The Red Blood Cell (RBC) is a metabolically-driven cell vital for processes such a gas transport and homeostasis. RBC possesses at its surface exposing antigens proteins that are critical in blood transfusion. Due to their importance, numerous studies address the cell function as a whole but more and more details of RBC structure and protein content are now studied using massive state-of-the art characterisation techniques. Yet, the resulting information is frequently scattered in many scientific articles, in many databases and specialized web servers. To provide a more compendious view of erythrocytes and of their protein content, we developed a dedicated database called RESPIRE that aims at gathering a comprehensive and coherent ensemble of information and data about proteins in RBC. This cell-driven database lists proteins found in erythrocytes. For a given protein entry, initial data are processed from external portals and enriched by using state-of-the-art bioinformatics methods. As structural information is extremely useful to understand protein function and predict the impact of mutations, a strong effort has been put on the prediction of protein structures with a special treatment for membrane proteins. Browsing the database is available through text search for reference gene names or protein identifiers, through pre-defined queries or via hyperlinks. The RESPIRE database provides valuable information and unique annotations that should be useful to a wide audience of biologists, clinicians and structural biologists.Database URL:http://www.dsimb.inserm.fr/respire Blood is essential to life for (i) the transportation of oxygen and carbon dioxide alongside metabolites, cells and nutrients, (ii) for homeostasis by participating in temperature regulation and pH maintenance, and (iii) for blood vessel protection from injury via platelet aggregation. In volume, blood is composed of about 55% plasma containing water, proteins, electrolytes, glucose and amino acids and about 45% of red blood cells (RBCs), known as erythrocytes. The red blood cells derive from hematopoietic stem cells that undergo several differentiation steps ,2 leadinTable 1). In these databases, gene or protein-driven, sometimes genetic, clinical or genomic information is provided. It is however difficult to gather a cell-centric view of the RBC protein content in different conditions, for instance how variants of a given protein will be linked to new RBC group antigens definitions [Repository of enriched structures of proteins involved in the red blood cell environment (RESPIRE).Many attempts have been made to assemble and organize the existing knowledge on genes and proteins important in RBC in open/free dedicated databases  experimentally determined three-dimensional structures, and (v) binding partners [http://www.labex-grex.com)Many RBC proteomics studies were performed to list the proteins available in the final stages of the red blood cell differentiation ,6–11. Thpartners . For mempartners   a BLAST  search wThe domain decomposition was computed using InterProScan . The domtarget) were initially extracted from UNIPROT. Since PDB numbering can be different from the protein sequence in UNIPROT, each PDB is split by chains and renumbered to UNIPROT numbering using ProDy [i.e. a large structural coverage (see below), the secondary structure assignment of the most representative structure is analysed with DSSP [Structural annotations for each protein entry (ng ProDy  to ensurith DSSP . When thith DSSP .templates” in the following) in Protein Data Bank  using Bnk  the coverage on the target sequence (%cov), which is defined as the percentage of amino acids aligned between the template and the target sequences, (ii) the sequence identity (%id) between the target and the template sequence once aligned and (iii) the number of experimental structures needed to obtain a complete model.The categories detailed in ab initio fragment-based assembly method was applied. For soluble proteins or single membrane-spanning proteins, MODELLER software [ab initio category, Rosetta [Thresholds were adapted to account for the differences in sequence characteristics between soluble and membrane proteins due to the different environment in which they are embedded. For comparative modelling , the thrsoftware ,54 was usoftware . For thesoftware  was used Rosetta  and Rose Rosetta  were pri Rosetta . Each mo Rosetta . Dependiin-house routines based on bioperl [http://getbootstrap.com/), jQuery (http://jquery.com/) and BioJS [The database is stored in MySQL version 5.5.32 and tables where created using the ORM implementation as available in the DJANGO framework. Data analysis, parsing and import were performed with  bioperl  or biopy bioperl . The prond BioJS . Interacnd BioJS . Interacnd BioJS .see above). Data gathering is performed regularly from upstream sources according to their specific release schedule and updated monthly in RESPIRE (Table 3). In addition to the upstream databases stored locally, each protein contains on average 35MB of processed data and predictions, for a total of 80GB. Altogether the computing time dedicated to add structural information and predictions on the database represents multiple months on a dedicated cluster of 200+ cores.The database is architected around 10 main tables filled in into two steps . Half of proteins have  experimental structural data and more than a quarter of proteins have a model produced exclusively for RESPIRE. More than 1800 diseases are linked to RBC proteins from 3100 OMIM entries. Nearly 8500 gene ontology functions divided in three classes allow to regroup proteins . More than 380 proteins are annotated as integral or single-pass transmembrane proteins. These statistics are regularly updated and presented in detail on the “Statistics” tab in RESPIRE, with dedicated links to each category to restrict the protein search. According to the structural content available for each protein, 376 proteins were classified in the experimental category , 595 proteins belong to the comparative modelling category, 496 proteins belong to the threading category and 306 do not possess any structural content and therefore need to be modelled using Fig 1A).For a given protein entry, the first tab displays the protein function as annotated in UNIPROT and when this information was updated in UNIPROT . A membrane annotation is indicated if this protein is either referenced (i) as a Single-Pass or Multiple-Pass protein in UniProt, (ii) as a plasma membrane protein in Gene Ontology, or (iii) if the TOPCONS prediction has detected at least one transmembrane segment.The sequence frame contains the protein sequence enriched with a color-coded conservation index , and the DSSP secondary structure  assignmeFig 1C).In the domains tab, the protein decomposition into subdomains performed using InterProScan is displayed. Subdomains are assembled into coloured sections with hyperlinks to their corresponding families in InterPro  and hoveFigs 2).When either a protein structure or a model is available, the structure frame presents the three-dimensional coordinates in an interactive window .RBC proteins may interact with many proteins to fulfil their function; these interactions are assembled from various tissues in BioGrid . To inteFig 1F).Many inferences between proteins are derived automatically from data-driven knowledge associations as performed by the Gene Ontology Consortium ,66. ThisFig 1G).According to their prevalence in populations, the knowledge concerning some RBC diseases such as sickle-cell disease is widespread in the community . For morFig 1H). These expression data were carefully determined by our partners of the GR-Ex consortium [The data presented in our database mainly present the content of the latest stages of the RBC differentiation. In order to get a broader overview of the protein expression levels during the RBC maturation process, an interactive diagram is available under the Expression tab (nsortium .https://feedly.com) or Reeder (https://reederapp.com/) to be informed rapidly when a modification happened .Many entries in the database come from external databases, it is therefore important to track the changes appearing in the protein card. The History tab references all the modification of the protein entry in the database. It is also possible to register to the protein feed using tools such as Feedly .This condensed view allows also interactively to display and retrieve a PDB file or the model produced for RESPIRE. When the user clicks on a pdb entry, its title and reference are indicated, when a model is selected, the details of its prediction are provided . The results of these queries can be retrieved in Comma Separated Value (CSV) format for further processing off-line in any spreadsheet editor . It is also possible to browse the database by clicking on hypertext links provided at various places, like for instance clicking on a given gene ontology category under the Genetics tab to retrieve all proteins involved in a specific process . It is also possible to use the “search as you type” box to search by UNIPROT accession id or protein name .Complex queries are also available in a dedicated menu for specific categories, namely proteins with a model (RESPIRE), proteins at the cell membrane (as defined in UNIPROT), protein defining blood group antigens (ISBT) or proteins associated to a disease (OMIM) ”, (b) Protein name “transport” and (c) Protein Size (AA) “200–800”, there will be 14 answers in RESPIRE related to a protein containing the “transport” keyword in its name without models in the RBC membrane. Again, this query result can be downloaded as a CSV file.For more complex queries, a more advanced form allows the combination of criteria. In this form, selecting (a) These multiple search strategies are important to retrieve a protein list concerning close but not overlapping queries. As an example, the membranous status of a protein is complex: (i) many proteins can have a transient membranous contact for maturation or trafficking within the cell, (ii) even by limiting only the definition of membranous proteins to the cell membrane localization, it is still possible to classify a protein as an integral or as a single-pass membrane protein. This complexity of membrane definition including localizations and/or organisations is assembled differently in different databases, giving different results for close queries. For instance, for membrane proteins, 384 proteins are membranous in RESPIRE if the “single-pass or multiple-pass membrane” annotation of UNIPROT is considered while in Gene Ontology, the “plasma membrane” annotation (GO:0005886) leads to 545 entries. The possibility to address both queries in RESPIRE is the best way for tackling these difficulties in a flexible manner.RESPIRE is a combination of upstream data references and of specifically produced protein models. Our main objective is to propose a high quality of service with regularly updated information. Importantly, any expert in RBC is welcomed to contribute in RESPIRE evolution and update. In this perspective, a contact form with pre-defined subjects is available if a user is willing to further enrich a given protein entry or report problems. The “Collaboration” subject is dedicated to more complex demands such as the incorporation of new data in the database, the additions of links to or the processing of other reference databases. The “Feature Request” is mostly for database updates on specific subjects like the addition of pre-defined queries, demands for updating a given protein model. The “Bug report” subject is mostly to pinpoint specific problems for a given entry. If no demand can be classified with previous subjects, the “General” subject is open to any remark or contribution. Each request will be processed regularly, and responses to demands should be answered within a week. Depending on the amount of work required, these demands will be answered rapidly or shall be incorporated in the next RESPIRE release.The importance of RBCs in vital processes has driven the extensive characterization of protein abundance and expression level using large-scale studies. Up to now, it is difficult to assemble protein information linking these experiments and reference databases. To address these needs, we have set up a new database called RESPIRE devoted to red blood cell proteins, starting from a list of proteins available from the literature. The RESPIRE database combines sequence, structure and functional annotations altogether with original data obtained using up-to-date bioinformatics methods ,69,71–72In the future, we shall continue to enrich this database with experimental results as they become available in the literature but also with manual curation involving biologist members of the research consortium GR-Ex. This curation effort is expected to focus on inherited disease such as sickle-cell disease or Diamond-Blackfan anemia, or to focus on infectious diseases such as malaria. We will expose convenient access to these data to ease cross-referencing in more general-purpose databases.The database is freely accessible, with an unrestricted access to data for download and external analysis. The evolution of the database is detailed per protein in a dedicated tab, and globally in a dedicated page.


Numerous proteins are molecular targets for drug action and hence are important in drug discovery. Structure-based computational drug discovery relies on detailed information regarding protein conformations for subsequent drug screening in silico. There are two key issues in analyzing protein conformations in virtual screening. The first considers the protein’s conformational change in response to physical and chemical conditions. The second is the protein’s atomic resolution reconstruction from X-ray crystallography or nuclear magnetic resonance (NMR) data. In this latter problem, information is needed regarding the sample’s position relative to the source of X-rays. Here, we introduce a new measure for classifying protein conformational states using spectral representation and Wigner’s D-functions. Predictions based on the new measure are in good agreement with conformational states of proteins. These results could also be applied to improve conformational alignment of the snapshots given by protein crystallography. Proteins are flexible biomolecules whose conformations change in response to physical and chemical conditions. However, protein functions depend on their specific conformation. A protein’s 3D conformation is determined by the primary sequence of amino acids . KnowingIn this paper, we introduce a simple mathematical measure to classify protein conformations, which is defined in reciprocal space and uses the corresponding structure factors of the given protein. Reciprocal space has advantages over real space since we can compare two proteins with different numbers of atoms using the same dimensions in reciprocal space, i.e., reciprocal space is independent of protein sizes . This caTo prove the reliability of our method based on MED we examine below three different synthetic datasets. The first dataset is a simple shape-simulated dataset of non-structured 12-mer peptides, the second is a simulated protein dataset and the third dataset is a simulated X-ray diffraction dataset of ADK.Simple shape dataset. To show the robustness and reliability of MED we first use a simulated conformational dataset for shapes ranging from an open to a closed conformation, and two examples out of 61 cases analyzed are shown in Protein dataset. The MED values obtained confirm the prediction regarding the corresponding conformations. We now perform the second test by examining conformations between quasi-real data to the protein structures, and use the data generated by MOE software of Chemical Computing Group Inc. (available online: http://www.chemcomp.com/). We have created a library of 4-mer peptides for surfaces with different characterization, which is the starting point for finding longer (more closed) peptides, which can recognize and bind to a given surface with high affinity and specificity. These data have been obtained by molecular dynamics (MD) simulations for 4-mer peptide sequences. We have first computed structure factors and then produced the distance matrix in order to find the MED for all 44 conformations of non-structured 12-mer peptides . For example, we see that the MED for s14-3 is Diffraction snapshots dataset. One of the main issues in the reconstruction of the biological systems from X-ray scattering snapshots is that some information, which is known in crystallography, is unknown to the biological system, e.g., the structure’s orientation. Conversely, information exists in the biological system’s reconstruction, e.g., conformation, which does not exist in crystallography, since only one sample may be used in crystallographic measurements while a biological system such a protein may involve many samples in a statistical ensemble. Hence, a large number of XFEL snapshots are combined with different conformations and unknown orientations. Thus, before reconstruction, we should cluster these data and then use the reconstruction methods that use X-ray diffraction patterns such as crystallography. E. coli in the open state (Protein Data Bank entry: 4AKE) were placed in a spherical droplet of water and simulated using NAMD software [In the third test of our method, we use the dataset simulated by MD of the unfolding of ADK , which csoftware . Figure To perform the required computations, one needs several hundred megabytes of memory and a computation time of about 950 s for all of 12,500 snapshots of this dataset by using a CPU-i3 with 4GB RAM. The three tests show that MED is a reliable and low-computational cost measure for the prediction of conformational states of proteins. It is especially useful to cluster these patterns before having the protein’s structure obtained from X-ray diffraction in the dataset. In the time-resolved X-ray crystallography methods, for structure reconstruction we need to have the crystal’s orientation in crystallographic measurements. In the corresponding biological assays, due to significant effects of radiation damage one needs to use replicate samples . Hence, Working in reciprocal space with structure factors has substantial advantages such as dependence on the topology and geometry of proteins and independence of the protein’s size . Using tThe weighted similarity value (WSV) is an alternative to root-mean-squared deviation (RMSD) that allows a similarity comparison between two proteins ,33. WSV It has been shown ,36 that e factor , hence Cof atoms . In this paper, we use another advantage of working in reciprocal space, i.e., replacing a time series by spectral representation in reciprocal space as a set of eigenvectors. This is a result of the Fourier transform of the time derivative of a function, which is equal to the spectral of the Fourier transform of the function:ith protein’s conformational state. If Here The maximum eigenvalue plays an important role in data analysis ,39,40,41


Characterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology.We present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call “drug-protein interaction signatures” from the integration of large-scale heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extracting informative drug-protein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which is made possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers.Our method infers a set of drug-protein interaction signatures consisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules. We argue the these signatures are biologically meaningful and useful for predicting unknown drug-protein interactions and are expected to contribute to rational drug design. Target proteins of drug molecules are classified into a primary target and off-targets. The former is the desired target, whereas the latter could lead to adverse drug reactions  or unexpSeveral computational methods have been presented for predicting drug-protein interactions (or compound-protein interactions) from chemogenomic and pharmacogenomic viewpoints on a large-scale. The basic idea behind the chemogenomic approach is that chemically similar drugs are expected to interact with similar proteins, with which the similarity of drugs and proteins are defined based on their side-effects and the amino acid sequences, respectively –8. On thThe characterization of drug-protein interaction networks with biological characteristics has become a challenging problem in modern pharmaceutical science toward better understanding of poly-pharmacology. It is hypothesized that polypharmacology is involved in various features of drugs and target proteins  and complicated associations between the heterogeneous features.A variety of feature extraction methods have recently been proposed for automatically characterizing drug-protein interactions. A data mining method was proposed for extracting molecular substructure pairs appearing frequently in interacting drug-target pairs . MachineWe present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call “drug-protein interaction signatures”. We develop a new efficient algorithm for extracting informative drug-protein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which is made possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers. In the results, our method infers a set of drug-protein interaction signatures consisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules. We argue that these signatures are biologically meaningful and useful for predicting unknown drug-protein interactions. To the best of our knowledge, this is the first report on characterizing a large-scale drug-protein interaction network with various biological features of drugs and proteins in an integrative framework. The drug-protein interaction signatures comprehensively inferred with our method are expected to contribute to rational drug design.We got the information on drug-protein interactions from five databases: ChEMBL , KEGG 2, DrugBanchemical profile.We described drug chemical structures by 17,017 chemical substructures using the KEGG Chemical Function and Substructures (KCF-S) descriptor . We reprADR profile.We obtained the information about adverse drug reactions (ADRs) from the public release of the adverse event reporting system (AERS) of the US Food and Drug Administration (FDA) . We deriFinally, we constructed an integrative feature vector of each drug by concatenating the chemical and the ADR profiles into a single one. The dimension of the resulting feature vector of each drug was 27,560.We obtained functional domains, biological pathways, and pathway modules (compactly clustered pathways) about proteins from the KEGG  and the domain profile. Based on 270 KEGG pathway maps, we represented each protein by a 270 dimension binary vector where the presence or absence of the involvement in a biological pathway is coded as 1 or 0. The resulting vector is referred to as a pathway profile. Based on 107 KEGG pathway modules, we represented each protein by a 107-dimension binary vector where the presence or absence of the involvement in a pathway module is coded as 1 or 0. The resulting vector is referred to as module profile.Based on 2678 PFAM domains, we represented each protein by a 2678-dimension binary vector where the presence or absence of a functional domain is coded as 1 or 0. The resulting vector is referred to as Finally, we constructed an integrative feature vector of each protein by concatenating the domain, pathway, and module profiles into a single profile. The dimension of the resulting feature vector of each protein was 3055.We address the problem of extracting features characterizing drug-protein interaction networks in the framework of supervised classification.C be a drug (or a drug candidate compound) and let P be a target protein (or a target candidate protein). We represent a drug-protein pair  as a high dimensional feature vector Φ and present a linear function, f=wTΦ, whose output is used to predict whether a  is an interacting pair or not. The weight vector w is estimated such that each drug-protein pair is correctly classified into the interaction class (positive class) or non-interaction class (negative class) based on the training set.Let Φ corresponds to an element of w, effective features can be selected by extracting highly weighted features. However, the performance of the linear model depends heavily on the feature vector design.An advantage of the linear model is that one can interpret features effective for predictions from learned models. Since each element in C is defined as a D-dimension binary vector:We represent each drug-protein pair as a high dimension feature vector by taking the tensor product of a drug profile and protein profile. The representation is similar to that in previous studies , 16. TheΦ(C)=T,ci∈{0,1}, i=1,...,D. The profile of a P is defined as a D′-dimension binary vector: pi∈{0,1}, i=1,...,D′. We compute the tensor product between a drug profile Φ(C) and protein profile Φ(P), and define a feature vector Φ as follows: Φ is composed of all possible products between elements in Φ(C) and those in Φ(P). The resulting feature vector is a D×D′-dimension binary vector, i.e., fingerprint, for encoding cross-integrated biological features. This is referred to as a “tensor-product fingerprint”.where Φ(C) was a 27,560-dimension binary vector, and Φ(P) was a 3055-dimension binary vector. Thus, the tensor-product fingerprint Φ of each drug-protein pair is a 84,195,800-dimension binary vector.In this study, Φ(C) and Φ(P) into a single feature vector as Φ=(Φ(C)T,Φ(P)T)T  and (k−1)-th item S[k−1] as [k]−S[k−1]) and keep the results in a new set S′. We can recover S by cumulatively adding the items in S′.Figure succinct data structures that achieve space-efficient representations of data structures while preserving the property of fast operations. The first one is a variable-length array for compactly representing fingerprints. The S′ is represented by two bit strings Rij and Pij which are indexed by rank/select dictionary, i.e., a succinct data structure for bit strings. We can randomly access any element in S′ in O(1) time by using fast operations in the rank/select dictionary [VLA.We constructed our two space-efficient representations of fingerprints by leveraging the idea behind ctionary . We refeSUCTRIE.The second one is a type of succinct trie for representing fingerprints. The trie is a data structure for strings, and it is also practical for representing fingerprints. A standard point-based implementation of trie consumes a huge amount of memory, resulting in limited scalability. Alternatively, we present a compact representation of trie by using a succinct data structure called LOUDS . We can We applied the proposed method (L1LOG-tensor) to extract drug-protein interaction signatures from drug profiles  and protein profiles , based on a large-scale drug-protein interaction network. Each signature is the association between a drug feature and protein feature, where two features in the same signature are thought of as being associated in terms of drug-protein interactions. The results of all extracted drug-protein interaction signatures are presented in the supplemental material .L1LOG-tensor extracted 105,684 signatures, while L2LOG-tensor extracted 7,843,218 signatures. Note that the number of all possible combinations of drug features and protein features is 84,195,504. The number of signatures from our L1LOG-tensor method was much less than that of L2LOG-tensor, due to the sparsity induced by L1-regularization. This makes it easier to analyze the extracted drug-protein interaction signatures for biological interpretation, so we focused on analyzing the results from L1LOG-tensor below.Figure We constructed biological interpretations for the drug-protein interaction signatures extracted with L1LOG-tensor. We give only two examples due to space limitation. The result of all analyzed signatures and the figures/tables are presented in the supplemental material .Figure Table Figure Figure If the extracted signatures are biologically meaningful in terms of drug-protein interactions, they need to have good generalization to predict drug-protein interactions.We tested five feature extraction methods: L1LOG-tensor, L2LOG-tensor, L1LOG-concat, L2LOG-concat, and L1LOG-LIBLINEAR-tensor on their abilities to reconstruct known drug-protein interactions. As mentioned above, L1LOG-tensor is our proposed method. The others are previous methods based on current algorithms or conventional fingerprints , which is defined as a plot of true positive rates against false positive rates based on various thresholds, and the precision-recall curve (PR curve), which is defined as a plot of precision  against recall (sensitivity) based on various thresholds, as evaluation measures for prediction performance.We computed the area under the ROC curve (AUC score) and the area under the PR curve (AUPR score). The parameters involved in each method  were fit with AUC and AUPR as the objective functions.Figure L1LOG-LIBLINEAR-tensor did not perform well with an increasing number of negative examples in the training set because of the memory storage problem. The learning process with the LIBLINEAR algorithm consumed all the memory of our machine with 128GB-memory. In contrast, the other four methods with our space-efficient algorithm were able to finish the training process. This suggests that our space-efficient algorithm is more suitable and powerful for learning a predictive model on extremely high-dimensional data.L1-LOG-tensor and L2LOG-tensor performed better than L1-LOG-concat and L2LOG-concat, which suggests that the tensor-product fingerprint can capture relevant information for drug-protein interaction prediction. On the other hand, the concatenated fingerprint cannot capture enough information, even though calculation is faster.Table In the pair-wise cross-validation, drugs and proteins in test pairs often overlap with those in the training set. We conducted a different 5-fold cross-validation to avoid the overlap of drugs and proteins in test pairs between those in the training set, which we call “block-wise cross-validation”. The results of this block-wise cross-validation are shown in Fig. Finally, we tested SUCTRIE, VLA, and SET on their space-efficiencies of fingerprint representations. Note that SET is a standard representation, and SUCTRIE and VLA are those constructed with our proposed method. Figure We proposed a novel method of extracting the underlying features characterizing overall drug-protein interactions, which we call “drug-protein interaction signatures”. We extracted a set of drug-protein interaction signatures consisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules, and argued that the extracted drug-protein interaction signatures were biologically meaningful. Our proposed method is original in that the space-efficient representations for high-dimensional fingerprints of drug-protein pairs, in the characterization of a large-scale drug-protein interaction network with various features in an integrative framework, and in the interpretability for the extracted feature associations.Our proposed method will be useful for various applications in drug discovery. A limitation of the method is that it cannot extract the associations between different attributes of drugs or proteins. For example, it cannot detect the associations between drug-chemical substructures and adverse drug reactions or the associations between protein domains and biological pathways. Extension of the method for analyzing such more complicated features is an important future work.


The Gene Ontology (GO) is a resource that supplies information about gene product function using ontologies to represent biological knowledge. These ontologies cover three domains: Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). GO annotation is a process which assigns gene functional information using GO terms to relevant genes in the literature. It is a common task among the Model Organism Database (MOD) groups. Manual GO annotation relies on human curators assigning gene functional information using GO terms by reading the biomedical literature. This process is very time-consuming and labor-intensive. As a result, many MODs can afford to curate only a fraction of relevant articles.GO terms from the CC domain can be essentially divided into two sub-hierarchies: subcellular location terms, and protein complex terms. We cast the task of gene annotation using GO terms from the CC domain as relation extraction between gene and other entities: (1) extract cases where a protein is found to be in a subcellular location, and (2) extract cases where a protein is a subunit of a protein complex. For each relation extraction task, we use an approach based on triggers and syntactic dependencies to extract the desired relations among entities.We tested our approach on the BC4GO test set, a publicly available corpus for GO annotation. Our approach obtains a F1-score of 71%, a precision of 91% and a recall of 58% for predicting GO terms from CC Domain for given genes.We have described a novel approach of treating gene annotation with GO terms from CC domain as two relation extraction subtasks. Evaluation results show that our approach achieves a F1-score of 71% for predicting GO terms for given genes. Thereby our approach can be used to accelerate the process of GO annotation for the bio-annotators. The Gene Ontology (GO)  is a reshttp://www.arabidopsis.org/), Saccharomyces Genome Database (http://www.yeastgenome.org/), Mouse Genome Informatics (http://www.informatics.jax.org/), WormBase (http://www.wormbase.org/), PomBase (http://www.pombase.org/), FlyBase (http://flybase.org/), and ZFIN (http://zfin.org/). This process is very time-consuming and labor-intensive. As a result, many MODs can only afford to curate a fraction of relevant articles. For example, the curation team of TAIR has been able to curate less than 30% of newly published articles that contain information about Arabidopsis genes [GO annotation is a process which assigns gene functional information using GO terms to relevant genes in the literature. It is a common task among the Model Organism Database (MOD) groups. Manual GO annotation relies on human curators assigning gene functional information using GO terms by reading the biomedical literature. Currently manual annotations are made by experienced biocurators from annotation projects including TAIR  and the kind of textual evidences for each type are different, current approaches for automatic GO annotation tend to use a single approach for annotations of all types of GO terms. This may be the reason that previous methods are not good enough to be used in automatic GO annotation. In this paper, we present a novel approach of annotating genes using GO terms from the CC domain, where we cast the task of GO annotation as relation extraction between gene and other entities. Evaluation shows our approach achieves a F1-score of 71% (91% precision), thereby can be used by the bio-annotators to accelerate the process of GO annotation.Based on our study, we found that GO terms from the CC domain can be divided into two sub-hierarchies: 1) subcellular location terms: i.e., terms in GO under CC category, which are in the sub-hierarchy rooted by 19 GO terms (shown in Table  subcelluexample sentence 1), the relation between the protein complex and its subunit is indicated by word “subunit”. Similarly, the word “dimerizes” can be taken as a trigger for the relation between two protein complex components as indicated in the sentence “Mad1 dimerizes with Max” (example sentence 2).We use trigger-based approach for the relation extraction tasks in this paper. A trigger will be a word or multi-word expression that indicates a relation. For example, in sentence “CSC-1 is a subunit of the Aurora B kinase complex”  the trigger in a verb form appears in the sentence; (2) One of the entities, of type protein (“Mad1”), is the nominal subunit of trigger “dimerizes”; and (3) “Max” is the modifier of trigger “dimerizes”, where the modification relation is given by nmod: with.example sentence 2, after specifying the triggers and the syntactic dependency conditions, an “argComponent” edge is added from the trigger to each protein entity, as shown in Fig. We use an existing framework in our lab to develop rules for confirming relations. Given one sentence, the framework uses the BLLIP parser , 11 to oexample sentence 3). Based on the syntactic dependencies, we can see the subject and object attached with “contains” are nodes “complex” and “components”. We just need to write rule to detect the relation between these two nodes. The framework will handle the member-collection structure and add new edges, as shown in Fig. This framework also detects other extra-syntactic information such as is-a and member-collection relations and use them to propagate the original relations to form new relations. For example, Fig. found-in relation, where triggers such as “found” and “detected” are used. A key aspect of the rules for these triggers is the presence of a locative preposition such as “in”, “at” and “on”. Figure example sentence 4). Given the word “found” as trigger, we can see “SIRT2” is the nominal subject of the trigger, and “cytoplasm” is the modifier of the trigger (there is an “nmod:in” edge from “found” to “cytoplasm”). Thus, we can confirm the found-in relation between “SIRT2” and “cytoplasm”.This annotation for a protein requires the identification of text that relates a protein with a subcellular location. The first relation we are interested in is the example sentence 5). Because of the use of the copular structure, the syntactic argument structure is similar to the verb case where the nsubj and nmod:in (or other locative preposition) identifies the protein and the location.Note the triggers do not need to appear only in the verbal forms but can also appear in their nominal forms. For example, the trigger “detected” is used in its nominal form “detection” as in the phrase: “the detection of ZmHK1 in endoplasmic reticulum”. The use of the trigger in adjectival form is illustrated in Fig. found-in relation, we also consider “movement” verbs. For these cases, we need to ensure that the moved entity is a protein and the new/old location is indicated by a locative prepositional phrase. An example of this kind can be found in Fig. example sentence 6). Unlike previous cases, since the verbs used as triggers indicate movement of their objects, these prepositions are likely to be “to” and “from”, rather than “in”, “on”, “at”.In addition to the trigger words that correspond to a broadly-defined found-in or movement relation explicitly. Instead, this type of relation covers cases where the proteins are arguments of an event which occurred in some subcellular location and hence the protein-subcellular location relation is implied. For example, from sentence “Fbxo45 interacts with Par-4 in the cytoplasm” (example sentence 7), we can infer that “Fbxo45” and “Par-4” must both be in the cytoplasm since the interaction took place in this region. Figure The next type of relation we consider does not include a trigger that indicate the example sentence 8), we can know “SGLT1” must be in “plasma membrane” since it is expressed there.Note that there are a variety of verb structures that can be used including passives. Also, the event need not be indicated by words in verbal forms but can also appear in their nominal forms. For example, in Fig. If we were only interested in protein-subcellular location relation, then we can consider triggers that are adjectives that are modifiers specifying subcellular locations . In such cases, we will look for the protein which these adjectives modify, e.g., in the phrase “the nuclear transcription factors AP-1 or NFIL-2A”. However, our interest here is not just in detection textual mentions of a protein’s subcellular location. Rather, we are interested in annotation of a protein. Thus, the text must indicate not only the relation but that it is a finding. Therefore, we will not consider cases such as “cytoplasmic CD3” in this work, because we believe it refers to existing knowledge.C. elegans embryos, using the affinity-purified anti-CeCDC-14 antibodies for immunofluorescence microscopy. Later in mitosis, during telophase, this staining compacted to a single dot that was positioned between the two daughter cells, highly reminiscent of the midbody”. The word “staining” above indicates protein localization. While we haven’t developed concrete rules for these cases, we look for location terms in the same sentence. In this case, we thus obtain the location of “midbody”. Next, we attempt to find which protein’s staining is detected or observed. For this purpose, we try to see if there is a unique protein (other than tags such as GFP) mentioned in the same or previous sentence. In this case, there is only one protein mentioned, i.e., “CeCDC-14”. We use this (as a low confident clue) to infer that protein “CeCDC-14” is in location “midbody”.In addition to the above rules, we also attempted to capture descriptions of experimental results on protein localization, through fluorescent microscopy. Consider the following text: “The localization of CeCDC-14 was analyzed in wild-type Finally, as for the detection of subcellular location mentions, we use a dictionary-based method, where the subcellular location dictionary is created using terms from GO subcellular location sub-hierarchies  protein complex and component relation, and (2) protein complex component and component relation. After the relations are detected, we will infer the proper GO terms under the protein complex sub-hierarchy based on the text to annotate the protein.example sentence 9). In this case, the “part whole” structure is indicated by trigger “subunit”. However, the “part whole” structure can be indicated by more general English words. E.g., in sentence “MRE complex contains Sp1 or related proteins” (example sentence 10), the “part whole” structure is indicated by word “contains”.We found the protein complex and component relation is often indicated by the “part whole” semantic structure, where a protein mention is found in the “part” argument position and a protein complex is found in the “whole” argument position. Consider sentence “CSC-1 is a subunit of the Aurora B kinase complex”  of the output using the three types of rules described in the previous paragraph.By specifying the triggers and the conditions, we add an “argComponent” edge from the trigger to the protein entity, and an “argComplex” edge from the trigger to the protein complex entity if the conditions are met. Figures example sentence 14), if we know “JAB1” is a subunit of a protein complex, this is enough for us to select this sentence as GO annotation evidence sentence. Thus, in addition to detecting protein complex names using a dictionary-based approach, where the dictionary is created based on PRO [Noted that we only need to confirm whether a protein is mentioned as a component of a protein complex. To achieve this goal, for the protein complex and component relation extraction task in this paper, we do not need to limit ourselves to cases when the protein complex is named. Some general phrases that denote a protein complex such as “a protein complex” can also be used as valid context. For example, in Fig. d on PRO , Complexd on PRO , we alsod on PRO  and furtd on PRO . An f-te“/(complex|dimer|trimer|tramer|hexamer|nonamer|tamer|decamer|octomer|oligomer)$/”.To extract the protein complex component and component relation from individual sentences, we use two types of rules: (1) “protein and protein form a complex”, (2) rules based on protein protein interaction (PPI). For the latter, we first use a set of triggers and rules described in  to detecexample sentence 15, trigger f-term needs to be the object of trigger “form”). The first type of rules also covers cases like “protein complexes with protein”, as shown in Fig. example sentence 16), and the nominal form of the verb where words such as formation (of complex) and dimerization are used as triggers.The first type of rules covers the most basic and straightforward cases. Triggers used here are “form” with any protein complex f-term such as “complex” and “trimer”. Noted that the word “form” and protein complex f-term do not need to be adjacent, as shown in Fig. example sentence 17).The second type of rules to extract protein complex component and component relation is based on the detection of the PPI relation. Triggers used for detecting the PPI relation are words such as “interact”, “bind”, and “associate”. We consider three cases where the PPI indicates protein complex component and component relation. Since a protein complex is often defined as a stable set of interacting proteins, the first case we consider is when the PPI is stable (PPI + stable), as shown in Fig. example sentence 18), based on the trigger “binds” and the syntactic dependencies between the trigger and the two proteins “CD47” and “TSP-1”, we can confirm there is a PPI relation. Since there is a conjunction edge from the PPI trigger “binds” to another verb “inhibits”, we can infer that “CD47” binds to “TSP-1” to perform some function, i.e., “inhibits angiogenesis”.The second case we consider is when protein interacts with other protein to perform some function, or the interaction itself performs some function (PPI + function). Our hypothesis is that if the interaction results in some function being performed, it is stable enough or lasts long enough to be called a complex. For example, in Fig. example sentence 19). Since there are more than two proteins involved in the interaction, we can infer that those proteins are components of one same protein complex, as shown in Fig. Finally, if more than two proteins are found to be involved in one interaction, we treat these proteins as components of one same protein complex (PPI + multiple components). Consider this sentence “Moreover, Bmh1p and Bmh2p associate with Ste20p in vivo” (example sentence 20) suffices to assert that CAR-1 belongs to a complex.Noted that we do not need to require all the components involved to be explicitly named proteins. Thus, for example, the following sentence .The BC4GO corpus  is a pubFrom the BC4GO test set, we extract the PMID, Gene ID and GOID triplets with annotated GO terms from the CC domain. Altogether. 97 PMID, Gene ID, and GOID triplets are extracted and used as gold standards for evaluation. According to the annotation guideline, we only try to predict GO terms based on the text from the abstract, results section, discussion section, and conclusion section.We use the standard measures of precision  and the false negatives (FNs) and found the FPs were mainly due to the errors of dependency parsing and entity recognitions. Since many of the annotations used text evidence from multiple sentences, while our approach mainly captures the ones from one single sentence, majority of the FNs were due to fact that we didn’t anaphoric expressions such as “both proteins” and “it”. For example, in sentence “We monitored the subcellular localization of ZmOST1-GFP and ZmSNAC1-GFP constructs in Nicotiana benthamiana and found that both proteins are localized in the nucleus and the cytoplasm of tobacco epidermal cells.” from PMID 23469147 (PMC3585266). If we can anaphoric “both proteins” to “ZmOST1” and “ZmSNAC1”, then we can capture the relations for the two proteins to location “nucleus” and “cytoplasm”, based on the syntactic dependencies. The remaining FNs correspond to cases where there is no clear-cut syntactic dependency between the protein and other entities. Thus, the syntactic dependency driven approach is unlikely to have captured these cases. We found sentential co-occurrence with careful restrictions might help to improve the recall without hurting the precision. This can be a potential future investigation topic.Evaluation results show treating GO annotation as relation extraction tasks can yield good performance. This idea can also be applied to other types of GO terms. For example, based on our study, we believe our approach can be especially useful for two major sub-hierarchies of the Molecular Function domain: protein binding terms and catalytic activity terms. Annotation with terms from these two sub-hierarchies can be benefit from: (1) extracting cases where a protein binds to other proteins, and (2) extracting cases where a protein is involved in catalytic activity. We believe approaches following these processes can yield high precision as well, thus can assist in the process of GO annotation.In this paper, we have described a novel approach to automatically annotate genes using GO terms from cellular component domain. In contrast to previous approaches, we treat annotation with different types of GO terms as individual subtasks, where we cast each subtask as relation extraction between gene and other entities: (1) extract cases where a protein is in a subcellular location, and (2) extract cases where a protein is a subunit. Evaluation results shows that our approach achieves a F1-score of 71% (91% precision) in predicting GO terms for given genes. Thereby it can be used to accelerate the process of GO annotation for the bio-annotators. In the future, we plan to investigate how to improve the recall of our approach. One possible direction is to apply sentential co-occurrence heuristic with careful restrictions.


In mass spectrometry-based proteomics, protein identification is an essential task. Evaluating the statistical significance of the protein identification result is critical to the success of proteomics studies. Controlling the false discovery rate (FDR) is the most common method for assuring the overall quality of the set of identifications. Existing FDR estimation methods either rely on specific assumptions or rely on the two-stage calculation process of first estimating the error rates at the peptide-level, and then combining them somehow at the protein-level. We propose to estimate the FDR in a non-parametric way with less assumptions and to avoid the two-stage calculation process.p-values of proteins are calculated from the null distribution and the BH procedure is applied to the p-values to achieve the relationship of the FDR and the number of protein identifications. The Permutation+BH method generates the null distribution by the permutation method, which is inefficient for online identification. The logistic regression model is proposed to infer the null distribution of a new sample based on existing null distributions obtained from the Permutation+BH method.We propose a new protein-level FDR estimation framework. The framework contains two major components: the Permutation+BH (Benjamini–Hochberg) FDR estimation method and the logistic regression-based null inference method. In Permutation+BH, the null distribution of a sample is generated by searching data against a large number of permuted random protein database and therefore does not rely on specific assumptions. Then, In our experiment based on three public available datasets, our Permutation+BH method achieves consistently better performance than MAYU, which is chosen as the benchmark FDR calculation method for this study. The null distribution inference result shows that the logistic regression model achieves a reasonable result both in the shape of the null distribution and the corresponding FDR estimation result. In shotgun proteomics, the identification of proteins is a two-stage process: peptide identification and protein inference . In peptp-value based approaches and false discovery rate (FDR) approaches: p-value based approaches provide a single protein-level p-value for each reported protein.FDR approaches apply a single threshold to all proteins identified from the data rather than generate individual significance values for each protein.Inferred proteins are the most biologically relevant outcome of a shotgun experiment. Therefore, the ability of accurately inferring proteins and directly assessing such inference results is critical to the success of proteomics studies. To date, many effective protein inference algorithms have been developed such as ProteinProphet, ComByne and MSBayesPro. However, the problem of accurate assessment of statistical significance of protein identifications remains an open question , 9. Pastp-value based approaches and FDR approaches aim at controlling the quality of identified proteins, though they consider this problem from different perspectives. Unfortunately, available methods still deserve certain drawbacks, as summarized below: Reliance on specific assumptions. Most methods depend on particular assumptions regarding the model or the distribution of false positive matches. For instance, the p-value based PROT_PROBE approach [approach  assumes approach  is basedReliance on the two-stage calculation process. Generally, the protein-level confidence measure is obtained by combining peptide-level p-values . Such ps  method:Proteins that are present in an experimental sample are true positives; others are false positives. Each protein is associated with a score measuring its confidence. The higher the score, more confident we are that the protein is in the sample. If we treat the protein score as a test statistic, the distribution formed by scores of false positives is the null distribution. Given Given a subset of proteins obtained by setting a protein score threshold, we can also determine the FDR according to the BH procedure.p-value calculation method and the BH procedure are well-established statistical routines. The major source of errors in estimating the FDR may come from the null distribution estimation. Generally, the more data we have, the more accurate the null distribution. Thus, we estimate the null distribution by using a permutation method, which can generate plenty of data for robust data analysis. However, the permutation method is inefficient. This method becomes computational expensive when handing large datasets. This motivates us to develop a method that can infer the null distribution of a new dataset from null distributions of known datasets. We can store the previous estimated null distributions and conduct the protein-level FDR estimation in an off-line mode. In this way, both accuracy and efficiency can be achieved. Details are provided in the following sections.In the method, the We employ the target-decoy technique to determine null distributions. In the permutation step, each sequence in the original protein database is randomly shuffled . The shuM decoy proteins in the above step. Let zl values into K bins of equal length: Suppose we obtain k-th bin. Define yk as the count in the k-th bin: Here, xk be the center point of and let p-value can be approximated as: When a protein belongs to N proteins, we can estimate their p-values. The determination of the relationship of the FDR and the number of proteins is straightforward by applying the BH procedure mentioned in the previous section.Given In the permutation method, we need to shuffle and identify proteins multiple times. Thus, the notable limitation of the permutation method is its low efficiency. Null distributions of different samples can be stored in the protein database for future use in an off-line mode.The protein identification result can be affected by various reasons such as the tandem MS peak count and the sample complexity. Null distributions built on existing samples may not be applicable in analyzing data with different tandem MS peak counts and a different sample complexity. Determining the null distribution of new data is time consuming by applying the permutation method. Thus, we design a way to infer the null distribution from existing null distributions in the case that high efficiency is desired.I existing samples and each sample can be described by J features. Denote features of the i-th sample as  and let r0,1,r0,2,...,rJ0,) and our objective is to infer its null density function A raw data can be described by many features. For instance, tandem MS peak counts and tandem MS spectral quality measured by the mean noise level. Suppose we have k-th bin J features can be described by the following logistic regression model: For a protein score belonging to the After fitting the logistic regression model, we estimate the the probability βk,j as shown in Table For bins mentclass2pt{minimThen, the density function k=1, When We can use a feature table to organize our data. A feature database is shown in Fig. i is high (e.g. the correlation of features is above 0.9), we use When a new raw data is input, protein inference and feature extraction are performed. We can use existing protein inference algorithms such as ProteinProphet to identify proteins from the protein database in the feature database. Then, we can compare the new sample with samples in the feature database based on their features. We can measure the similarity of two samples by calculating the correlation of their feature vectors. Similar samples are often encountered when we analyze replicate samples. If the similarity between the new sample and an existing sample function  and use The permutation method takes lots of time. When the number of bins in the null distribution and the number of features are large, the logistic regression fitting may also take a great amount of time. The off-line information (i.e. the feature table and the coefficient table) is used to achieve a new null distribution without the permutation step and the logistic regression fitting step. Thus, it makes the protein-level FDR estimation efficient.When applying the proposed protein-level FDR estimation framework, two key points are: features and similarity measurement. A sample can be described by features. When a novel sample is similar to an existing sample by comparing their features, the null distribution of the existing sample will be used. Otherwise, a new null distribution is inferred by applying the logistic model based on sample features.The error propagation from the peptide-level to the protein-level is non-trivial. Features of raw data such as tandem MS peak counts are faraway from the final protein inference result. Thus, these kinds of features may not have a clear connection with protein scores. In our current implementation, we determine to directly select features from protein scores.i into 10 bins of equal length. The probabilities of protein scores falling in 10 bins are denoted as Pi,1, Pi,2,...,Pi,10. Then, we choose sample j as a reference sample. The similarity of protein identification results of sample i and sample j is measured by their Kullback-Leibler (KL) divergence: First, we partition the range of protein scores of sample Di,j, the more similar sample i and sample j. We choose the KL divergence from each sample to the reference sample as a feature, which is used to infer the null distribution and measure the sample similarity.The smaller the value of The whole framework consists of two parts: The first part employs the permutation method and the BH procedure to estimate the FDR (Permutation+BH); the second part provides a logistic regression model to infer the null distribution of a new sample based on existing null distributions. We first conduct the experiment to verify Permutation+BH in FDR estimation. The performance of our method is compared to MAYU based on three datasets with groundtruth. Then, we conduct another experiment to illustrate the performance of our null distribution inference method. In the last part of our experiments, we discuss the reference dataset issue in our current implementation of our framework.The whole framework is implemented in Ruby (v1.9.2p290). Target-decoy concatenated databases are generated from UniProtKB/Swiss-Prot (Release 2011_01) by appending shuffled protein sequences into the original protein database. Peptides are identified by X!Tandem (v2010.10.01.1) . Then, PIn our experiments, we use six public available datasets: ISB, ABRF, Yeast, Yeast_Train, Human and Human_Test. The ISB dataset was achieved from a 18 standard protein mixture . The samIn this experiment, the first three datasets are used: ISB, ABRF and Yeast. For the ISB dataset, the 18 standard proteins together with 15 contaminants are marked as the groundtruth . For theThe permutation method includes two steps to obtain a null distribution: a shuffling step and a protein inference step. In the shuffling step, each protein sequence is shuffled and appended into the original protein database. In the protein inference step, proteins are identified from the target-decoy concatenated database with TPP. The shuffling step and the protein inference step repeat for 20 times. The protein mapping to a decoy sequence is considered to be a false positive. The protein probabilities of all false positives from the 20 protein identification results are collected. Then, the histogram of the protein probabilities is built and used as the null probability density function. In general, the smaller the bin size, the more detail the null distribution contains and more data points are desired to build the null distribution. In our experiments, the length of each bin is empirically chosen to be 0.003.p-value of each protein is calculated according to Eq. , we just plug coefficients in the coefficient table in Eq.  and use Figure In our current implementation of our framework, we need to reference dataset. The reference dataset is chosen to calculate the KL divergence of each sample to the reference dataset. Then, the KL divergence is used as a feature in both similarity measurement and null distribution inference. In the previous experiment, we take the ISB dataset as the reference dataset. The KL divergence is a non-symmetric measure of the difference between two probabilities. Thus, a different reference dataset may lead to a different null distribution inference result. In the following experiment, we take the ISB dataset, the ABRF dataset and the Yeast dataset as the reference dataset, respectively. The result is shown in Fig. Readers may be interested in the results of inferring the null distributions of other samples (other than Human_Test) by using the ISB dataset as the reference dataset. In the following experiment, we conduct two extra experiments to inferring the null distributions of the ABRF dataset and the Yeast dataset. In each of the two experiments, we treat either the ABRF dataset or the Yeast dataset as test data and using remaining datasets as training data. The result is shown in Fig. The data used in fitting the logistic regression model may be neither typical nor enough. Data representative to different conditions are desired to obtain a robust regression model. When information of some typical conditions are missing, it is may be hard to make it up by using mathematical models.In our experiment, we only consider one feature in our logistic model. The single feature may not explain all kinds of variation in the protein inference process. A more accurate model can be achieved by using more features and plenty of data in building the feature database.Even if we obtain an ideal logistical model with perfect coefficients, it is not guaranteed that the new data is not an outlier. It is often the case that the new data locates at a point that has a certain distance to the ideal model.The difference between the inferred null distribution and the empirical null distribution obtained by the permutation method may be caused by the following reasons: The biggest advantage of the null inference method is its efficiency. Once the coefficient table is obtained beforehand, the inference of the null distribution is just the calculation of deterministic functions  and 6)..6). The In the Permutation+BH method, we use the permutation method to generate the null distribution and apply the BH procedure to estimate the FDR. The method does not relies on specific assumptions and works directly at the protein-level. Thus, the problems related to improper assumption and error propagation are avoided. The flexibility of our method also implies that it can be used with any search method or protein inference method of the user’s choice. According to our experimental results based on three datasets, our method performs better than MAYU. We believe that this is partly due to a more accurate estimation of the null distribution through the increased sampling by our permutation method, than in a typical 1:1 target-decoy approach as used in MAYU.In the Permutation+BH method, the efficiency is low because we need to shuffle protein sequences and conduct protein inference multiple times. We propose an off-line strategy to handle this issue. In the off-line strategy, a feature protein database is built beforehand with null distributions obtained from existing samples, a feature table and a coefficient table. When a new sample cannot find a match in the feature table, a new null distribution is inferred by directly plugging the coefficients in the coefficient table into the logistic model. The logistic regression model provides an efficient way to infer the null distribution. Our model is currently trained only on a few samples and including only 1 feature, limiting its accuracy. We will seek to improve this model by adding many more features and training the model on more datasets, as part of our future work.In this paper, we propose a protein-level FDR estimation framework. The framework includes two major components: the Permutation+BH FDR estimation method and the logistic regression-based null distribution inference method. The Permutation+BH method first applies the permutation to generate the null distribution and then uses the BH procedure to estimate the FDR. However, this method is inefficient for online identification. Therefore, we propose the logistic regression-based null distribution inference method to handle this issue. In our experiment based on three public available datasets, our Permutation+BH method achieves consistently better performance than MAYU, which is chosen as the benchmark FDR calculation method for this study. The null distribution inference result shows that the logistic regression model achieves a reasonable result both in the shape of the null distribution and the corresponding FDR estimation result.


Long non-coding RNA (lncRNA) studies play an important role in the development, invasion, and metastasis of the tumor. The analysis and screening of the differential expression of lncRNAs in cancer and corresponding paracancerous tissues provides new clues for finding new cancer diagnostic indicators and improving the treatment. Predicting lncRNA–protein interactions is very important in the analysis of lncRNAs. This article proposes an Ant-Colony-Clustering-Based Bipartite Network (ACCBN) method and predicts lncRNA–protein interactions. The ACCBN method combines ant colony clustering and bipartite network inference to predict lncRNA–protein interactions.A five-fold cross-validation method was used in the experimental test. The results show that the values of the evaluation indicators of ACCBN on the test set are significantly better after comparing the predictive ability of ACCBN with RWR, ProCF, LPIHN, and LPBNI method.With the continuous development of biology, besides the research on the cellular process, the research on the interaction function between proteins becomes a new key topic of biology. The studies on protein-protein interactions had important implications for bioinformatics, clinical medicine, and pharmacology. However, there are many kinds of proteins, and their functions of interactions are complicated. Moreover, the experimental methods require time to be confirmed because it is difficult to estimate. Therefore, a viable solution is to predict protein-protein interactions efficiently with computers. The ACCBN method has a good effect on the prediction of protein-protein interactions in terms of sensitivity, precision, accuracy, and F1-score. LncRNA refers to a class of non-coding RNAs that are greater than 200 nucleotides in length and do not encode proteins , 2. RNA In recent years, bioinformatics has developed rapidly, and a good deal of lncRNAs has also been found. Although some lncRNAs have been well studied, the function of most lncRNAs remains unknown and needs further study. Typically, most lncRNAs act by interacting with the corresponding RNA binding proteins . As a reThe aforementioned algorithms are based on the sequence features. However, in general, lncRNAs often exhibit low sequence conservation , and theIn this paper, we present a novel prediction method named ACCBN. The ACCBN method can predict unobserved lncRNA-protein interactions more effectively for the following reasons. Firstly, lncRNA is represented as a feature vector and lncRNA is used as a data point in the feature space. Secondly, the similarity is enhanced by using the Ant Colony Clustering method. Thirdly, an effective prediction of lncRNA-protein interactions is achieved by applying a lncRNA-protein bipartite network.Clustering is the important content of data mining, which is an unsupervised learning process. The basic principle is to cluster data sets according to different features between data and find the hidden pattern in data. In recent years, the application of clustering algorithms has been a research hotspot. At present, clustering algorithms can be roughly divided into four categories, namely hierarchical, partitioning, density-based and grid-based clustering methods. Recently, scientists have proposed an ant colony clustering algorithm based on the intelligence of ant colony.The first studies of ant-based clustering algorithms were performed by Deneubourg et al.. Deneubourg et al. proposed a basic model that allowed ants to randomly move, pick up, and deposit objects in clusters on the basis of the number of similar surrounding objects. The clustering method based on the food-seeking principle of ants has got the name from the food-seeking process, in which an ant releases a chemical substance called pheromone along the path and other ants can perceive this pheromone. The ant colony behavior done by a large number of ants is presented as a kind of positive feedback of information, and clustering is realized through this kind of positive feedback mechanism. In the clustering process based on the food-seeking principle of ants, the data to be clustered are regarded as ants of different properties and the clustering center is considered as the food source to be sought. Therefore, the data clustering process can be considered to be the process of ants seeking for the food source. During each search cycle, the ants would calculate the transition probability (which is concerned with the amount of information to reach the clustering center) and heuristic information to decide the next transition location.The idea of ant colony clustering algorithm based on ant colony foraging principle is as follows:Tij(0) = 0, and set various parameter values, such as the radiusr of cluster, p0 and α, β is conducted.First of all, the initialization of the algorithm, initialize the pheromone on various paths, setTij(t) on various paths is calculated:And then, during the algorithm operation process, the pheromone xi and data object xj belong to the same cluster is calculated:pij(t) > P0, it indicates that data object xiand data object xj belong to the same cluster, and combine xi to the field of xj. αis the heuristic factor of information, which reflects the importance of accumulated pheromone Tij(t) by ants during the operation. βis the expected heuristic factor, which reflects the importance of heuristic information ηij(t) of ants during the movement. Tij(t)refers to the pheromone on the path from data xi to the j − th clustering center in the t − th clustering. ηij(t)is the visibility function, which reflects a priori certainty factor of ants during the movement. dij refers to the Euclidean distance from the data object i to the clustering center cj, which is presented as follows:During the algorithm operation process, the probability that the data object ρ refers to the volatilization degree of pheromone; (1 − ρ) refers to the residual degree of pheromone; ΔTij(t) represents the increment of pheromone from data i to cluster j during this cycle; Q is a constant value. The bigger the Qvalue, the faster the pheromone accumulates on the path where the ants have passed. In a word, the Q value affects the convergence speed of the algorithm to a certain degree.However, every time ants complete one clustering, the clustering center will change, and the pheromones from each data to the clustering center are adjusted according to the following rule:α ∈ , β ∈ , ρ ∈ , Q ∈ .Each transition of ants between different clustering centers will result in a change of clustering center, and the next clustering process will start until the clustering result is stable. In this process, most initial parameters are determined by the experience, and the common ranges areDuring the application of ant colony clustering algorithm, the algorithm has slow convergence speed, and especially during the initial period of iteration, due to a slow update of pheromone, it is difficult to distinguish pheromones on each path. However, during the later period of iteration, the pheromones on some paths will be continuously accumulated, as a result, it would be more probable for ants to choose the path with more pheromones in a later operation, but it cannot ensure that the solution is a global optimal pollution, which results in the premature phenomenon. There has been a significant amount of research recently conducted on the improved performance and wider applications of ant colony clustering algorithms. The ant colony clustering algorithm with variation characteristics is thus proposed here for an improvement. The algorithm flow chart is as shown in Fig. F to the clustering center; F _ temp refers to the mean square error of various properties of various sample points to their corresponding clustering center under the variation path; F _ min represents the minimum mean square error of various properties to their corresponding clustering center in t − th iterations.As Fig. The variation times in the algorithm are random. However, through the introduction of variation, the algorithm can break through its original operation mechanism, and in other words, during the tolerable convergence process is optimized at random, which improves the performance of the original algorithm in a certain degree.G to describe the lncRNA-protein interaction network. L = {l1, l2, ⋯, ln}denotes lncRNA set. P = {p1, p2, ⋯, pm}denotes the protein set. E = {eij| li ∈ L, pj ∈ P}denotes the edge set, and eij is the edge connecting the nodes li and pj. The bipartite network is shown in Fig. We use a graph In this section, we study the association profile of lncRNA and the associated profile of proteins based on a binary network. In Fig. Referring to ,we calcum lncRNAs, a similarity matrix W is computed, and then we make up a directed graph in which lncRNA is used as the node and its similarity is used as the weight of the edges. We use the known association between the specified protein and all lncRNAs as the initial mark for the node. For the proteinPk, the k − th column of the association matrix Zis known as the initial labels of the nodes, and is written asZ. In the directed graph, for the labels of nodes are updated, the labels of neighbors with the probability ρ are absorbed and reserve the initial labels with the probability 1 − ρ. Let t − th iteration, we use the following formula to infer the update of step t − 1 to stept.For given 1, p2, ⋯, pm}, the above formula will be represented in matrix form as follows:Meanwhile, if we take into account the labels for all proteins {pAccording to the above formula, we can calculate the prediction matrix for the lncRNA-protein bipartite network.At present, several commonly used public databases for lncRNA–protein interaction prediction include NPInter , NONCODEhttps://github.com/BioMedicalBigDataMiningLabWhu/lncRNA-protein-interaction-prediction.In order to compare the prediction results with the prediction method proposed in reference , we usedIn this section, we used a five-fold cross-validation method to assess the predictive performance of our proposed method. The test set was randomly divided into five subsets. Each time we run, one of the subsets was selected as the test set and the remaining four subsets were used as the training set. Afterwards, the training model was used to predict the test set and evaluate the performance of the model. To ensure that each subset would be tested, the process was repeated five times. Because there are some data deviations for each test, we have performed 20 times of five-fold cross-validation during the experiment and then average them as the final evaluation result.TN denotes the number of true negatives, FP denotes the number of false positives, and FN denotes the number of false negatives.We use seven evaluation metrics as follows: the area under the precision-recall curve (AUPR), the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, and F1-score.All the evaluation indicators we mentioned above show that the larger the value, the better the performance.In this section, we compared the predictive ability of ACCBN with RWR , ProCF , LPIHN , and LPBWe performed a five-fold cross-validation experiment on the test dataset with the predictive models mentioned above to compare the results. The results are shown in Fig. To sum up, the ACCBN method can produce better predict results than the RWR, ProCF, LPIHN, and LPBNI methods in predicting unknown lncRNA-protein interactions.There have been many studies on protein interaction at home and abroad . There aThe first is to determine how proteins interact experimentally. For example, some of the websites mentioned above, such as DIP , record The second is to predict the existence and function of protein interactions with biological theories. This kind of research relies on bioinformatics , 30. ComThe third category is computer algorithms that predict protein interactions. On the basis of the second method, in order to be able to process large data, there are many algorithms for computer prediction interaction –36. ThisThe protein interaction network is huge and complex, and the protein reaction confirmed by experiments is only a small part at present. How to expand the known protein interaction network has become a major focus of the research on protein interaction. Biological experiments are time-consuming and expensive, and it is not feasible to test protein pairs one by one. So an effective method commonly used in bioinformatics to expand known protein interaction network rapidly is as follows: first forecast the potential of protein interactions with the known data and then predict the results of the experiment and verify them again.Our article aimed at developing an efficient and accurate protein prediction method. Only by using the bipartite network prediction algorithm to predict protein interaction, there will be a lot of irrelevant data to reduce the coupling between the data and affect the prediction quality. The ACCBN uses ant colony algorithm to first conduct data clustering, and then constructs a bipartite network for prediction, solving the above problems effectively.The above experimental results have shown that the prediction results of ACCBN are better than those of other comparison algorithms, and that the prediction results of ACCBN are better than those of other comparison algorithms as well.ρon the prediction accuracy rate results, as shown in Fig. Let’s discuss the effect of parameters ρ value increases, the value of the prediction accuracy also increases, but afterρ reaches 0.6, as the ρ value increases, the value of the prediction accuracy begins to decrease. The best prediction accuracy is obtained atρ = 0.6. So we usually set ρ = 0.6 in the experiment.As can be seen from Fig. We proposed a novel prediction method for lncRNAs and proteins based on the known lncRNA-protein association bipartite and linear neighborhood similarity. We use the Ant-Colony-Clustering-Based Bipartite Network method (ACCBN) to predict unobserved lncRNA-protein associations. The experimental results show that the ACCBN method is superior to other comparison methods in predicting protein interactions. What’s more, the ACCBN method provides a new idea for researchers to identify key proteins by combining protein interaction information with other biological information.


B. distachyon using a novel docking approach. Further, we systematically investigated the docking site (D-site) of BdPP2C which plays a vital role for recognition and docking of BdMAPKs. D-site analysis revealed that there were 96 pairs of PPIs including all BdMAPKs and most BdPP2Cs, which indicated that BdPP2C may play roles in other signaling networks. Moreover, most BdPP2Cs have a D-site for BdMAPKs in our prediction results, which suggested that our method can effectively predict PPIs, as confirmed by their 3D structure. In addition, we validated this methodology with known Arabidopsis and yeast phosphatase-MAPK interactions from the STRING database. The results obtained provide a vital research resource for exploring an accurate network of PPIs between BdMAPKs and BdPP2Cs.Protein-protein interactions (PPIs) underlie the molecular mechanisms of most biological processes. Mitogen-activated protein kinases (MAPKs) can be dephosphorylated by MAPK-specific phosphatases such as PP2C, which are critical to transduce extracellular signals into adaptive and programmed responses. However, the experimental approaches for identifying PPIs are expensive, time-consuming, laborious and challenging. In response, many computational methods have been developed to predict PPIs. Yet, these methods have inherent disadvantages such as high false positive and negative results. Thus, it is crucial to develop in silico approaches for predicting PPIs efficiently and accurately. In this study, we identified PPIs among 16 BdMAPKs and 86 BdPP2Cs in  In particular, mitogen-activated protein kinase  is a crucial protein kinase plays vital role in converting environmental and developmental signals into distinct nuclear responses3. MAPKs are activated by their specific activator MAPK kinases  through double phosphorylation on both conserved threonine (T) and tyrosine(Y) residues located in the kinase activation loop5. In contrast, inactivation of MAPKs is induced by dephosphorylation of cognate residues by various tyrosine phosphatases6, serine/threonine phosphatases(e.g.PP2C)7 and/or dual-specificity phosphatases (DUSPs)8.Protein kinases or phosphatases in eukaryotic cells are main basics of signal transduction mechanisms by catalyzing covalent addition or subtraction of phosphate groups to serine and threonine/tyrosine residues in their substrates, which causes the quick and reversible modification of proteins and thus regulates plant living situations to adapt to their environment rapidly and precisely9. The docking interactions increase the efficiency of all the enzymatic reactions and may help to regulate the specificity of molecular recognition10. It was discovered that MAPKs contain a common docking domain (CD domain) that is featured by a cluster of negatively charged amino acids in the C-terminal region outside the catalytic domain that binds the basic residues at the N terminus of the docking site (D-site) in MAPK-interaction proteins12. Such D-sites has more consecutive positively charged amino acids and promote binding specificity and high affinity interactions with cognate MAPKs. D-sites are also found in MAPK regulating proteins such as MKKs14, scaffold proteins and MAPK phosphatases and substrates10. The D-sites in these proteins consist of a cluster of basic residues followed by a hydrophobic sub-motif containing Leu, Ile or Val separated by one residue (R/K1–3-X1–6- φ-X-φ) (φ is any hydrophobic residue)16. The specificity of MAPK docking interactions may be determined by the two hydrophobic residues at the distal end of the D-site in MAPKKs17. However, by comparing the current and previous structures, we found that MAPK have actually three (not two) hydrophobic pockets on this surface, which collectively form a “docking groove”, that bind the motif LxLxL/I17. Of course, it is least critical for MAPK binding about the most C-terminal hydrophobic residue in the LxLxL/I motif19. Furthermore, MAPK phosphatase MKP-1 harbor another type of MAPK-docking site named the DEF motif 20. Notably, D-sites bind to acidic residues in the CD domain of MAPKs, while DEF motifs interact with a hydrophobic pocket that is only exposed upon MAPK activation21. Moreover, the other one atypical MAPK-docking site within Msg5 named the IYT motif mediates the interaction of Slt2 and Mlp123.The formation of complex between MAPK and its connate activator, substrate, scaffold or inactivator is normally achieved through specific docking interactions24. Consequently, the construction of dynamic PPI networks is a critical step towards better understanding biological function of relevant proteins. Recently, many experimental technologies have been proposed for the large-scale PPIs detection, for example, two-hybrid-based screens25, protein chips26, mass spectrometry27, bimolecular fluorescent complementation (BiFC)28 and phage display. However, these methods have itself inherent limitations such as high false positive rate and very low coverage30. Hence, more and more computational methods, such as evolutionary information and physicochemical characteristics method31, alignments of multiple sequences32 or weighted sparse representation model33, and so on, have been proposed to predict PPIs efficiently and accurately. While in silico-prediction of PPIs using “Docking” strategy about MAPK has been developed in this study, which can avoid these disadvantages mentioned above.Naturally, the precondition of kinase-substrate reaction is the interaction between MAPK and cognate target proteins. However, it is difficult to identify the PPIs about these proteins, primarily because MAPK-substrate interactions are much transient and unstable. The identification of PPIs about their critical roles is a great challenge of unravel many interactomes for deciphering the molecular mechanisms and further providing insight into numerous physiological and pathological processes36. In order to better understand the PPIs about MAPKs and PP2Cs, we have generated a PPI network based on docking approach to predict the interactions about MAPKs and PP2Cs in Brachypodium distachyon. And further analysis also has performed to study the key basis about MAPK-docking on PP2C proteins. In general, the network of PPIs can provide novel insights into the molecular mechanism and the clues of function about MAPKs and PP2Cs in B. distachyon.The above observations clearly demonstrate that MAPK-PP2C docking is crucial for efficient signal transmission. However, less is known about the role of MAPK-PP2C docking in specificity and the identification of D-site in PP2C that is only found in subgroup B PP2CB. distachyon. MAPKs are prominent components of protein phosphorylation cascades transduce extracellular signals to plant defense responses. Thus this paper identifying the interacting MAPKs with PP2Cs of B. distachyon which is relate to disease signaling process resorting to docking approach. Docking studies is a method which predicts two proteins (PP2C and MAPK) when bound to each other to a complex, the more stable structure  higher the probability of their interaction.Networks of PPIs supply a framework for understanding the biological processes and can give insights into the molecular mechanism inside the cell. Thus the scholarship of PPIs promotes the understanding of biological mechanisms. MAPK is regulated through gene expression between cell receptor and cell response by transcription factions. To better elucidate the molecular mechanism, our knowledge no work in the literature has been executed regarding the interaction prediction of PP2Cs with MAPKs in 37. Moreover, not all of BdMAPKs and BdPP2Cs are only connected into a single network component  of two proteins is higher than 65.74 that is the maximum value of the interaction between all subgroup B PP2Cs and MPKs, which means them interacting with each other. Docking studies revealed that 96 from 1376 pairs of PPIs, that is 6.98℅, exhibit higher possibility of interaction including all MAPKs and 49 PP2Cs Fig. . The resent Fig. . These rB. distachyon which also showed interaction with BdMAPK through docking studies (Table RRGRRRPDILTI) that it found in residues 29–40 of BdPP2C55  from es Table . There aes Table . D-finde55 Table . It coulRRSLSCKA; BdMAPK6, KMLTFDPRQRITVEGAL-visible regions marked in yellow; Fig. 38. Therefore, we choose them to evaluate the possibility of PPIs between BdPP2Cs and BdMAPKs, which was also used as the criterion of cutoff. It is found that the position of BdPP2C6 in “φ-X-φ” locate in protuberant edge which is easier to insert into the hydrophobic pockets. Moreover, BdMAPK6 in common docking motif has three side chain docking pockets which are consistent with the interaction situation  Arabidopsis and yeast PPIs less than 65.74, except 4 pairs PPIs  -X(1–6)-[L/I]-X-[L/I]) in subgroup B phosphatases38. BdPP2C6, BdPP2C25, BdPP2C73 and BdPP2C78 belong to the subgroup B PP2Cs, which also has a typical KIM40. In addition, it only has reported that the interactions and structural features of PP2Cs with MAPKs in model plant Arabidopsis. So, we also independently investigated the AVLE of PPIs with BdMPK3/4/6 for the clues of their interaction each other  Fig. 10. As ex42. The expression of AtPTP1, a protein Tyr phosphatase, was manifold increased under salinity stress while decreased obviously in cold stress43. DOG1 can interact with the type 2C protein phosphatases AHG1 and AHG3 regulated the ABA signaling pathway to control seed dormancy36. Furthermore, little or no information is available on the molecular regulation network about MAPK cascades including activators or regulators. In addition, previous searching for MAPK target network using protein microarrays yielded 570 MPK phosphorylation substrates44 and MKKK-MKK-MPK molecular regulation network using co-expression systems showed some signaling pathways45. Moreover, despite a large amount of computational approaches have been developed to effectively and accurately predict protein interactions32, to date we still need develop more effective methods to investigate the PPIs situation of them. However, construction of a PPI network is the first step for the systematically study of a given organism, which not only provides clues for the signaling pathway but also contributes to comprehend protein functions46. Therefore, we took a high-throughput approach to understand the PP2C/MAPK signaling pathways. Specially, we identified 96 pairs PPIs and generated a PPI network  model and Bi-gram Probabilities (BiGP) for PPIs detection from protein sequences had developed using protein evolutionary information47. Moreover, the combining weighted sparse representation based classifier (WSRC) and global encoding (GE) of amino acid sequence had also performed33. It has reported a method for inference of protein-protein interactions from protein amino acids sequences32. While we also propose a novel computational method for predicting PPIs based on the energy of the protein 3D structure closed. Subsequently, we verified the feasibility of our method through a series of models, including the 3D structure reconstruction, putative protein docking domain prediction and public STRING database validation. For example, BdMAPK6 in common docking motif has three side chain docking pockets which are consistent with the situation of interacting BdPP2C25 which contain a D-site  classifier had proposedIn this study, we proposed in silico “Docking” strategy to predict the PPIs network about between 16 BdMAPKs and 86 BdPP2Cs. Docking studies showed 96 pairs of probable PPIs including all BdMAPK and 49 BdPP2C, which indicated that BdPP2C may be involved in other signaling pathway. In addition, 34 BdPP2C proteins harbor D-site in identified interaction with BdMAPK, which means that D-site plays important role in BdPP2C protein bind to BdMAPK. Moreover, we evaluate the reliability of prediction results and docking site by compare the 3D protein structure of BdMAPK6 and BdPP2C25 which can provide the structural support for our study. Furthermore, STRING database also indicated that our prediction results have low false positive rate and dependability. These findings provide insight into docking interactions in MAPK signaling networks and should be related to efforts to predict new MAPK substrates and regulators by in silico-prediction methods.B. distachyon (BdMAPKs and BdPP2Cs) were downloaded from PLAZA (https://bioinformatics.psb.ugent.be/plaza/)48 in FASTA format. Homology modeling was performed with the help of MODELLER and NCBI BLAST. For reconstructing the 3D conformation of all investigated in this study, a template for homology modeling was searched with PDB search in NCBI BLAST. Six structures were generated in Protein Data Bank proteins for each protein molecule, out of which the minimized average models with maximum score, lowest E-value and with a cut off identity of >30% were selected. The final structures generated by MODELLER 9.17 were used to construct and evaluate 3D models. Structures editing through energy minimization model was implemented using structure minimization tool from enhanced UCSF Chimera (http://www.rbvi.ucsf.edu/chimera/) which was a program for the analysis of molecular structures and related data and interactive visualization using a toolkit Opal49. Therefore, the default parameter value of all structures are retained, in details as follows: Steepest descent steps: 100, Steepest descent step size(A): 0.02, Conjugate gradient steps: 10, Conjugate gradient step size: 0.02, Update interval: 10, Fixed atoms: none, Method: also consider H-bonds(slower), Standard residues: AMBER ff14SB, Other residues: AM1-BCC Protonation states for: histidine, Residue-name-based (HIS/HID/HIE/HIP = unspecified/delta/epsilon/both)50. The minimized energy structures were finally saved as *.pdf files which were checked and validated by PROCHECK, ERRAT and VERIFY_3D. Subsequently, the refined structures of BdMAPKs were taken as receptor and the structures of BdPP2Cs were taken as ligand for the docking studies on the online Patch Dock server which is based on shape complementary principle and keeping parameter value Clustering RMSD:4.0, Complex Type: Default52. The results obtained were refined using Fire Dock online server which rearranges the interface side chains and adjusts the relative orientation of the molecules53. The AVLE is in solutions number 1000. At last, we can obtain the AVLE of all phosphatase-MAPK interaction pairs , BiFC assays and so on. According to the principle that homologous genes have similar functions, we therefore analyzed the AVLE of the interaction between all the subgroup B PP2Cs  and BdMAPKs . Specifically, the AVLE of the interaction between BdPP2C6 and BdMAPKs  is 60.33, 50.23 and 62.83, respectively . A profile HMM architecture, composed of linked main, insert, and delete states was performed in the programming language Java to implement the computational analysis and prediction. An HMM model of length 19 was designed to be a minimal prescreen that simply checks for a basic residue followed after a spacer of 1–5 residues by a hydrophobic-X-hydrophobic (as defined above)55. Thus, interologs are homologous pairs of protein interactions across different organisms. To assess the PPIs of BdPP2Cs and BdMAPKs, protein 3D structures are used to check their probability of interaction from spatial conformation, especially docking site.The docking-based and interolog method uses domain interaction information and sequence similarity to infer the potential PPIs, respectively. If two proteins contain an interacting domain pair, it is expected that these two proteins may interact with each other. While if two proteins have interacting homolog in another organism such as Arabidopsis, it is also thought that these two proteins are conserved interaction56 (https://string-db.org/cgi/input.pl) were performed. Subsequently, we compared the AVLE of other species phosphatase-MAPK interaction, including Arabidopsis and yeast.To validate the feasibility and reliability of our methods for phosphatase-MAPK interaction, the appraisement about AVLE of PPIs that have confirmed with experiments in STRING databaseSupplementary Information