Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy.The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on  All living organisms are composed of cells, which are intricately arranged chemical factories that obtain matter from their environment and use this raw matter to generate copies of themselves . Behind Several methods have been proposed for characterization of protein functions. The early and conventional techniques were generally based on the most fundamental type of information about proteins i.e., their amino acid sequence, utilizing tools such as Basic Local Alignment Search Tool (BLAST) . SequencDue to technological advancements, our understanding of biological processes is improving and new features describing proteins are emerging on regular basis . It is iSaccharomyces cerevisiae species proteins which is obtained from Uniprot database  = {y ∈ U|xEy}. The fundamental notion of rough set theory, i.e., approximations and the three regions are defined using equivalence classes as follows.Three-way Decisions) given by ∩C≠⌀}.The threThe deferment decision option which is exercised based on the boundary region is useful in at least two aspects. Firstly, it provides hints for seeking and incorporating anticipated future information in the decision making model for making decisions on the deferred cases. It is hoped that as information matures, the number of deferred cases will reduce thereby leading to more precise decisions. Secondly, the deferred cases which are typically associated with high levels of uncertainty and therefore, no obvious immediate decisions, the deferment decision option may help avoiding some false decisions. The former aspect is of particular interest from protein functions classification perspective We further elaborate this in the next section.The information describing the functions of proteins are evolving. An interesting issue is how to build effective decision making model for taking advantage of evolving information of protein functions. In this section, we elaborate the role of rough sets based three-way decisions as one of the possibility. In particular, we explain, how evolving information leading to new features can be effectively utilized in three-way decision making based on rough sets. We consider a demonstrative example for this purpose based on an information table of Pi’s and the columns describe the feature or characteristics of proteins. The last column labeled as “Function” represents the decision attribute. The Function = Yes means that a protein performs the function and Function = No means that the protein does not perform the function. Let us assume three instances in time, i.e., t0, t1 and t2 with t0 < t1 < t2. Further assume that at time instance t0, we only have information about the “Localization” of the proteins. At time instance t1, we have additional information of “interacting proteins” of the proteins and at time instance t2, we have more additional information about the “No. of Domains” of the proteins. Using the information available at time instance t0, i.e., “Localization”, we have the following equivalence classes.The rows of the t1 and t2, when we have additional information in the form of “interacting proteins” and “No. of Domains”. t1 and t2. In other words, the additional information about the “interacting proteins” and “No. of Domains” of the objects has increased the size of the positive and negative regions. This means that we can make more decisions in the form of acceptance or rejection when the level of available information increases. In this article, we argue that this property of three-way decision making can be quite useful for making decisions on protein functions classification.In the same way we can compute the three regions at time instances In order to see the same phenomena visually, we include Three-way Decisions using Rough Sets) provide a useful approach for inducing and making three-way decisions. However, this conventional rough sets model which is also sometimes referred to as Pawlak rough set model suffers from a key limitation. Researchers argue that the conditions in the upper and lower approximations may be too strict when it comes to real applications. Specifically the conditions in Eqs ,,Three-waC are defined using a pair of thresholds  as [P(C|[x]) denotes the conditional probability of a concept C with an equivalence class [x]. Given that an object x ∈ [x], the conditional probability highlights the evaluation of an object x to be in C. The three rough set regions based on lower and upper approximations are defined as,α,β)((C), NEGα,β)((C) and BNDα,β)((C) in Eqs  as ,apr_≥α},NEG|[x])<α}.The POS(β) model, where due to restricted pairs of thresholds, the determination and interpretation of thresholds are ignored, the GTRS and ITRS allows for investigation and examination of thresholds based on different aspects.Firstly, compared to some of the earlier probabilistic models, such as, 0.5-probabilistic rough set model and  such that s1 ∈ S1, s2 ∈ S2 and sn ∈ Sn. Each order pair in S is called a strategy profile and represents a certain situation encountered in a game.α, β) thresholds. Depending on the initial values of thresholds, we may have different types of strategies. For instance, if the initial values of  are set to , then the strategies may be formulated as decreasing levels of α and β. Alternatively, when the initial values of  are set to , then the strategies may be formulated as decreasing levels of α and increasing levels of β. Please note that in order to keep the regions disjoint, it is assumed that 0 ≤ β < α ≤ 1.0. The strategies of the players in a game lead to effective modification of the thresholds which ultimately determines the final configuration of the thresholds.The strategies in GTRS are realized as different changes and modifications in the . Each ui is a real valued utility function for player i and it maps the strategy profiles to real values, i.e., ui: S ↦ ℜ. In particular, the payoffs reflect the utilities of performing or selecting a certain strategy. Recall the game players in GTRS which are represent different aspects or properties of rough sets, the payoff function for a certain player is based on particular measure employed for evaluating its respective property.In a game setting, every player wants to perform a strategy that will maximize its payoff. The selected strategies of the players however affect their opponents payoffs. The game solution is used to choose a balanced and trade off point based on the utilities of all the players. The game solution of Nash equilibrium is commonly used in GTRS for this purpose.si− = , which means a strategy profile without player i strategy. Moreover, the strategy profile  may be denoted in revised notation as . The strategy profile  =  is a Nash equilibrium if ) ≥ α′}3:  POS4:  α′, β′)((C) = {x ∈ BNDα,β)((C)|β′ < P(C|[x]) < α′}5:  BNDα,β)((C) = POSα′, β′)((C)⋃POSα,β)((C)6:  POSα,β)((C) = NEGα′, β′)((C)⋃NEGα,β)((C)7:  NEGα,β)((C) = BNDα′, β′)((C)−BNDα,β)((C)8:  BNDelse9: α, β) using GTRS and ITRS.10:  Determine thresholds ((C) = {x ∈ U|P(C|[x]) ≥ α}11:  POSα,β)((C) = {x ∈ U|P(C|[x]) ≤ β}12:  NEGα,β)((C) = {x ∈ U|β < P(C|[x]) < α}13:  BNDend if14: return POSα,β)((C), NEGα,β)((C), BNDα,β)((C)15: α,β)((C), NEGα,β)((C), and BNDα,β)((C), respectively. In line 1, the algorithm evaluates the positive and negative regions by employing some quality criteria denoted as QPOS and QNEG . These notations are introduced to represent the general notion of any criteria that is employed for evaluating the three regions. They may be interpreted in terms of cost, risks, uncertainty, accuracy or precision. The quality of the regions may be measured based on the notions such as risks, cost, uncertainty, accuracy or precisions. As discussed in the previous subsection titled Three-way Decisions and Evolving Information, when the features evolve, the positive region gradually converges to the concept C  and the negative region gradually converges to the complement of the concept Cc , respectively. As a result, the quality of the two regions improves. As improvement in quality is a gradual process in this case, at the current level of information, the quality of the positive and negative regions may or may not be effective (please be noted that the term effective here may have different interpretation based on the underlying applications). We deal with these two cases separately.The algorithm accepts information table containing information about a new feature and the three regions based on the previous features, i.e., positive, negative and boundary regions denoted as POSc1 and c2, we will only examine the objects in the boundary region and will not further investigate the positive and negative regions. The boundary is expected to shrink further as we have access to new features. In any other case, we will examine the full information table to obtain the three regions. In other words, we are not satisfied with the quality of the positive and negative regions (they are below the levels c1 and c2) and we expect that additional information may improve their respective quality levels. We first deal with the former case. In line 3, we determine thresholds based on the reduced information table with U = BNDα,β)((C). As new information becomes available in the form of a new feature, we may be able to confidently classify further objects in the boundary. This is shown in line 4-7 where we further divide the objects in the boundary region. In line 6-8, we update the three regions based on further examination of the boundary. From line 10-13 we examine the case when the positive and negative regions based on the previous knowledge were not of acceptable quality. We therefore examine the full information table and update the three regions accordingly. The If the quality of the regions are above some acceptable levels c1 and c2 may be defined in different ways depending on the application needs and requirements. For instance, if we want to reduce the processing overload, we may define them moderately. On the other hand, if processing overload is not an issue and we are more concerned about the accuracy, then we may define them more strictly. Other ways in which they may be defined are by making comparison with the quality of the regions obtained with the standard Pawlak models or other known models in the domains or by considering the improvement in quality based on the new features.It may be noted that the constants Saccharomyces cerevisiae species proteins [Uniport database [is a special case of the parent or is a part of the parent process i.e., a sub-process or component. For the evaluation of our methodology we operate on molecular function category of gene ontology. To reveal the evolving nature of biological information, we present features in the order in which they are evolved over the time i.e., most basic type of information is presented first and so on [Fi.To evaluate the use of three-way approach, we examine the application of three-way decisions on well studied proteins , 64, obtdatabase . From vadatabase  classifind so on . For claF1)Protein Sequence Length , as the length of protein sequence which is extracted from Uniprot database [database .F2)Protein Localizations Biological Processes , we count the number of biological processes of a protein. It is obtained by retrieving counts of Biological Process ontology terms from the Gene Ontology database [database .F4)Number of Interacting Proteins  data to predict proteins function. In a living cell, protein-protein interactions are amongst the most ubiquitous types of interactions and their precise knowledge helps in understanding the activities performed by a protein as well as the processes it is part of. A protein having many different interacting partners can be said to be part of many different functions. Thus the number of interactions (F4) of a protein can be linked to the wide variety of activities it performs. We obtain PPI data from most widely used PPI databases, namely, IntAct [STRING [, IntAct  and STRI [STRING . Since pF5)Number of Domains  the number of Interpro conserved domains within a query protein sequence.ed e.g.,  to defined e.g., . One of database . InterPrF6)Number of Conserved Motifs  we count the number of conserved motifs in a protein sequence using Prosite motif database [database .F7)Number of Protein Structures  [F7) to characterize its function.ta Bank)  is by faF8)Molecular Weight of Protein  from Uniprot Database [F8) for our 3-way classifier.Database  and use F9)Number of Interfacing Residues in Protein Structure  for our 3-way classifier.A server , to retrF10)Binding sites in the Predicted Interface  at its surface. To determine the specificity of a protein for binding and performing wide variety of functions we calculate a feature (namely (F10), which is the number of binding sites on its surface that are predicted using PDBeFold Server [d Server .F1 to F10, are extracted using the Feature Extractor module , from the world wide biological databases using the knowledge discovery module. The Feature Extractor module also has the capability to incorporate any new feature, say F11 in the predictive task. To imitate the ever evolving nature of biological information, we selected and ranked features from most basic type to the latest type i.e, F1 namely, sequence similarity, is a basic type of feature and F10 namely, number of binding sites on a protein interface, is a specific feature known after information evolution.The above features namely, We performed experiments with five three-way decision making approaches based on GTRS and ITRS. Specifically three of these approaches are based on GTRS and two of them are based on ITRS.Three-way Decisions using GTRS. The essential difference in these games are the consideration of different types of game players. Two of these games are based on examining a balance between the uncertainties of probabilistic rough set regions. These games are based on two players, namely, immediate decision region, denoted as I and deferred decision region, denoted as D. The player I reflects the collective uncertainty in probabilistic positive and negative regions and the player D denotes the uncertainty in the probabilistic boundary region. By realizing changes in thresholds as game strategies, the players in a game compete in a game by selecting appropriate changes in the thresholds which are used in determining the final settings of the thresholds. Two games are constructed with these game players, i.e., player I and D by realizing different interpretation and computation of uncertainty. In one game, the uncertainty is measured with the Shannon entropy and in another game it is measured with gini coefficient. These two games will be referred to as GTRSE and GTRSG, respectively. These game were previously examined in the context of text categorization and medical decision making [A,G) and performed three-way decisions using the five approaches discussed above in this section. We considered four feature sets. In each feature set, we consider the features whose relevant information was previously available or which emerged roughly at the same time. In particular, the first feature set comprise of F1, F2 and F3 (please refer to Section Data Preparation for their details). We denote the first feature set as FS1. The second feature set denoted as FS2, is given by FS1 ∪ F4. The third and fourth feature sets, denoted as FS3 and FS4 are given by FS3 = FS2 ∪ {F5, F6} and FS4 = FS3 ∪ {F7, F8, F9, F10}, respectively. Please be noted that the FS1 contains the oldest available information about proteins while FS4 is the represents the most recent information comprising the previous knowledge and newly evolved information. Finally, all the results are based on 10 folds cross validation.In all experiments, we considered the top five most frequent protein functions in the database. For each protein function , we learn the probabilistic thresholds , we are able to make classification decisions for more proteins.We report the results of accuracy and generality for the considered three-way approaches. The accuracy and generality may be defined as ,AccuracA,G) while maintaining the same or similar level of accuracy. In order to highlight this observation, we constructed two figures.Figs In this section, we compare our method to the most widely used group of function prediction techniques. The first choice of comparison is a most recent function prediction tool, namely INGA (Interaction Network GO Annotator) , which iSaccharomyces cerevisiae proteins. Clearly, our method outperforms IGNA in both aspects. The strength of our method mainly comes from the fact that it is able to defer instances  for which there is less characterization evidence at present, thus improving prediction accuracy.To attain the most accurate comparative results, we run our algorithm for wider range of target classes of the molecular function ontology in a ten-fold cross-validation setting. We present the comparative results  challenge . The CAFSaccharomyces cerevisiae proteins. However, the results of this method cannot be directly compared with our approach for a number of reasons. Firstly, this method operates on fixed ontology sizes thus giving results for only 16 GO terms (target classes) out of more than 30,000 GO terms. Secondly, the fixed GO terms chosen by the authors limit pertinence of their method to proteins directly annotated to those GO terms, hence limiting the applicability of their algorithm to only a small number of proteins . On the other hand, our algorithm has much wider GO coverage and results presented include all the yeast proteins.Another method, that is more recently proposed, by Mitrofanova et al. in , combineAs a final remark, it is pertinent to mention here that although three way classification achieved far better results than the earlier proposed schemes but the main purpose of this study was not the optimization of performance (in terms of precision or accuracy) of the earlier schemes. But rather it should be looked at from the perspective of an examination, feasibility and appropriateness of considering three way classification schemes based on evolving biological information for the task of protein function predictions.In conclusion, the three way approaches considered in this study achieve an average accuracy of 80%. The incorporation of future information is useful as it improves the generality or applicability of the models while maintaining similar level of accuracy. In particular, there is an increase in generality by more than 40% for the GTRS based approaches and more than 14% for the ITRS based approaches. From the general trend in the results, it is suggested that as more information becomes available, the generality may improve further. These results advocate for the use of three-way approaches for protein functions classification.Uniprot database indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy. In particular, an average accuracy of 80% (±%2) was reported for the considered approaches with an average generality improvement of 33% (±%5) as we increase features.Proteins are involved in almost every biological phenomena and the precise knowledge of their functions plays an essential role in understanding biological processes. Intelligent mechanisms are generally employed to assign and predict functions of proteins. The technological advancements are continuously resulting in new information and features describing protein functions which in turn can be utilized for improving the quality of protein function predictions. An important issue in this context is to develop effective classification schemes and models for classifying protein functions by incorporating evolving information. We propose a three-way decision making approach to address this issue. The approach includes a deferment decision option which is practiced in situations characterized by insufficient and incomplete information. In particular, we considered probabilistic rough sets based models i.e., game-theoretic rough sets and information-theoretic rough sets for inducing and making three-way decisions. An architecture of protein function classification with three-way decisions is also proposed and explained. Experimental results on dataset from We investigated the probabilistic rough sets which is one possible way for inducing three-way decisions. Other approaches such as shadowed sets, statistical testing, interval sets and ortho-pairs may also be examined to investigate the potential benefits of three-way approach to protein function classification. Moreover, the three-way approach for protein function classification may further be evaluated and extended by incorporating new features resulting from next generation sequencing data or from other high throughput experiments.S1 FileThe code (Python/Bash/Matlab) and data files along with instructions are provided as a zip file.(ZIP)Click here for additional data file.


Despite extensive efforts in elucidating direct protein interactions, our knowledge on the complete spectrum of protein associations remains limited. We therefore developed a new approach that detects protein associations from identifications obtained after re-processing of large-scale, public mass spectrometry-based proteomics data. Our approach infers protein association based on the co-occurrence of proteins across many different proteomics experiments, and provides information that is almost completely complementary to traditional direct protein interaction studies. We here present a web interface to query and explore the associations derived from this method, called the online Tabloid Proteome. The online Tabloid Proteome also integrates biological knowledge from several existing resources to annotate our derived protein associations. The online Tabloid Proteome is freely available through a user-friendly web interface, which provides intuitive navigation and data exploration options for the user at  Protein–protein interactions are well known to play key roles in living cells ,2. TheseIn this approach, we have used publicly available mass spectrometry-based human proteomics experiments from the PRIDE database . These eInterestingly, however, only very few of the obtained associations are explained through direct protein interactions (<3% of obtained associations are known as protein interactions), which indicates that our method is very complementary to traditional protein-protein interaction analyses. Moreover, protein associations with little or no annotation were also observed, typically because one or both proteins in the pair are very poorly annotated in existing databases.http://iomics.ugent.be/tabloidproteome).To make the results of our analysis easily available to the research community, we here present the online Tabloid Proteome as a convenient and user-friendly means to access and query all obtained protein associations, along with extensive annotation based on existing knowledgeb bases (Mass spectrometry-based human proteomics experiments were retrieved from the PRIDE database (release May_2015). The biological annotation included in online Tabloid Proteome is collected from six resources Figure : biologihttps://neo4j.com). The Tabloid Proteome currently stores 551 420 distinct associations for 4562 unique proteins pairs, all with a minimum Jaccard similarity of 0.1. These associations map against 1231 Reactome leaf pathways, 1104 CORUM complexes, 13 593 protein–protein interactions from BioGRID and IntAct, 53 tissue annotations from The Human Protein Atlas and 9697 DisGeNET diseases. These associations are currently derived from 99 distinct PRIDE projects comprising a total of 1063 assays.All data underlying the Tabloid Proteome are stored in the Neo4j graph database (https://jquery.com), Bootstrap (http://getbootstrap.com) and Primefaces (https://www.primefaces.org). Visualization of the protein association graphs is performed by the Cytoscape JavaScript library (The web interface was developed using the Java Server Faces (JSF) framework, with JQuery .A RESTful Web Service is also provided for other applications to access the Tabloid Proteome. The JSON structure and detailed information about the web service is available in the API section of the Tabloid Proteome website , or a single UniProt accession number (e.g. P02790) or Entrez gene ID (e.g. 3263) in the corresponding text field. When a protein or gene name, or an Entrez gene ID is used for the query, a dialog will pop up to show the matching proteins for confirmation and/or disambiguation (e.g. searching MTA1 provides two possible matching proteins). Clicking the magnifying glass icon in front of the desired protein reveals all known association partners for that protein.The first way to access the online Tabloid Proteome is via a single protein or gene search. In this case, the user provides either a single protein (e.g. Hemopexin) or gene name (e.g. GC) with a Jaccard similarity score of 0.58, revealed that it has been found to be co-overexpressed with Hemopexin in breast tumor , for instance, four associated proteins are found, all four of which are already well known to directly interact with MTA1. On the other hand, querying by protein Hemopexin (P02790), yields seven associated proteins, none of which have any prior knowledge linking it to Hemopexin. The system does indicate that many of these have a shared role in diseases. A literature search for the first associated protein, Vitamin-D binding protein  search option. The supported input types are identical to the single protein or gene search. Also, as for the single search, when using anything but a UniProt accession number, a dialog pops up listing all possible protein associations for confirmation and/or disambiguation.MTA1.Importantly, for a two protein query, the online Tabloid Proteome also automatically retrieves indirect associations if the searched pair does not have a direct association. For instance, when queried with the protein accession numbers Q96ST3 and Q8TDI0, no direct associations are found but the system presents the user with a possible indirect relation between both proteins, as both are associated to A third use case is to query the online Tabloid Proteome for multiple protein association pairs, which is supported by the multiple pair protein search option. Here, the user can upload a file with protein pairs, which are then queried. Interestingly, the multiple pair protein search also allows users to provide their own links for these pairs in the uploaded file, which are then visualized in the graphical view alongside the other edges.The online Tabloid Proteome search is not limited to only protein or gene-based searches.Indeed, the fourth use case allows the system to be queried by a Reactome pathway, using either a pathway name or Reactome accession number. The input text can be generic, such as ‘metabolism’, or can be specific, for instance ‘Pyruvate metabolism’ or Reactome accession number ‘R-HSA-70268’. The result is a list of all known associated protein pairs in which both members are involved in the provided pathway. Each individual pair can then be explored in more detail by clicking the magnifying glass icon for that pair in the overview table.The fifth use case then, consists of retrieving all associated protein pairs that are detected for a particular disease. This can be achieved by querying the online Tabloid Proteome with either a disease name or a DisGeNET ID. As for the pathways, the query can be generic , or selective (e.g. ‘Cancer of Nasopharynx’ or DisGeNET ID: ‘C0238301’).The sixth case allows the user to restrict their search for associated protein pairs by tissue annotation, as provided by The Human Protein Atlas. This search can be performed by selecting one of the 53 tissue names, for example, ‘lung’, resulting in 653 protein pairs. Similar to the disease and pathway search, the results are shown in an overview table.The online Tabloid Proteome is an easily searchable website that presents protein associations derived from public mass spectrometry-based proteomics datasets, along with annotation of these associations using information from a variety of existing knowledge bases. Due to the strong complementarity with existing approaches (most notably with direct protein-protein interaction studies) the online Tabloid Proteome provides unique added value for researchers that are interested in understanding the relations between proteins.Future plans for the online Tabloid Proteome focus on the inclusion of association data obtained from public proteomics data from other model organisms . This will also allow orthologous association pairs that have been conserved across evolution to be derived. Additionally, we also compared the protein pairs to information from the STRING database  and founhttp://iomics.ugent.be/tabloidproteome. The documentation for database usage is available via http://genesis.ugent.be/uvpublicdata/Tabloid_Proteome/TabloidProteome1.2_documentation.pdf and the documentation for API REST is available via http://iomics.ugent.be/tabloidproteome/tabloidApi.html.The online Tabloid Proteome is freely available via Supplementary DataClick here for additional data file.


As compared to the previous HOMCOS server, the current server targets all of the molecules in the PDB including proteins, nucleic acids, small compounds and metal ions. Their binding relationships are stored in the database. Five services are available for users. For the services “Modeling a Homo Protein Multimer” and “Modeling a Hetero Protein Multimer”, a user can input one or two proteins as the queries, while for the service “Protein-Compound Complex”, a user can input one chemical compound and one protein. The server searches similar molecules by BLAST and KCOMBU. Based on each similar complex found, a simple sequence-replaced model is quickly generated by replacing the residue names and numbers with those of the query protein. A target compound is flexibly superimposed onto the template compound using the program fkcombu. If monomeric 3D structures are input as the query, then template-based docking can be performed. For the service “Searching Contact Molecules for a Query Protein”, a user inputs one protein sequence as the query, and then the server searches for its homologous proteins in PDB and summarizes their contacting molecules as the predicted contacting molecules. The results are summarized in “Summary Bars” or “Site Table”display. The latter shows the results as a one-site-one-row table, which is useful for annotating the effects of mutations. The service “Searching Contact Molecules for a Query Compound” is also available.The HOMCOS server ( Usually, template-based modeling is performed first, because it has advantages in terms of accuracy and computation costs, if proper templates are available.Molecular interactions are the essence of molecular functions in all forms of life, and thus characterizing interacting molecular pairs and interaction sites is fundamental for molecular biology. Huge numbers of interacting protein–protein pairs, and compound-protein pairs have been analyzed and stored in databases , 2. The Various methods for template-based modeling have been proposed. They can be roughly classified into two categories: “complex threading” and “template-based docking”. Complex threading is modeling from two or more amino acid sequences, and these sequences are aligned (threaded) on the template structure. Szilazyi and Zhang further classified complex threading into two subcategories: “monomer threading and oligomer mapping” and “dimeric threading” . The “moThe “template-based docking” method basically requires two or more monomeric 3D structures of the target proteins as inputs. For each target input structure, its similar structures are searched in the PDB. If a template structure found for one target structure forms a complex with that for another target structure in the biological units in PDB, then the target structures are superimposed on the corresponding template structure to generate a complex 3D model of the target structures. The reason why this method is called “template-based docking”, is that the standard de novo docking program also requires two monomeric protein 3D structures. The modeled 3D structure can be used instead of experimentally obtained 3D structures. To enhance the coverage of the prediction, most methods employ the monomer 3D modeling calculation as their first step –21. The Template-based modeling has also been applied to the modeling of small compound-protein complexes. For this approach, a target chemical compound is superimposed onto the template compound using the 3D structural alignment programs of chemical compounds . The staNucleotide-protein 3D complex structures can been modeled using the template-based approach. To determine the target sites of DNA-binding proteins, the DNA sequences within protein-DNA complexes were replaced and the interaction energies were evaluated . RecentlMany WEB servers for modeling complex structures have been developed. Most of their targets are hetero protein dimers , 23, 38,http://homcos.pdbj.org) keeps the name of the server established in 2008 (http://strcomp.protein.osaka-u.ac.jp/homcos) [Our server HOMCOS  . However/homcos) , and the/homcos) , 40, 41.unitmol, asmblmol, assembly and contact, as shown in Figs. The HOMCOS database system extracts 3D structural information from mmCIF files of the PDB , and stounitmol table describes the molecule in the asymmetric unit. Its primary key is a combination of pdb_id and asym_id. The asym_id is a new molecular identifier introduced in the mmCIF format, is often written as a capital letter . The classical PDB format also has a chain identifier, which is uniquely assigned to each protein and nucleotide polymer. In contrast, the new asym_id is assigned for all of the molecules, except waters, in the asymmetric unit , their SDF files are stored in the server for the chemical structure search. It does not include any multi-residue molecules, such as peptides and oligosaccharides.The nit Fig. b. Therefbilities . The amiT search . Note thblastp comparison among all of the amino acid sequences of the unitmol molecules [unitmol molecule.To summarize the predictions performed by the HOMCOS server, the redundancies of the sequences must be reduced. We perform an all-vs-all olecules , and exeasmblmol describes the 3D structure of the molecule in the biological unit. The mmCIF file contains information about the biological unit provided by both the authors and the software. Some of the biological unit are constructed by combining transformed unitmol molecules as shown in Fig. asmblmol is the combination of three ids, pdb_id, asym_id and oper_expression. The XYZ coordinates of the asmblmol molecule are also separately stored in a classical PDB format file. By definition, the amino acid sequence and the 2D chemical structure of each asmblmol molecule are the same as those of the corresponding unitmol molecule.The assembly summarizes the information about an assembly , which is composed of several asmblmol molecules. Its primary key is a combination of pdb_id and assembly_id. A list of the asmblmol molecules of the assembly is stored in two array variables, asym_ids and oper_expressions. Note that many PDB entries have more than one candidate of their biological unit. For example, the PDB entry 3gyr has eight biological unit candidates; two homo hexamers (assembly_id = 1 and 2) defined by the author, and six homo dimers  defined by the softwares PISA and PQS. Since there is no reliable standard for choosing one correct biological unit, the HOMCOS server contains all of the biological units described in the mmCIF file.The table contact contains contacting residues between two asmblmol molecules belonging to the same biological unit (assembly data). If the distance between one of the heavy atom pairs of the two molecules is within 4 Å, then these two molecules are regarded as contacting molecule pairs and registered in the contact table. Its primary key is the combination of five keys: pdb_id, and one asmblmol key  and another asmblmol key . The array variable seq_id_con contains the residue numbers (seq_id) of the residues in the asmblmol molecule  contacting with another asmblmol molecule .The table http://homcos.pdbj.org) is shown in Fig. The top page of the HOMCOS server . The molecules contacting these homologous unitmol proteins are obtained by searching the contact table, and they are regarded as predicted contacting molecules with the query protein.We first introduce the service for searching contact molecules for a query protein. An overview of the service is shown in Fig. p search  with theThese results can be summarized in two ways: Summary Bars and Site Table. The Summary Bars view shows the information about the predicted 3D complexes in a colored-bar format. An example of the Summary Bars view is shown in Fig. blastp E-value, only if the number of additionally aligned residues >10. For the 3D complex bars, a single linkage clustering is performed for all of the homologous dimers in the same interaction class. Only one representative dimer is shown for each cluster. The criteria of linking are as follows: (1) “type” of contact molecule is identical. (2) Tanimoto coefficient of binding sites is more than 0.2. The “type” of the contact molecule is set to cluster95 for proteins (see the previous section about data architecture), the three-letter code (comp_id) for compounds, metals, precipitants, and sequence for nucleotide, and the pdbx_description for others . The server then checks if a 3D dimeric structure of the homologues exists, in which one of the proteins is homologous to one query protein, and another protein is homologous to the other query protein. If the homologous multimers are found, then they can be used as the template for complex modeling. In contrast to the previous version of HOMCOS, our new server can model not only dimeric structures, but also multimeric structures composed of two different proteins, such as the tetrameric structures of hemoglobin alpha and beta chains. Figure This service is for modeling the complex 3D structures of hetero multimers from two query proteins. The computation procedure is described in Fig. searches  with thehttp://www.jmol.org). This model is created simply by replacing the residue names and numbers with those of the query protein according to the BLAST alignment. The model can be generated with much less computation time than a full atomic modeling, and it is sufficiently useful for observing its molecular geometry at residue-level resolution. Of course, it cannot be used for docking and molecular dynamics simulation, because its substituted side chains are not correctly modeled and all of the inserted residues are missed. For users who desire more detailed models, the HOMCOS server provides a script file for the MODELLER program [We use the common page for 3D model viewing for our five HOMCOS services. As an example, we will explain the case of the 3D model of the hetero protein multimer. On the results page of the hetero protein multimer in Fig.  program , which cIn the model linked directly from the hetero oligomer page, only two protein molecules are always included Fig. . Howeverblastp search [unitmol sequences in the PDB, to make a list of the homologous proteins (unitmol). The server also performs a chemical similarity search with the given query chemical compound for all of the chemical compounds appearing in the PDB, by the dkcombu program [fkcombu [fkcombu aligns the query target compound based on the atomic correspondences with the template compound, by changing the conformation of the target. The atomic correspondences are calculated by the maximum common substructure algorithm [This service is for modeling the complex 3D structure of one protein and one small chemical compound. The computation procedure is described in Fig. p search  with one program , 41. The[fkcombu . The prolgorithm , as showlgorithm . Users hhttp://p4d-infor.nig.ac.jp/vapros/). The VaProS is an integrated database system for structural life science, it includes not only the 3D structural database, but also the databases of sequences, molecular interactions, expressions, and diseases. It stands for “Variation effect on PROTein Structure and function”, because one of the important goals of the VaProS system is analyzing phenotypic effects of mutation using protein 3D structures. The page of the service “contact molecules for query protein” is shown in the VaProS page, with the name “3D Interaction”. For fast searching from the VaProS, the HOMCOS server stores pre-calculated BLAST results for all of the human sequences stored in UniProt. The VaProS server has the “Molecular Interaction” view, which shows molecular interactions as nodes and edges. At each edge for a protein–protein interaction, a link is placed to the service “modeling complex 3D structure of hetero protein multimer”.Some of the services of the HOMCOS server can be used through the VaProS server  , 23, 28.blastp and dkcombu), and provides only sequence-replaced models as a default, and makes time-consuming atomic modeling optional. We think that our strategy provides a good balance between fast response and precise prediction for template-based modeling. We expect that users would always like to quickly know whether templates are available for their target molecules, because template-based modeling is useless when templates are not found. Detailed predictions will only be required when the user performs more detailed analyses, such as molecular simulations.Another advantage of the HOMCOS server is its fast computation. Some WEB servers take more than half an hour for calculations , 23, or blastp for protein sequence searches, but more sensitive profile-based programs or structure comparison programs are sometimes required to detect remote homologues. They can increase the coverage of the model at the expenses of the precision, and can provide longer and more accurate alignments with templates. The technical problem in implementing them in our server is their larger computation time. We first plan to include PSI-BLAST [The current HOMCOS server has many more functions than our previous server, established in 2008. However, the server still has room for improvement. First of all, we only employ the program SI-BLAST  in the nSecond, quality evaluations of the model should be introduced. Currently, the HOMCOS server only shows the sequence similarity and chemical similarity between the target query and template molecules. These similarities are reported to be good measures for the quality of models , 54, andblastn program [Third, some molecules such as nucleotides and polysaccharides cannot be searched and modeled by the current HOMCOS server, although our database contains all of the molecules in the PDB. As shown in Fig.  program . We may Fourth, we maximally accept two query protein sequences and one chemical query compound; however, more query molecules may have to be accepted. In principle, the modeling of multiple query sequences is not impossible. The problems are rather technical: its high computation cost and the need for a user-interface to input many query molecules.Finally, our predictions of binding molecules have limited accuracies. They are performed by our two services, “searching contacting molecules for a query proteins” and “searching contacting proteins for a query compound”. These predictions are based on the similar property principle, which states that “similar molecules have similar common binding properties”. However, this rule is simply empirical, and exceptional cases cannot be ignored. Fukuhara et al.  reportedOur new server has been greatly improved in comparison to its previous version, established in 2008. It contains all of the different types of molecules in the PDB, and provide a useful summary of the contact molecules for a query protein. We hope our server will be useful for a wide range of life-science researchers, and will contribute toward making the full use of the 3D structural data stored in the PDB.


Some computational methods have been proposed, which detect remote homology proteins based on different features and algorithms. As noted in previous studies, their predictive results are complementary to each other. Therefore, it is intriguing to explore whether these methods can be combined into one package so as to further enhance the performance power and application convenience. In view of this, we introduced a protein representation called profile-based pseudo protein sequence to extract the evolutionary information from the relevant profiles. Based on the concept of pseudo proteins, a new predictor, called “dRHP-PseRA”, was developed by combining four state-of-the-art predictors  via the rank aggregation approach. Cross-validation tests on a SCOP benchmark dataset have demonstrated that the new predictor has remarkably outperformed any of the existing methods for the same purpose on ROC50 scores. Accordingly, it is anticipated that dRHP-PseRA holds very high potential to become a useful high throughput tool for detecting remote homology proteins. For the convenience of most experimental scientists, a web-server for dRHP-PseRA has been established at  In the post-genomic age, protein sequence database  Since remote homologous proteins share very low sequence similarity (<30%), a more accurate protein representation by incorporating the evolutionary information into the profiles is needed. (2) The outcomes of homology detection methods based on different techniques and models are complementary with each other; hence, it would be much more efficient to develop a new framework by which to combine them into one. (3) Although several tools or web-servers have been proposed, most of them are not suitable for large scale analysis due to the high computational cost; in this sence an easy-to-use web-servers or stand-alone tools will be certainly welcome.http://bioinformatics.hitsz.edu.cn/dRHP-PseRA/. The detailed usage about this webserver can be found in the ReadMe page.To address the aforemention three points, we construct a profile-based pseudo protein sequence to replace the original protein sequence. This protein representation approach can transform the evolutionary information of profiles into a pseduo protein sequence. Furthermore, the new approch is featured by combining a rank aggregation method. The newly proposed predictor thus formed is called dRHP-PseRA. Finally, a web-server for dRHP-PseRA is established, and it is avaliable at The four state-of-the-art predictors, namely PSI-BLASTAs shown in http://bioinformatics.hitsz.edu.cn/dRHP-PseRA/.Protein remote homology detection is a key technique for studying protein structures and functions. However, it is still a big challenging task since remote homologous proteins usually share very low sequence similarities (<30%). Although several computational methods have been proposed, their performances are still too low for many practical applications. In this paper, we introduced the profile-based pseudo protein sequence formulation derived from protein profile, and found that it was quite useful to improve the performance compared with their individual approaches. Based on such interesting findings, a novel predictor called dRHP-PseRA is proposed by combining the aforementioned four state-of-the-art predictors into one framework through the pseudo protein approach. Experimental results show that dRHP-PseRA outperforms each of the individual predictors based on ROC50 scores. Furthermore, a user-friendly web-server for dRHP-PseRA has been established at It is instructive to point out that, in addition to the four basic predictors selected in the current study, there are some other methods as well in the area of protein remote homology detection, such as FFAS1213In this study, we adopted a commonly used benchmark dataseti = 1, 2,…, 1070) represents the i-th superfamily; k = 1, 2,…, 1824) represents the k-th family, and the symbol ∪ represents the ‘union’ in the set theory. The benchmark dataset is given in the where P, we search its potential homologues against R with its components in a decending orderFirst, for a given query protein pi  represents the i-th homologous sequence with P in R; n is the total number of potential proteins in the ranking list of R; and T denotes the traspose operator. If all the query protein’s homologous proteins are ranked before the non-homologous ones, then the prediction is perfect.where For the reason of diversity and mutually complementary, here we selected the following four state-of-the-art ranking methods as the basic predictors: PSI-BLASTPSI-BLAST is a profile-sequence alignment method, which uses the query proteins to construct profiles and iteratively searches the sequence database. In this study, the PSI-BLAST version 2.2.30 was employed with the iterations times set at 3.P and the sequences in the database, and then iteratively searches the query HMM profile against the database of HMM profiles. HHblits version 2.0.16 was employed with the default parameters except that the maximum time of iterations was set at 2.HHblits is a HMM-HMM alignment method, which constructs a HMM model for both the sequence of the query protein Hmmer is a method based on probabilistic inference and HMM model. In this study, the Hmmer version 3.1b2 with default parameters was used.Coma is a profile-profile alignment method adopting position-dependent gap penalties and a global score system. The multiple sequence alignments generated by using PSI-BLAST version 2.2.30 are fed into the Coma for calculation. In this study, the Coma version 1.10 with default parameters was employed.Remote homology proteins have very low sequence similarities (<30%), therefore only based on sequence information is not enough for accurate homology detection. As demonstrated in previous studies5P, it is searched against the NCBI’s nrdb90 database by running PSI-BLASTP, a matrix M of size 20*L (20 is the number of native amino acids and L is the length of sequence P), can be calculated based on the frequency of each amino acid at each site in generated MSA.Firstly, for a protein sequence M, we sort the amino acids in the descending order according to their frequency values, and then select the amino acids with the maximal frequency value in each column. These selected amino acids are combined to form a new pseudo protein sequence, which is called profile-based pseudo protein sequence. The higher scores in M represent more conserved sites in protein sequence P. Such representation of proteins defined by frequency profiles would be more sensitivity than raw protein sequences for detecting remote homologs.Secondly, for each column in The profile-based pseudo protein sequences were used to replace the raw protein sequences as inputs for the aforementioned four predictors without the need to modify the programs.The aim of rank aggregation is to combine different ranking lists  so as tok ranking lists  between a query protein P and a potential homologous protein pi in the database Given ng lists  generatewj  is the weight of j-th predictor; pi)  is the normalized alignment score between protein P and protein pi calculated by the j-th predictor; and pi) can be calculated bywhere sj(pi)  is the aligned score between query protein P and pi given by the j-th predictor, max(sj) and min(sj) present the maximum and minimum aligned scores returned by the j-th predictor for the query protein P, respectively.where S(pi) means the query protein P and protein pi has closer homologous relationship. Consequently, the rank aggregation approach will automatically sort the proteins in Larger rank aggregation score The jackknife or leave-out-out test was employed in remote homology detection. The jackknife test is deemed the most objective cross-validation approachHow to cite this article: Chen, J. et al. dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation. Sci. Rep. 6, 32333; doi: 10.1038/srep32333 (2016).


GTPase-activating proteins (GAPs) and guanine exchange factors (GEFs) play essential roles in regulating the activity of small GTPases. Several GAPs and GEFs have been shown to be present at the postsynaptic density (PSD) within excitatory glutamatergic neurons and regulate the activity of glutamate receptors. However, it is not known how synaptic GAP and GEF proteins are organized within the PSD signaling machinery, if they have overlapping interaction networks, or if they associate with proteins implicated in contributing to psychiatric disease. Here, we determine the interactomes of three interacting GAP/GEF proteins at the PSD, including the RasGAP Syngap1, the ArfGAP Agap2, and the RhoGEF Kalirin, which includes a total of 280 interactions. We describe the functional properties of each interactome and show that these GAP/GEF proteins are highly associated with and cluster other proteins directly involved in GTPase signaling mechanisms. We also utilize Agap2 as an example of GAP/GEFs localized within multiple neuronal compartments and determine an additional 110 interactions involving Agap2 outside of the PSD. Functional analysis of PSD and non-PSD interactomes illustrates both common and unique functions of Agap2 determined by its subcellular location. Furthermore, we also show that these GAPs/GEFs associate with several proteins involved in psychiatric disease. The cellular actions of GTPases are frequently initiated by GTP binding, enabled by guanine nucleotide exchange factors (GEFs), and finalized by GTP hydrolysis facilitated by GTPase-activating proteins (GAPs)2. The large number of existing GTPases require a multitude of GEFs and GAPs that utilize a variety of combinatorial mechanisms to achieve functional specificity4. These proteins not only need to respond to different inputs, but also direct their output to specific small GTPases. Proteins containing GAP and GEF domains are usually distributed in families containing a variety of protein domains5. These domains are arranged in multi-domain architectures including a family specific GAP/GEF domain together with domains that have the capacity to respond to different chemical messages and associate in protein complexes through protein-protein interactions5.Small GTPases are molecular switches that can rapidly interconvert between two conformational states, depending on association to GTP or GDP9. Proteomics analyses have localized a number of GAPs and GEFs at the postsynaptic specialization of glutamatergic excitatory neurons known as the postsynaptic density (PSD)11. The PSD contains a collection of more than 1500 proteins arranged in macromolecular complexes and many of these proteins have been linked to a variety of brain disorders16. PSD protein complexes link glutamate receptors to downstream signaling pathways through a number of scaffold proteins with multi-modular protein domain architectures13. However, it is not known how synaptic GAPs and GEFs are organized within this signaling machinery, if they have overlapping binding partners, and if they also associate to brain disease risk factors.Small GTPases can regulate a variety of cellular functions. In neurons, they have been shown to be involved in different aspects of synaptic function such as regulation of the actin cytoskeleton, spine remodeling, and synaptic plasticity21. We determined 281 protein interactions within the PSD involving these proteins. We described the functional properties and organization of these complexes, along with their role in the organization of GAP and GEF families within the PSD. Because synaptic proteins may function within multiple subcellular locations, we utilized Agap2 as an example of this scenario and determined the interactome of Agap2 in non-PSD fractions, which involved an additional 110 interactions. By comparing domain architecture and binding interactions in different subcellular fractions, we illustrate both common and unique functions of Agap2 based on its cellular localization. With Agap2, Kalirin, and Syngap1 being implicated in contributing to the risk of psychiatric disease, we also show that they interact with and cluster proteins involved in autism spectrum disorder (ASD), schizophrenia (SCZ), and intellectual disability (ID).Here, using immmunoisolation and high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS), we determined the interactomes of the RasGAP Syngap1, the RhoGEF Kalirin, and the ArfGAP Agap2 in PSD fractions of adult mouse cortex. These proteins are able to interact with each other at the PSD and have also been described to be involved in intellectual disability and psychiatric disease23 and mapped them to 1524 PSD proteins isolated from mouse cortex and identified by HPLC-MS/MS  and Agap2 (ArfGAP) in the CA1 area of mouse hippocampustex Fig.  and deteely Fig. . Proteinely Fig. .Figure 226. Thus, Agap2, SynGAP1 and Kalirin share three main functional associations through: (a) interactions to the core-scaffold machinery of the PSD, (b) association with PSD kinases, and (c) clustering of proteins involved in GTP signaling mechanisms.We first focused on analyzing different functional groups associated to each interactome individually. Clustering the interactors of the PSD GAPs and GEFs by function shows that Agap2 and Syngap1 were mainly associated to PSD scaffolds, protein kinases, and proteins involved in GTP signaling Fig. . AlthougSimilarly to Syngap1, Agap2 and Kalirin also associate with the core-scaffold machinery of the PSD, composed of MAGUKs (Dlg1-4), DLGAPs (Dlgap1-4) and SHANKs (Shank1-3) Figs . This as28. We recently reported that the induction of LTP increases Agap2 and Syngap1 phosphorylation at multiple CamkIIa phosphosites11. Moreover, induction of LTP also induced the increase of Akt1 phsophorylation sites in Agap211, which is reflected in the diverse set of kinases associated to Agap2 . The same was true for Kalirin complexes . While Syngap1 complexes were highly enriched in GAP proteins (P = 6.08 × 10−6), they lacked enrichment within GEF proteins (P = 0.79)  Fig. . Overall22  domains . Within the Kalirin complex, the SPEC (spectrin) domain has the highest enrichment, corrosponding to the increased number of cytoskeletal interactors (P = 5.02 × 10−4). The enrichment of S_TKc (serine/threonine kinase) and RasGAP domains in Agap2 PSD complexes  and RasGAP domains within Syngap1 (P = 0.03) confirms the clustering of multiple kinases families and RasGAP proteins in these complexes. Moreover, Agap2 is also eniched in the G_alpha domain (P = 1.64 × 10−5), corresponding to the association of Agap2 to 6 out of the 10 G-protein alpha subunits localized within the PSD , but also contained significant enrichment in SPEC domains (P = 7.30 × 10−4) which correspond to an increased quantity of cytoskeletal interactors outside of the PSD . Surprisingly, a majority of the G-protein alpha subunit proteins found in Agap2 PSD complexes are found to be in common with Agap2 non-PSD complexes, leading to a significant enrichment of G_alpha domains among proteins in common (P = 1.21 × 10−4)  followed by a decreased enrichment in Kalirin complexes (P = 0.02). In the single nucleotide variants gene list, Agap2 PSD and non-PSD complexes gained significance through the interaction of proteins with very little overlap between the two cellular compartments   Fig. . This sh−3 for all). However, Kalirin and Agap2 non-PSD complexes, failed to reach significance   Fig. . As seenThrough immunoisolation followed by HPLC-MS/MS we were able to determine protein complexes involving the interacting GAP/GEF proteins Agap2, Syngap1, and Kalirin which comprised a total of 281 interactions. In addition, we analyzed non-PSD Agap2 complexes (110 interactions), which was able to show functions of Agap2 based on differential localization. In analyzing the composition of GAP/GEF proteins complexes, we show for the first time that PSD GAPs and GEFs are clustered in subsets of protein complexes highly enriched in other GAPs and GEFs and that these protein complexes are associated to the PSD through its core-scaffold machinery.47 and Kalirin has been shown to interact with Dlg48, here we are able to show that all three of the GAP/GEF proteins associate with multiple layers of the core-scaffolding machinery at the PSD. All of the complexes analyzed here also associate with glutamate receptors, reflecting their ability to relay signaling information from the receptors while relying on the scaffolding machinery for positioning.While we and others have shown Syngap1 to interact with the PSD core-scaffolding machineryIn addition to the widespread interactions with the core-scaffolding machinery, the three PSD complexes were also enriched in the quantities of GAP and GEF proteins contained within each complex. This suggests that, GAP and GEF proteins are highly associated with and can cluster other GAPs and GEFs within protein interaction networks. In addition, Agap2 and Syngap1 complexes were also highly associated with G-protein subunit alpha proteins of the heterotrimeric G protein complexes with Agap2 complexes containing all four classes of G-protein alpha subunits and Syngap1 complexes mainly associated to the G-protein alpha subunit group I signature. This suggests a wide regulation of small G-protein signaling mechanisms by Agap2 and more specific modulation by Syngap1 at the PSD. In addition, all of the G protein alpha subunits within Agap2 PSD complexes were also shared with Agap2 non-PSD complexes. Thus, this represents a core functional unit within Agap2 complexes, while different interactors within each subcellular compartment provide the differential modulation of Agap2 function in a location-specific manner.48. For example, the interactomes of the Alzheimer’s Disease (AD) risk factors, APP and PSEN1, have been shown to be significantly associated to AD risk factors identified through GWAS data48. This provides evidence for the concept that proteins linked to a particular disease may be closer together within protein-protein interaction networks than by chance49.With Syngap1, Agap2, and Kalirin being individually implicated in psychiatric disease, here we show that their protein interactors are also highly enriched in recurrent mutations found in ASD. However, the enrichment for PSD complexes is not universal as Kalirin PSD complexes are not enriched and any SCZ gene set analyzed. While Agap2 non-PSD interactors are also not enriched in SCZ risk factors, Agap2 PSD and non-PSD complexes have similar levels of enrichment with respect to all ASD SNVs and genes implicated in contributing to ID. This reflects the diverse sets of Agap2 interactors implicated in contributing to psychiatric disease as the majority of interactors that contribute to the similar levels of enrichment between PSD and non-PSD complexes are unique to Agap2 based on its subcellular location. Furthermore, protein interactions contributing to disease relevant aspects of Agap2 have been verified within this study. While we have shown that the interactomes of baits implicated in contributing to psychiatric disease are enriched with other psychiatric disease risk factors, this concept has also been shown to be true with respect to neurodegenerative diseases33. It was proposed that Agap2 contributed in the dysregulation of metabotropic glutamate receptors 1/5 (Grm1/5) signaling observed in FXS. Our results show that Agap2 can associate not only to Grm1 and 5, but also to type II Grm2 and 3, suggesting a wide regulation of metabotropic glutamate signaling by Agap2. Moreover, Agap2 was also able to interact with Fmr1 binding partners Cyfip1 and Cyfip2, indicating that Agap2 might modulate Fmr1 function not only at the level of glutamate receptor inputs but also through protein-protein interactions within the Fmr1 protein interaction network. A number of these protein interactions were also identified in non-PSD fractions, including Grm1, Grm2 and Cyfip2. In addition, we were also able to identify Fmr1 as an Agap2 protein interactor in non-PSD fractions, confirming a role of Agap2 in the Fmr1 protein network. Interestingly, recent studies have also suggested that altered signaling pathways observed in Syngap1 mutation mice, might converge with FXS50. In line with this, we also identified Grm5 and Cyfip2 as Syngap1 interactors, suggesting a convergence in a protein interaction network within glutamate metabotropic receptors, synaptic Agap2, and Fmr1 protein interactors.Recently, increased Agap2 expression have been associated to the impaired signaling, synaptic function, and behavior in observed in fragile X syndrome (FXS)We have identified numerous interactors of PSD GAPs and GEFs and provide an example of how interacting proteins can change based on cellular compartmentalization. This shows that GAPs and GEFs may play roles in multiple cellular processes and future studies will need to address the role of GAPs and GEFs not in isolation but within protein interaction networks. This will allow the investigation of how disease-relevant mutations affect not only their activity, but also the composition of protein interactions within the synapse.All experimental protocols used in this study were approved by the University of Southern California (USC) Institutional Animal Care and Use Committee (IACUC), protocol number 11782-CR003. All methods were carried out in accordance with relevant guidelines and regulations.51. Briefly, adult mouse (3–4 months in age) cortex was homogenized in sucrose buffer , 2 mM EDTA, 30 mM NaF, 20 mM β-glycerol phosphate, 5 mM sodium orthovanadate, and Roche cOmplete protease inhibitor cocktail) and centrifuged at 500 g for 6 minutes. Supernatant was collected and then spun at 10,000 g for 10 minutes. The resulting pellet was solubilized in triton buffer (50 mM Hepes (pH 7.4), 2 mM EGTA, 2 mM EDTA, 50 mM NaF, 20 mM β-glycerol phosphate, 5 mM sodium orthovanadate, Roche cOmplete, and 1% Triton X-100. The solubilized pellet was centrifuged at 30,000 rpm for 30 min and supernatant was collected for non-PSD fractions. The resulting pellet was collected and solubilized in DOC buffer (50 mM Tris (pH 9), 30 mM NaF, 5 mM sodium orthovanadate, 20 mM β-glycerol phosphate, 20 µM ZnCl2, Roche complete, and 1% sodium deoxycholate) and served as the PSD fraction.Postsynaptic density preparations were performed as described49. Lysate containing 2 mg of total protein was incubated with the indicated primary antibody at a concentration of 1 µg/µl at 4 degrees Celsius overnight with rotation. The following day, IPs were incubated with Dynabeads protein G (Novex) for 2 hours at 4 degrees Celsius with rotation. IPs were washed three times with IP wash buffer (25 mM Tris (pH 7.4), 150 mM NaCl, 1 mM EDTA, and 1% Triton X-100 or sodium deoxycholate where appropriate). IPs were re-suspended in 2X LDS sample buffer and incubated at 95 degrees Celsius for 15 minutes to elute protein complexes. The elutant was incubated with DTT at a final concentration of 1 mM at 56 degrees Celsius for 1 hour followed by incubation with Iodoacetamide at a final concentration of 20 mM at room temperature for 45 minutes. Primary antibodies used for immunoprecipitation in this study included PIKE (Agap2) , Kalirin , Syngap1 , Rabbit IgG Isotype Control  and GST . All antibodies were used as obtained from the manufacturer.Immunoprecipitation experiments were performed as previously describedet al.11. Briefly, samples were separated on 4–12% Bis-Tris gels (NuPAGE Novex) followed by staining with InstantBlue (Expedeon) for protein visualization. Following destaining, lanes were cut and placed in 96-well perforated plates for destaining and peptide digestion via trypsin at 37 degrees Celsius overnight. Peptides were then extracted with acetonitrile, dried down using a Savant SPD 1010 SpeedVac Concentrator (Thermo Scientific), and then suspended in 3% ACN/0.1% FA. A Nano/Capillary LC System Ultimate 3000 (Thermo/Dionex) was used for desalting and reverse-phase separation of peptides. The LC system was coupled to a hybrid linear ion-Fourier transform ion cyclotron resonance LTQ-FT (FTICR) 7 Tesla mass spectrometer (LC/MS) for data acquisition.Samples were prepared for mass spectrometry as described in Li 52.Proteome Discoverer 1.4 (Thermo Scientific) was used to process MS data which was analyzed using both the Sequest and Mascot V2.4 (Matrix science) against a modified mouse database from Uniprot combined with its decoy database. With respect to analysis settings, the mass tolerance was set 10 parts per million for precursor ions and 0.8 daltons for fragment ions, no more than two missed cleavage sites were allowed, static modification was set as cysteine carboxyamidation, and dynamic modification was set as methionine oxidation. False discovery rates (FDRs) were automatically calculated by the perculator node of Proteome Discoverer and a peptide FDR of 0.01 was used for cut-offs. Peptides with high confidence were considered as true hits and proteins with at least two different peptides were accepted. Protein Interactions were considered positive if a minimum of two peptides were present in at least two assays and absent in anti-GST controls. The datasets generated and/or analyzed during the current study have been deposited to the ProteomeXchange Consortium via the Proteomics IDEntifications (PRIDE) partner repository under the dataset identifier PXD006326Immunoprecipitation was carried out as described above in non-PSD (triton soluble) for Agap2 and PSD (DOC soluble) fractions for Agap2, Kalirin. and Syngap1. For conformation of protein interactions, total lysate (20 ug) and a control rabbit IgG immunoprecipitation were ran alongside Agap2, Kalirin, or Syngap1 immunoprecipitation experiments. Protein was loaded onto 4–12% Bis-Tris gels (NuPAGE Novex) and separated at 135 V for 1.5 hours. Proteins were transferred to a PVDF membrane using a BIO-RAD Trans Blot Turbo System. Membranes were then blocked using 5% bovine serum albumin (BSA) in 0.05% TBST (TBS-Tween 20) for 1 hour at room temperature and then incubated with the primary antibody at a 1:1000 dilution overnight at four degrees Celsius. Primary antibodies include PIKE (Agap2) , Syngap1 , Kalirin , Camk2a , Cit , Dlg4 , Fmr1 , Git1 , NR2B , Gsk3β , mGluR2 , mGluR5 , Nckap1 , and Tnik . All antibodies were used as obtained from the manufacturer. The following day, membranes were washed with 0.05% TBST four times for 10 minutes each, incubated with the respective secondary antibody for 1 hour, and subsequently washed 4 time for 5 minutes each. Western blots were incubated with Pierce ECL Western Blotting Substrate (Thermo Scientific) for 5 minutes and then imaged using a Carestream Image Station 4000 MM Pro.Enrichment of GAP and GEF domain containing proteins within PSD protein complexes was carried out using the one-tailed Fisher’s exact test with the option “greater,” followed by the Bonferroni correction for multiple comparisons using the (fisher.test) and (p.adjust) packages in R, respectively. The total number of proteins within the PSD served as the background.53 using the default settings and the number of protein coding genes expressed in purified mouse cortical neurons derived from Zhang et al.54 as the background for each gene list analyzed. To obtain the background, we accessed processed transcriptomic data from NCBI Gene Expression Omnibus (GEO) accession number GSE52564 and compiled a list of genes that were consistently expressed at a statistically significant level as defined by Zhang et al.54 (FPKM > 0.1) between biological replicates. After filtering out non-coding genes, this resulted in a list of 13,761 protein coding genes. Only domains that reached statistical significance following Bonferroni correction of multiple comparisons are reported in the text.SMART protein domain enrichment was completed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6.8et al.17 which encompasses a list of 57 genes found to have recurrent de novo likely gene disrupting (LGD) mutations in probands with autism from a collection of exome sequencing studies45, (2) Supplementary Table et al.43 which contains 353 genes harboring validated de novo LGD mutations in probands with autism, (3) Supplementary Table et al.45 which included 107 genes with an FDR < 0.3 implicated in contributing to autism through the TADA statistical model, 4) SFARI gene database44 as of February 2016 which was restricted genes containing the SFARI gene rankings 1 – 4  and S (syndromic) (5) Supplementary Table et al.14 which contains de novo SNVs identified in probands with SCZ excluding silent mutations, (6) Supplementary Table et al.55 restricted to loci reaching genome wide significance in patients with SCZ and implicating a single gene, (7) Supplementary Table et al.56 which contains a list of 1537 genes known to contribute to ID. For all tests, protein coding genes expressed in purified mouse cortical neurons derived from Zhang et al.54 served as the background measured by one-tailed Fisher’s exact test with the option “greater,” followed by the Bonferroni correction for multiple comparisons using the (fisher.test) and (p.adjust) packages in R, respectively.For disease gene set enrichment, we incorporated several different lists encompassing genes that are likely to contribute to autism spectrum disorder (ASD), schizophrenia (SCZ), and intellectual disability (ID). These included: (1) Supplementary Table Supplementary InformationSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4


Planarians are emerging as a model organism to study regeneration in animals. However, the little available data of protein–protein interactions hinders the advances in understanding the mechanisms underlying its regenerating capabilities.Schmidtea mediterranea transcriptomic sequence datasets. Then, using Neo4j as our database manager, we developed PlanNET, a web application to explore the multiplicity of networks and the associated sequence annotations. By mapping RNA-seq expression experiments onto the predicted networks, and allowing a transcript-centric exploration of the planarian interactome, we provide researchers with a useful tool to analyse possible pathways and to design new experiments, as well as a reproducible methodology to predict, store, and explore protein interaction networks for non-model organisms.We have developed a protocol to predict protein–protein interactions using sequence homology data and a reference Human interactome. This methodology was applied on 11 https://compgen.bio.ub.edu/PlanNET. The source code used is available at https://compgen.bio.ub.edu/PlanNET/downloads.The web application PlanNET is available at Bioinformatics online. Schmidtea mediterranea, a platyhelminth of the class Turbellaria, has become a model for studying regeneration in animals due to its ability to regenerate its whole body even from small parts of it. Planarians only have one cell type able to divide by mitosis, named neoblasts, which are responsible for the extraordinary regeneration capabilities of these organisms  that may reveal driver components of planarian-specific processes such as regeneration. Furthermore, it is possible that those approaches will also suggest homologous functional candidates to test in planarians as an Instead of studying and analyzing individual genes or proteins, focusing on the environment of such elements where those components play their roles may reveal interesting insights. Molecular medicine based on gene and protein networks has been expanding rapidly, and has shown that most disease-causing genes often work together, either forming protein complexes or participating in the same signalling pathways.Several approaches have been developed in order to infer protein interactions networks from different sources. Sequence homology can be used to predict interactions that have been conserved between species, and the information about these protein interactions can be transferred from one species to another using different approaches  symbols , TrEMBL The mapping of HGNC identifiers against human sequences was done sequentially. First, priority was given to SwissProt sequences, followed by ENSEMBL and finally TrEMBL sequences. Each sequence was assigned to a specific HGNC symbol using the aforementioned synonyms table, looking for sequences in the next database only if a symbol remained unassigned. This constitued the H-Prot dataset.D.melanogaster transcript sequences to train the random forest classifier were downloaded from FlyBase release r5.56 , Dresden  for each of all the transcript sequences. These ORF were used for the two following procedures.The transcript sequences were aligned to the H-Prot dataset using BLASTX and TBLASTN, with an hmmsearch , restricting the dataset to only those domains that contained a human protein with an HGNC identifier. The program hmmsearch was used in order to annotate the PFAM domains on the transcript sequences, using an E-value cutoff of 10−10 and the hidden markov model database of PFAM-A, release 27.0.The alignment to the EggNOG hidden markov models were performed using mmsearch , with anBoth domains should be equal and consecutive.Both domains annotated over the ORF should represent different regions of the domain. In order to decide if this condition was met, the overlap between both annotations had to be <25% of the real length of the PFAM domain.The distance between the domains over the ORF had to be equal or less than the real length of the domain that is not annotated over the transcript, plus a 25% of the total length of the domain.The redundancy of the annotation of the domains over the transcripts was reduced by joining several consecutive domains. The conditions used for that merge were the following:Needleman-Wunsch algorithm, with a match value of +30, a missmatch value of −30, and a gap value of −5. The match score was also adjusted to the percentage of the domain annotated on the transcript sequence. Best reciprocal hits were also selected.Once the PFAM domains were annotated, each transcript sequence and each protein of the H-Prot dataset was transformed into a meta-sequence where the annotated domains were concatenated, producing a string of domain symbols suitable for a meta-alignment. Those constructs were then aligned using the If a protein is a unique best reciprocal hit in the EggNOG alignment, set it as the best homolog for that particular transcript.Contrarily, if a unique protein has the largest number of supporting evidences from all the different methods, select it.E-value), set it as an homolog.Otherwise, if a unique sequence is the best hit in the EggNOG alignment , select it.Or then, if only a sequence is the best BLAST hit  were removed. In order to speed up the prediction, all the shortest paths between all the human proteins were pre-computed using the python module graph_tool  that are annotated as interacting in the 3did database.GO normalized term overlap (NTO) between the homologous proteins , and 853, 023 random pairs filtered against the DroiD pairs constituted the non-interacting protein pairs. All the features were manually discretized into fixed ranges specific to each variable. We used the R module randomForest .For all the performance validation measures the out-of-bag (OOB) votes reported by the module were used. A cutoff of 0.6 votes was set to decide if a pair is interacting. This cutoff was selected by looking for the value that maximized the F-measure see . In ordeAll the predicted interactomes, as well as the annotations of the different planarian transcripts were stored in a Neo4j database, version 3.1.1  experiment ; see We analyzed the relative importance of the 19 features used for the classification of each contig pair using the ‘Gini importance’ index provided by the R randomForest package, as it has been shown to be useful for feature selection in classification problems . HoweverE-value in the EggNOG alignment for each of these cases.The contigs of the 11 planarian transcriptomes were aligned to the H-Prot dataset using BLAST searches, HMMER alignments to EggNOG models, and PFAM meta-alignments. We selected the best hit for each contig and we computed the 19 features for each possible pair of contigs required for the random forest classifier. Although each planarian contig had only one selected homolog, several human proteins had more than one homologous planarian contig in the selected pairs used for the prediction. However, among the selected contig pairs, most human proteins had between one and two homologous contigs for all the datasets . Two humSpearman’s rho = 0.945, P-value < 10−10). The number of contigs in each predicted interactome is also dependant on the initial contig count . The overlap between the 11 predicted networks and the human reference interactome is shown in The classifier was used to predict interactions in 11 planarian transcriptomes. As it can be seen in pathlength = 1) was <1%, while this proportion increased significantly when considering only the available predicted interactions (https://compgen.bio.ub.edu/PlanNET/datasets).In order to compare the confidence of each prediction, we plotted the distribution of votes of the classifier for each dataset (including the OOB votes for the testing dataset). As can be seen in ractions . AdditioTo explore the predicted interactomes and the sequence annotations of the planarian sequences, we implemented a web application called PlanNET using the python web development framework Django and the javascript plugin cytoscape.js , PFAM identifiers, KEGG pathway identifiers, or GO codes. In KEGG: hsa04010) projected over the Smed454 transcriptome.We also provide a way to explore the predicted interaction networks utilizing cytoscape.js in PathwayFinder. This application looks for all the possible paths between two protein/contigs in the specified interactome, rating all these paths depending on their overall confidence. This score was defined as the mean of the random forest votes for each of its predicted interactions. Just like in NetExplorer, users can search by human protein symbols, PFAM identifiers, contig identifiers, KEGG accessions and GO codes.Thanks to the graph-based database manager Neo4j, traversing the networks to retrieve any subset of them does not have a huge performance impact; we took advantage of this capability to implement For the sake of completeness, we also implemented a BLAST web form to look for contigs on the graphs using sequence homology searches.S.mediterranea transcript datasets. As a result, we provide PlanNET, a web application that allows researchers to explore these networks in different ways, as well as to access to all sequence annotations performed in order to predict the interactions.In this work, we introduce a tool to predict protein–protein interactions from transcript sequences using sequence alignments and a reference Human interactome. This tool was then used to predict 11 different protein interactions networks from 11 Given the OOB performance evaluation of the predictor, we conclude that this random forest classifier is useful for inferring interologies between two species, for instance planarian and human. The area under the ROC curve of 0.82 strongly indicates the significant improvement from a random predictor of our tool. The performance of the random forest classifier is similar to a previously described interaction predictor developed by . The lowE-values are next in importance. The GO similarities and the EggNOG E-values paired together, ensure that both proteins have a high sequence similarity to their respective human homologs, that those homologs are known to be in a similar cellular location (cellular component) and that they share GO biological process and molecular function annotations. Thus, our tool not only predicts interactions between putative proteins translated from transcripts, but it essentially clusters these contigs according to their functional similarities . The relative importance of the EggNOG E-values may be due to two reasons: first, the performed protocol favors EggNOG alignments when selecting best hits for each contig, and secondly, hidden markov models are known to detect more distant homologies than sequence searches such as BLAST . It is worth to note that, even without the most useful feature, the resulting classifier still shows good performance, with an AUC of 0.781, highlighting the robustness of the other features when trying to predict protein–protein interactions. The GO similarities considered together contribute to the performance of the predictor more than the feature ‘PATH_LENGTH’ alone; and the EggNOG alignment Contigs with an homolog’ and ‘Number of interactions’ suggests. Therefore, the different level of fragmentation of the contigs, apparent from the dissimilarities between homologous proteins found for each dataset , we have also implemented two additional ways to explore the protein networks. NetExplorer . This data can be projected over the graphs, coloring the nodes according to the expression levels; right know, the application allows to color the nodes using expression data from one sample (binning colors by percentils of absolute expression levels) or to compare two samples at the same time from the same experiment [assigning colors as a function of log2(FoldChange)]. We are planning to add further RNA-seq expression data to this tool, which will allow more complex queries to be performed, such as retrieving subnetworks having correlated expression levels across different experiments and samples. Neo4j manager and the Cypher query language make those complex queries simple and fast (We can also integrate gene expression data from different sources, which has been tested by showing results from a DGE experiment performed by Rodriguez-Esteban When designing experiments with the aim to unravel the underlying mechanisms of planarian regeneration, the biological context of any given candidate gene is just as important as its annotations. Thus, a predicted protein–protein interaction network for many planarian transcriptomes will be useful in determining that context from a transcript-centric point of view. Our applications allow researchers to compare the human homologs found in all the transcriptomes, to look for possible interacting proteins, to retrieve sub-networks using GO codes and PFAM domains, and to compare expression levels across the transcriptomes. Our approach focuses on the planarian contigs instead of the annotated genes on a reference genome; giving researchers the flexibility to work with any contig as a proper separate entity with its own annotations. When new refined transcriptomes will be available in the future they will be easily incorporated to PlanNET using our current pipeline, without interfering with all the previous datasets and maintaining the relevance of the application.S.mediterranea and the human interactome in the database, making quick queries to compare and traverse the networks easy. Our current database design will facilitate the inclusion of genetic interactions, as well as the extension with new interfaces to explore them in the future. In conclusion, this database engine provides a very adaptable framework for storing, modelling and visualizing many networks projected over a reference interactome. This has been crucial to implement a responsive interactive interface, PlanNET, over the extensible interologs network that projects planarian transcriptomes towards human sequences and vice versa. The analysis pipeline can be applied to any species transcriptomic datasets to map it over any model organism reference interactome, which makes our protocol extensible to a broader range of similar research problems.At last, in this work we provide one of the first practical applications of the database manager Neo4j to store and analyze multiple protein–protein interaction networks. Sequence homologies predicted for the planarian contigs allow us to link the Supplementary DataClick here for additional data file.