Plasmodium, revealed potential drug and vaccine candidates. However, genomics-driven target discovery has been significantly hampered by our limited knowledge of the cellular networks associated with parasite development and pathogenesis. In this paper, we propose an approach based on aligning neighborhood PPI subnetworks across species to identify network components in the malaria parasite P. falciparum.Malaria causes over one million deaths annually, posing an enormous health and economic burden in endemic regions. The completion of genome sequencing of the causative agents, a group of parasites in the genus P. falciparum and E. coli. 1,082 P. falciparum proteins were predicted as functional orthologs of known transcriptional regulators in the E. coli network, including general transcriptional regulators, parasite-specific transcriptional regulators in the ApiAP2 protein family, and other potential regulatory proteins. They are implicated in a variety of cellular processes involving chromatin remodeling, genome integrity, secretion, invasion, protein processing, and metabolism.Instead of only relying on sequence similarities to detect functional orthologs, our approach measures the conservation between the neighborhood subnetworks in protein-protein interaction (PPI) networks in two species, In this proof-of-concept study, we demonstrate that a subnetwork alignment approach can reveal previously uncharacterized members of the subnetworks, which opens new opportunities to identify potential therapeutic targets and provide new insights into parasite biology, pathogenesis and virulence. This approach can be extended to other systems, especially those with poor genome annotation and a paucity of knowledge about cellular networks. P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi, cause human malaria. P. falciparum is the most virulent and widespread one.Malaria is a major threat to public health and economic development in endemic regions. About 300-500 million cases are reported, and 1-2 million people die from malaria every year. Children and pregnant women are among the hardest hit of malaria victims. Five parasite species, P. falciparum and its sibling species and strains [The continuous morbidity or mortality of malaria is largely due to the rapid development of parasite resistance to currently available drugs and the increasing insecticide resistance of mosquito vectors. It is imperative to search for new lines of antimalarial drug and vaccine targets. The complete genome sequencing of  strains -6, the s strains -30, prot strains -46, meta strains -54, inte strains -60 and,  strains -63 efforP. falciparum, as over 60% of the predicted open reading frames (ORFs) were annotated as "hypothetical" without functional assignment [We propose to circumvent this limitation using a subnetwork alignment approach. It has been shown that network alignment offers an effective means to elucidate network structure and predict protein orthologs -69. Our signment . Previousignment , and presignment , which wsignment . In thisPPI network alignment algorithms are designed to match nodes in two PPI networks such that the conserved interactions between the orthologs in the networks are captured or maximized in counts. The current network alignment algorithms are either local or global approaches. Local network alignment -69 aims P. falciparum shares very few orthologous proteins with other species, the conserved interactions between P. falciparum PPI network and the PPI network of model organisms are too few to reveal meaningful alignments. Thus, network alignment is not directly applicable to the study of P. falciparum PPI network. Instead of focusing on detecting alignment, we propose to measure the functional relation between P. falciparum proteins and the annotated proteins in another species by aligning the neighborhood subnetworks of the two proteins. The neighborhood subnetwork of a protein  contains the nearby neighbors reachable by the protein through a small number of hops in the PPI network. Our assumption is that the neighborhood subnetwork captures information on the functional role of the central protein. Based on this assumption, if two proteins are functional orthologs, their neighborhood subnetworks will share similar paths or other structural patterns. Our subgraph alignment approach is designed to summarize the structural similarity between neighborhood subnetworks for ortholog prediction.Since P. falciparum. It was chosen because: (1) parasite employs exquisite regulatory machinery on gene expression to assure timely and accurate coordination on parasite growth, development, infection, and virulence. (2) Very little is known about the components, dynamics, and design principles of this network. New discoveries of network components could significantly fill our knowledge gaps and possibly lead to new short lists of proteins that are poorly understood and poorly annotated for functional characterization. The correspondent network used was from Escherichia coli. Detection of network similarities among Eukaryotes and among Prokaryotes have been demonstrated [P. falciparum, where the immediate relatives reveal comparatively little about its functional subsystems.As a proof-of-concept, we chose to predict the components in the transcriptional regulation network in nstrated ,78, but It is a common belief that the malaria parasite possesses a complex and orchestrated transcriptional regulatory system ,80. HoweP. falciparum proteins were functional orthologs of known transcriptional regulators in the E. coli network  that has a functional domain RPC4 which comprises a subunit of the tRNA specific polymerase RNA Pol III. The third interacting protein is a merozoite surface protein 7 (MSP7) precursor (PF13_0197), which is a regulator of parasite growth and a surface antigen regarded as a potential vaccine target [Plasmodium membrane protein (PF14_0315) that contains two FYVE/PHD zinc fingers for binding to potential target molecules. The remaining two proteins associated with PF14_0374 are 40S ribosomal proteins S10 (PF07_0080) and S20e (PF10_0038), indicating the interactions between transcription and translation  was predicted to be a transcriptional regulator. It has two functional domains YL1 (Pfam accession PF05764) and YL1 C-terminal domain PF08265), both of which are typical DNA binding domains. This protein may be related to chromatin remodeling. In addition, a Y2H assay using this protein as a bait pulled out a chloroquine resistance marker protein (PF14_0463)  domain. AP2 transcription factors play a pivotal role in floral development in plants . The recadiation . These Aadiation -95. P. fs Figure . AnalyziThe ApiAP2 protein with highest connectivity is PFD0985w, which has 17 interaction partners Figure . It has The ApiAP2 protein with second largest connectivity is PF07_0126. It has 15 PPI partners Figure  that canThe role of ApiAP2 proteins in transcriptional and epigenetic regulation is also indicated by a direct PPI between a putative ApiAP2 with 3 AP2 domains PF10_0075 and a histone acetyltransferase GCN5 PF08_0034), an enzyme for histone modification and chromatin remodeling . This Ap, an enzyP. berghei was an indispensible regulator for sporozoite development in the mosquito stage [P. falciparum. As shown in Figure Plasmodium genus. The PPI between PTEX150 and ApiAP2 suggests that this export machinery may have parasite-specific regulation. PTEX is becoming an attractive therapeutic target due to its importance to virulence and parasite survival and its distant evolutionary relatedness to the human host. (2) PF11_0442. Its counterpart in P. berghei is a transcription factor that regulates ookinete-specific gene expression for parasite invasion of the mosquito midgut. PF11_0442, however, may play a role in the red blood cell (RBC) stage: It has one PPI partner, rhoptry-associated protein 1 . RAP1 is an escort protein required to localize RAP2 to the rhoptries, apical organelles essential for RBC invasion [Two of these 11 ApiAP2 proteins have been experimentally characterized to some extent: (1) the crystal structure of the AP2 domain of PF14_0633 has been determined, revealing a multiple-site binding pattern , and gento stage . Howeverinvasion .In summary, ApiAP2 proteins are a family of stage-specific transcriptional regulators for diverse processes ranging from epigenetic modification, chromosome organization and dynamics, invasion, protein sorting and trafficking, protein turnover, and metabolism.Arabidopsis thaliana, to physically interact with GCN5, a histone acetyltransferase and a potent transcriptional activator [P. falciparum [Module based subnetwork alignments predicted additional proteins that are likely involved in transcriptional regulation Table . Two proctivator ,103. Thelciparum  revealedOther putative transcriptional regulators include PFE0870 and PF14_0170. PFE0870 contains two functional domains: a FACT complex subunit (SPT16/CDC68) domain which was reported to facilitate transcriptional initiation and interact with nucleosomes and histones , and a hP. falciparum. Our results predicted general transcriptional regulators that may regulate gene expression in a global or pleiotropic mode. Our results also imply a group of parasite-specific transcriptional regulators in the ApiAP2 family that play roles in diverse cellular processes ranging from chromatin remodeling, protein sorting and secretion, signal transduction, and invasion. Finally, our analysis has identified other potentially important proteins involved in transcriptional regulation. Our present knowledge of the transcriptional machinery and its regulatory capacity is rudimentary. The identification of network components in this machinery will open new avenues to the development of novel therapeutic targets and provide new insights into parasite biology, pathogenesis and virulence.A functional-module based alignment approach was used to predict system components in transcriptional regulatory networks in malaria parasite a priori information as to the identity or function of a gene is not necessary for the gene to be placed in a network. Thus genes identified only because of their key role in a network become potential targets. Furthermore, placement of the gene product in a systems context could, in itself, serve to identify the function of the gene product. If successfully applied, a systems biology approach circumvents the limiting factor in comparative genomics - the difficulty in obtaining reliable functional assignments.The premise of our subnetwork alignment approach is that functional annotations of the proteins can be transferred across species through conserved interactions in the aligned PPI networks. Under this framework, P. falciparum proteins, we formulated the problem as subnetwork querying. We first mapped the annotated E. coli transcriptional factors (GO:0003700: transcription factor activity) into the E. coli protein-protein interaction network. For each transcriptional factor, nearby neighbors were selected to form its neighborhood subnetwork. Similarly, each P. falciparum protein was mapped into the P. falciparum PPI network and a neighborhood subnetwork was built to include its nearby neighbors. Since the E. coli network and the P. falciparum network differ in size and density, the nearby neighbors were selected with a rule to control the neighborhood size. Let kN(p) denote the set of proteins that are exactly k hops from the central protein p. The neighborhood of central protein p is N(p) = N1(p) ∪ N2(p) ... ∪ kN(p) such that |N(p)| ≤ 500. Specifically, we first included the neighboring proteins that are 1 hop from the central protein. If the size of the neighborhood was less than 500, we continued to include the proteins that were 2 hops from the central protein. We kept increasing the hop distance until the neighborhood size was larger than 500. In other words, nearby proteins were selected by their distance to the central protein and the neighborhood size was kept below 500 unless the central protein has more than 500 direct neighbors in the PPI network.To predict functional orthologs for E. coli transcriptional factors and all the P. falciparum proteins, we aligned each E. coli subnetwork against all the P. falciparum subnetworks. The central protein of the best-aligned P. falciparum subnetwork was identified as the functional ortholog of the E. coli transcriptional factor.After we obtained the neighborhood subnetwork for the P. falciparum neighborhood subnetwork aligned with an E. coli neighborhood subnetwork, we assigned a score for each possible alignment and summarized the alignment scores with a graph kernel. Graph kernels are effective approaches to measure the similarity between two labeled networks [p Swith central protein p and q Swith central protein q, we define a simple shortest path similarity function,To evaluate how well a networks ,106. Givnetworks -109. Altnetworks . Since owhere,σ = 10 measures the sequence similarity between proteins x and y based on the E-value of the sequence alignment, and dist is the length of the shortest path connecting proteins x and y in the PPI subnetwork. Since the scores were small numbers, the computation was done in -log10 scale. In this similarity function, we took each pair of the proteins  in one subnetwork and identify the  in the other subnetwork that gives the maximum ratio between their sequence similarity with respect to  and the closeness in the subnetworks. Specifically, we computed the shortest path through the central protein between all pairs of proteins in the neighborhood subnetwork. The shortest paths of two neighborhood subnetworks are then compared and scored pairwise. The total of the alignment scores was reported as the subnetwork alignment score. Our strategy is to incorporate both the sequence similarity of the proteins and the role of the central proteins in the subnetwork in the similarity measure, which summarizes the functional coherence between the two subnetworks and between the two central proteins of the two subnetworks.E. coli protein-protein interactions were obtained from IntAct database. IntAct database provides binary protein-protein interactions derived from literature curation or direct user submissions. The complete set of protein-protein associations for P. falciparum was extracted from the STRING database [The database ; each asdatabase  and Plasdatabase ) assignmdatabase  and convdatabase . The defdatabase  to deterP. falciparum genomic sequence and annotation data [http://www.plasmodb.org) [ion data , transcrion data ,9,12, maion data ,35,39,40ion data  data forodb.org) . Conservodb.org) . Multiplodb.org)  and T-coodb.org) , followeodb.org) . BootstrEBA: erythrocyte binding antigen; DBL: Duffy binding-like; GO: Gene Ontology; HMG: High Mobility Group; HSP: heat shock protein; MCM: minichromosome maintenance; MSP: merozoite surface protein; MRF: Markov Random Field; ORF: open reading frame; PPI: protein-protein interaction; RAP: rhoptry-associated protein; RBC: red blood cell; SMC: structural maintenance of chromosomes; SVM: support vector machine; Y2H: yeast 2-hybrid.The authors declare that they have no competing interests.YW and RK conceived and designed the study. All authors performed bioinformatics data analysis and drafted the manuscript. All authors read and approved the final manuscript.P. falciparumFunctional orthologs involved in transcriptional regulation in . The query genome is P. falciparum, and the target genome is E. coli. GO: Gene Ontology. BP: Biological Process. MF: Molecular Function. CC: Cellular Component.Click here for fileP. falciparumThe protein-protein associations involving 11 ApiAP2 transcriptional regulators in .Click here for file


The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine.To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS). Dr. PIAS has a meta-database that stores various types of information  retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM). Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV) proteins identified to date.The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data. The importance of PPIs as targets for drugs, especially small molecule drugs, has increased greatly in recent years -4. Over in silico drug target identification, stores diverse information on drug target proteins identified by the web-based tool Target Fishing Docking. SuperPred [in silico pharmacology and medicine. All of them, however, deal only with single proteins, rather than PPIs.Several databases and web-based tools specializing in drug targets have been published. For example, TTD ,8, a datuperPred , a web-sDB [DB mainly focuses on protein/protein and protein/inhibitor interfaces in terms of various physicochemical parameters such as atom and residue properties, pocket volume, and accessible surface area [DB and TIMBAL can provide useful information to researchers developing PPI inhibitors. Both databases, however, contain only known drug target PPIs, so only a very small number of PPIs and PPI-inhibiting chemicals are stored. As a next step, in order to efficiently utilize the databases such as 2P2IDB and TIMBAL, it is needed to apply the information obtained from known drug target PPIs and their inhibitors to other PPIs not presently targeted by inhibitors.Recently, two databases (2P2IDB  and TIMBDB ) specialace area . TIMBAL ace area . 2P2IDB DB and TIMBAL, other properties associated with the biological function of PPIs are also included in the assessment. This is important because, to select a drug target PPI, a researcher considers not only information on the tertiary structure of the PPI and its known inhibitors but also that on the biological function of the PPI. All information on the PPIs used in the assessment is stored in Dr. PIAS. Users can search for druggable PPIs in Dr. PIAS by using various words and terms such as protein/gene name, tertiary structure, disease, pathway, and drug/chemical name as keywords.Here we describe a novel database system, Dr. PIAS, which focuses on the druggability of PPIs. Dr. PIAS assesses the druggability of PPIs, currently not targeted by inhibitors, by utilizing the information obtained from known drug target PPIs. Dr. PIAS holds not only known drug target PPIs but also all PPIs identified to date for human, mouse, rat, and HIV proteins. In addition to information on the properties of the tertiary structures of PPI interfaces and that on the properties of drugs/chemicals related to interacting proteins, which are dealt with in 2P2IThe most distinctive characteristic of Dr. PIAS is that the system assesses the druggability of PPIs by our original SVM-based method . Thirty in vitro and/or in vivo assays.• A PPI-inhibiting small chemical has been identified, and its potency as a PPI inhibitor has been validated by • A binding pocket for the PPI-inhibiting small chemical has been located on the tertiary structure of a protein, and it overlaps with the PPI interface.Structural, drug/chemical, and functional attributes  is a druggable protein that has been already targeted by a drug approved by the United States Food and Drug Administration (FDA). Several other known target PPIs are novel drug targets, and both interacting partners have no FDA-approved drug targeting them. In our SVM-based method, we used the number of drugs as the PPI attributes to assess whether the fact that an interacting protein has been already targeted by existing drugs influences the selection of PPIs as drug targets. The information on the FDA-approved drugs was retrieved from the DrugBank database . The numThe information on chemicals that experimentally assayed for the activity to each of the two interacting proteins was retrieved from the ChEMBL database .Information on human diseases caused when a protein is heritably or somatically mutated is essential for assessing the druggability of the protein. We retrieved information on human diseases from the OMIM database . When usTo repress a disease state with drugs, it would be desirable in some cases to target the proteins that function as 'hubs' in a PPI network. In other cases, targeting proteins that function in a peripheral part of a network could be more feasible for the treatment of a disease. After the PPI network was constructed based on the PPI data stored in Dr. PIAS, the number of all interacting proteins for each partner of a PPI was counted and stored in our database.As with the number of interacting proteins, proteins that function in a large number of biological pathways may be more desirable targets for therapeutic intervention for some diseases, while proteins involved in a limited number of pathways may be more desirable targets for other diseases. We retrieved information on biological pathways from the KEGG  and PID To assess the degree of similarity in biological function of the two interacting proteins, we utilized Gene Ontology (GO) . GO termFor a protein to be selected as a drug target, it is advantageous if the protein functions in a limited number of tissues/organs including the tissues/organs that develop the disease. Because of a scarcity of information on protein expression profiles in human, we utilized gene expression profiles. Information on gene expression profiles was retrieved from the UniGene database . To asseThe number of paralogs is an important factor in order for a protein to be selected as a drug target, since the researchers must consider potential adverse effects caused by the drug binding to non-target paralogs. Target proteins with a large number of paralogs may be associated with more severe adverse effects. The information on paralogs was retrieved from the KEGG and PIRSF  databaseAll information described above is stored in a relational database (MySQL).etc). Users can also use the amino acid sequence of a protein as a query. Amino acid sequence similarity search is performed by the BLASTP program, and then PPIs of the query protein and those of homologs of the query are listed as a search result.Screenshots of Dr. PIAS are shown in Figure Figure If a user selects the 'Draw PPI network' radio button in the 'Advanced search' form, a PPI network derived from the search result is drawn by the computational program eXpanda  Figure . Nodes aBy clicking the headers of the summary table shown in Figure In this section, the usage of Dr. PIAS is illustrated by applying it to search for potentially druggable PPIs from two points of view: one is from a disease point of view and the other is from a protein.Lung cancer is one of the most common cancers in both men and women worldwide . Many stWhen using Dr. PIAS, users can easily set versatile criteria to search for druggable PPIs. To search for PPIs that are potentially druggable for lung cancer, we set the following criteria Figure .• A PPI is derived from human.• The PPI is involved in biological pathways associated with lung caner.• Genes coding interacting proteins are expressed in lung cancer cells.• Tertiary structure of the protein complex of the PPI has been solved.in silico drug design and development. If a user does not intend to design and develop drugs by in silico technologies, this criterion is superfluous. In total, 116 PPIs satisfy all the above criteria  of this pocket is the largest among the three pockets, and the volume (314.62Å3) is the second largest. GRB2 protein is a well-studied drug target [Among the 17 PPIs, GRB2/VAV1 has the highest score of 0.9662 when all attributes were used for the assessment. Figure g target . One FDAInterestingly, the list of PPIs in Table DB and TIMBAL have been already published that hold drug target PPIs and their inhibitors. By using an identical protein as a query when searching for druggable PPIs in Dr. PIAS and the two databases, we demonstrate the differences and similarities in output results obtained from the three databases.As described in the former section, 2P2IL protein was adopted as a query. The PPIs of BCL-XL with BAX, BAK, and BID proteins are well-studied drug target PPIs [DB and TIMBAL contain the PPIs. From 2P2IDB, a user can obtain the information on 8 tertiary structures (as of 2010/12) of BCL-XL/BAK protein/protein and BCL-XL/inhibitor complexes and the information on various physicochemical properties of the inhibitor-binding pockets. If a user search for BCL-XL in TIMBAL, the user can obtain the information on 26 chemicals (as of 2010/12) inhibiting the PPIs of BCL-XL with BAX, BAK, and BID. The two databases provide users with the structural and drug/chemical information on already-studied drug target PPIs.BCL-Xget PPIs , and botL) as a keyword in searching Dr. PIAS, the user can obtain 66 PPIs (as of 2010/12) of BCL-XL with many other proteins as well as with BAX, BAK, and BID  have the tertiary structures of protein/protein complexes in the PDB, and the druggability assessments were conducted in all four ways  that focus on drug target PPIs. These databases hold known drug target PPIs and their inhibitors. In contrast, Dr. PIAS holds all PPIs of human, mouse, rat, and HIV proteins identified to date as well as known drug target PPIs. For each PPI, we assessed the druggability by the SVM-based method by using structural, drug/chemical, and functional attributes of the PPIs. These two characteristics of Dr. PIAS are what make Dr. PIAS distinct from other existing databases. 2P2IDB and TIMBAL primarily focus on the structural aspects of protein pockets and the chemical properties of PPI-inhibiting ligands, respectively. The information on the biological function of a protein is also essential for selecting drug target PPIs. Dr. PIAS stores functional information on interacting proteins, such as disease associations, pathways, GO terms, gene expression profiles, and paralogs. Therefore, Dr. PIAS can help researchers select drug target PPIs by evaluating each PPI from the three viewpoints of the tertiary structures of protein/protein complexes, drugs/chemicals relevant to interacting proteins, and the biological roles of PPIs in living cells. A cross reference of the three databases can provide researchers with a synergistic power to prompt studies on drug target PPIs and chemicals inhibiting them.Currently, there exist two databases  from the collected data. In this sense, the design concept of Dr. PIAS is completely different from that of all other PPI databases published to date.The number of PPIs for which small molecule inhibitors have been discovered has gradually increased over the last decade. This trend will continue. As more information on known drug target PPIs accumulates, our assessment system based on information obtained from known target PPIs will be improved. We will intensively incorporate information on novel drug target PPIs to make Dr. PIAS more useful for researchers focusing on the development of PPI-inhibiting drugs. In addition, PPI data and all omics information will be updated every half year. The number of PPIs stored in Dr. PIAS will rapidly increase due to the accumulation of PPI data in public databases and in the literature.Dr. PIAS is a database system aimed at assessing the druggability of PPIs. Of the huge number of currently unidentified PPIs, there could be many latent PPIs that are highly druggable. Dr. PIAS will aid the efficient discovery of these druggable PPIs from the continuously growing amount of PPI data.http://asp.gridasp.net/drpias/index.php. Academic non-profit users can freely access all of the contents stored in Dr. PIAS without paying a licensing fee. Commercial and for-profit users must obtain a license to access Dr. PIAS by paying a licensing fee to and entering into a license agreement with Beyond Computing, Co. Ltd. and PharmaDesign, Inc.Dr. PIAS is available at Dr. PIAS: druggable protein-protein interaction assessment system; FDA: United States Food and Drug Administration; GO: gene ontology; HIV: human immunodeficiency virus; PPI: protein-protein interaction; SVM: support vector machine.NS conceived this study, developed the database and user-interface of Dr. PIAS, and drafted the manuscript. FT helped to draft the manuscript. All authors have read and approved the final manuscript.Supplementary tables. Table S1 lists the positive set PPIs used in our SVM-based method to assess the druggability of PPIs. Table S2 is the list of the attributes of PPIs used in the assessment. Table S3 lists the PPIs satisfying the criteria set to search for potential drug targets for lung cancer (see text). Table S4 lists the PPIs associated with BCL-XL protein in Dr. PIAS.Click here for file


Today large scale genome sequencing technologies are uncovering an increasing amount of new genes and proteins, which remain uncharacterized. Experimental procedures for protein function prediction are low throughput by nature and thus can't be used to keep up with the rate at which new proteins are discovered. On the other hand, proteins are the prominent stakeholders in almost all biological processes, and therefore the need to precisely know their functions for a better understanding of the underlying biological mechanism is inevitable. The challenge of annotating uncharacterized proteins in functional genomics and biology in general motivates the use of computational techniques well orchestrated to accurately predict their functions.We propose a computational flow for the functional annotation of a protein able to assign the most probable functions to a protein by aggregating heterogeneous information. Considered information include: protein motifs, protein sequence similarity, and protein homology data gathered from interacting proteins, combined with data from highly similar non-interacting proteins . Moreover, to increase the predictive power of our model we also compute and integrate term specific relationships among functional terms based on Gene Ontology (GO).Saccharomyces Cerevisiae and Homo sapiens species proteins. The aggregation of different structural and functional evidence with GO relationships outperforms, in terms of precision and accuracy of prediction than the other methods reported in literature. The predicted precision and accuracy is 100% for more than half of the input set for both species; overall, we obtained 85.38% precision and 81.95% accuracy for Homo sapiens and 79.73% precision and 80.06% accuracy for Saccharomyces Cerevisiae species proteins.We tested our method on  Homo sapiens more than half of the total proteins are uncharacterized, likewise about one-third of the proteins in the Saccharomyces Cerevisiae, which is arguably one of the most well characterized model organisms, remain functionally unknown.Proteins are macromolecules that serve as building blocks and functional components of a cell, and account for the second largest fraction of the cellular weight after water. Proteins are responsible for some of the most important functions in an organism and the knowledge of their functions is a crucial link in the development of new drugs, better crops, and even the development of synthetic biochemicals such as biofuels. However, rapid advances in genome sequencing technologies are revealing new proteins at a rate that have resulted in a continually expanding sequence-function gap for the discovered proteins . For exaThis large set of conserved proteins whose function is still unknown, represents one of the main challenges for a deep comprehension of an organism as a biological system. Moreover, better understanding of protein functions can help biologists to successfully investigate new lines of attack against different diseases. Due to their enzymatic nature, proteins are generally among the preferred targets in drug and vaccine manufacturing processes. This makes the knowledge of their functions a critical step in any drug target discovery effort, and fully justifies the necessity of effective computational techniques for the precise annotation of uncharacterized proteins.Until recently, numerous high-throughput experimental procedures have been developed to investigate the mechanisms leading to the accomplishment of a protein's function. Different information sources including sequence similarity, protein 3D structure, phylogenetic profiles, protein-protein interactions (PPI), gene expression profiles, protein complexes, etc., represent the ground for the development of these techniques . The mosModule-assisted, 2- Direct methods, and 3- Probabilistic methods , , , 18], a, a18], aSeveral techniques have been proposed to use GO term relationships to functionally characterize proteins, e.g., , 21], ,  and 23]23]. MitrCombining functional information from heterogeneous biological sources has also been proven to increase the overall predictive power of automated protein function annotation techniques , 19]. F. F19]. FYKL033W-A (UniProtID: Q86ZR7) of Saccharomyces cerevisiae does not show any interaction in its own network. Nevertheless, it has two interactions with high homolog similarity with protein HDHD1 (UniProtID: Q08623) of Homo Sapiens species and with protein CG15441 (UniProtID: Q94529) of Drosophila Melanogaster species networks. Another type of biological information that could be exploited to link characterized and uncharacterized proteins is the set of motifs conserved in those proteins. Several functionally conserved proteins are found to have motifs that associate them to a particular molecular activity. For example in Table YIL169C (UniProtID: P40442) is conserved with Chemotaxis_Transduce_2 and T_SNARE motifs, while uncharacterized protein Truncated TBY (UniprotID: E9PAE3) is conserved with INTEGRASE and ASP_PROTEASE motifs. Similar motifs in known proteins can be used to link functional information with these proteins.Protein homology among different species could be exploited for this purpose. Many hypothetical proteins show no interactions  in their own network, but are associated with high confidence edges to homologs of other species networks. An example of this type of association is shown in Figure Saccharomyces Cerevisiae and Homo sapiens species proteins.This work is an extension of our previous work ; with thOur protein annotation pipeline exploits the associative nature of proteins that interact and collaborate on a common biological activity. The functions of an uncharacterized protein can therefore be inferred when the functions of its binding or interacting partners is known. Figure 1. building the protein-protein interaction network,2. filtering the network for reliable interactions,3. enriching the network with a set of non interacting highly similar proteins, and4. computing a GO based function similarity score to propagate functions from characterized to uncharacterized proteins.uP) identified using its UniProt  measures the motif similarity of the two proteins, whereas SS ∈  measures the sequence similarity of the two proteins. Integrating different information in a single score is particularly important as each type of data typically captures distinct aspects of cellular activity.where PIS. ProSite enables to query for a protein and to obtain a list of conserved motifs associated with a particular protein functional activity. We define the motif similarity score between uP and iPj as the number of common motifs conserved between the two interacting proteins (dividend of eq. 2) normalized to the minimum number of motifs obtained for the two proteins in isolation (divisor of eq. 2):Proteins often have several motifs with distinct evolutionary histories. The identification in the sequence of an annotated protein of patterns including evolutionarily conserved motifs can be associated to a specific biochemical function. Similar conserved motifs can be identified in the sequence of uncharacterized proteins, as well. Therefore, counting the number of common motifs conserved in two connected proteins represents a good opportunity to identify strong functional associations for uncharacterized proteins. Motif information has been taken into account gathering data from the ProSite database  and usinMS is in general biased toward either 0  or 1 .High scores indicate proteins sharing several conserved motifs and therefore with higher possibility of sharing the same function. Experimental results showed that uP and protein j iPas a normalized pairwise BLAST score [The second measure that contributes to increase the PIS is the sequence similarity. Sequence similarity between two proteins is a strong hint for interaction relevance. Proteins with highly similar sequences are found to have been involved in similar functional activities. To capture sequence similarity between proteins we therefore define a sequence similarity score between protein ST score . The BLAPIS are more likely to correspond to reliable interactions and therefore to identify proteins that actually participate in common functions. A threshold th PISis used to filter low scored interactions from the PPIN, and to identify a reliable set of interacting proteins. Different threshold values have been investigated in the performed experiments . Following [MS = 0), proteins with less than 50% sequence similarity (SS <0.5) are good candidates to be discarded motivating the maximum considered threshold of 0.5. If shared motifs are identified, also proteins with less than 50% sequence similarity can be still considered as valid interactors.Interacting nodes with high ollowing , if no sTogether with a large set of false positives, PPI information are also prone to false negatives . This is due to the fact that a large fraction of uncharacterized proteins are only known with their amino acid sequences. Sequence information can be used to enrich the filtered PPIN with additional interactions with other known proteins through sequence alignment.blastp [uP against all sequences contained in the Non Redundant Protein Sequences (nr ) database [nr database compiled by the NCBI is one of the largest and most prominent databases that accumulates and stores almost all the available protein sequences. It contains non-redundant sequences from GenBank, CDS translations, PDB, Swiss-Prot, PIR, and PRF. If the similarity of uP spans the whole sequence, blastp also accounts a global alignment, which is the preferred score used to rank the sequence similarity.We use the blastp  tool of database  and to ouP , only the first 10 ranked non-interacting  highly similar proteins (Similactors) out of the full set of proteins returned after alignment from the nr database has been considered for the PPIN enrichment. This set has been further filtered removing all PDB structures and uncharacterized proteins in order to enrich the PPIN with a very small set of reliable similactors.Enriching the PPIN with additional proteins may, on the one hand, reduce the number of false negatives, but, on the other hand, it can introduce new false positive interactions. It must be therefore limited to a very small set of proteins that show very high similarity. In the experiments performed in this paper, for each uP . Functions are represented according to the GO taxonomy as GO terms . However, GO is organized into three principle ontologies namely: molecular function, biological process and cellular component, whereas each ontology is structured in a DAG of terms. Each term is therefore part of a GO hierarchy. For our scheme we focus on the molecular function GO hierarchy, which describes activities performed by a protein at the molecular level. This is particularly important to understand the gene product in detail. Since GO nodes are connected to other nodes through parent-child relationships, and a protein known to a term in GO is also known to all the parent terms of the hierarchy, we can represent each annotation of an interacting protein iP  between two annotations of two different proteins on the basis of their relative positioning in the GO hierarchy according to eq. 4. The dividend of eq. 4 measures how much the two annotations overlap, counting the number of common terms in the hierarchy. The divisor of eq. 4 normalizes the overlapping to the hierarchy size of the shortest annotation.thGOSS) is used to filter GOSS results and to select those annotations that likely represent a valid function for uP. For all scores that cross the threshold, the minimum length hierarchy annotation out of the two that have been compared during the score calculation is selected and used to annotate uP. The shortest annotation is selected because up to that level the molecular activity of interacting proteins is certain.GOSS is computed for all couples of annotations and proteins available in the filtered and enriched PPIN resorting to GO structural data downloaded from the GO database  for the MAP kinase kinase MKK1/SSP32 protein , which is annotated in the UniProt database with three molecular functions: (1) ATP binding, (2) Protein binding, and (3) Protein serine/threonine Kinase activity functions. We assume MKK1 to be our target uncharacterized protein and we try to predict its functions using our scheme, as reported in Figure To help understanding the computational steps involved in the annotation of a protein we consider the example of MKK1 has 21 interactors obtained from IntAct and DIP databases resulting in a PPIN network of 22 nodes with a subset of them depicted in Figure PISth = 0.5, obtaining a set of 4 candidate interactors: (1) Protein Kinase C-Like1 , (2) Serine/threonine-protein kinase BCK1/SLK1/SSP31 , (3) Mitogen-activated protein kinase SLT2/MPK1 , and (4) Serine/threonine-protein kinase GIN4 . For the sake of simplicity no similactors are included in this example.binding, and catalytic activity. For protein annotations under the same functional context, we computed the GOSS score according to eq. 4. For instance, let us compare the ATP binding annotation of protein PKC1 ATP binding annotation of protein BCK1 binding context. Since both proteins are annotated with the same GO terms  and this term is elaborated at the sixth level in the GO hierarchy, according to eq. 4 GOSS MKK1 both interacting proteins PKC1 and BCK1 are found to be involved in ATP binding activity with high GO similarity. We therefore annotate protein MKK1 with this functional term. Likewise, we calculate GOSS for other couples of annotations of the interacting proteins and obtain two additional valid annotations Protein binding, and Protein serine/threonine Kinase activity with high GO similarity compared to other terms. In summary, all original annotations of MKK1 have been properly predicted by the proposed protein annotation flow.Potential interactors are annotated with a number of functions. We map them on GO terms to obtain related term dependencies as shown in Figure Saccharomyces Cerevisiae and Homo sapiens species protein datasets. The protein functional annotation data used for our model were obtained from the Uniprot [P in our dataset has been selected as a candidate unknown protein and its functional annotations predicted resorting to our methodology. Predicted functions have been then compared with the protein's original annotations in order to understand the overall prediction performance. The process has also been repeated under several different thresholds settings. We present the results for 763 proteins annotated with 2,099 GO terms of Saccharomyces Cerevisiae species, and 793 proteins annotated with 2,178 GO terms of Homo sapiens species.To validate the pipeline described in the Methods section, we applied the annotation process to predict the functions of two  Uniprot  database Uniprot . To calcConceptually, protein activities are very much related to each other. To precisely understand and evaluate the proposed experimental results it is necessary to provide a clear definition of how True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN) predictions are defined. These definitions may significantly impact not only the overall statistical strength of the experiments, but also the comparison with other methods.P under test can be classified. Three main sets of annotations can be defined:Figure True annotation set: the set of GO terms for which the target protein is actually annotated in UniProt. It represents the reference set to which our predictions can be compared;1. Full annotations set: is the set of all GO terms found in the annotations of all interactors extracted for the target protein (before applying the filtering) plus the annotations of all selected Similactors. This represents the actual set of functions that our method is able to predict for a given protein.2. Predicted annotations set: is the set of GO terms predicted by the proposed computational model for the selected protein.3. Based on these three sets of annotations, our predictions can be classified as follows:• TP terms are the intersection between the predicted annotations  and the real annotations . It is important to remember here that nodes in GO are hierarchically arranged from the most abstract term to the more detailed levels of activities and, if a protein is annotated to a child, it is also annotated to its parent. Therefore, annotating a protein with the functional activity of the parent node in the GO hierarchy may be considered as a TP. Nevertheless, methods that consider the prediction to parent terms as TP actually reduce the protein annotation information. Therefore, their prediction strength cannot be fairly compared with those methods that aim at predicting only the actual annotations of the proteins. Our results are compiled by considering as TP only those terms that correspond exactly to one of the true annotations. All other predictions are considered as false positives.• FP terms are all terms in the predicted annotations set excluding the previously defined TP terms.• FN terms are those functions that are present in the true annotations set but are not present in the predicted annotations set . Within the FN set, it is possible to identify a subset of annotations that we call Unpredictable False Negatives (UFN). It corresponds to the set of true functions of the test protein that are not part of the full annotation set. The UFN terms cannot be strictly called false negatives because our method does not actually reject those annotations. The bottleneck is rather to find enough biological information that could be used to include interacting proteins annotated with those functions in order to include them in the full annotations set. However, in order to provide worst case results, UFN have been included in the computation of FN.• TN terms are all the terms in the full annotations space excluding the predicted true terms.We computed TP, TN, FP and FN for each of the 1,556 proteins composing the two considered datasets. Cumulative TP, TN, FP and FN for each dataset have been then used to compute the following set of performance measures:th PISthresholds and with th GOSS= 0.99.Table PIS with the introduction of similactors in the PPIN significantly improves precision, accuracy, and FP rate. This can be appreciated by comparing these results with the ones published for Homo sapiens species in [Results show that combining the filtering capability of the ecies in .th PISon the overall prediction performance, experiments have been repeated with three different thresholds: th PIS= 0.5, th PIS= 0.25 and th PIS= 0. High values of th PISguarantee better performances in terms of precision, accuracy, and F1, since the algorithm only selects highly reliable interactors. Reducing th PISresults in downgraded precision and accuracy values for both species. This is caused by the fact that a lower th PISmeans selecting all interactors of the test protein along with its similactors as potential proteins. This leads to a too heterogeneous set of potential annotations and consequent lower precision and accuracy.To understand the effect The presented results were compiled including UFN terms, i.e., considering UFN as FN. However, the inclusion of UFN terms in the analysis does not render the complete predictive strength of the experiments. Table th GOSS= 0. This means directly annotating the test protein with all functions of the identified potential interacting proteins. Results are shown in Table To better evaluate the contribution of the GO-based annotation transfer, we repeated the experiments using a direct annotation, i.e., Another important observation is that the use of the GOSS similarity measure enables to decrease the False Positive Rate (FPR) for both data sets with increasing similarity values. We calculate the FPR as:The FPR is decreased from 77% to 14% for Homo sapiens dataset and from 76% to 21% for Saccharomyces Cerevisiae dataset as shown in Figure Homo sapiens dataset. The results reported in Figure In this section, we compare our method with other techniques that also integrate multiple sources of information for protein annotation. The first technique we compare with is presented by S. Jaeger's et al. . This teSaccharomyces Cerevisiae proteins providing prediction performance indicators for both methods in Figure Another successful technique has been proposed by Nariai et al. . This meFinally, we compare our results with a more recent technique proposed by A. Mitrofanova et al. . In thisTo complete the evaluation of our proposed methodology, we report a set of measures on the coverage of the GO terms that appear in the cross validation test of our protein datasets. The complete term wise prediction results can be seen in the supplemental material (Additional file For each functional term in the GO hierarchy we reportthe Total Appearance Count, which is the number of proteins in the dataset that are annotated with that functional term;• Total Prediction Count, which is the number of times that a term has been correctly predicted;• the Term Coverage, which is the percentage of Total Predictions over the Total Appearance of each term.• the Homo sapiens species we predicted 201 terms with 100% precision; likewise for 263 unique GO terms for Saccharomyces Cerevisiae species we predicted 165 terms with 100% precision.For 330 unique GO terms appearing in the annotation of the dataset for In this work, we presented a methodology that uses existing biological data with Gene Ontology functional dependencies to infer functions of uncharacterized proteins. We combined different sources of structural and functional information along with Gene Ontology relationships to predict multiple but related functional categories of unannotated proteins. These term-specific relationships, defined to clearly identify the functional contexts of activity of the interacting proteins, enables a dramatical improvement of the annotation accuracy with respect to previous approaches. The presented methodology may be easily extended to integrate more sources of biological information to further improve the function prediction confidence.The authors declare that they have no competing interests.Term wise prediction-Homo sapiens.pdf Term wise prediction results for Homo sapiens data set.Click here for fileTerm wise prediction-Cerevisiae.pdf Term wise prediction results for Saccharomyces Cerevisiae data set.Click here for file


The molecular network sustained by different types of interactions among proteins is widely manifested as the fundamental driving force of cellular operations. Many biological functions are determined by the crosstalk between proteins rather than by the characteristics of their individual components. Thus, the searches for protein partners in global networks are imperative when attempting to address the principles of biology.We have developed a web-based tool “Sequence-based Protein Partners Search” (SPPS) to explore interacting partners of proteins, by searching over a large repertoire of proteins across many species. SPPS provides a database containing more than 60,000 protein sequences with annotations and a protein-partner search engine in two modes (Single Query and Multiple Query). Two interacting proteins of human FBXO6 protein have been found using the service in the study. In addition, users can refine potential protein partner hits by using annotations and possible interactive network in the SPPS web server.http://mdl.shsmu.edu.cn/SPPS/.SPPS provides a new type of tool to facilitate the identification of direct or indirect protein partners which may guide scientists on the investigation of new signaling pathways. The SPPS server is available to the public at  The flowchart representation of the SPPS web server is shown in http://www.ncbi.nlm.nih.gov), UniProt (http://www.uniprot.org/), and PIR (http://pir.georgetown.edu).The input format of query protein sequences for SPPS server is the standard FASTA. The sequences of proteins can be either derived from in-house experiments, or directly taken from the databases embodying protein sequences, such as NCBI . In addition, a download link is available for all known interactions of the query protein if “Uniprot Entry Name” of the query protein is submitted, which were collected from several PPI databases .As the result of ‘Single Query’, the output in SPPS is split into three main sections, namely, “Protein Partner List”, “Protein Partner Network” and “Result annotation”. “Protein Partner List” includes all predicted protein partners along with their overall confidence scores in probability. The confidence score is provided for each potential protein-protein interaction as described in the Contrary to “Single Query”, the output of “Multiple Query” mainly divides into “Direct Interaction Prediction” and “Indirect Interaction Prediction” sections. The probability score is always provided for direct PPI predication. One or two intermediate proteins linking two query proteins are also estimated if “Consider indirect interaction” option in “Multiple Query” mode is checked. All probabilities of prediction in the output are defined the same way as “Single Query”.In addition to the “Search” option, SPPS also offers all training data, models and standalone software under its “Download” page, which facilitate users in their local machines if they have a great number of data to predict.To test the reliability of the SPPS server, we searched for the interacting protein partners for human F-box protein 6 (FBXO6) using “Single Query” and predicted a few direct interactions not included in the training models from the latest literature among several species using “Multiple Query”.FBXO6 protein is involved in the endoplasmic reticulum-associated degradation pathway by mediating the ubiquitination of glycoproteins. FBXO6 interacts with the innermost chitobiose in N-glycans of glycoprotein substrates by a small hydrophobic pocket in FBA domain and the introduction of point mutation into the residues in that pocket (FBXO6 null) impaired the binding activity toward its glycoprotein substrates Besides“Single Query”mode, SPPS server also provides “Multiple Query” to quickly evaluate whether two proteins are directly and indirectly interactive based on the score of probability, which may help biologists to choose the most probable protein-protein interaction pairs for further experimental validation. Many directly interacting proteins from the latest literatures in 2011 SPPS server has been running since 2007 and is updated annually. Competitive prediction of our algorithm has been evaluated in 2010 among sequence-based PPI prediction methods By bringing together a protein-partner search engine and protein databases in a single program, the SPPS server is a convenient tool for the identification of potential protein partners of query proteins such as kinases, regulatory factors, and other components of much more complex protein machineries. This web server can also be used in constructing the signal transduction network for a known protein or a novel protein whose function is unknown. In general, one protein may interact with at least several partners including upstream and downstream regulators. As illustrated by the example of FBXO6, SPPS provides a good predictive ability for potential protein partner hits. These are useful guidelines for further experimental validation of signaling network around any given protein.However, SPPS still has certain limitations, one of them being that the number of the models is not enough for covering all the species. The second one is that SPPS server has not considered interspecific interactions, such as the interactions between viral and human proteins, which may be vital in exploring targets responsible for infectious diseases. The running time for “Single Query” job is still slow due to large database and limited CPUs. To overcome these shortages, we are currently (i) collecting original interaction data produced by using the yeast two-hybrid based methods, mass spectrometry, protein chips and hybrid approaches to construct training models for more species, (ii) developing new kernel of SVM to adapt crossover interactions between different species, (iii) planning to provide more CPUs to accelerate the running process.http://mdl.shsmu.edu.cn/SPPS/.Discovering protein partners in large-scale network has been unprecedentedly appreciated due to the requirement to address the complicated process of biological systems by means of integrated technology. SPPS provides a new type of tool to facilitate the identification of direct or indirect protein partners and guides scientists to design new experimental directions. The SPPS server is available at a public web service http://www.uniprot.org/), which is carried out by a Python script “Updater” at a 6-month interval. The database currently consists of more than 60,000 non-redundant protein sequences, with species covering “Homo sapiens”, “Mus musculus”, “Caenorhabditis elegans”, “Drosophila melanogaster”, and “Saccharomyces cerevisiae”, as shown in SPPS requires a sufficient number of known protein sequences covering a diverse range of species. The protein sequences in our database were retrieved from UniProt f(x of observation ixi)g(x to derive probabilities by Eq. (1)A and B are estimated by minimizing the negative log-likelihood of the training data, Eq. (2),For the SPPS server, we further enhanced the algorithm by probability. Putative protein partners are ranked by the value of probabilities. Platt's approach was used to derive posterior probabilities for the estimated class membership 293T cells transfected with either Flag-FBXO6 WT or Flag-FBXO6 Null were lysed in 6 ml of lysis buffer  for 20 min with gentle rocking at 4°C. Lysates were cleared using centrifugation , the supernatant was subjected to immunoprecipitation (IP) with 50 µl of anti-FLAG M2 affinity resin (Sigma) overnight at 4°C with gentle inversion. Resin containing immune complexes was washed with 1 ml ice cold lysis buffer 4 times followed by three 1 ml Tris Buffered Saline (TBS) washes. Proteins were eluted with two 50 µl 150 µg/ml 3×Flag-peptide (Sigma) in TBS for 30 minutes, and the elutions were pooled for a final volume of 100 µl. Proteins in each elution were precipitated with cold acetone and the resulting pellet washed 2 times with cold acetone. Proteins were separated by 10% SDS-PAGE and transferred to NC membrane . After blocking with 5% nonfat milk in PBS, membranes were immunoblotted with indicated antibodies, followed by HRP-linked secondary antibodies . The signals were detected by SuperSignal West Pico Chemiluminescent Substrate kit  according to manufacturer's instructions.Figure S1WT or 293T FBXO6Null were immunoblotted with the indicated antibodies.Immunocomplexes from either 293T FBXO6 Both FBXO6WT and FBXO6Null interacted with Cullin1, only FBXO6WT interacted with the DDOST and RPN1.(TIF)Click here for additional data file.


The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the “few articles - many proteins” phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments. Experiments and observations are the vehicles used by science to understand the world around us. In the field of molecular biology, we are increasingly relying on high-throughput, genome-wide experiments to provide answers about the function of biological macromolecules. However, any experimental assay is essentially limited in the type of information it can discover. Here, we show that our increasing reliance on high-throughput experiments biases our understanding of protein function. While the primary source of information is experiments, the functions of many proteins are computationally annotated by sequence-based similarity, either directly or indirectly, to proteins whose function is experimentally determined. Therefore, any biases in experimental annotations can get amplified and entrenched in the majority of protein databases. We show here that high-throughput studies are biased towards certain aspects of protein function, and that they provide less information than low-throughput studies. While there is no clear solution to the phenomenon of bias from high-throughput experiments, recognizing its existence and its impact can help take steps to mitigate its effect. Functional annotation of proteins is an open problem and a primary challenge in molecular biology today One concern surrounding the capture of functional data from articles is the propensity for high-throughput experimental work to become a large fraction of the data in the GO Consortium database, thus having a small number of experiments dominate the protein function landscape. In this work we analyzed the relative contribution of peer-reviewed articles describing all the experimentally derived annotations in UniProt-GOA. We found some striking trends, stemming from the fact that a small fraction of articles describing high-throughput experiments disproportionately contribute to the pool of experimental annotations of model organisms. Consequently we show that: 1) annotations coming from high-throughput experiments are overall less informative than those provided by low-throughput experiments; 2) annotations from high-throughput experiments are biased towards a limited number of functions, and 3) many high-throughput experiments overlap in the proteins they annotate, and in the annotations assigned. Taken together, our findings offer a picture of how the protein function annotation landscape is generated from scientific literature. Furthermore, due to the biases inherent in the current system of sequence annotations, this study serves as a caution to the producers and consumers of biological data from high-throughput experiments.The increase in the number of high-throughput experiments used to determine protein functions may introduce biases into experimental protein annotations, due to the inherent capabilities and limitations of high-throughput assays. To test the hypothesis that such biases exist, and to study their extent if they do, we compiled the details of all experimentally annotated proteins in UniProt-GOA. This included all proteins whose GO annotations have the GO experimental evidence codes EXP, IDA, IPI, IMP, IGI, IEP (See Methods for an explanation of GO evidence codes). We first examined the distribution of articles that are the source of experimentally validated annotations by the number of proteins they annotate. As can be seen in Single-throughput articles are those articles that annotate only one protein; low throughput articles annotate 2–9 proteins; moderate throughput articles annotate 10–99 proteins and high throughput articles annotate over 99 proteins. The results are shown in To better understand the consequences of such a distribution, we divided the annotating articles into four cohorts, based on the number of proteins each article annotates. To understand how the log-odds distribution affects our understanding of protein function, we examined different aspects of the annotations in the four article cohorts. Also, we examined in greater detail the top-50 high-throughput annotating articles. “Top-50 high throughput annotating articles” are the articles describing experimental annotations that are top ranked by the number of proteins annotated per article. An initial characterization of these articles is shown in Table S1 in contribution is the number of annotations by any given GO term in the top 50 articles divided by the number of annotations by that GO term in all of UniProt-GOA. For example, as seen in D. melanogaster, 88% of the annotations using the term “precatalytic splicosome” in articles experimentally annotating this species that are contributed by the top-50 articles.We examined the relative contribution of the top-50 articles to the entire corpus of experimentally annotated proteins in each species. Unsurprisingly, all the species found in the top-50 articles were either common model organisms or human. For each species, we examined the five most frequent terms in the top-50 articles. We then examined the contribution of this term by the top-50 articles to the general annotations of that species. The D. melanogaster and C. elegans where the dominant terms were from the Biological Process ontology, and in mouse, where “protein binding” and “identical protein binding” are from the Molecular Function Ontology. D. melanogaster's annotation for the top terms is dominated (over 50% contribution) by the top-50 articles.For most organisms annotated by the top-50 articles, the annotations were within the Cellular Component or Biological Process ontologies. Notable exceptions are The term frequency bias described here can be viewed more broadly within the ontology bias. The proteins annotated by the cohorts of single-protein articles, low-throughput articles, and moderate-throughput articles have similar ratios of the fraction of proteins annotated. Twenty-two to twenty-six percent of assigned terms are in the Molecular Function Ontology, and 51–57% are in the Biological Process Ontology and the remaining 17–25% are in the Cellular Component ontology. These ratios change dramatically with high-throughput articles (over 99 terms per article). In the high-throughput articles, only 5% of assigned terms are in the Molecular Function Ontology, 38% in the Biological Process Ontology and 57% in the Cellular Compartment Ontology, ostensibly due to a lack of high-throughput assays that can be used for generating annotations using the Molecular Function Ontology.How many of the top-50 articles actually annotate the same set of proteins? Answering this question will tell us how repetitive experiments are in identifying the same set of proteins to annotate. However, even when annotating the same set of proteins and within the same ontology, different experiments may provide different results, lacking consistency. Therefore, the annotation consistency was also checked. Repetition is given as C. elegans. Of course, a higher number of articles is expected to increase repetitive annotations simply due to increased sampling of the genome. However, the goal of this analysis is to present the degree of repetition, rather than to try to rank and normalize it. As an additional repetition metric, M. tuberculosis (14% in two articles). Taken together, these results show that there is repetition in choosing the proteins that are to be annotated in most model organisms using high-throughput assays, although the rate of this repetition varies widely.The results of the repetition analysis are shown in A. thaliana, 1941 proteins are annotated by 15 articles and 18 terms in the Cellular Component ontology. The mean maximum-consistency is 0.251. The highest mean consistency is for the annotation of 807 mouse proteins annotated in Cellular Component ontology with an annotation consistency 0.832. However, that is not surprising given that there are only three annotating articles, and two annotating terms. We omitted the ontology and organism combinations that were annotated by less than three articles or two GO terms, or both.Consistency analysis took place as described in Methods. The consistency measure is normalized on a 0–1 scale, with 1 being most consistent, meaning that all annotations from all sources are identical. A common assumption holds that while high-throughput experiments annotate more protein functions than low-throughput experiments, the former also tend to be more shallow in the predictions they provide. The information provided, for example, by a large-scale protein binding assay will only tell us if two proteins are binding, but will not reveal whether that binding is specific, will not provide an exact The results of both analyses are shown in only using high-throughput experiments? We have seen that high-throughput experiments annotate a large number of proteins, but still some 80% of experimentally determined proteins are annotated via medium-, low- and single-throughput experiments. Given the lower information-content of high-throughput experiments, it is important to know which organisms have a substantial fraction of the proteins experimentally annotated by high throughput studies only. To do so, we analyzed all species with more than 200 genes in the NCBI taxa database for the fraction of the genes that are exclusively annotated by high throughput studies. The results are shown in Of interest is the fraction of proteins that are exclusively annotated by high-throughput experiments. The question here is: from the experimentally annotated proteins in an organism, how much do we know of their function As can be seen, although the fraction of high-throughput annotated proteins is large, not many species are affected with a large fraction of proteins that are exclusively annotated by high-throughput studies. However, the few species that are affected are important study and model species. It is important to note that some redundancy due to isoforms, mutants and duplications may exist.assertion terms in which the nature of the assay is given. For example, an enzyme-linked immunosorbent assay (ELISA) provides quantitative protein data in vitro while an immunogold assay may provide the same information, and cellular localization information in situ. We manually assigned Evidence Codes Ontology (ECO) assertion and evidence terms to the top-50 articles. The assignment is shown in detail in Table S2 in The twenty GO evidence codes, discussed above, encapsulate the means by which the function was inferred, but they do not capture all the necessary information. For example, “Inferred by Direct Assay” (IDA) informs that some experimental assay was used, but does not say which type of assay. This information is often needed, since knowing which experiments were performed can help the researcher establish the reliability and scope of the produced data. RNA, used in an RNAi experiment does not traverse the blood-brain-barrier, meaning that no data from the central nervous system can be drawn from an RNAi experiment. The Evidence Code Ontology, or ECO, seeks to improve upon the GO-attached evidence codes. ECO provides more elaborate terms than “Inferred by Direct Assay”: ECO also conveys which assay was used, for example “microscopy” or “RNA interference”. In addition to evidence terms, the ECO ontology provides The most frequent ECO term used is ECO:0000160 “protein separation followed by fragment identification evidence”: this fits the 27 papers that essentially describe mass-spectrometry studies. Consequently this means that the assignment procedure is limited to the cellular compartments that can be identified with the fractionation methods used. So while Cellular Component is the most frequent annotation used, fractionation and mass-spectrometry is the most common method used to localize proteins in subcellular compartments. A notable exception to the use of fractionation and MS for protein localization is in the top annotating article C. elegans, D. melanogaster and one in C. albicans. RNAi experiments use targeted dsRNA which is delivered to the organism and silences specific genes. Typically the experiments here used libraries of RNAi targeted to the whole exome . Several types of studies fall under this ECO. Those include microscopy, RNAi, some of the mass-spectrometry studies that used microscopy, and a yeast-2-hybrid study. As imaging information is used in a variety of studies, this ECO term is not informative of the chief method used in any study, but rather the importance of imaging assays in a variety of methods. The third most frequent experimental ECO term used was “Cell fractionation evidence” which is closely associated with the top term, “Protein separation followed by fragment identification evidence.” The fourth and fifth most frequent ECO term used were “loss-of-function mutant phenotype evidence” (ECO:0000016) and “RNAi evidence” (ECO:000019). These two terms are also closely associated, in RNAi whole-genome gene knockdowns in We have identified several annotation biases in GO annotations provided by the GO consortium. These biases stem from the uneven number of annotations produced by different types of experiments. It is clear that results from high-throughput experiments contribute substantially to the function annotation landscape, as up to 20% of experimentally annotated proteins are annotated by high-throughput assays. At the same time, high throughput experiments produce less information per protein than moderate-, low- and single- throughput experiments as evidenced by the type of GO terms produced in the Molecular Function and Biological Process ontologies. Furthermore, the number of total GO terms used in the high-throughput experiments is much lower than that used in low and medium throughput experiments. Therefore, while high throughput experiments provide a high coverage of protein function space, it is the low throughput experiments that provide more specific information, as well as a larger diversity of terms.C. elegans and A. thaliana studies comprise 36 of the top-50 annotating articles, or 72%.We have also identified several types of biases that are contributed by high throughput experiments. First, there is the enrichment of low information-content GO terms, which means that our understanding of the protein function as provided by high-throughput experiments is more limited than that provided by low-throughput experiments. Second, there is the small number of terms used, when considering the large number of proteins that are being annotated. Third is the general ontology bias towards the cellular component ontology and, to a lesser extent, the Biological Process ontology: there are very few articles that deal with the Molecular Function ontology. These biases all stem from the inherent capabilities and limitations of the hight-throughput experiments. A fourth, related bias is the organism studied: taken together, studies of We have discussed the information loss that is characteristic of high-throughput experiments, as shown in Taken together, the annotation trends in high-throughput studies affect our understanding of protein function space. This, in turn, affects our ability to properly understand the connection between predictors of protein function and the actual function - the hallmark of computational function annotation. As a dramatic example, during the 2011 Critical Assessment of Function Annotation experiment To remedy the bias created by high throughput annotations, the provenance of annotations should be described in more detail by curators and annotation software. Many function annotation algorithms rely on homology transfer as part of their pipeline to annotate query sequences http://github.com/FriedbergLab/Uniprot-Bias/ and on http://datadryad.org (the latter upon acceptance).We used the UniProt-GOA database from December 2011. Data analyses were performed using Python scripts. The following tools were used in the analyses: Biopython et al. (2012). Briefly, this procedure consists of six steps which include sequence curation, sequence motif analyses, literature-based curation, reciprocal BLAST evidence code which the curator assigns to explain how the association between the protein and the GO term was made. Experimental evidence codes include such terms as: Inferred by Direct Assay (IDA) which indicates that “a direct assay was carried out to determine the function, process, or component indicated by the GO term” or Inferred from Physical Interaction (IPI) which “Covers physical interactions between the gene product of interest and another molecule.”  Computational evidence codes include terms such as Inferred from Sequence or Structural Similarity (ISS) and Inferred from Sequence Orthology (ISO). Although the evidence in computational evidence codes is non-experimental, the proteins annotated with these evidence codes are still assigned by a curator, rendering a degree of human oversight. Finally, there are also computational, non-experimental evidence codes, the most prevalent being Inferred from Electronic Annotation (IEA) which is “used for annotations that depend directly on computation or automated transfer of annotations from a database”. IEA evidence means that the annotation is electronic, and was not made or checked by a person. Different degrees of reliability are associated with different evidence codes, with experimental codes generally considered to be of higher reliability than non-experimental codes. (For details see: http://www.ebi.ac.uk/GOA/ElectronicAnnotationMethods).Proteins in UniProt-GOA are annotated with one or more GO terms using a procedure described in Dimmer We used two methods to quantify the information given by GO terms. First we used edge counting, where the information contained in a term is dependent on the edge distance of that term from the root. The term “catalytic activity”(one edge distance from the ontology root node) would be less informative than “hydrolase activity” (two edges) and the latter will be less informative than “haloalkane dehalogenase activity” (five edges). We therefore counted edges from the ontology root term to the GO term to determine term information. The larger the number of edges, the more specific -and therefore informative- is the annotation. In cases where several paths lead from the root to the examined GO term, we used the minimal path. We did so for all the annotating articles split into groups by the number of proteins each article annotates.While edge counting provides a measure of term-specificity, this measure is imperfect. The reason is that each of the three GO ontologies is constructed as a directed acyclic graph (DAG) where different areas of the GO DAG have different connectivities, and terms may have different depths unrelated to the intuitive specificity of a term. For example “D-glucose transmembrane transporter activity”, (GO:0055056) is 10 terms deep, while “L-tryptophan transmembrane transporter activity”, (GO:0015196) is fourteen terms deep. It is hard to discern whether these differences are meaningful. For this reason, information content, the logarithm of the inverse of the GO term frequency in the corpus, is generally accepted as a measure of GO term information content To examine annotation consistency, we employed the following method: given a protein maximum annotation consistency for protein The number of total annotations for a protein in an ontology is S. cerevisiae is annotated four times by three articles using the Cellular Component ontology, as shown in mitochondrion, 2), divided by the total number of annotations, 4: 0.5.For example, the protein “Oleate activated transcription factor 3” (UniProtID: P36023) in When choosing a measure for annotation consistency, we favored a simple and interpretable measure. We therefore examined identity among leaf terms only, rather than use a more complex comparison of multiple subgraphs in the GO ontology DAG (Directed Acyclic Graph). Doing so without manual curation is unreliable, and may skew the perception of similarity Text S1Table S1: The top 50 annotating articles. N: article rank; Proteins: number of proteins annotated in this article; Annotations: number of annotating GO terms; Species: annotated species; ref. annotating article; MFO/BPO/CCO: number of proteins annotated in the Molecular Function, Biological Process and Cellular Component ontologies, respectively. Table S2: The Top-50 studies and the ECO terms we have assigned to them. PMID: Articles' PubMed ID; ECO terms/ECO ID's: terms and ID's we assigned to the articles. Table S3: ECO terms were assigned by us to the top-50 annotating papers. The table entries are ranked by the frequency of the assignments, i.e. 27 papers are assigned with term ECO:0000160, 21 were assigned ECO:0000004, etc. Entries in boldface are for computational methods, which were used in many papers in combination with experimental methods to assign function. Table S2 lists the ECO terms.This file contains supplementary tables. (PDF)Click here for additional data file.


Many functionally important proteins in a cell form complexes with multiple chains. Therefore, computational prediction of multiple protein complexes is an important task in bioinformatics. In the development of multiple protein docking methods, it is important to establish a metric for evaluating prediction results in a reasonable and practical fashion. However, since there are only few works done in developing methods for multiple protein docking, there is no study that investigates how accurate structural models of multiple protein complexes should be to allow scientists to gain biological insights.We generated a series of predicted models (decoys) of various accuracies by our multiple protein docking pipeline, Multi-LZerD, for three multi-chain complexes with 3, 4, and 6 chains. We analyzed the decoys in terms of the number of correctly predicted pair conformations in the decoys.fpair and propose to use it for evaluation of the accuracy of multiple protein docking.We found that pairs of chains with the correct mutual orientation exist even in the decoys with a large overall root mean square deviation (RMSD) to the native. Therefore, in addition to a global structure similarity measure, such as the global RMSD, the quality of models for multiple chain complexes can be better evaluated by using the local measurement, the number of chain pairs with correct mutual orientation. We termed the fraction of correctly predicted pairs (RMSD at the interface of less than 4.0Å) as  An essential part of protein structure prediction is to establish methods to evaluate computed models. For single protein structure prediction, the research community, which is partly driven by the Critical Assessment of Protein Structure Prediction (CASP), often uses the global RMSD as well as its variant, the GDT-TS score . On the Earlier works on multiple protein docking used the global RMSD for evaluating the model accuracy -7. Of cofpair (fraction of pairwise hits) that accounts for the proportion of correct pairwise predictions among all chain pairs in a whole multiple chain complex.Here, we generated decoys of multiple protein complexes using Multi-LZerD ,9, a mulWe used Multi-LZerD ,9 to conA conformation of a multiple chain complex can be uniquely specified by denoting which pairwise docking decoys to combine from the pool of the pre-computed pairwise decoys by LZerD. Multi-LZerD explores different conformations of the whole complex by altering pairwise decoys using a genetic algorithm . The fitfpair value, which is defined as the fraction of pairwise combinations that are considered hits, from the total pairwise combinations:Three protein complexes of different number of chains, 3 (PDB ID: 1A0R), 4 (1NNU), 6 (1I3O) were used in this study. These decoys are classified by the RMSD into 6 classes, 0-4Å, 4-8Å, 8-12Å, 12-16Å, 16-20Å, and 20Å or larger. Additionally, for each decoy, we computed the iRMSD for all pairs of chains included in the complex by comparing each pair in the decoy to the corresponding pair in the native structure. If the iRMSD is lower than 4Å the pair is considered a hit. An iRMSD of 4Å is a criterion of acceptable prediction for pairwise docking used in the CAPRI. Using the pairwise hit count in a whole complex decoy we calculate the P is the set of all pairwise combinations. I is the indicator function that represents 1 if the predicate is true  and 0 otherwise.where fpair is 2/3 = 0.67 for the predicted complex structure.For example, a 3-chain complex with chains A, B, and C has 3 pairwise combinations, A-B, A-C, and B-C. A-B from a predicted structure is superimposed onto A-B structure taken from the native complex structure, without taking the chain C into account. The same process is repeated for A-C and B-C. If 2 out of these 3 pairwise combinations are hits then Figure Figure Figure In Figure fpair for assessing the fraction of correctly predicted pairs among all pairs in a whole complex. By using fpair one can distinguish models that have partially accurate subcomplexes from models with the same global RMSD but do not contain any correctly predicted pairs. fpair will complement the traditional global measurements like RMSD and fnat for evaluating quality of models for multiple protein complexes.We have shown that, while the global C-α RMSD is a clear indication of high quality predictions for multiple protein docking, a predicted structure with a higher RMSD should not be simply discarded as unsuccessful prediction since in many cases such decoys contain correctly predicted subcomplexes. We propose a measure named The authors declare that they have no competing interests.JER developed the multiple protein docking prediction method, Multi-LZerD, performed the computational experiments and wrote the manuscript draft. DK conceived the study and participated in its design and coordination, as well as drafting and finalizing the manuscript. All authors read and approved the final manuscript.


Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity.ρf, as ρf= (density)ρ*(modularity)ρ1-, and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S.cerevisiae show that compared with the results of fitness equal to density (ρ = 1) or equal to modularity (ρ = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0<ρ<1). Compared with the results of seven competing protein complex detection methods  in S.cerevisiae and E.coli, LF-PIN outperforms other seven methods in terms of matching with known complexes and functional enrichment. Moreover, LF-PIN has better performance in identifying protein complexes with low density or with low modularity.To identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, By considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity. Identifying protein complex is important in understanding the cellular organizations and functional mechanisms. However, the experimental methods to discover protein complexes are costly and time-consuming. Fortunately, with the development of high-throughput techniques such as yeast-two-hybrid , mass spDensity-based methods, such as CPM ,16, CP-DS.cerevisiae which are provided by Pu S et al. in , , are all higher than those identified by other seven algorithms. All these mean that LF-PIN is more effective for identifying significant proteins complexes than other algorithms in S.cerevisiae.As shown in Table E.coli. The percentage of insignificant complexes identified by LF-PIN is lower than those identified by other seven algorithms. On the contrary, compared with the results of other seven algorithms, the percentage of significant complexes identified by LF-PIN is improved 44% to 133%. The statistical results of Table From Table S.cerevisiae show that compared with considering only density (ρ = 1) or only modularity (ρ = 0), LF-PIN has better performance when considering both density and modularity (0<ρ<1). To compare algorithm LF-PIN with other protein complexes detection methods, we apply LF-PIN and other seven competing algorithms, including CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, and NFC, to the protein interaction network of S.cerevisiae and E.coli. The experimental results in both S.cerevisiae and E.coli show that LF-PIN identifies much more significant protein complexes and generates much less insignificant protein complexes than other algorithms. When matching with the known protein complexes of S.cerevisiae, for each overlapping score's threshold, LF-PIN has the highest percentage of matched predicted complexes and the highest F-score. These quantitative comparisons reveal that our algorithm LF-PIN outperforms the other previous competing algorithms in identifying protein complexes. Moreover, algorithm LF-PIN has good performance for identifying protein complexes with low density or low modularity. When matching with the known protein complexes of S.cerevisiae with density less than 0.5, the percentage of matched predicted complexes and the F-score of LF-PIN are both higher than those of other seven algorithms, especially to the four density-based algorithms, CMC, Core-Attachment, CPM and DPClus. When matching with the known protein complexes of S.cerevisiae with modularity less than 0.3, the percentage of matched predicted complexes and the F-score of LF-PIN are also both higher than those of other seven algorithms, especially to the modularity-based algorithms NFC.In the post-genome era, one of the most important works is to discover the protein complexes with various density and modularity. In this paper, we propose a novel fitness function by considering both density and modularity and develop a fitness-based local search algorithm, named LF-PIN, to identify protein complexes with different density and modularity in PPI network. By tuning the value of parameter ρ in the fitness function, we can adjust the importance of density and modularity in the fitness. Experimental results in The authors declare that they have no competing interests.JR obtained the protein-protein interaction data, essential proteins and Orthologous data. JR and JXW designed the new method, LF-PIN. JR and ML analyzed the results. JR, JXW and LSW drafted the manuscript together. All authors have read and approved the manuscript.S.cerevisiae. Description: This file provides the input PPI network of LF-PIN for S.cerevisiae. It has three columns, 'Protein A', 'Protein B', and 'Weight'. 'Protein A' and 'Protein B' are PPI's two proteins and 'Weight' is PPI's weight. The original un-weighted PPI network of the input PPI network is downloaded from DIP database (version 20100614). To generate the input PPI network, we first removed all self-connecting interactions and repeated interactions, then change the DIP ID of all proteins to ORFname by tool ID Mapping (http://www.uniprot.org/mapping/), finally generate the input PPI network by pre-processor. Here, the value of α is set as 0.2.The input PPI network of LF-PIN for Click here for fileE.coli. Description: This file provides the input PPI network of LF-PIN for E.coli. It has three columns, 'Protein A', 'Protein B', and 'Weight'. 'Protein A' and 'Protein B' are PPI's two proteins and 'Weight' is PPI's weight. The original un-weighted PPI network of the input PPI network is downloaded from DIP database (version 20101010). To generate the input PPI network, we first removed all self-connecting interactions and repeated interactions, then change the DIP ID of all proteins to UniProtKB ID by tool ID Mapping, finally generate the input PPI network by pre-processor. Here, the value of α is set as 0.2.The input PPI network of LF-PIN for Click here for fileS.cerevisiae. Description: This file provides the known protein complexes of S.cerevisiae which is obtained in [The known protein complexes of ained in .Click here for fileThe original data of Figure Click here for file