem, we added a weight to each correct class prediction in the multi-label neural network, where the weight is inversely proportional to the number of the times that class is present in the training set. For a protein, v = , the regularization strength , the learning rate  and the number of epochs . Shown in the parentheses are the values tested for each hyper-parameter.Training was performed with backpropagation using the ADAM optimizer, implemented in TensorFlowNested cross-validation provides robust and unbiased training and testing using the full data available from the dataset. In the nested cross-validation there were two cross validation loops. In the outer loop, the dataset was divided into k (=5) subsets, where one subset was considered as the test set and the rest are used for training & validation set, and the test set was changed for k times. Furthermore, the inner loop was to perform a cross-validation on the training & validation set, i.e. the set was divided into k (=5) pieces again and one of them was considered as the validation set. Each different combinations of hyper-parameters were trained on the training set and tested on the validation set. This was performed for k times by changing the validation set. Then, the best hyper-parameter was chosen based on the average error on the k validation set, and the model trained using the hyper-parameter set on all training and validation set was applied to the testing set. This is repeated for k times, and the final result was the average performance on the k test sets.We examined the performance of NNTox using two sets of GO terms for proteins. First, we tested NNTox using the GO annotations of proteins obtained from UniProtKB-SwissProt. This is to test the performance of the architecture of NNTox in the best possible cases when all the correct GO terms are known. Second, we used a GO-term prediction method, PFP, to predict GO terms of each protein and trained NNTox on the predicted GO terms. This is to simulate the situation when true GO terms for a query protein are not present.27 to retrieve similar sequences from a database to a query sequence and obtains GO-term annotations from the sequences with an E-value of up to 125. Then, each GO term will be assigned with a score that reflects the E-value of sequences that have the GO term in their annotation as well as the conditional probability that the GO term occurs given other GO terms are observed. For the sequence database, we used UniProtKB Swiss-Prot downloaded in March 2018. To avoid retrieving GO terms from the query protein itself, sequences retrieved with an E-value of 0 were discarded.PFP was developed in our group and has been successful in the Critical Assessment of protein Function Annotation algorithms (CAFA). PFP uses PSI-BLASTPFP provides a confidence score to each GO term predicted that ranges from 0.0 to 1.0 with 1.0 for the highest confidence  has 100% of the toxin specificity. Proteins with this GO term are indeed toxins, e.g. alpha-conotoxin in a sea snail (Uni-Prot ID: CDKA_CONVX) and cobrotoxin in Chinese cobra (UniProt ID: 3S1CB_NAJAT). These toxins bind to nicotinic acetylcholine receptors, inhibiting them, and impairing neuromuscular transmission. Thus, it is involved in neurotoxicity and ion channel impairing toxicity. “N-acylphosphatidylethanolamine-specific phospholipase D (NAPE-PLD) activity”  has a high toxin specificity of 75.35%. Phospholipid D catalyzes the hydrolysis of sphingomyelin and induces complement-dependent hemolysis, dermonecrosis, blood vessel permeability, and platelet aggregation. Thus, it is involved in dermonecrotic and complement system toxicity. It is possessed by recluse spiders and causes necrotic damage. “Phospholipase A2 activity” (GO:0004623), the last one in the table, has a toxin specificity of 58.41% with neurotoxin specificity of 22%, myotoxin specificity of 14%, and hemostasis impairing toxin specificity of 23%. Phospholipase A2 catalyzes the calcium-dependent hydrolysis of the 2-acyl groups in 3-sn-phosphoglycerides. It affects neuromuscular transmission by blocking acetylcholine release from the nerve termini. It also has anticoagulant activity and weakly inhibits ADP-induced platelet aggregation. The protein with this activity exists in venomous snakes, e.g. Chinese krait (UniProt ID: PA2B1_BUNMU) and Nikolsky’s Viper (UniProt ID: PA2B2_VIPBN). Overall the results show GO terms are promising features for predicting protein toxicity.In this section we discuss the performance of our NNTox on distinguishing toxin and non-toxin proteins. We compare the performance with the baseline methods. Table The baseline method is explained in Methods. NNTox (GO Annotation) used the GO annotations of proteins from UniProtKB-SwissProt. “PFP” checked if the “toxin activity” GO term was predicted with 0.9 or a higher confidence score. NNTox (PFP) uses predicted GO terms by PFP using 0.1 as the prediction confidence cutoff value Fig. . PFP + NIn the first three rows of Table In contrast, prediction by NNTox performed substantially better than the baseline method. The precision and recall for detecting toxin proteins was 0.903 and 0.898, respectively, indicating that the predictions made for toxin and non-toxin proteins were well balanced. The NNTox F1 score was 0.900, which is a clear contrast compared to baseline method that showed substantially lower F1 score.The second half of Table In Fig. In Fig. Outputs from each of the three hidden layers of the neural network for toxin (red) and non-toxin (green) proteins are visualized by PCA. The x- and the y-axis are the first and the second principal components of the output values of the layer through the sigmoid activation function.28, where the number of correctly predicted modes for each of the target proteins was counted. NNTox (Mode of action) showed good performance with an accuracy of over 0.8, even when predicted GO terms were used. The high accuracy indicates that the method was overall successful in not only for pointing out the correct mode of the toxin proteins but also in avoiding over predicting incorrect modes.Next, we developed a multi-label neural network model, which predicts the mode of action of a toxin protein. The input to the model is the same feature vector of GO terms and the output is a binary vector for the 11 modes of action. Multiple action predictions are also allowed for a protein, which makes the prediction task more complex. To evaluate the prediction performance of the model, we computed the elementwise accuracy of the predicted vector Table  as usualThe values are the average for test sets in the five-fold nested cross-validation. In this multi-class prediction, a prediction output for a protein is a binary vector of 11 values, where 1 indicates the class is predicted and 0 for a negative prediction for the class. The accuracy was computed by counting the agreement of the predicted binary class for each toxin mode of action in all the proteins.11 modes shown on the x-axis are: C, cardiotoxin; EN, enterotoxin; N, neurotoxin; IC, ion channel impairing toxin; M, myotoxin; D, dermonecrotic toxin; H, hemostasis impairing toxin; GCR, G-protein coupled receptor impairing toxin; CS, complement system impairing toxin; CA, cell adhesion impairing toxin; V, viral toxin. In the parentheses, the number of proteins of the mode is shown. 173 toxin proteins that have only one mode of action were analyzed. Black bars, predictions using GO annotations from UniProtKB; gray bars, predictions using PFP’s GO term predictions.Figure Among the toxin protein dataset with the mode of action, there are 88 proteins that have two mode labels. Here we examine predictions made to the two largest toxin groups with two labels. 54 out of 88 proteins are labeled as neurotoxin (N) and Ion channel impairing toxin (IC). Out of them, 30 (55.6%) had the exactly correct predictions, i.e. correct positive predictions for the two labels and correct negative predictions to the other modes. For 9 other cases, the two labels, N and IC, were correctly predicted but with other false positive predictions. Finally, 48 of them (88.9%) had at least 1 mode, either N or IC, correctly predicted. The second-largest group with two modes were with hemostasis impairing toxin (H) and cell adhesion impairing toxin (CA), with 16 proteins. For this group, five of them have the exact correct prediction, and another protein was counted if we include the prediction with the two correct modes and one more over-predicted mode (37.5%). The number of proteins with at least one correctly predicted mode, H or CA, was 12 (75.0%). Thus, overall, NNTox (Mode of action) was able to capture the dual labels of the proteins reasonably well.29, which can be predicted with a stable accuracy.Here, we developed NNTox, which predicts the toxicity of proteins via GO term annotation. In contrast to existing methods that compare a query protein sequence to known toxin proteins, NNTox’s approach is less dependent on the known similar toxin proteins because prediction is made via GO terms. This approach exploits the success of general function predictors that have constantly been improving in the past years. We used PFP for the current development because it was developed by our lab and is one of the top-performing methods in the field. As the function prediction method improves, the toxin prediction by NNTox will also improve. Performance is also expected to improve by using additional input features, such as protein local structure information, e.g. protein main-chain conformationThe multi-label classification performed for toxin action mode prediction showed high elementwise accuracy Table . NaturalIn this work, we trained the network model so that the overall F1 score was maximized. The method can also be trained differently, for example, in a way to increase the sensitivity of toxin detection , considering that missing life-threatening toxins can cause a catastrophic outcome.Supplementary Table S1, S3, S4Supplementary Table S2


Tailored therapy aims to cure cancer patients effectively and safely, based on the complex interactions between patients' genomic features, disease pathology and drug metabolism. Thus, the continual increase in scientific literature drives the need for efficient methods of data mining to improve the extraction of useful information from texts based on patients' genomic features. An important application of text mining to tailored therapy in cancer encompasses the use of mutations and cancer fusion genes as moieties that change patients' cellular networks to develop cancer, and also affect drug metabolism. Fusion proteins, which are derived from the slippage of two parental genes, are produced in cancer by chromosomal aberrations and trans-splicing. Given that the two parental proteins for predicted fusion proteins are known, we used our previously developed method for identifying chimeric protein–protein interactions (ChiPPIs) associated with the fusion proteins. Here, we present a validation approach that receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by our new method, ProtFus, using an online literature search. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as a training set for a Naïve Bayes classifier, to identify predicted fusion proteins that have reliable evidence in the literature and that were confirmed experimentally. Next, for a test group of 1817 fusion proteins, we were able to identify from the literature 2908 PPIs in total, across 18 cancer types. The described method, ProtFus, can be used for screening the literature to identify unique cases of fusion proteins and their PPIs, as means of studying alterations of protein networks in cancers.http://protfus.md.biu.ac.il/Availability:  Tailored therapy aims to cure cancer patients in a fully personal way. Thus, the continual increase in scientific information and, particularly, in published literature, drives the need for efficient methods of data mining to find unique personal genomic features and their connections. Fusion proteins, which are derived from the slippage of two parental genes or chromosomal translocations, are frequently drivers of cancers. We used our previously developed method for identifying chimeric protein–protein interactions (ChiPPIs) for multiple fusion proteins. In this paper, we present a validation approach, ProtFus, which receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by online literature searches. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as training set. Next, for a test set of 1817 fusion proteins, we were able to identify 2908 previously published PPIs across 18 different cancer types. The described method can be used for screening the literature to identify unique cases of fusion proteins and their PPI networks, as means of studying alterations of protein networks for personalized approaches in cancers. PLOS Computational Biology Methods paper.This is a Fusion proteins resulting from chromosomal translocations have important roles in several types of cancer and are extensively discussed in cancer research literature. The current biomedical literature resources, such as PubMed, comprise more than 28 million citations, with approximately 14,000 cancer-related papers from 2018 alone, and more than 3 million abstracts in total that mention ‘cancer’. Similarly, the number of PubMed articles that mention ‘fusion proteins’ is also increasing rapidly. Thus, there is a growing need to catalog as well as curate this information. Hence, text mining-based methods to identify fusion proteins from PubMed are extremely important. Moreover, information regarding fusion proteins mentioned in the current literature have no standard format. The upshot is that identifying a certain fusion protein is non-trivial; for example, a fusion protein such as BCR–ABL1 is represented in variable forms in different texts  protein-[ABL] protein [causes] action-verb [leukemia] cancer’. The ProtFus method performs a search in PubMed abstracts or uploads a full text file that is based on a specific input text. For example, in the case of an input text, the result is displayed in a separate pop-up window, and the fusion proteins are highlighted. Similarly, in the case of PPIs among fusion proteins, the result window includes the input text, and the interactions are highlighted. Thus, another feature of ProtFus is direct searching using PubMed articles. Users can select from the drop-down menu of 100, 200 or more, the number of articles to be considered for searching fusion proteins and their interactions. The result includes the abstracts of the articles that match best with fusion protein keywords . This file can be further used for highlighting the fusion proteins and their interactions. Thus, Table 5 represents Precision and Recall for NER.The tokens were used to parse the texts for performing named-entity recognition (NER) . NER locTable 1). Other datasets served as test sets to evaluate the model that was built and a 10-fold cross-validation was performed, each time using 40% of the entities to train an extraction model and the remaining 60% to test it [Supporting information).We downloaded abstracts from PubMed to generate both training and test datasets. For a training set we used several datasets . The tokenizer module segregates the text into ‘Biological’, ‘Miscellaneous’, ‘Function’ and ‘Literal’ tokens. For example, given the following text, “The small molecule BCR-ABL-selective kinase inhibitor imatinib is the single most effective medical therapy for the treatment of chronic myeloid leukemia”, the tokenization output is: Biological Tokens—‘small’, ‘BCR-ABL-selective’, ‘single’, ‘medical’ and ‘chronic’; Miscellaneous Tokens—‘molecule’, ‘kinase’, ‘imatinib’, ‘therapy’, ‘treatment’, ‘myeloid’ and ‘leukemia’; Function Tokens—‘effective’ and ‘inhibitor’; Literal Tokens—‘is’, ‘the’, ‘for’ and ‘of’.The word-token tagger identified the property of words from the text for fusion proteins, like ‘fusion proteins’, ‘fusion transcripts’, ‘chimeric proteins’, ‘chimeric genes’ and ‘fusion gene transcripts’; and "Synonyms’ (fusion corpus) consists of ‘entity’ ‘relation token’, such as ‘fusion’ ‘fusions, fusion transcript, fusion transcripts, fusion protein, fusion proteins, fusion gene, fusion genes’, whereas ‘Synonyms’ (PPI corpus) consists of ‘entity’ ‘relation token’, such as ‘Activate’ ‘activate, activates, activated, activating, activation, activator’. Similarly, the back-end ‘Synonyms’ (fusion) consists of ‘Fusion proteins’ ‘Synonyms’ ‘Alternate representations’, such as ‘EWS-FLI1’ ‘TMRPSS2-ERG’ ‘ews: fli1, EWSR1: EWS, EWSR1/FLI1, EWS/FLI-1’. The ‘parser’ and the entity recognition module used ‘Rule-base’ and ‘Short Form Recognition’ back-end resources for identifying the final ‘best-suited’ entities and tokens, and also for filtering out the false positives. The ‘Rule-base’  consisted of ‘Rule’ ‘Input’ ‘Output’ ‘Reg Ex’, such as ‘Normalization of case’ ‘BCR-ABL, bcr-abl, BCR:ABL, bcr:ABL, BCR/ABL, bcr/abl’. Similarly, the ‘Rule-base’ (for regular expression) consisted of ‘Characteristics’ ‘Description’ ‘Rule’ ‘Reg Ex’, such as ‘Fusion token’ ‘Tokens with fusion word occurrence’ should be separated by space/tokens (‘fusion|fusions|fusion genes|gene fusion|fusion protein|fusion transcripts’) etc.Here, we present the structure of the corpus that was used for validation and testing. ProtFus considered all possible combinations of representing fusion proteins in text, by looking the back-end Rule-base as well as the fusion and PPI corpus. Now, we define the different keywords and tokens used by our method, as part of entity recognition. The back-end ‘The fusion protein information extracted by ProtFus was validated using the ChiTaRS-3.1 . The fusS5 Table, Supporting information). For example, in the case of the BCR-ABL1 fusion protein (PubMed ID = 9747873), ProtFus identified its occurrence in all PubMed articles, like, ‘Both Bcr-Abl fusion proteins exhibit an increased tyrosine kinase activity and their oncogenic potential has been demonstrated using in vitro cell culture systems as well as in in vivo mouse models’ . Similarly, ProtFus also identified interactions among fusion proteins , such as in the case of the BCR-ABL1 fusion protein (PubMed ID = 9747873), ‘The SH2-containing adapter protein GRB10 interacts with BCR-ABL’ . Particularly, the essential parameters for examining the accuracy of text-mining based algorithms involved the identification of Precision, Recall and F-Score. Moreover, ProtFus identified fusion proteins with Precision ranging from 0.33 to 1.0 (average = 0.83), Recall = 0.4 to 1.0 (average = 0.84) and F-Score = 0.4 to 1.0 (average = 0.81); whereas for PPIs with: Precision = 0.42 to 1.0 (average = 0.81), Recall = 0.5 to 1.0 (average = 0.81) and F-Score = 0.59 to 1.0 (average = 0.83). A high scoring system would typically have a Precision of ~0.8–1, Recall of ~0.8–1 and F-score of ~0.8–1, depending on the quality of data. Thus, the overall accuracy of ProtFus enabled extracting different attributes of fusion proteins and their interaction appearances in the biomedical texts.Next, ProtFus was tested on 358 fusion proteins (based on the Mitelman Database) from PubMed articles; and the result statistics of the top 100 fusion proteins were provided based on their identification from the text (Table 1) and a set  that was used for testing the algorithm in all PubMed references (2013–2017) (Table 2). There was no overlap between training and testing data. Subsequently, decisions were modeled for assigning labels to raw input data. This type of classification algorithm can also be thought of as a convex optimization problem, in which one needs to identify the minima of a convex function ρ, associated with an input vector v, having n entries (a(i)∈nZ are training instances (1≪n),y(i)∈nZ that act as labels. To examine the accuracy of our algorithm, we performed a 10-fold cross-validation. For this purpose, we partitioned the input text into ten equal-sized sub-samples, of which five were retained for testing and five were used for model building. We also used the standard Precision, Recall and F-score values for validating the results. Precision (P) was defined as the fraction of retrieved instances that was relevant to the study. Precision can also be defined as the probability that randomly selected retrieved information is relevant  is defined as the fraction of relevant instances that are retrieved for the study. Recall can also be defined as the fraction of the information relevant to the query that is successfully retrieved  tokens = n, n, n, a . In this case, Precision = 0.75, Recall = 0.75 and F-score = 0.75. Similarly, the corresponding accuracy plot can be drawn by providing information about Precision, Recall, and F-score values, and the number of runs. ProtFus still had a high false-positive rate, due to the diverse corpus of texts and different forms of fusion mentions. However, this rate automatically decreased when the corpus was updated with better literals.We used a classical Naïve Bayes algorithm for training as well as extraction. The datasets were partitioned based on known fusion proteins and their interactors from the literature. This resulted in a training set (40%) , supported by Apache Tomcat and My-SQL. This is an open source Big Data processing framework that supports ETL  and machine learning, as well as graph generation. Some classical text mining tasks can also be performed by identifying biological, functional, literal and miscellaneous tokens, as well as chunks from text. Further, for the purpose of entity recognition, the word-token tagger has back-end Synonyms (with a synonym resource), whereas the RegEx tagger has back-end synonyms (with rule base).To display the results of ProtFus in a user-friendly manner, we also built the Protein-Protein Interaction of Fusions (PPI-Fus) database  from non-essential ones  for better data extraction.Interestingly, the vely see . SimilarFig 3).Considering discrete protein domains as binding sites for specific domains of interacting proteins, we catalogued the protein interaction networks of more than 11,000 cancer fusions in order to predict PPIs of fusion proteins using ChiPPI . MappingTable 7 represents the accuracy of ProtFus as compared to, ChimerDB-3.0 [Fig 4 shows representative ROC curves generated in a typical experiment using ‘abstracts’ data. Compared to full-text articles, the extraction was better for abstracts. This is because the size of feature space is too large for full-text articles. For text classification purposes, abstracts may yield better results than full-text scientific articles. We also used various full-text journal corpus information for the purpose of evaluating our method’s performance over others [To demonstrate the added value of the ProtFus tool, we performed a direct comparison with existing services. erDB-3.0 , FusionCerDB-3.0  and FusierDB-3.0  resourcer others . Thus, tThis study focused on investigating large-scale biomedical text classification downloaded from PubMed. We utilized classical text-mining and machine learning strategies, and also Big Data infrastructure to design and develop a distributed and scalable framework. This was applied to identify fusion proteins and their interactions for classifying information extracted from tens of thousands of abstracts and full-text articles with associated MeSH terms. The accuracy of predicting a cancer type by Naïve Bayes using the abstracts was 92%. In contrast, its accuracy using the 103,908 abstracts (for fusion proteins only), 90,639 full texts (for fusion proteins only), 185,606 abstracts (for fusion protein interactions) and 353,535 full texts (for fusion protein interactions) was 88%. This study demonstrates the potential for text mining of large-scale scientific articles using a novel Big Data infrastructure, with real-time updating from articles published daily. ProtFus can be extended to other areas of biomedical research to improve searches in multiple medical records and medical data mining approaches.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their “undruggable” binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery. Rational drug discovery and design has progressed at a precipitous pace with the aid of in silico strategies and innovations in hardware and computational power. While still in its infancy as compared to traditional drug discovery techniques, computer-aided drug discovery (CADD) has helped deliver several success stories ,9,10,11 In the last couple of decades, protein-protein interactions (PPIs) have become popular therapeutic targets. Proteins interact with other biologically important molecules such as peptides, proteins (homo or hetero), DNA, and RNA to carry out their functions. In general, PPIs are involved in effecting regulatory changes in response to external stimuli. Different PPI targets have been implicated in cancer, metabolic diseases, neurological disorders, and many other diseases ,13. The In this review, we will initially discuss the significance of PPIs as therapeutic targets in the drug discovery paradigm, and how this has been dealt with so far in the research field. Next, we will expound on how various in silico methods and recent advances in this field can facilitate PPI drug discovery, as exemplified in several case studies. Lastly, the benefits and pitfalls of CADD techniques in relation to PPI drug discovery will be reviewed. Due to the breadth of PPI drug discovery, we limit the scope of this review to the computer-aided rational discovery and the development of small molecules, peptides, and peptidomimetics for PPI.With an estimated 650,000 PPIs as part of the human interactome , it is e2) [PPI interfaces are shallow and highly hydrophobic with large contact surfaces (>1000 Å2) ,17 that 2) ,19. Thou2) . Aside f2) ,24,25,262) ,28,29,302 are noted to be more flexible and prone to induced-fit conformational changes [Different types of PPI complexes are found in biological systems. Obligate complexes are PPIs that require permanent interaction between protein partners or subunits to establish function, such as in the cases of the P22 Arc repressor  and V-AT changes ,80.PPI complexes display a wide range of affinity and stability. However, high specificity is still observed, even with proteins that have multiple partners or “protein hubs” . When deWhile hot spots are considered to be the main contributors for binding affinity and stability, not all hot spots participate in specificity, as a portion are often shared among several partners that bind to the same site of a protein hub. Separate specificity-determining hot spots that can distinguish between cognate and non-cognate partners are present in PPI binding interfaces ,83. The PTM is an alternative process where proteins recognize different protein partners to carry out their specific functions. A protein can be modified using different PTMs in one or different residues at the same time, or in a sequential manner, to increase its complexity and functional scope. Data statistics from dbPTM show that over 60% of PTM sites are situated in protein functional domains involved in PPIs, implying that PTMs play a crucial role in the modulation of PPI function . PhosphoStructural flexibility can also greatly affect binding affinity and specificity in PPIs. The dynamic nature of proteins allows them to alter conformations based on the required interactions for binding with a partner and inducing a corresponding function. Some proteins can subtly influence both specificity and affinity by altering the orientation of only one or a few conserved residues in the binding interface, such as MDM2  and protFor the design of efficient PPI modulators, a great level of understanding of the target is needed: the type of complex formed, elucidation of binding epitopes and hot spots, determination of binding site flexibility, etc. While more information is progressively coming to light regarding PPI binding requirements, further characterization of features that synchronize molecular recognition, affinity, and specificity within the target protein interface is vital in designing therapeutics for a specific PPI and function to improve selectivity and off-target toxicity.Increased understanding of druggability and molecular recognition in PPIs has undoubtedly stimulated drug discovery efforts for these previously intractable targets, offering hope in identifying small molecule candidates for PPI-regulated pathways. However, the development of PPI modulators is still at a relatively slow pace in comparison to conventional small molecule drugs, due to the challenges conferred by the structural optimization required to improve the affinity and pharmacokinetic properties of the PPI ligands. The complexity of a PPI inhibitor primarily depends on the interface structure, and this is usually equivalent to that of its targets. Analysis of known PPI modulators showed that these compounds are larger, more hydrophobic, and contain more multiple bonds and aromatic rings ,103,104.Studies suggest that 15–40% of the human interactome is comprised of protein-peptide interactions . NaturalTraditionally, potency was the chief facet used for the early evaluation of lead candidates. However, it has been established that potency alone does not completely explain bioactivity against a target, and other physicochemical properties should also be considered . AdvanceExperimental methods are widely accepted as the standard for any analysis as they illustrate biological scenarios in either in vitro or in vivo systems. However, the immensity of the human interactome requires considerable cost, effort, and time to study. Moreover, PPI studies are highly dependent on dynamics, PTMs, and physiological conditions, causing difficulties in distinguishing true interactions from experimental artifacts and inconsistencies in findings, especially in cases involving transient interactions and IDPs. In silico methods have emerged as alternative methods or as complements to experimental techniques, to fill in the gaps regarding vital PPI information, and to provide a foundation for further analyses .PPI targets have become a class of their own throughout the years. In contrast to well-known protein families, like GPCRs and kinases, this class of targets encompasses exceedingly diverse structures and interactions. Efforts have been made to compile information about experimentally-determined PPIs into databases or platforms. Network analysis and sequence-based methods are valuable strategies that have widespread application in various stages of drug discovery, including target identification, binding site prediction, and polypharmacological studies . Due to PPINs exhibit typical web-like structures, wherein most proteins (nodes) display interactions (edge) with only one or a few partners, while some proteins exhibit contact with multiple partners, and are hence called hubs . The corIn a recent study done by Vinayagam and colleagues , they pePPI prediction is essential for functional annotation. Constraints in experimental approaches necessitated the application of in silico methods, such as binding site prediction  and protTo extend the concept of in silico models, intelligent and contemporary ML algorithms have been implemented in the identification of PPIs. ML is a data-driven or knowledge-based approach which requires an adequate number of training sets and features. Statistical and ML methods have been applied at varied stages: assimilation of diverse heterogenous datasets, assessment of predictions, prediction of prospective PPIs, and investigation of extrapolated PPI networks ,187,188.During the past, several ML algorithms , naïve Bayesian ,189, neuJones and Thornton developed a method for the prediction of PPI interaction sites by analyzing a group of residue spots on the protein interface. Each residue patch was examined using six parameters, including the accessible surface area, hydrophobicity, planarity, protrusion, residue interface propensity, and the solvation potential. The relative combined score was calculated to predict the probability of surface patch formation in the PPIs . FurtherE. coli, Drosophila, and C. elegans). The models developed using an autocovariance (AC) coding method showed the top results on 10-fold cross validation and varied external datasets ranging from 87.99% to 99.21% (prediction accuracy). Another group developed a method known as DeepPPI, which applies DNNs to obtain high-level discriminative characteristics from common protein descriptors [Besides supervised ML algorithms, unsupervised ML approaches were also implemented in PPI prediction. Deep learning has become a new dimension of ML field, which attempts to learn multiple layered models of inputs, known as NNs. A deep learning algorithm is capable of handling extensive raw and complex data where it operates by mimicking deep neural networks (DNNs) and learning processes of the human brain. The central idea of deep learning has been detailed in . It has criptors . This meHunter [ML tools can also be applied in the preparation of PPI libraries or in the analysis of initial PPI hits through different filtering routes to evaluate drug-likeness or ADME/Tox properties. Information regarding PPI modulators is available in 2P2Idb ,228, TIMHunter , based oHunter .The breakthrough of hot spot residue contribution to PPI binding, in conjunction with innovations in protein structure determination in the last several years, has greatly helped in characterizing protein interfaces. Even with current efforts to fully characterize the complete human interactome using well-known genetic and biochemical techniques, numerous targets are still without structures, and the sheer number of partner interactions involved leads to difficulties in fully elucidating all of them. PPIs mainly transpire in conserved regions of protein interfaces, where hotspots are usually identified, and they can cause conformational changes in the overall protein structure . HoweverAlanine scanning mutagenesis (ASM) has been an indispensable tool for the identification of hot spot residues of different relevant protein targets, but this method has only been applied in a limited number of PPIs. More to the point, experimental alanine scanning entails the mutation of several residues in the interface, along with biophysical evaluation of changes in the binding energy for each mutation, making it a costly approach . ComputaDocking methods, like Optimal Docking Area (ODA)  and pyDoThe current rise of the big data era has aided the return and further innovation of knowledge-based techniques, especially in the context of drug discovery. The availability of sequence and structural information has allowed the exploration and identification of distinguishing features for the analysis and prediction of hot spots. Sequence-, structural-, and physicochemical-based features have been used in the development of different data-driven models and in the characterization of relevant PPIs . ProteinL protein, which exhibits drastic conformational differences between its unbound and peptide-bound structures. While BCL-XL is known to be experimentally druggable [L structure considerably fluctuates, resulting in the formation of a highly druggable binding region and implication that much of its druggability can be attributed to its flexible structure [MD simulations have also been employed in the identification of druggable PPI pockets, particularly for flexible and transient systems . A typicruggable , its aporuggable . Analysitructure .It has been estimated that the amount of interactions in the human interactome range from 130,000–650,000 ,273,274.Elucidation of protein-protein or -peptide structural complexes through computational docking has evolved significantly due to the accumulating data on protein structures and interactions, improved energy functions, and powerful techniques to accelerate the sampling process . ProteinThe docking phase can either be rigid or flexible. In rigid-body docking, there are no changes in the structural features. However, in flexible docking, conformational changes in the structures are considered . The docS-glutathionylation has been linked to several human diseases. It has been reported that glyoxalase II (GlxII), an antioxidant glutathione-dependent enzyme, plays a role in the S-glutathionylation mechanism. However, its mechanistic underpinnings remain unrevealed. In a recent study by Galeazzi et al. [Several studies have shown the applicability of protein-protein docking in PPI research ,327,328.i et al. , PPIs ofSimilarly, once druggability and potential binding sites in the protein interface have been established, virtual screening (VS) experiments can be performed to find potential binders that can disrupt or stabilize the target PPI. VS can be categorized as structure-based (SBVS) or ligand-based (LBVS), but the former is more useful for PPIs, since ligand information for these targets is sparse when compared to other drug targets. SB pharmacophore VS can be employed for a given target, wherein interface features are explored to generate a 3D pharmacophore model used to identify ligands with diverse scaffolds that complement the binding site and have the potential to exhibit a desired bioactivity (inhibition or stabilization) . Some prL structure in complex with BCL-2-associated death promoter (BAD) BH3 peptide, identifying three key hydrophobic features which were used to create a pharmacophore model. To identify scaffolds with suitable pharmacological and safety profiles, they screened an in-house database of FDA-approved drugs using the generated pharmacophore model, identifying three classes of compounds with different cores. From this, they performed molecular docking on the BCL-XL protein to predict and study the binding modes of Lipitor and Celexocib, both of which consist of bis-aryl substituted five-membered heterocyclic scaffold. Both compounds mimicked two out of three hydrophobic features required for interaction in the BCL-XL binding pocket, leading to the design of BM-501, which showed suitable affinity to both BCL-2 and BCL-XL and can function as an excellent scaffold in the design of inhibitors for the Bcl-2 protein family [Here, we present a few case studies where VS and ligand docking protocols have been incorporated to identify small molecule PPI inhibitors or stabilizers. HIV-1 Nef is involved in infection, pathogenicity, and disease development by interacting with its own SH3 binding surface . Betzi en family . In a din family . Jian etFBDD has been widely employed for different disease targets in the last several years to acquire new chemical entities, while efficiently scouring a much larger chemical space than traditional high-throughput screening (HTS) can cover. Compared to extensive compound libraries commonly used for VS, which comprise of millions of complex structures, fragment libraries often contain only hundreds to a few thousand low-MW structures . DespiteDuring optimization, fragment hits can undergo linking or growing strategies. For those found to bind in adjacent pockets in the protein interface, appropriate linkers can be selected to connect the two fragments together. On the other hand, fragments can also be grown synthetically to the neighboring pocket based on binding site analysis and rational design. Between the two, linking is found to be more difficult, since the best linkers must be identified to avoid detrimental effects on lead potency and pharmacokinetic properties. While fragment binders initially show weaker affinity in screens, their smaller size and low complexity make it easier to manage physicochemical properties during the optimization than a high-affinity HTS lead. Nevertheless, these weak binding fragments are discovered to form excellent interactions in the binding site, and thus, they contribute more than half of the favorable binding energy if the interactions are preserved during fragment-to-lead optimization .L in complex with ABT-737 and previous assay results to identify a fragment that could be linked to BM-501. Using part of ABT-737 as a linker, they were able to rationally design low nanomolar affinity leads for BCL-2 and BCL-XL. They further employed structure-activity relationship and modeling studies to improve potency and cellular activities of their compounds [While the expansive interface of PPIs is not very conducive for small molecule inhibitor discovery, the presence of multiple hot spot regions spread across the surface suggests the applicability of fragment-based methods. Distinct fragments can be identified for each hot spot, and can be later linked or grown into whole molecules with novel structures that are specifically designed for a particular PPI target . FBDD haompounds .It is important to note that hot spot identification and analysis of the receptor protein are important elements of fragment-based design of PPI inhibitors. Pertinent fragments must be screened and bound to essential hot spot clusters, and not to negligible areas in the PPI interface. Otherwise, while tight binding compounds can be designed from suitable fragments, it may not produce the desired outcome . An excellent example for this is shown by Geppert and colleagues , where tDespite steady progress in the growth of PPI data, comprehensive understanding of such complexes and their dynamics remains inadequate. The scarcity of 3D structural data for protein-protein or -peptide interacting complexes delays PPI research. Moreover, crystallography cannot detect the presence of druggable hot spots and transient pockets on protein-protein interfaces, due to their active motions. MD simulations surpass these limitations by facilitating in-depth analysis of structural, functional, and dynamic aspects of PPI models. They also provide valuable insights into PPI mechanisms which could be utilized in the design of PPI modulators.MD simulation is a commonly applied in silico approach for calculating the time-dependent motion of biological molecules. The initial step in MD simulation of a PPI is the acquisition of a starting complex structure, which could be obtained through experiments ), modeling approaches (homology modeling or protein-protein docking), or PPI databases. Once the structural template is available, the next step is to prepare the system and subject it to atomistic simulations in line with software-dependent simulation procedures. Firstly, the system’s initial positions and velocities are fixed. Subsequently, the forces among all atoms are defined by using a preconfined interaction potential, where time-dependent motions of a system could be traced by solving classical equations of motions . Key utiDixit et al. investigated the functional mechanism of heat-shock protein (Hsp) 90 using atomistic MD simulations, along with the modeling of principal correlated motions and energy landscape analysis . ThroughMD simulations can also assist in PPI hot spot prediction to provide clues for the design of potential PPI inhibitors. Survivin protein is found to be over-expressed in several solid tumors; hence disruption of its interaction with substrate proteins is indispensable for the treatment of cancer. Sarvagalla et al.  provided1–42 oligomers with each of the four models of the full-length Amylin1–37 oligomers were explored using extensive MD simulations, resulting in the elucidation of the link between type 2 diabetes and Alzheimer’s disease [It has been widely accepted that most proteins function as oligomers. However, the oligomerization process often remains unclear. In such scenarios, MD simulations can assist experimentalists in unraveling the oligomerization processes. Zhang et al. investigated the formation of small oligomers in the amyloid fibril-formation process of the peptide GNNQQNY from the yeast prion-like protein Sup35, using explicit solvent MD simulations. Their results demonstrated that primarily antiparallel dimer forms, and subsequently novel peptides may complement the assemblies in parallel arrangement, which is in line with the experimental microcrystal structure of the amyloid fibril . In anot disease . Thus, MThere is no doubt that computational methods have become one of the cornerstones of drug discovery, especially with the advances in technology and the increase in experimental data in the last several years. However, as with any other drug discovery and design methods, CADD has its own strengths and weaknesses when dealing with a variety of drug targets, including PPIs.Prediction and filtering tools are rapid and dependable strategies, particularly for targets with insufficient information at the start of a drug discovery effort. In the absence of 3D information, sequence-based methods can provide quick and useful insights about conservation and evolution information, which may also be critical for ligand design. However, information that is taken from these techniques might not translate well into 3D systems . In factFBDD is notably more efficient than conventional HTS, both in experimental and virtual form. However, several things are needed before pursuing this strategy. First, structural information is required for fragment screening, making it an unsuitable method for relatively unknown targets. Furthermore, information about hot spot regions is crucial, as these are the focus for any type of screening efforts. Second, binding affinity and orientation of fragments may differ from that of the complete molecule. Linker groups and attachment points must be considered conscientiously, as their addition to identified fragment hits can greatly affect both the potency and pharmacokinetic properties of the lead. Third, and the same as other computational strategies, virtual fragment hits must be validated. However, validation for these low affinity structures often requires more sensitive and sophisticated methods and expertise, as compared to conventional HTS .Perhaps the most notable challenge for many computational methods is the dynamic nature of proteins, as the inclusion of flexibility is extremely computationally expensive, leading to the use of rigid protein structures by the majority of CADD modules. This drawback becomes more prominent in PPIs, whose conspicuously flat interfaces are postulated to be highly flexible. MD simulations assuages these concerns as it can approximate protein movements in a carefully monitored system. Improvements in hardware computational power have also aided increased interest and utilization of MD for the analysis of PPIs ,365,366.The listed shortcomings for each method should not be taken as a dissuasion of their use. In fact, this is a reinforcement that more studies are needed to further improve current computational tools. Moreover, the strengths of each method can be used to make up for the weaknesses of the others. The combination of two or more CADD tools, along with the incorporation of experimental techniques, can result in better insights and success in drug discovery endeavors.5. Conclusion and Prospects of PPI Drug Discovery.PPIs have become prime drug targets due to their integral roles in cell signaling and regulatory pathways. While increased interest for these targets encouraged comprehensive studies about PPINs, interface conformations and interactions, and PPI modulators, much is still needed to completely characterize this vast system and to overcome hindrances associated with PPI drug discovery. Current experimental techniques have facilitated our growing understanding of PPIs, resulting in the construction of informative databases containing valuable information involving the human proteome and interactome. Unfortunately, the immensity of the human interactome renders experimental methods insufficient, necessitating the use of their computational counterparts. CADD impacts various stages of PPI drug discovery by assisting in the characterization of PPIs and its unique chemical space, thereby providing better insights into the design of PPI modulators. Innovations in computational power and algorithms, along with the growing knowledge and better parameterization of macromolecular dynamics and energetics relating to PPIs, have led to notable contributions of in silico methods in various PPI drug discovery efforts.Overall, we can expect a remarkable future for PPI drug discovery as both in silico and experimental methods continue to evolve. While each approach has their own significance in the field of drug discovery and development, it is important to remember that one is not designed to outshine the other, but to complement it. The integration of different tools, especially in the study of diverse drug targets such as PPIs, can enhance our understanding of these targets and aid in the design of potent and effective PPI modulators.


Identifying protein complexes from protein-protein interaction (PPI) network is one of the most important tasks in proteomics. Existing computational methods try to incorporate a variety of biological evidences to enhance the quality of predicted complexes. However, it is still a challenge to integrate different types of biological information into the complexes discovery process under a unified framework. Recently, attributed network embedding methods have be proved to be remarkably effective in generating vector representations for nodes in the network. In the transformed vector space, both the topological proximity and node attributed affinity between different nodes are preserved. Therefore, such attributed network embedding methods provide us a unified framework to integrate various biological evidences into the protein complexes identification process.In this article, we propose a new method called GANE to predict protein complexes based on Gene Ontology (GO) attributed network embedding. Firstly, it learns the vector representation for each protein from a GO attributed PPI network. Based on the pair-wise vector representation similarity, a weighted adjacency matrix is constructed. Secondly, it uses the clique mining method to generate candidate cores. Consequently, seed cores are obtained by ranking candidate cores based on their densities on the weighted adjacency matrix and removing redundant cores. For each seed core, its attachments are the proteins with correlation score that is larger than a given threshold. The combination of a seed core and its attachment proteins is reported as a predicted protein complex by the GANE algorithm. For performance evaluation, we compared GANE with six protein complex identification methods on five yeast PPI networks. Experimental results showes that GANE performs better than the competing algorithms in terms of different evaluation metrics.GANE provides a framework that integrate many valuable and different biological information into the task of protein complex identification. The protein vector representation learned from our attributed PPI network can also be used in other tasks, such as PPI prediction and disease gene prediction.The online version of this article (10.1186/s12859-018-2555-x) contains supplementary material, which is available to authorized users. With the advent of the post-genomic era, the focus of life science research has shifted from genomics to proteomics. One important task in proteomics is to detect protein complexes from protein-protein interaction (PPI) networks. The discovery of protein complexes is not only critical to reveal the principle of cellular organization and functions, but also helpful to predict protein functions, disease genes and drug-disease associations. With the advances of high-throughput technologies, many large-scale PPI networks have been constructed , 2. HencIn recent years, many computational methods have been proposed to predict protein complexes from PPI networks. A PPI network is usually modeled as an undirected graph, where the nodes in the graph represent proteins and the edges represent the interactions between proteins. Roughly, most of these protein complexes identification methods are based on the principle that densely linked regions in the PPI network correspond to actual protein complexes . TherefoExisting computational methods for predicting protein complexes can be approximately divided into two broad categories : (1) TheRecently, network embedding methods have shown to be effective in many graph data analysis tasks such as link prediction and network clustering . NetworkStructure-preserved network embedding methods focus on preserving the topological structure of the original network. Motivated by the similar power-law distribution of the vertices appearing in short random walk and the words in natural language, DeepWalk  regardedAttributed network embedding targets at leveraging both the topological proximity and node/edge attribute affinity. MMDW  is a semThe attributed network embedding method provides a general framework for incorporating both network structure information and additional node attribute information to generate a unified low-dimensional representation. This salient feature is particularly desirable in the context of protein complexes identification since using the additional biological information. The use of additional biological information sources often boost the identification performance significantly. Unfortunately, there are still no researches that exploit the attributed network embedding approach for protein complex detection. To fill this gap, we take the first attempt to investigate the feasibility and advantage of utilizing the attributed network embedding idea for protein complexes detection.To our knowledge, although some methods incorporate several biological information in different ways, our method is the first piece of work that incorporates the attributed network embedding idea into the protein complexes identification problem.Our method provides a framework that integrate many valuable biological information into the task of protein complex identification.The protein vector representation learned from our attributed PPI network can also be used in other tasks, such as PPI prediction and disease gene prediction.We propose a new method called GANE to predict protein complexes based on Gene Ontology(GO) attributed network embedding. The PPI network is represented as an attributed network in which the protein nodes are associated with GO slims. GANE first learns the vector representation for each protein from the GO attributed PPI network. Then, it uses a clique mining method to generate candidate cores. Consequently, a set of seed cores are generated from the set of candidate cores with density-based clique ranking and redundancy-based clique updating. For each seed core, its attachments are the proteins whose correlation score is larger than a threshold. The seed cliques with attachments are reported as the predicted protein complexes. In order to evaluate our method, we compared GANE with six classic protein complex identification methods, which are COACH , CMC 1010, MCODEThe remainder of the paper is organized as follows. In “The GANE method for protein complex prediction is a two-step procedure. Firstly, it learns the vector representation for each protein from the GO attributed PPI network. Based on the pair-wise vector representation similarity, a weighted adjacency matrix is constructed. Secondly, it uses a clique mining method to generate candidate cores. A set of seed cores are generated from the set of candidate cores with density-based clique ranking and redundancy-based clique updating. For each seed core, its attachments are the proteins whose correlation score is larger than a threshold. The seed cores with attachments are the predicted protein complexes. Figure The network embedding technique transforms graph-structured data into vectorial data by learning the low-dimensional vector representation for each node in the network. Among existing network embedding methods, the AANE  aims at G = , where the nodes in V represent proteins and the edges in E represent the interactions between proteins. In order to preserve the topological proximity between proteins in the original PPI network, a loss function is defined as: In the GANE, a PPI network is represented as an undirected graph φi and φj are the vector representations of protein i and protein j, the matrix A∈Rn×n represents the adjacency matrix of the PPI network, aij=1 only if there is an interaction between protein i and protein j. Minimizing the penalty part aij(φj−φj)2 means to minimize the embedding difference between φi and φj when aij=1. Hence, proteins with similar topological structures will be forced to have similar vector representations. As a result, this model preserve the topological structures of the original PPI network.where Bp), molecular function (Mf), and cellular component (Cc). GO slims is the cut-down version of GO, it contains a subset of the terms in the whole GO. They provide an overview on the ontology content without the details of the specific fine grained terms. GO slims give a comprehensive description on the biological attributes of proteins. Since GO slims of Cc include some protein complexes information, we only select GO slims of Bp and Mf as protein attributes.Gene Ontology (GO) is currently one of the most comprehensive ontology databases in the bioinformatics community . It provO∈Rn×m, where n represents the number of proteins and m represents the number of GO slims attributes. Each entry oij in the matrix O describes whether protein i has a corresponding GO slim j or not with oij=1 or 0. Based on the matrix O, we construct a protein attribute affinity matrix S∈Rn×n. Each entry sij is calculated as below: After getting the attributed information for all the proteins in the PPI network, we generate an attribute matrix To preserve the proximity with respect to protein attributes, a loss function is defined as: S∈Rn×n is the protein attribute affinity matrix. Minimize this loss function means minimize the difference between the dot product of the vector representation φi and φj with the corresponding attribute similarity sij.where Since topological and biological properties are both important for protein complexes identification, we use the topological model and GO attributed model together to learn the representations of proteins. The final loss function is defined as: λ is a parameter that controls the trade-off between topological and GO attributed properties. Since ℓ is separable for φi, the corresponding minimization problem can be reformulated as a bi-convex optimization problem. T‘he original embedding problem is split into 2n small convex optimization sub-problems. As shown in AANE [n updating steps of φi is assigned to different workers in a distributed way. The distributed algorithm is guaranteed to converge to a local optimal point [φ∈Rd, where d represents the length of the embedding representation.where  in AANE , the dis in AANE , 36 can al point . After sφ∈Rd, we generate a weighted adjacency matrix W∈Rn×n as below, where cos_sim is the function for calculating the cosine similarity between two connected proteins based on the embedding representations. After obtaining the vector representation of each protein Gavin et al.  proposedAlternative_core set. Since not all the cliques in Alternative_core are suitable to be the cores of protein complexes, we prune the Alternative_core set to generate the Seed_core set based on the following procedure: Alternative_core are sorted in the descending order by density_score, denoted as Clique1,Clique2,…,Cliquec. This density_score function considers both the inside connective density and biological correlation of each clique. Cliques in Clique1 from Alternative_core and put it into the Seed_core set.Remove Cliquei∈Alternative_core that has an overlap with Clique1, Cliquei is updated with Cliquei−Clique1. After that, if |Cliquei|<3, remove Cliquei from Alternative_core.For any other clique To generate cores, we use the cliques mining algorithm proposed by Tomita et al.  to enumeAlternative_core is empty. The cliques in Seed_core are regarded as the core proteins in protein complexes.This process repeats until Seed_core and a candidate attachment protein is calculated as below: To detect attachments for each core, we focus on the strength of topological and biological connectivity between the core and the corresponding attachments. The correlation score between a clique in the pi is one of the neighbors of the corresponding core Cliquej. If the correlation score between protein pi and Cliquej is larger than a threshold value θ, pi is considered as one attachment of the corresponding clique.where protein Finally, each protein complex is generated by combining the core and its corresponding attachments.https://downloads.yeastgenome.org/curation/literature/go_slim_mapping.tab. To compare the predicted results with the reference complexes, we have constructed a standard complexes set by selecting all the protein complexes that had at least three proteins from MIPS, CYC2008, SGD, Aloy and TAP06. Consequently, there was a total 789 protein complexes in the reference set.Five yeast PPI networks were used in the performance comparison: DIP , Krogan-To formally evaluate the performance of our method, we use the same evaluation metrics as other methods , 14.P denotes the set of predicted protein complexes from one method, the performance of this methods is mainly determined by the number of matched complexes between P and the set of gold standard protein complexes B. To determine if a predicted protein complex p∈P matches a known protein complex b∈B, we use the neighborhood affinity score NA defined in in Eq.  is larger than 0.25.where Ncp is defined as the number of predicted complexes that match at least one real complex, and Ncb is the number of real complexes that match at least one predicted complex. Based on the neighborhood affinity score, ω is threshold parameter, which is typically specified to be 0.25.In Eqs.  and 10)10), ω isPrecision, Recall and F-score. Precision is the proportion of predicted protein complexes that match at least one reference complex. Recall is the proportion of reference protein complexes that match at least one predicted complex. F-score is the harmonic mean of Precision and Recall. The first three measures used in the experiments for evaluating the performance of different methods are Sn), clustering-wise positive predictive value (PPV) and geometric accuracy (Acc). Given |B| reference complexes and |P| predicted complexes, let Tij denote the number of proteins that are found both in reference complex i and predicted complex j, and let Ni denote the number of proteins in reference complex i. Then, Sn, PPV, Acc are defined as follows: The other three metrics we used are clustering-wise sensitivity  of each method. In Fig. F-score and Acc. As shown in Fig. In order to visually observe the comparative results, Fig. P-value by the tool GO::TermFinder [P-value represented the minimum P-value of the matched GO analysis results, it indicated that the collective occurrence of these proteins in a complex did not occur merely by chance. Thus, the predicted complex had a high probability to be real.To examine the biological sense of the predicted protein complexes generated by GANE, we calculated the rmFinder . Some ofExpect GO slims, we also utilized gene expression profile as attribute information for complexes detection. The performances were shown in Additional file d, the harmonic value λ and the threshold value θ.In this part, we examined the sensitivity of GANE with respect to three parameters: the length of vector representation d was varied from 32 to 224. Figure In the experiment, the embedding vector dimension λ balanced the contributions of topological and biological information for GANE. To investigate the impact of λ, we varied it from 0.00001 to 1000. When λ was relatively low, topological information contributed much to the performance of our method. With the increasing of λ, biological information contributed much. As shown in Fig. λ. Here, we set λ=0.1 as default value.The harmonic factor θ determined whether the neighbors of a core are included as its attachments. When the value θ was higher, it was harder for each neighbor to become an attachment. In other words, internal connections of the resulting protein complex were tighter. As shown in Fig. θ was less than 0.1, the performance was relatively low. This was because when θ was small, most of the neighbors can be regarded as the corresponding attachments. The performance reached its peak when θ=0.3, so we set 0.3 as its default value.The threshold value In this article, we propose an efficient method called GANE to detect protein complexes from PPI networks. GANE integrates biological evidences into the detecting process by learning vector representations for proteins from GO attributed network. As experimental results shown, GANE outperforms six protein complex detection methods on five different datasets. We concluded that the GO attributed network embedding can effectively enhance the quality of predicted complexes.How to learn the vector representations by incorporating more biological attributes in the PPI network? The incorporation of more biological evidences will further boost the identification performance.How to apply the attributed network embedding methods to other biological networks, such as drug-drug interaction network and gene-phenotype networkIn the future, we will focus on investigating the following two questions: Additional file 1Table S1. The performances of GANE with different attribute information. (DOCX 25 kb)


Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at  Three-dimensional structures of proteins provide valuable insights into their function on a molecular level and inform a broad spectrum of applications in life science research. Often, complexes of proteins are central to many cellular processes. A detailed description of their interactions and the overall quaternary structure is essential for a comprehensive understanding of biological systems, how protein complexes and networks operate and how we can modulate them ,2. GivenSeveral approaches have been developed to address the computational prediction of protein-protein interactions . Co-evolWith more experimentally determined structures of protein complexes becoming available, it has been observed that interacting interfaces are often conserved among homologous complexes  and thathttps://swissmodel.expasy.org was the first fully automated protein homology modelling server and has been continuously improved during the last 25 years (SWISS-MODEL 25 years . Its mod25 years .https://cameo3d.org) (SWISS-MODEL currently generates ∼3000 models a day (∼2 models per minute), up from ∼1500 models a day in 2014 (o3d.org) , a fullyo3d.org) , allowinInput data: The target protein can be provided as amino acid sequence, either in FASTA, Clustal format or as a plain text. Alternatively, a UniProtKB accession code  (e (GMQE)  and Quate (GMQE) . Top-ranModel building: For each selected template, a 3D protein model is automatically generated by first transferring conserved atom coordinates as defined by the target-template alignment. Residue coordinates corresponding to insertions/deletions in the alignment are generated by loop modelling and a full-atom protein model is obtained by constructing the non-conserved amino acid side chains. SWISS-MODEL relies on the OpenStructure computational structural biology framework , available at  release . Every d release , and thehttps://swissmodel.expasy.org/repository) is a database of automatically generated homology models for relevant model organisms and experimental structure information for all sequences in UniProtKB  and NGL (https://github.com/arose/ngl)  . ProMod3The modelling engine is the heart of SWISS-MODEL. It builds an atomistic protein model given a template structure and a target-template sequence alignment. Until recently, the software package ProMod-II , using Mab-initio techniques to perform this step. Final candidates are selected using statistical potentials of mean force scoring methods. If no suitable fragments can be found, a conformational space search is performed using Monte Carlo sampling. Non-conserved side-chains are modelled using the 2010 backbone dependent rotamer library from the Dunbrack group   . Taking de novo modelling targets, other methods perform better in the CAMEO assessment  Reductase (FNR) and its physiological electron donor Ferredoxin (Fd). In higher plants, these proteins are part of the electron transport chain of thylakoid membranes where they catalyse the last step of NADP+ reduction. In non-photosynthetic tissues, i.e. roots, the reaction operates in the opposite direction and is mediated by the tissue specific isoforms of the enzymes . Crystalynthesis . Only reynthesis . To illuZ. mays were submitted to SWISS-MODEL. Results of the template search are shown in Figure The amino acid sequences of root FNR (UniProtKB: B4G043) and root Fd (UniProtKB: P27788) from Computational structural modelling methods have established themselves as a valuable complement to experimental structural biology efforts towards increasing our understanding of the protein universe and of its properties. In this endeavour, comparative modelling techniques have matured into fully automated pipelines, providing easy access to reliable 3D models and broadening the spectrum of users and applications of protein models. SWISS-MODEL pioneered the field of fully automated comparative modelling servers 25 years ago and it has been continuously developed and improved since then.With the new version of SWISS-MODEL presented here, we aimed at extending the scope of automated homology modelling to address the modelling of protein assemblies by efficiently using the information on quaternary structures available in the PDB. The success of this approach clearly depends on the availability of homologous complexes that can be used as templates for modelling. As such, ongoing structural biology efforts leading to structures of macromolecular complexes being determined at unprecedented speed are tremendously beneficial for making our approach increasingly applicable and effective. An important aspect is the ability to handle ambiguous or conflicting information present in available structural data, which is crucial for the development of stable and fully automated pipelines. Here, we showed how our PPI fingerprint analysis and model quality estimates could provide additional criteria to improve the automatic identification of templates, which in turn results into more accurate models and a biologically meaningful representation of their oligomeric state. Finally, we introduced an improved modelling engine and increased the precision of model quality estimates, leading to more accurate models and realistic error estimates at the same time.Supplementary DataClick here for additional data file.