Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database.We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions.Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology. S. cerevisiae proteins, approximately 24, 000 interactions between 7, 600 D. melanogaster proteins, and over 5, 100 interactions between 2, 900 C. elegans proteins However, a search method based on keyword matching or ID-matching is purely syntactic and does not consider biological relations between the keywords or IDs. If the user gets too many protein-protein interactions, selecting the protein-protein interactions to focus on entirely relies on the discretion of the user. If the user gets too few protein-protein interactions or no results at all, the user will probably have to look for other resources. For example, BOND returns 5, 100 protein-protein interactions for a keyword query of 'ATP binding' whereas it returns only 96 interactions for a keyword query of 'nucleotide binding'. The term 'nucleotide binding' is at a higher level than 'ATP binding' in the Gene Ontology (GO) hierarchy , but it p annotated with a GO term f, find the interaction partners of p", the search engine considers not only the GO annotation f but also all the GO annotations below f in the GO hierarchy. Several computational methods have been developed to elucidate protein function from the analysis of protein-protein interaction data [Recently a few ontology-based information retrieval methods have developed for biological literature or databases -11, but ion data , and oura1, a2, ..., an} byGödel numbers are typically used to uniquely encode any list of positive integers {pk is the kth prime number [where e number .However, the original Gödel numbers defined by equation 1) cannot represent the Directed Acyclic Graph (DAG) structure of the Gene Ontology. Consider an example shown in Table  cannot rTherefore, we modify the Gödel numbers as follows:1. Assign each term a prime number instead of a natural number using Algorithms 1 and 2.2. Represent each relation between the terms by a modified Gödel number using Algorithm 3. The modified Gödel number is the product of the prime numbers representing the terms in the relation and their ancestors, including the root term in the Gene Ontology hierarchy.For example, relation R3 of Table Table In this example, the relation that domainA interacts with domainB is represented by a modified Gödel number 143, which is the product of 11 (representing domainA) and 13 (domainB) (equation 2). The hypothesis that proteinA interacts with proteinB is represented by a modified Gödel number 46, 189 from the multiplication of 187 (proteinA) by 247 (proteinB) (equation 3). Since the remainder after dividing 46, 189 (representing the hypothesis that proteinA interacts with proteinB) by 143 (representing the relation that domainA interacts with domainB) is 0 (equation 4), the hypothesis that proteinA interacts with proteinB turns out to be true.Algorithm 1 Generate modified GödelnumbersThis algorithm assigns prime numbers to GO terms, stores the relation of the GO terms in a local DB by calling Algorithm 2, and generates the modified Gödel numbers by multiplying the prime numbers.T = {t1, t2, ..., tG} {G is the number of GO terms.}1: P = {p1, p2, ..., pG} {P is an ordered set of prime numbers.}2: for all i ∈ {1, 2, ..., G} do3: ti ← pi {Assign a prime number to a GO term.}4:   end for5: for all t ∈ {t1, t2, ..., tG} do6: t. key, t) {Store the prime number assignment in a local DB.}7:   Algorithm210:   Modified Gödel number ← Algorithm3This algorithm stores the prime number assignment of the GO term and its parents by a recursive call until the parameter Term is a root term of the hierarchy.if Term.isRoot == false then1: for all t ∈ Term.parent do2:   Term.key, t.key, t.prime) {Store the prime numbers assigned to Term and its parents.}3:      SetRelation {Recursive call for the parent of Term}4:      Algorithm2(end for5:   end if6: Algorithm 3 ModifiedGödelNumber(Term.key)This algorithm calculates a modified Gödel number by multiplying the prime numbers representing the parameter Term.key and its ancestor terms in the ontology hierarchy.list = GetRelation(Term.key) {Retrieve the prime numbers for Term and its ancestors.}1: ArrayList var = 12: for all t ∈ list do3: var = var × t.prime4:   end for5: return var6: A prototype of the ontology-based search engine has been implemented in the C# programming language . We geneThe user can also get protein-protein interactions from the web service of the search engine using the Web Services Description Language (WSDL). WSDL is an XML format for describing network services as a set of endpoints operating on messages containing either document-oriented or procedure-oriented information . The intWe tested the ontology-based search engine on the interaction data of HPRD  and compThe GO term ID of GO:0008150 is the root node of the GO hierarchy for biological process. With a query of GO:0008150, the ontology-based search engine found 36, 523 interactions , but the ID-matching search retrieved only 5 interactions . With a query of GO:0008152 for metabolic process, which is the descendent node of GO:0008150 in the GO hierarchy  using modified Gödel numbers. This representation is hidden from users but enables a search engine to efficiently find protein-protein interactions in a biologically meaningful way. A prototype of the search engine is available at . The seap with function f" or "for every protein p with function f, find the interaction partners of p". To the best of our knowledge, the ontology-based search engine presented in this paper is the first one that can deal with such queries. This paper presented preliminary results of the ontology-based search engine, and it is being expanded into a full-featured search engine.The OWL Web Ontology Language  was estaThe authors declare that they have no competing interests.Byungkyu Park conceived the idea of using modified Gödel numbers, implemented the system and prepared the first draft of the manuscript. Kyungsook Han supervised the work and rewrote the manuscript.


Currently, several systems have been proposed to classify viruses and indicate the relationships between different ones, though each system has its limitations because of the complexity of viral origins and their rapid evolution rate. We hereby propose a new method to explore the relationships between different viruses.A new method, which is based on the virus-host protein-protein interaction network, is proposed in this paper to categorize viruses. The distances between 114 human viruses, including 48 HIV-1 and HIV-2 viruses, are estimated according to the protein-protein interaction network between these viruses and humans.The results demonstrated that our method can disclose not only relationships consistent with the taxonomic results of currently used systems of classification but also the potential relationships that the current virus classification systems have not revealed. Moreover, the method points to a new direction where the functional relationships between viruses and hosts can be used to explore the virus relationships on a systematic level. Viruses can be classified according to different aspects ,2, such Phaginae (attacks bacteria), Phytophaginae (attacks plants), and Zoophaginae  according to the host type. These classifications are accepted by some virologists, but not all the virologists are satisfied with the current virus classification systems [All of the three main classification systems mentioned above, the Baltimore classification, LHT System, and Casjens and Kings Classification, are based on certain chemical or physical characteristics of viruses. On the other hand, the ICTV classification system is based on the hypothesis that the members of the order might have a common ancestor. However, sixty-four of the total 84 families in the ICTV classification system are still unplaced. The Holmes classification sorts viruses into  systems . Some of systems , it is hA virus needs to use the DNA replication and protein synthetic systems of its host to complete its life cycle and proliferation. Therefore, some viruses exhibit strong co-evolutionary relationships with their mammalian host organisms ,13. The Our method based on the virus-host protein-protein interaction network has shown us the relationships among different viruses according to molecular function. As it was demonstrated in the molecular function tree Figure , 114 virExamination of HIV classification using our method reveals additional insights. HIV has two subtypes, HIV-1 and HIV-2, which are extracted from chimpanzees and sooty mangabeys respectively . Both suIn evolution research, different indicators, such as the similarity between conserved sequences, have been used to determine the distances between organisms. In our method, we have defined the smallest special score derived from the SSBP of proteins between two sets as the distances between different viruses. The mathematical definition of the distance between two sets is the infimum of the distance between any components of the two sets. Our definition of the distance between two viruses is consistent with the mathematical distance definition of two point sets. Moreover, some human proteins, with which a viral protein interacts, could exert their function in relatively general processes. These general proteins contribute less to differentiate viruses. In an ideal situation, we should use the proteins that have more specific functions and participate in more special processes to reflect the relationship between different viruses. The infimum represents the most specific similarity between two protein sets and could reflect the relationship between two viruses on the most specific level. Considering the definition of GO term is at a general level, the smallest special score also has the tendency to get rid of the non-specificity of some GO terms in our sets.In our new approach, the ability to detect the relationship between distinct viruses relies on the quality of the virus-host protein-protein interaction network explicitly. If the network is reliable and contains enough information to bridge the connection between viruses and their hosts, the relationship disclosed based on PPI network would reveal more functional associations to the virologists who are interested in the relationships between different viruses. In total, 9683 human proteins are confirmed to interact with these viral proteins of 114 viruses, and among them 8249 human proteins are verified to interact with 48 HIV viruses, while 66 non-HIV viruses correspond only to 1434 human proteins. This number is relatively low compared to the number of human proteins that interact with HIV proteins. As discussed above, the classification of HIV viruses displayed much more reliable result than for the rest groups of the viruses. This might be caused by the difference between the amounts of data in the two corresponding datasets that are currently available. It is expected that more verified virus-host protein-protein interaction data of other viruses may lead to more reliable and valuable results for exploring potential relationships between distinct viruses. Our method points to a new direction to elucidate the relationship between viruses on the systematical level and provides rich information for virologists to study the relationships among various viruses.http://molvis.vbi.vt.edu/pig/index.php) describing the pathogen and human protein-protein interaction. We combined the data from the two sources mentioned above to build the protein interaction networks between each virus and its host, human, in our paper. Each interaction pair in the network is composed of a viral protein and a human protein. This network is the foundation that we used to evaluate the relationships between different viruses. The distance between distinct viruses is calculated according to the functional similarity of the host protein that interacts with the corresponding viral protein in the network. In the GO system, each human protein has been normally assigned GO terms in three different categories - 'Cellular Component', 'Biological Process' and 'Molecular Function', we used the GO term - "Molecular Function" to carry out the analysis since researchers usually are more interested in the molecular function of a protein when they study the virus and host interaction.The protein-protein interaction network used in this paper is constructed mainly according to the results of Dyer et al. , which fAs some viruses exhibit co-evolution with their host ,13, asseThe distance between two sets C and D is defined as:The method of smallest shared biological process, which is used to measure the functional similarity between different proteins in the previous work ,25, is tThe authors declare that they have no competing interests.Data collection: FX. Programming: FX, CZ, LJ. Design of the analysis process: TLS, YD and FX. Data analysis: FX, LJ, TLS. Paper Writing: FX, YHL, TLS.This file contains the GO terms of human proteins that correspond to separation of HIV-1 and HIV-2.Click here for fileTree files based on the GO terms of "Biological Process", "Cellular Component" of HIV and all of the viruses in our network have been stored in this supplementary file. These tree files can be viewed with the help of "treeview" and other similar software.Click here for file


Protein-protein interactions (PPIs) play a crucial role in initiating infection in a host-pathogen system. Identification of these PPIs is important for understanding the underlying biological mechanism of infection and identifying putative drug targets. Database resources for studying host-pathogen systems are scarce and are either host specific or dedicated to specific pathogens.Here we describe "HPIDB” a host-pathogen PPI database, which will serve as a unified resource for host-pathogen interactions. Specifically, HPIDB integrates experimental PPIs from several public databases into a single, non-redundant web accessible resource. The database can be searched with a variety of options such as sequence identifiers, symbol, taxonomy, publication, author, or interaction type. The output is provided in a tab delimited text file format that is compatible with Cytoscape, an open source resource for PPI visualization. HPIDB allows the user to search protein sequences using BLASTP to retrieve homologous host/pathogen sequences. For high-throughput analysis, the user can search multiple protein sequences at a time using BLASTP and obtain results in tabular and sequence alignment formats. The taxonomic categorization of proteins  involved in PPI enables the user to perform category specific BLASTP searches. In addition, a new tool is introduced, which allows searching for homologous host-pathogen interactions in the HPIDB database. http://agbase.msstate.edu/hpi/main.html.HPIDB is a unified, comprehensive resource for host-pathogen PPIs. The user interface provides new features and tools helpful for studying host-pathogen interactions. HPIDB can be accessed at  Proteins are the work horses of living organisms; they interact with other proteins to carry out most of the biological functions such as signal transduction, protein transport, immune response and other essential functions. PPIs can be classified into two main categories: "Intra-species PPI," where two proteins from the same species interact with each other, and "Inter-species PPI," where two proteins from two different species interact. Host-pathogen protein–protein interactions (HPIs) that play a vital role in initiating infection are a subset of inter-species interactions. Identification and study of HPIs is critical for understanding molecular mechanisms of infection and subsequent development of drug targets.Although a number of databases that store PPIs are described in the literature -3, only Here we describe HPIDB, a unified resource that integrates HPIs from multiple resources into a single, non-redundant set in a user friendly web accessible format. The user interface provides multiple options for querying the database content and facilitates BLASTP  based sehttp://agbase.msstate.edu/hpi/main.html). Only inter-species interactions were selected from the repository. For proper classification of PPI into HPI, we classified the taxon ids present in the inter-species interactions into two groups: group A had the taxonomic ids for host species  and group B had the taxonomic ids of pathogenic species . All PPIs where a protein from “group A” interacts with a protein from “group B” are selected and considered as possible Host pathogen interactions (HPIs). To eliminate the possibility of redundant HPIs, all entries were converted into UniProt accession and duplicate entries were eliminated. Where UniProt accession conversion was not successful, duplicate PPI entries were removed based on the protein sequence identity. All the identified interactions were organized into a relational database with additional features like synonym, taxon id, sequence, function, interaction type, experimental information used to identify PPI, and literature information (PubMed id and author information). A web based user interface was designed to query the database using various identifiers, perform BLASTP based protein sequence searches and provide a tool for searching homologous interactions.The HPIDB database is implemented using MYSQL 5.1, with the user interface and web server designed using CGI and Perl. Figure All the protein sequences in the database are grouped into major taxonomic groups like plant, animal, bacteria, virus, fungi, and protist. BLASTP sequence alignment functionality was added to the database to search against similar protein sequences. Scripts were written to perform the BLASTP sequence searches in batch mode (search multiple protein sequences at a time) and process the results. The taxonomic classification of protein sequences is integrated with BLASTP, to search only a particular group of sequence databases such as bacteria, virus, animal, all pathogen, all host, etc. Taking advantage of this taxonomic classification of proteins into host and pathogen, we designed a "Search Homologous HPIs" module within the HPIDB. This tool enables the user to search for homologous HPIs in the database for a given set of host and pathogen protein sequences. Internally, this tool is executed in three steps:1. Input user provided host protein sequences (A) in FASTA format, conduct BLASTP searches against all host proteins in HPIDB and output homologous host protein (HA). 2. Input user provided pathogen protein sequences (B) in FASTA format, conduct BLASTP searches against all pathogen protein in HPIDB and output homologous pathogen protein (HB).3. Combine the results from step 1 and step 2; any interactions found between HA and HB in HPIDB database are called homologous host-pathogen interactions for proteins A-B. This module provides the user with a set of homologous interactions for further analysis and wherever possible, the results obtained in this module  can be used to transfer annotation to a new species (A-B).Currently HPIDB contains 22,841 interactions between 49 host and 319 pathogen species. Table The database can be accessed using the web interface, which is divided into three separate modules based on specific user needs. Alternatively the whole database can be downloaded from the website in tab delimited file format.The web interface is divided into three separate modules:1. "Simple search" is used to search the database based on user defined identifiers like UniProt id, alias name, symbol, taxonomy id, interaction type, literature information . The results include a set of host-pathogen PPIs along with additional taxonomic categorization in tabular format. A search result with all information about all PPIs is available for download in tab delimited text format, which can be further used in other programs like Cytoscape  for netw2. "Advanced BLAST search" provides the ability to perform BLASTP sequence searches in batch mode. Users can provide more than one protein sequence at a time in FASTA format. Apart from the features provided in a simple BLAST query, users have the option to either get the top hit result for each query or get multiple hits below a user specified E-value.3. "Search Homologous HPIs" is used to search for homologous HPIs in the HPIDB. For a given set of host and pathogen proteins, first the program tries to identify similar host and pathogenic proteins (based on BLASTP results) in the database. If the identified homologs were involved in HPI interaction in HPIDB, it would be called a homologous HPI. This tool can also be used for only host or pathogen sequences to search homologous host/pathogen proteins and their interacting partners. The user interface includes a statistics page which summarizes the interactions present in the database Figure . A help Here we describe three case studies that demonstrate the utility of HPIDB to researchers in achieving their objective:A researcher is studying a particular bacterial species and its related strains that cause infection in humans and animals. In order to identify the host specificity as well as the source for varying infectivity of the bacterial strains of interest, he wants to get a list of all host-pathogen PPIs available for each strain. Instead of searching various databases and filtering inter-species PPIs from them individually, this researcher can search the HPIDB using the "simple search" feature and use the taxon ids of all the bacterial species in the search field (one by one) to get the desired PPI dataset.A researcher has sequenced a new bacterial genome and wants to identify proteins in the genome that are similar to known bacterial proteins involved in host-pathogen interactions. Existing PPI/HPI resources do not provide sequence searches for a particular taxonomic category. An advantage that HPIDB has over other comparable databases is that it provides the categorization of pathogen protein sequences into categories like bacteria, fungi, protist, virus, etc. and host protein sequences into categories such as animal, plant, etc. based on taxonomy. The researcher can use the "Advanced BLAST search" option to perform a BLASTP search in batch mode with all protein sequences from the genome against the bacterial protein database. Streptococcus  pneumoniae. In the absence of any experimental PPIs between human and S.pneumoniae, the user needs to identify putative HPIs based on similar homologous interactions  present in the database to generate a testable hypothesis.A user is studying pneumonia, a disease caused by the human pathogen  Currently, there is no web based tool available that enables the user to search for homologous HPIs. S. pneumoniae proteins sequences (2105) were downloaded from NCBI (ftp://ftp.ncbi.nih.gov/genomes/Bacteria/Streptococcus_pneumoniae_TIGR4/NC_003028.faa). In the HPIDB, the “Search Homologous HPIs" can be selected to identify homologous HPIs. Here option A is selected as HPIDB already has human proteins in the database and no further predictions for homologs host proteins are desired. “Form A” should be used which inputs the pathogen protein sequences in FASTA format, the BLASTP parameters can be set to have an E-value < 10-20 and the “bacterial proteins” should to selected as database. When we conducted this search, we identified 2001 HPIs between 492 pathogen proteins and 1153 host proteins (mostly human). The dataset can be used further to transfer the homologous interactions and to predict new interactions between human and S.pneumoniae. For example, the predicted interactions include previously known virulence factors of S. pneumoniae [eumoniae  like 3 dWe developed a new host-pathogen protein-protein interaction database "HPIDB" which will serve as a unified and comprehensive resource for HPIs. The user interface provides multiple options to search the database. HPIDB allows high throughput sequence searches in which the user can submit multiple protein sequences at a time and search against a selected taxonomic category. HPIDB also includes a tool that can search for homologous HPIs in the database for user provided sequences. All these features of HPIDB will be helpful for studying host-pathogen interactions. Project name: HPIDBhttp://agbase.msstate.edu/hpi/main.htmlProject home page: Restrictions for use by non-academics: nonePPI: (protein-protein interaction); PPIs: (protein-protein interactions) ; HPI: (host-pathogen interaction) ; HPIs: (host-pathogen interactions); HPIDB: (host-pathogen interaction database)RK contributed to the design of HPIDB, wrote all of the scripts for the database construction and implementation, and wrote the draft of this manuscript. BN conceived this study, contributed to the design of HPIDB, helped to analyze and interpret the results, and helped to draft the manuscript. All authors read and approved the final manuscript.All authors have declared that there are no competing interests.S. pneumoniae and human proteins.Description: Selected homologous HPIs identified during Case study 3 for Click here for fileS. pneumoniae and human proteins visualized using Cytoscape.Description: HPI network for selected Click here for file


Besides the pinpointing of individual disease-related genes, associating protein complexes to human inherited diseases is also of great importance, because a biological function usually arises from the cooperative behaviour of multiple proteins in a protein complex. Moreover, knowledge about disease-related protein complexes could also enhance the inference of disease genes and pathogenic genetic variants. Here, we have designed a computational systems biology approach to systematically analyse potential relationships between diseases and protein complexes. First, we construct a heterogeneous network which is composed of a disease-disease similarity layer, a tissue-specific protein-protein interaction layer, and a protein complex membership layer. Then, we propose a random walk model on this disease-protein-complex network for identifying protein complexes that are related to a query disease. With a series of leave-one-out cross-validation experiments, we show that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We further predict a landscape of associations between human diseases and protein complexes. This landscape can be used to facilitate the inference of disease genes, thereby benefiting studies on pathology of diseases. With a vast amount of genetic variants detected by such techniques as traditional genome-wide association studies , 2 and rTo achieve this goal, quite a few approaches have been proposed from the perspective of computational systems biology. For example, Endeavour resorted to the guilt-by-association principle  to rank However, a biological function usually arises from the cooperation of multiple proteins. These proteins link to each other by noncovalent interactions, forming a protein complex. Hence, genetic variants occurring at different loci might affect the structure of a member protein of a complex, alter the function of the entire complex, and cause a disease. For example, it has been reported that seven pathogenic genes responsible for a heterogeneous syndrome called Fanconi anemia (FA) form a protein complex with functions related to DNA repair . TherefoSome methods for identifying disease genes have paid attention to linking protein complexes to diseases and then made use of such information to facilitate the prediction of disease genes. For example, Lage et al. proposed to identify the aggregates of proteins connected to a candidate protein in a PPI network as a protein complex by a virtual pull-down procedure and infer the association between the candidate protein and a query disease based on members of the protein complex . Vanunu http://bioinfo.au.tsinghua.edu.cn/jianglab/complex.With the above understandings, we propose in this paper a computational systems biology approach for the identification of protein complexes that are related to a query disease via a random walk model on a heterogeneous network that is composed of a disease-disease similarity layer, a tissue-specific protein-protein interaction layer, and a protein complex membership layer. Starting from the query disease at the disease layer, our method simulates the process in which a random walker travels in the three-layered disease-protein-complex network, scores a protein complex using the probability that the walker stays in the protein complex at the steady state, and then ranks candidate protein complexes according to their scores. With a series of large-scale leave-one-out cross-validation experiments, we systematically show that our method not only possesses high performance but also demonstrates robustness to parameters involved and the network structure. As an application of our approach, we predict a landscape of associations between human diseases and known protein complexes and provide free downloads of the prediction results at We model the problem of identifying protein complexes associated with a query disease as a prioritization problem and propose to solve this problem with a three-step approach. As illustrated in The disease-protein-complex network is composed of three layers. The top layer is a disease-disease similarity network derived from a phenotype similarity profile . The midk-nearest neighbour (k-NN) strategy (used as the default in our study), we link each disease to its k nearest neighbours, which correspond to the k highest phenotype similarity scores. Second, with a δ-threshold strategy, we set up a cut-off value δ and then connect two diseases by an undirected edge if and only if their similarity is greater than or equal to the cut-off. In both strategies, we further consider two variations for edges: weighting edges by the original similarity values or treating edges as unweighted.At the top layer, given a disease phenotype similarity profile  that quantifies pairwise overlaps of diseases in their clinic traits, we construct the disease-disease similarity network by using two strategies. First, with a α.At the middle layer, given generic PPI network and tissue-specific gene expression data, we get a tissue-specific PPI network from the literature . These nβ.At the bottom layer, given a collection of protein complexes, we connect each of them to all of its member proteins in the PPI network at the middle layer by undirected edges, while leaving protein complexes unconnected. We weight the introduced edges by a positive real-valued parameter D = (dij)l×l, where l is the number of diseases and dij is the weight of the edge between the ith and jth diseases or 0 if the edge is absent. We describe the tissue-specific PPI network by a weight matrix P = (pij)m×m, where m is the number of proteins and pij is the weight of the edge between the ith and jth proteins or 0 if the edge is absent. We describe connections between the diseases and proteins by a weight matrix A = (aij)l×m, where aij = α is the weight of the edge between the ith disease and the jth proteins or 0 if the edge is absent. We describe connections between proteins and complexes by a weight matrix B = (bij)m×n, where bij = β is the weight of the edge between the ith protein and the jth complex or 0 if the edge is absent. Put together, the disease-protein-complex network can be represented using a block matrix, asT stands for the transposition of a matrix.Formally, we describe the disease-disease similarity network by a weight matrix We achieve the goal of identifying protein complexes related to a specific query disease by calculating a score for each candidate complex and then rank the candidates to obtain a ranking list. The higher the rank, the more likely to be related to the query disease. For this purpose, we adapt the random walk with restart model , 27 to tγ or move on with probability 1 − γ. When moving on, the walker may move at random to one of its direct neighbours in the same layer, jump from the disease layer to the protein layer or vice versa, or jump from the protein layer to the complex layer or vice versa.At a quick glance, our model simulates the process that a random walker wanders on the three-layered disease-protein-complex network. When starting on, the walker chooses the query disease of interest as the starting point. In each step of the walking process, the walker may start on a new journey with probability q(0) = (qi(0))l+m+n)×1   being the probability that the walker initially starts from the ith node. In this vector, the element corresponding to the query disease is set to 1, and all of the other elements are set to 0. We normalize each row of the weight matrix H for the disease-protein-complex network to obtain a transition matrix T = (tij)l+m+n)×(l+m+n)( = (qit)l+m+n)×1 being the probability that the walker stays on the ith node. We then have the iterative updating formula asqt)( and qt+1)( − qt). With the steady-state probability (denoted by q∞) obtained, we further calculate a normalized score si for the ith complex asFormally, as illustrated in α = 1, protein-complex weight β = 1, and restart probability γ = 0.5. By simulation studies, we find that our model is not sensitive to these parameters (see results for details).In this paper, we set the default values for the parameters as disease-protein weight We adopt a leave-one-out cross-validation experiment to assess the capability of our method to identify protein complexes that are associated with human diseases. For this reason, we define a protein complex as associated with a disease if at least one member protein of the complex has been annotated as associated with the disease, and we collect a set of test protein complexes as those associated with at least one disease. Then, in each validation run, we take a test protein complex, identify a query disease as the one with which the complex is associated, pretend that all annotated associations between the query disease and proteins (or corresponding genes) are unknown, and then rank the test protein complex against a collection of control protein complexes.In the context of the disease-protein-complex network, the above validation procedure is equivalent to remove all edges connecting the query disease and proteins and see whether protein complexes containing these proteins could receive high ranks. In the context of genetics, this validation procedure is equivalent to hide all known genetic bases of the query disease and see whether some of them could be recovered at the protein complex level.N validation runs and collected the same number of ranking lists. We calculate a criterion named TOP which is the number of test protein complexes ranked first in their corresponding list. We also divide this number by N to obtain the fraction of first ranked test protein complexes and call this fraction precision (PRE). Second, we calculate the average rank of all test protein complexes as the second criterion called mean rank (MR). Alternatively, we normalize ranks of test protein complexes by the lengths of ranking lists to obtain relative ranks, and we calculate the average relative rank of all test protein complexes to obtain mean relative rank (MRR). Third, given a threshold of the relative rank, we calculate the sensitivity (true positive rate) as the fraction of test protein complexes ranked above the threshold and the specificity (true negative rate) as the fraction of control protein complexes ranked below the threshold. Varying the threshold value from 0.0 to 1.0, we draw a rank receiver operating characteristic (ROC) curve and further calculate the area under this curve (AUC). Obviously, larger TOP (PRE)/AUC and smaller MR/MRR indicate higher performance.We adopt three classes of criteria to quantify the performance of our method. First, let us suppose that we have performed a total of We obtained disease-tissue associations from the literature . BrieflyWe obtained disease-disease similarity scores from the literature . BrieflyWe obtained tissue-specific PPI networks from the literature . Given aWe extracted disease-protein associations from the Ensembl database using the tool Biomart , obtainiWe extracted 1,343 human protein complexes from the core set of the CORUM database (release in February 2013) , each ofk = 15, α = 1, β = 1, γ = 0.5), we constructed a disease-protein-complex network that was composed of 5,080 diseases, 9,998 proteins, and 1,343 protein complexes. There were a total of 107,661 edges in the network, among which 58,448 are between diseases, 41,049 are between proteins, 1,962 are connecting diseases and proteins, and 6,202 are connecting proteins and protein complexes.With the collected data and the default parameter setting  in the naïve approach, while with our method, such phenomenon will not happen because the probability of going out from the protein will be distributed uniformly to the multiple protein complexes in the random walk procedure. We performed a comparison between these two methods and showed the results in k-nearest neighbour (k-NN) strategy and the δ-threshold strategy. In both strategies, we further considered two variations: weighting edges by the original similarity values or treating edges as unweighted. We then conducted a comparative study of these strategies and presented the results in We considered two strategies for constructing the disease similarity network at the top layer of the disease-protein-complex network: the k-NN strategy and the default parameter setting, the PRE, MRR, and AUC are 8.84%, 12.59%, and 88.44% for the weighted variation, respectively, and 7.88%, 12.71%, and 88.32% for the unweighted one, respectively. Using the δ-threshold strategy (δ = 0.35) with the default parameter setting, the PRE, MRR, and AUC are 6.28%, 13.69%, and 87.34% for the weighted variation, respectively, and 5.64%, 13.72%, and 87.30% for the unweighted one, respectively. With these observations, we conjecture that the weighted disease similarity network is preferred by our method and will use this network as the top layer of our disease-protein-complex network in the rest of this paper.We first observe that our method with the weighted disease similarity network outperforms that with the unweighted one in terms of the precision of test protein complexes (PRE), and the difference between these two variations is subtle according to the other two criteria (MRR and AUC), though the weighted one slightly outperforms the unweighted one. For example, with the k-NN strategy. All of the three criteria only show small fluctuations in a wide range of the parameter k. Focusing on weighted networks, the PRE, MRR, and AUC are in general greater than 3.94% 16.25%, and 84.76%, respectively, when k is greater than 10 and less than 500, with the optimum values of these criteria achieved at k = 15, 20, and 20, respectively. For the δ-threshold strategy, our method is also quite robust when the cut-off value δ is not too large. Also focusing on weighted networks, the PRE, MRR, and AUC are in general greater than 3.30%, 20.97%, and 79.99%, respectively, when δ is greater than 0.25 and less than 0.45, with the optimum values of these criteria achieved at δ = 0.45, 0.35, and 0.35, respectively. With these observations, we conclude that the selection of the parameters k and δ is not critical and kind of flexible. To achieve a balance over all of the three criteria, we recommend to select k = 15 and δ = 0.35 as default values of these parameters.Second, we also observe that our method is quite robust to the number of neighboring diseases in the k-NN strategy gives us higher performance than the δ-threshold does in a wide range of parameter settings. When comparing the performance at the default parameters, the PRE, MRR, and AUC are 8.84%, 12.59%, and 88.44%, respectively, for the k-NN strategy and 6.28%, 13.69%, and 87.34%, respectively, for the δ-threshold strategy. Around these parameters, the k-NN strategy exhibits consistent higher performance than the δ-threshold strategy in terms of both MRR and AUC. Therefore, we recommend the use of the k-NN strategy in the construction of the disease similarity network.Third, we notice that the We considered two strategies to construct the tissue-specific PPI network at the middle layer of the disease-protein-complex network: the node removal strategy and the edge reweight strategy. Besides, we also considered the use of a tissue-nonspecific PPI network extracted from the HPRD database as the middle layer. We then performed a comparison study of these strategies and presented the results in We first observe from this table that the difference between the node removal strategy and the edge reweight strategy is subtle. For example, with the default parameter setting, the PRE, MRR, and AUC are 8.84%, 12.55%, and 88.49% for the node removal strategy, respectively, and 8.84%, 12.59%, and 88.44% for the edge reweight strategy, respectively. This observation is consistent with a previous study about relying on a tissue-specific PPI network to prioritize candidate genes . TherefoWe then notice from α), the weights of the protein-complex connections (β), and the restart probability in the random walk model (γ). To study the influence of these parameters on our method, we performed a comparative study on different values of these parameters and presented the results in There are three main parameters involved in our method: the weights of the disease-protein connections (α) determine the possibility of jumping from the disease layer to the protein layer and vice versa. With a large value of α, it is easier to travel between the two layers, while with a small value of α, it is harder to travel between the two layers. From −3 to 103), all of the three criteria show only tiny fluctuations. For example, at the lower end of the spectrum (α = 10−3), the PRE, MRR, and AUC are 7.56%, 13.64%, and 87.38%, respectively, while at the higher end of the spectrum (α = 103), the PRE, MRR, and AUC are 7.24%, 14.07%, and 86.95%, respectively. Moreover, at the optimum point (α = 1), the PRE, MRR, and AUC are 8.84%, 12.59%, and 88.44%, respectively. From these observations, we conjecture that the selection of this parameter is not critical to the performance of our method. We hence use α = 1 as the default value for this parameter.The weights of the disease-protein connections (β) determine the possibility of jumping from the protein layer to the complex layer and vice versa. With a large value of β, it is easier to travel between the two layers, while with a small value of β, it is harder to travel between the two layers. From −3 to 103), all of the three criteria show only tiny fluctuations. For example, at one end of the spectrum (β = 10−3), the PRE, MRR, and AUC are 7.88%, 13.29%, and 87.74%, respectively, while at the other end of the spectrum (β = 103), the PRE, MRR, and AUC are 8.84%, 13.26%, and 87.76%, respectively. Moreover, at the optimum point (β = 10), the PRE, MRR, and AUC are 9.05%, 13.05%, and 87.94%, respectively. From these observations, we conclude that the selection of this parameter is not critical to the performance of our method. Therefore, we use β = 1 as the default value for this parameter.Similarly, the weights of the protein-complex connections (γ) determines the possibility of jumping from any node in the network back to the starting point of the query disease. With a large value of γ, a random walker cannot go far away from the starting point and thus will mainly explore neighbouring nodes of this point, while with a small value of γ, the random walker is able to explore areas far away from the starting query disease. From γ = 0.1), the PRE, MRR, and AUC are 8.09%, 13.92%, and 87.1%, respectively, while at the other end of the spectrum (γ = 0.8), the PRE, MRR, and AUC are 8.73%, 12.86%, and 88.18%, respectively. At the optimal point (γ = 0.6), the PRE, MRR, and AUC are 8.63%, 12.49%, and 88.54% respectively. Moreover, at the middle point of the spectrum (γ = 0.5), the PRE, MRR, and AUC are 8.84%, 12.59%, and 88.44%, respectively, not very different from the optimum point. From these observations, we conclude that the selection of this parameter is not critical to the performance of our method. Therefore, we seek for the simplicity to select γ = 0.5 as the default value for this parameter.The restart probability   and the Second, although the PPI network provides a systematic view of functional similarities between genes, such genomic information as transcriptional regulation, noncoding RNA regulation, functional annotation, pathway annotation, and structure domain annotation also provides useful assessments on functional similarities between genes. Integrating such genomic information with tissue-specific gene expression data to obtain a more comprehensive characterization of tissue-specific functional similarities between genes and further enhance the performance of our method will be one of our future research directions.Third, protein complexes represent higher level functional units than proteins. Besides, gene modules such as pathways can be thought of as even higher level function units. Therefore, it also matters to pursue the goal of identifying pathways or gene modules that are associated with a given query disease. In technology, our method can be directly applied to solve this problem.Finally, the predicted genome-wide landscape of associations between human diseases and protein complexes provides a rich resource in understanding genetic bases of human inherited diseases. Using these prediction results to facilitate the analysis of prevalent genetic data such as single nucleotide polymorphisms identified in traditional genome-wide association studies or recent exome sequencing studies will also be a goal worth pursuing.


S. cerevisiae interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied.Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database.The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting. Computational methods that successfully predict protein complexes are of instrumental importance to our understanding of the functions that take place in a living cell. Moreover, these methods can be used to guide biological experiments in order to confirm predicted complexes, therefore resulting in a reduction in the cost of such experiments. Protein complexes in a living cell are of dynamic nature; their composition and cardinality vary according to cell state and the challenges set forth to the cell by its environment. Since the data provided by interaction-detecting experiments lack temporal information, the results provided by clustering the interactome graph depict only a snapshot of the dynamic composition of protein complexes.The problem of clustering protein interaction networks lies in the data itself, since methods that produce high-coverage of the proteome introduce a significant amount of noise in the form of false-positive interactions. On the other hand, methods that produce highly reliable interaction data, suffer from poor coverage of the complete interactome graph. This is known as the problem of noise inherent in protein interaction data, which is a drawback of the methods that detect protein interactions -4.Protein-protein interaction networks are represented as undirected graphs G, in which the nodes V denote the proteins, while the edges E correspond to interactions among them . In the The issue of assigning weights to parameters has long been a crucial issue in optimization and learning from data as this process reflects the importance of single variables or groups of them . On the Modha et al. present an abstract framework for integrating multiple feature spaces in the k-means clustering algorithm . The papSeveral approaches to PPI graph weighting make use of graph-theoretic methods (such as the Czekanowski-Dice distance ). HoweveIn this manuscript, we present a novel noise reduction method by weighting protein interactions assigning higher score to the most probable true ones and a lower score to the ones derived as artifacts of the used detection method. Experiments were carried out using the yeast subset of the iRefIndex database  and resuThe yeast subset of the iRefIndex database  is the dThe majority of the clustering procedures consist of two steps: First, each binary protein interaction is assigned a weight, reflecting the confidence that this interaction is a true positive interaction. Then, the results are clustered using several different clustering algorithms. We performed experiments using all possible combinations of four different weighting schemes (described below) and four clustering algorithms.et al. propose the PRODISTIN method of functional clustering of proteins based on the principle that the higher the number of common interactors shared by two proteins, the more likely they are to be functionally related [st degree and 2nd degree neighbors is calculated using the Czekanowski-Dice distance (CD-distance). More recently, Liu et al. proposed the Adjust-CD [Brun  related . In theidjust-CD  weightindjust-CD  and consLubovac et al. introduce two PPI graph weighing schemes ,22 that et al. [et al., interactions detected by different experimental methods provide a set of interactions that is more accurate, even if the coverage of the interactome is rather limited.Von Mering et al.  recognizet al. . Accordiet al. calculate the reliability of an experimental source by computing the fraction of interacting proteins that share the same function [et al. assigns to each interaction a weight that is calculated as a function of the reliability of the experiment that reports the interaction.Similarly, Nabieva function . The weiet al. separate experimental methods in 3 categories: in vivo, in vitro and yeast-two-hybrid methods. Subsequently, they assign weights to interactions according to the combination of experimental categories by which the interaction was detected [In a more recent contribution, Pinkert detected .et al. also apply a weighting scheme, which does not distinguish between high- or low-throughput experiments, but boosts interactions that are detected in different experimental methods [Pereira - Leal  methods .et al. [Using information derived from the work of von Mering et al. , we defiBelow we present two PPI graph weighting schemes that use the concept of experiment plurality, which is defined as the number of protein interactions reported by an interaction detection experiment. The first one is a novel weighting scheme, called MV scoring, and it is the main contribution of the present manuscript. The second one, called Simple Scoring, is based on the basic assumption that most scoring schemes use and it is used purely for evaluation purposes.Ne) that report an interaction  takes into account the statistical distribution of the number of experiments , the interaction will be assigned a high confidence score using the above formula (Equation 1).α affects only interactions reported by more than one experiment (Ne > 1). A higher value of α conveys higher confidence levels to such interactions, whereas a smaller value tends to give the relative advantage to interactions that are reported by one experiment, with smaller plurality number. The value producing the best results has been experimentally adjusted to α = 2.The user-assigned factor Purely for evaluation purposes, we define a naïve weighting scheme, which assigns a weight to an interaction based on the number of experiments (Ne) reporting the interaction, and the plurality of each one of those experiments. Based on the fact that high-throughput experiments are prone to detect false-positive interactions -4, if thA number of algorithms have been proposed for clustering protein interaction networks. In order to cluster the weighed PPI graph, we only used the algorithms that support weighted graphs, such as MCL , the CMCThe RNSC algorithm  searchesThe COACH algorithm  first deThe Markov Clustering algorithm (MCL)  finds clet al., propose and use the Adjust-CD [More recently Liu, Wong, and Chua proposed the Clustering on Maximal Cliques algorithm (CMC) . CMC firdjust-CD  weightinet al. spectral graph decomposition [Additional experiments were carried out by using two variations of a Spectral Clustering algorithm. Using the Ng position , we map position .Following the spectral decomposition, we apply to the set of resulting points two clustering algorithms, the recently proposed Kmeans++ algorithm  and the et al. [et al. extract such information using the eigenvalues of the decomposed graph and then sort the quasi-cliques according to their interconnectivity.Using a different approach, Bu et al.  proposedA more detailed report of the clustering algorithms used in PPI clustering is beyond the scope of this paper. A recent evaluation of clustering methods is given in .Eliminating the noise inherent in the data is of vital importance in order to produce a valid clustering result. This is especially true in the PPI clustering context, since interactome data feature much noise mainly in the form of false positive interactions. The results presented in Figures In order to compare the clustering results, we validate them against the list of known complexes present in the MIPS database . The ideet al. [Positive Predictive Value, Sensitivity and Accuracy. We also use a prediction rate metric which is defined as the percentage of predicted clusters that match known complexes [The metrics used to compare the algorithms' performances are mainly derived from the work of Brohee et al. , namely omplexes .As far as the complex prediction rate is concerned, all tested algorithms benefit from the application of the MV weighting scheme  rating for a clustering algorithm would mean that the algorithm predicts protein complexes in which every protein belongs to the matching confirmed complex. High PPV value  cannot go without a loss of recall (sensitivity). This is evident when comparing the PPV and sensitivity rating of the CMC algorithm (see Figure The price for this, however, is that the detected clusters lack several proteins that their corresponding reference set complexes feature. This holds also true for the results of Spectral methods when using the Adjust-CD weighting and the combination of the EM algorithm and the ETW weighting. Moreover, in contrast to all other tested algorithms, the sensitivity rating of the MCL algorithm was not hindered by the use of the Adjust-CD weighting scheme.The Accuracy results chart Figure  summarizSpectral methods feature the best results for the unweighted graph compared to other clustering algorithms, although their out-performance compared to the MCL algorithm is decreased when using weighted graphs. This is largely due to the beneficial effect of graph weighting to both the PPV and sensitivity performance of the MCL algorithm. Furthermore, the combination of spectral methods and Adjust-CD weighting is the only one that features worse results compared to the unweighted case. This is expected, taking into account the poor sensitivity performance of this combination of algorithms and weighting scheme (see Figure In Figure A stronger effect of interactome graph weighing is shown in Figure In Figure Summarizing, the combination of the MCL algorithm and the Adjust-CD features the best prediction rate and the second best sensitivity results. On the other hand, the combination of the Spectral-Kmeans++ algorithm with the MV weighting scheme features the best PPV and accuracy results. Taking all the above information into account, it becomes obvious that the best performing weighting schemes are the MV and the Adjust-CD ones. However, since the Adjust-CD one hinters the results of the spectral methods, we favor the use of the MV weighting scheme.Finally, we note that a direct comparison between the CMC and the other clustering algorithms tested is not feasible, because the CMC algorithm, in contrast to MCL, Spectral-Kmeans++ and Spectral-EM algorithms, can assign a protein to more than one clusters. Arguably, this is a desirable property of a PPI clustering algorithm. However, applying the CMC algorithm to the current input data using the default parameters produced an exceptionally large number of clusters, which accounts for the lower performance compared to other algorithms.In this paper, we applied four different methods to introduce weights to the edges of the interactome graph according to the likelihood that an edge corresponds to a true positive interaction and conducted experiments using four different clustering algorithms.The main contribution of this work is the development of a new weighting scheme, called MV scoring, which features superior noise reduction properties compared to known PPI weighting schemes.As an outlook, we plan to further investigate the application of soft cluster assignment algorithms in protein interaction networks and assess the biological significance of our results.The authors declare that they have no competing interests.GK conceived the study, performed the experiments and wrote the paper. CM helped conducting the clustering experiments and aided revising the manuscript. MV conceived the MV weighting scheme and provided expert view on the clustering algorithms used and experiments conducted. SK guided the study and assisted in writing the manuscript. All authors read and approved the final manuscript.


The oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. However, shifts in the normal composition of this microbiota may result in the onset of oral ailments, such as periodontitis and dental caries. In addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions (PPIs) between the host and microorganisms. Nevertheless, this kind of PPIs is still largely undisclosed. To elucidate these interactions, we have created a computational prediction method that allows us to obtain a first model of the Human-Microbial oral interactome.−7), leading to a set of 46,579 PPIs to be further explored.We collected high-quality experimental PPIs from five major human databases. The obtained PPIs were used to create our positive dataset and, indirectly, our negative dataset. The positive and negative datasets were merged and used for training and validation of a naïve Bayes classifier. For the final prediction model, we used an ensemble methodology combining five distinct PPI prediction techniques, namely: literature mining, primary protein sequences, orthologous profiles, biological process similarity, and domain interactions. Performance evaluation of our method revealed an area under the ROC-curve (AUC) value greater than 0.926, supporting our primary hypothesis, as no single set of features reached an AUC greater than 0.877. After subjecting our dataset to the prediction model, the classified result was filtered for very high confidence PPIs (probability ≥ 1-10http://bioinformatics.ua.pt/software/oralint.We believe this dataset holds not only important pathways involved in the onset of infectious oral diseases, but also potential drug-targets and biomarkers. The dataset used for training and validation, the predictions obtained and the network final network are available at  The majority of gene products that crowd a living cell interact, at least transiently, with other protein molecules. Virtually all cellular events, such as signal transduction, intracellular transport, DNA replication, transcription, translation, splicing, secretion, cell cycle control and intermediary metabolism, are mediated by protein-protein interactions (PPIs) . The samNumerous experimental techniques have been explored to attain the human interactome: two-hybrid screening ,10, affiet al.[et al.[et al.[As an alternative to experimental methods, a wide range of computational approaches for the prediction of intra-species PPIs have been proposed. Computational methods can be categorized according to the types of information they analyze. One common approach consists of using text mining to extract known PPIs from the biomedical literature . Additioet al. combinedet al., while Dl.[et al. adapted l.[et al.. To provl.[et al. applied l.[et al.,65.Here we present a computational model to predict inter-species PPIs within the human oral cavity, an environment particularly prone to bacterial colonization. This is mostly due to the fact that human, microbial and environmental factors interact in a dynamic equilibrium within the human oral cavity . DetermiRothia mucilaginosa microorganism are very specific and relevant. Moreover, our method not only predicted new PPIs between periodontal pathogens and the host, but also PPIs between different periodontal pathogens, suggesting a synergistic course of action.As a result of this work, analysis of the resulting PPI network revealed some interesting features. Some of the PPIs involving the We conducted a series of pre-test analyses to assess the performance of our model. Then, we proceeded to test our approach on high-quality experimental protein-protein interaction (PPI) data collected from five databases. The selected databases exclusively contain manually curated PPI data.Figure Since there is no well-established gold standard for PPIs, we collected data from five databases containing high-quality experimentally determined interactions as described further on in Methods. Extracted PPIs from the five databases were merged, creating our gold standard of positive interactions. The gold standard of negative interactions was obtained by randomly pairing the protein list on the premise that all protein pairs produced must differ from those on the positive dataset. A total of 18,371 positive and a similar number of negative pairs were obtained.Simultaneously, for each possible pair of proteins, we constructed five clusters of features based on: (1) literature; (2) primary protein sequence information; (3) orthologous profiles; (4) biological process similarity, and; (5) enriched conserved domain pairs. This was performed by accessing public databases, extracting, and then processing the collected data.The gold standard dataset was used to train a Naïve Bayes classifier and to perform further validations on the final model. The classifier was then applied to the set of all possible pairs of protein interactions. Finally, by aggregating all individual pairs of predicted interactions, the final network was obtained.In this section, we evaluate the performance of the proposed method when applied to the set of human proteins from the gold standard. We performed a 5-fold cross-validation to assess the combined and individual contributions of the clusters of features. Table The best performance is achieved through the ensemble of the five clusters, returning an area under the receiver operating characteristic (ROC) curve (AUC) of 0.926, a precision of 0.848 and a recall of 0.854. This result is above the performance of any individual feature and can only be achieved with the participation of all, meaning that all features are required and have a complementary contribution.The Sequence is simultaneously the feature with the best overall performance (AUC = 0.877) and the one that causes the most negative impact when removed from the classifier, making the AUC drop to 0.891. It also has a very interesting recall of 0.813, partially due to the fact that all protein sequences are recognized and therefore the feature has full coverage.In contrast, the clusters of orthologous groups  and domain-domain interactions  have the lowest individual AUCs, mainly due to the low coverage of their features. Despite that, they benefit from a considerably high precision that contributes positively to the final classifier. This is especially true for the COGs which, when removed, cause the major drop in precision.The Literature and the Gene Ontology (GO) features, while not outstanding in any particular metric, have consistent performance on almost all metrics. Nevertheless, they make a very relevant contribution to the final classifier while the removal of the Literature causes a drop of the AUC to 0.919 and the GOs to 0.916.−7). We neglected the recall for the sake of precision. As can be observed in Figure −7 is the lowest probability value where an increment does not imply a decrease in the number of interactions. This cut-off resulted in a total of 46,579 PPIs, with 37,407 being between human proteins, 6,394 between human and microbial proteins, and 2,778 between microbial proteins. The average number of protein interactions per protein after the cutoff was 8. Figure Rothia mucilaginosa, Leptotrichia buccalis, and Actinomyces odontolyticus (strain independent).The classifier returned a set of 1.9 million possible interactions with a probability higher than 0.5. This corresponds to an average degree of 404 interactions per protein, which is much above the range of 3 to 30 documented in previous studies . AdditioHomo sapiens with 3,030 proteins, the most represented organisms in the human oral cavity are Rothia mucilaginosa (strain DY-18) (Stomatococcus mucilaginosus), Actinomyces odontolyticus (strain ATCC 17982), and Streptococcus salivarius (strain SK126), with 68, 54, and 26 proteins, respectively. These organisms are opportunistic pathogens known to be associated with periodontitis [With the exception of dontitis  and caridontitis .The most frequent biological processes are related to host-microbial interactions: GO:0044281 , GO:0019048 , and GO:0045087 .Leptotrichia buccalis , a kinase domain protein from Parviromonas micra ATCC 33270 , and Ras-related protein SEC4 from Saccharomyces cerevisiae .We also identified the top three human hub-proteins present in our data: epidermal growth factor receptor (EGFR) , fibronectin , and cullin-associated NEDD8-dissociated protein 1 (CAND1) . In terms of non-human original hub-proteins, the most common are a serine/threonine protein kinase from Unsurprisingly, the most frequent GO biological processes in our final PPI dataset are associated with host-pathogen interactions. The preeminence of innate immune response and viral interaction with host as the most frequent biological processes are self-explanatory. However, the association between small molecule metabolism and host-microbial interactions is not so direct.When faced with an infection, the body will respond by initiating two major cellular signaling pathways with opposing functions: the nuclear factor (NF)-kB and glucocorticoid-mediated signal transduction cascades. While the NF-kB pathway promotes the immune response and inflammation, the glucocorticoid-mediated signal transduction cascade suppresses it. In order to explain the association between small molecule metabolism and host-pathogen interactions we must focus on the NF-kB cascade, as it is known to mediate the transcriptional activation of several cytokines  involved in immunity . Tumor nThe top three hub proteins identified share a common trait: these are exploited by pathogens in an attempt to gain entry to the host and survive inside it.Salmonella typhimurium[Candida albicans[Reovirus[Vaccinia virus[et al. [EGFR is a transmembrane protein mainly produced in the salivary glands and the kidneys . Its assphimurium, Candida albicans, Reoviru[Reovirus, and Vacnia virus. Apparens[et al. .1 integrins in the cell surface, and is generally seen as a key protein for bacterial adhesion within the oral cavity [Similarly to EGFR, fibronectin appears to also play the role of a “microbial-anchor”. This glycoprotein is found bound to the βl cavity ,80.The CAND1 protein, formerly TIP120A, was found to interact with most of the proteins in the Cullin family . The CulRothia mucilaginosa, a microorganism present in the normal human oral microbiome but considered an opportunistic pathogen [When the data analysis is focused on a particular disease such as periodontal disease four main features can be observed: i) pathogen , is the pathogen .Rothia mucilaginosa shares the characteristics previously described for the hub proteins with 37/638 interactions with the EFGR protein, 40/638 interactions with fibronectin and 34/638 interactions with CAND1. Furthermore, this sub-network presents two predicted interactions which have not been described before: R. mucilaginosa proteins D2NSF5 and C6R5R8, which are predicted to interact with human immunoglobulin chains (P01719 and P01781), and could be related to the immune response specific for this species, explaining why these interactions are worth investigating.Regarding the first observation, the analysis of the sub-network pertaining to A. actinomycemcomitans, P. gingivalis, and the host proteins. As mentioned before, this is due to the fact that these organisms are not well represented in the original protein data set. However, besides the interactions predicted between these bacteria and the human hub proteins described above, in the case of Porphyromonas gingivalis it is possible to identify at least two potentially interesting new types of interactions between bacterial ribosomal proteins and a major histocompatibility complex protein (P30461). Furthermore, we also identified a possible interaction between the bacterial enolase (Q7MTV8) and a host aquaporin which could interfere with the homeostasis mechanisms of the host. Additionally, when we consider the interactions of P. gingivalis with other bacteria, we find that the same enolase might interact with outer membrane proteins of Haemophilus influenza and Pasteurella multocida. The role of bacterial enolase as a multitask protein involved not only in carbohydrate metabolism but also in virulence has been recognized recently [If we consider the bacteria most associated with periodontal disease, our model predicts few interactions between recently .P.gingivalis proteases and those of other periodonto-pathogens such as Kingella oralis and Treponema denticola is interesting. This may even shed some light on the synergistic aspects of oral biofilm in periodontal disease [This suggests that previously unknown and important PPIs for oral colonization and biofilm formation may be present in this dataset. Finally the fact there are possible interactions between  disease .The continuous yield of large-scale data mainly from microarrays and yeast two-hybrid studies has made the study of PPIs very appealing. The main issue associated with PPI study is the high prevalence of false positives and negatives in experimental PPI data. Being the only “reliable” source of PPIs, inaccurate experimental PPI data will contaminate training datasets and therefore compromise the performance of computational PPI prediction methods. For this reason, we believe that an improvement in the quality of experimental PPI data will greatly impact the performance of new computational PPI prediction approaches. While this is not the case at present, we must consider how to avoid the effects of false positives and false negatives in the final PPI prediction model.We proposed a probabilistic Bayesian-based method to integrate several data sources, to obtain more robust and reliable PPI predictions. By applying naïve Bayes, we automatically up-weigh the most informative features and down-weigh the less informative ones, allowing for automatic error-correction.Our individual feature analysis results show a great relevance of the selected features. When applied on a naïve Bayes classifier, the individual features synergize, boosting the AUC up to 0.926. This suggests that the reliability of prediction improves with the increase of significant features, meaning that the ensemble final model actually reduces the disadvantages of the individual methods.Cytoscape was successfully used to validate the network when tested with real pathway examples, discovering new potentially interesting interactions in oral biology, both between the host and the periodontal pathogens and between different periodontal pathogens.We believe our work may be applied in several scientific areas, and even in other PPI related studies. An example is biomedical PPI screening, to assess if interactions of particular interest might occur and what the related interaction probability is. Another example is pharmacologic research, as a well-established PPI network can provide insights on potential drug targets, but also new uses for existent in-market drugs. Finally, and based on the fact that protein interaction networks are dynamic , our worAs a starting point for our study we used 4,707 proteins, 3500 from Human and 1207 from microbial, available on the OralCard database ,67.These proteins were identified via proteomic analysis of the saliva, frequently by using 2D electrophoresis/mass spectrometry or 2D liquid chromatography/mass spectrometry. By the end of 2012 the salivary proteome was determined to contain 3500 proteins from human origin and 1207 from microbial sources.The use of positive (interacting pairs of proteins) and negative (non-interacting pairs of proteins) examples is required for training and assessing the performance of the classifier. All data used in the construction of the positive data set (PDS) and the negative data set (NDS) was downloaded in March 2013.We collected experimental oral protein-protein interaction (PPI) data from five databases: 14,139 PPIs from BIOGRID , 254 PPIAll the interacting protein pairs were identified by their UniProtKB  AccessioPPI pairs from the five databases were merged and repeated entries were removed. From a total of 23,564 PPIs, 5,193 duplicated entries were removed, resulting in a PDS of 18,371 protein pairs.The selection of negative examples to integrate the negative data was based on two methods described in the literature . These mN is the number of proteins and K is the average degree for the final PPI network. In this study the N is 4,707 and for PPIs the typical value of K is between 6 and 16.where With this strategy we generated a NDS of a size similar to that of the PDS , and combined it with the PDS to obtain a training data set with 36,719 PPIs.In this section we describe the procedure for construction of the five clusters of features. The final results are summarized in Table et al.[et al.[U  – where Xi is in this case the protein of interest and Yj is any other concept in the vocabulary – as the weights used for creating the concept profiles:The literature-based protein-protein interaction scores were calculated by the method described in van Haagen et al.. This mel.[et al., we use H (X) is the entropy for X and H  is the joint entropy for X and Y, calculated based on document frequency counts.Where We used a corpus of nearly one million abstracts, obtained by searching Pubmed with 17,402 names and synonyms extracted from UniProtKB for 4,707 proteins in the dataset, after removing nonsensical names such as “uncharacterized protein”. To identify the concepts mentioned in the texts we used Gimli , a machiSeveral studies have been carried out where detection of protein-protein interaction is derived from information directly extracted from the amino-acid sequences -56. The X, Y) is represented by concatenating the corresponding features vectors Fx and Fy, represented by .Taking into account the primary protein sequence information, the following features have been considered in this work: occurrence of the 20 amino-acids in the protein sequence, protein atomic composition, molecular weight and atomic weight, forming a vector of 27 features. The interacting protein pair . In contrast, paralogs are genes related by duplication within a genome [By definition, clusters of orthologous groups (COGs) are sets of orthologous genes or orthologous groups of paralogs from three or more phylogenetic trees. In essence, this means that two proteins from different lineages belonging to the same COG are orthologous. Orthologs are genes in different species that evolved from a common ancestor by speciation   databaseWe were able to obtain the orthologous profile for 9,636 protein pairs from the training dataset and 1,324,230 proteins pairs for the classification dataset.Previous studies have explored the use of GO annotation similarity between two proteins as a PPI predictor ,102-105.We were able to obtain the gene ontology profile for 23,769 protein pairs from the training dataset and 5,130,103 protein pairs for the classification dataset.The Database of Protein Domain Interactions  (DOMINE)We were able to obtain the domain profile for 5,994 protein pairs from the training dataset and 516,609 protein pairs for the classification dataset.Ai given the class label C, from the training data. The Bayes rule is then applied to calculate the probability of C given the specific instance of A1 ,…, An, and then assessing the class with the greatest posterior probability, ensuing classification [The proposed approach was developed, tested, optimized and performed using Orange, an open-source bioinformatics tool featuring Python scripting and a visual and programmatic interface. We used the naïve Bayes  classifification .The receiver operating characteristic (ROC) curve, which is the plot of the true positive (TP) rate with the false positive (FP) rate, depicting the relative trade-off between both rates  was usedWe used Cytoscape to visualize and validate the obtained PPI network. The PPIs were classified as “HUMAN-HUMAN”, if the interacting proteins were only of human origin, as “MICRO-MICRO”, if the interacting proteins were only of microbial origin, or as “HUMAN-MICRO”.We imported the network data to Cytoscape defining the two proteins in the same interacting protein pair as Source Interaction (protein one) and Target Interaction (protein two). The chosen Interaction Type was the above-mentioned organism-organism classification. A file containing node attributes was also imported, containing microorganism and biological process information extracted from the UniProt database pertaining to each individual protein in the network.http://bioinformatics.ua.pt/software/oralint. This includes the unique list of UniProt AC for the proteins in the oral cavity, the gold standard of interactions, the dataset used for training and validation, the predictions obtained, and the Cytoscape project file with the network.All data required to analyze the results and re-run this experiment are available for download at The authors declare that they have no competing interests.EDC participated in the design of the study, constructed the positive and negative datasets, performed the analysis of hub proteins, characterized and analysed the human-microbial interactome, and drafted the manuscript. JPA conceived the study, participated in its design, performed feature construction and selection, parameterized the classifier, and helped to draft the manuscript. SM performed the text-mining analysis and helped to draft the manuscript. CP carried out primary protein sequence analysis and helped to draft the manuscript. NR and MJC analysed the role of the microbiome in periodontitis and helped to draft the manuscript. MB and JLO participated in the design and conception of the study, coordinated it, and helped to draft the manuscript. All authors read and approved the final manuscript.