Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction. RNA represents the direct output of genomic encoded genetic information, and a large part of the regulatory capacity of cells focuses on the synthesis, processing, transportation, modification, and translation of RNA. With the continuous improvement of RNA analysis, cell type isolation, and culture technology, people’s understanding of many biological functions of RNA is also getting higher and higher . StudiesBased on the physicochemical properties of peptide chains and nucleotide chains, Recently, many network-based models have been proposed for predicting lncRNA-protein interactions. Based on this, this study proposes a projection-based neighborhood non-negative matrix factorization (PMDKN) to predict potential lncRNA-protein interactions in heterogeneous omics data, which is also applicable to the prediction of new lncRNAs and new proteins. First, based on the lncRNA sequences, lncRNA expression profile, and protein sequences, we extracted a variety of features of lncRNA and protein. Second, based on multiple features of lncRNA and protein, lncRNA sequences, gene ontology annotation of the protein and the modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA and protein and fused to obtain more accurate lncRNA (protein) similarity network. Finally, PMDKN uses these features and fused similarity network to predict lncRNA-protein interaction scores. The results indicate that PMDKN exhibits higher predictive performance than other state-of-the-art methods for the prediction of lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further demonstrates that PMDKN can be an effective tool for lncRNA-protein interaction.The noncoding RNAs and protein related biomacromolecules interaction database (Npinter)  providesIn benchmark DATASET 1, all lncRNAs (proteins) interact with at least two proteins (lncRNAs), and the number of lncRNA-protein interactions is relatively dense. To investigate the predictive ability of the algorithm for sparse interactions, lncRNAs without sequence information were deleted from the data provided by Li et al., and a total of 4,679 interactions between 1,068 lncRNAs and 90 proteins were finally obtained. Meanwhile, sequence information of lncRNA and expression profile information of lncRNA in 24 human tissues and cells were extracted from the integrated knowledge database of non-coding RNAs database (NONCODE) , and seqlN lncRNAs and pN proteins obtained, respectively. In this section, we introduce the three features of lncRNA, the two features of the protein, and the similarity of lncRNA and the similarity of protein.Let We extracted three features of lncRNA, namely expression profile feature and two sequence-based features: pseudo-k-tuple nucleotide composition (PseKNC)  and paraf(iv)i=1,2,.···,73 of the occurrence of the conjoint triad in the amino acid sequence, where iv represents the i-th conjoint triad. Finally, normalizing f(iv), we could get the conjoint triplet descriptor feature CTriad(P)= of protein P as follows:The hydrophilicity and hydrophobicity of proteins play an important role in protein folding, environmental and molecular interactions, and catalytic effects. Combining the frequency of regularization of 20 amino acids in the protein sequence and the distribution pattern of hydrophilicity and hydrophobicity along the protein chain, we calculated the characteristics of the two proteins, which are the amphiphilic pseudo amino acid composition (APseAAC)  and the Where, In this section, we introduce the lncRNA-lncRNA similarity and the protein-protein similarity.L1 and L2 with m and n nucleotides, respectively, the dissimilarity L1 and L2, and k-tuple w occurring in L1 and L2 of lncRNA under the background model. wX and wY represent the frequencies at which the k-tuples in the sequences L1 and L2 occur, respectively. Further, the similarity of 1L and 2L is Where AT,EA), where AT represents the set containing item A and all its ancestor items in the GO diagram, and AE represents the set connecting all edges of GO item in DAG(A). Then, for any two GO annotation items A and B, their semantic similarity could be defined as:The semantic comparison of gene ontology annotations provides a quantitative method for calculating the semantic similarity of gene products . There aAS(t) and BS(t) represent the S-value of GO item t related to item A and item B respectively, and Where, shown in .In Section “Features for lncRNAs and Proteins”, we obtained three features of lncRNA and two features of protein, and the known lncRNA-protein interaction network also contains important lncRNA (protein) feature information. Based on these feature vectors, there are many methods for calculating similarities, such as Gaussian, linear neighborhood similarity  (LNS), aBased on the above steps, we obtained a total of 5 similarities of lncRNAs and 4 similarities of proteins, which reflected the similarity relationship of lncRNAs (proteins) from different perspectives. Due to the limitations of data and the selection of computational methods, these similarity networks may contain noise. Hence, we adopted a clusDCA proposed by N1 feature matrices of lncRNA, N2 feature matrices of protein, similarity matrix of lncRNA and protein are SL and SP respectively, A represents known lncRNA-protein interaction matrix, and Based on various features of lncRNA (protein) and the integrated lncRNA (protein) similarity network, we proposed projection-based neighborhood non-negative matrix factorization (PMDKN) to predict potential lncRNA-protein interactions. dR, that is, any lncRNA il and protein jp can be represented by non-negative latent vectors Ā. Since the observed interactions have been verified by experiments and have higher reliability than the unknown interactions, the observed lncRNA-protein interactions are assigned a higher level of importance and can be obtained as follows:First, we mapped lncRNA and protein to the common non-negative space C is the importance level distribution matrix, that is, if there is interaction between the lncRNA il and the protein jp, i,jC= δ, otherwise, i,jC = 1, where δ > 1 is an important level parameter. ||·||F denotes the F-norm and γ denotes the regularization parameter of latent vectors.where dR, and required the difference between it and U to be as small as possible, so as to obtain:In addition, in order to integrate different types of lncRNA features, we project all lncRNA features onto the non-negative space i-th feature matrix of lncRNA, lid represents the dimension of the feature, and iPL is required to be non-negative. The Weight vector η > 1 is the index of α, indicating that all features contribute to the generation of U. µ is the regularization parameter of projection matrix, and P is the k-th row of the matrix P. ||iPL|| represents the iPL  is as sparse as possible, and iPL. Therefore, equation (2) can be expressed as follows:where Similarly, for proteins, we have:j-th feature matrix of the protein, and non-negative matrix where It is generally believed that lncRNAs with higher similarity are more likely to interact with the same protein, but due to the incomplete data set, the similarity network of lncRNAs (proteins) obtained may contain noise. In order to eliminate the influence of non-neighborhood noise and improve the prediction accuracy, we only consider strong neighborhood similarity relationship of the samples. Therefore, lncRNA neighborhood similarity matrix  represents the K neighbor sets closest to lncRNA il. In order to adaptively select the number of neighbors according to the sample size, we make Among them, Where Furthermore, the objective function can be obtained as follows:where iα, jβ, and using the Lagrangin multiplier and the KKT condition, we can get the iterative formula of U, V, iPL and jPP as follows:We use the two-step method to solve (9). First, by fixing iPL and jPP, and let C1 represents the terms unrelated to iα and jβ (3.8). We can get the objective function about iα and jβ as follows:Then, fix U, V, iα and jβ can be obtained as follows:Using the Lagrangian multiplier, the iterative formula for iα and jβ always satisfy non-negative constraints. In formula (9), U and V are obtained based on the decomposition of the known lncRNA-protein interaction matrix. In order to reduce the prediction error of the new lncRNA (lncRNA without any protein interaction information) and the new protein, we utilized the method proposed by iũ the modified latent vector of lncRNA il, which can be calculated as follows:According to (14) and (15), N+(il) refers to the set composed of K lncRNAs with the highest similarity to il among lncRNA sets with protein interaction. In order to make the number of neighbors automatically adapt to the size of samples, we set where, the first item By using the modified latent vector j-th dimension of the i-th sample, then the normalized feature matrix F is as follows:In the process of model derivation, we assume that the features of lncRNA and protein are non-negative, so the original features need to be normalized before algorithm calculation. Let F.j) and max (F.j) represent the minimum and maximum of the j-th dimension, respectively. Algorithm 1 summarizes the general process of solving lncRNA-protein interaction prediction by KDMPN.Where, min .Fix end forfor jPP and V, calculate jβ according to formula (15).Fix end forrepeatU according to formula (10).3 Fix V according to formula (11).4 Fix 5 for U, Update iPL according to formula (12). Fix iPL and U, Update iα according to formula (14).Fix end for6 for V, Update jPP according to formula (13).Fix jPP and V, Update jβ according to formula (15).Fix end foruntil ConvergesU of lncRNA according to formula (16).7 Ũ was obtained by completing the subspace feature V of protein according to formula (17).8 9 According to previous studies, the performance of the interactive prediction method was evaluated by the 5-fold cross validation (CV), and the area under ROC curve (AUC), area under Precision-Recall curve (AUPR), and F1 value (F1) were used as evaluation indexes. Since the known lncRNA-protein interactions were much less than the unknown lncRNA-protein interactions, AUPR was usually used as the most important evaluation index to punish false positives .lN rows for lncRNAs and pN columns for proteins. In order to investigate the prediction ability for lncRNA-protein interactions, new lncRNAs and new proteins, we performed CV under three different settings, as follows:In addition, in order to eliminate the influence of random partition on the results in the crossover experiment, we selected the method ofaCV: CV on known lncRNA-protein interaction pairs. Specifically, we randomly divided the known lncRNA-protein interactions into 5 equal parts. Take turns to select one and all the unknown interactions to form the test set and the remaining four and all the unknown interactions to form the training set .lCV: CV on lncRNAs. Specifically, all lncRNAs are randomly divided into five equal parts; one is selected as a test set in turn, and the remaining four are training sets .pCV: CV on proteins. Specifically, all proteins are randomly divided into five equal parts; one is selected as a test set in turn, and the remaining four are training sets .aCV, we selected all zeros in A as the test set. For example, for DATA2, the test set of each crossover experiment contains 4,870/5 = 947 known interactions and 97,658 unknown interactions . This selection method ensures that all the unknown interactions can be included in each crossover experiment, which expands the search range and is also in line with the actual situation.It should be noted that with regard to η, the projection regularization parameter µ, the latent vector regularization parameter γ, the neighborhood Laplacian regularization parameter λ, the potential subspace dimension d, and the known interaction important level parameter δ. Among them, µ and γ control the influence of feature projection, γ controls subspace feature contribution, λ describes the effect of similarity network, and δ controls the importance level of observed interaction. In order to study the effect of parameters on the prediction results, we calculated all the parameter combinations. Specifically, η was selected from {2,3,4,5,6}, µ was selected from {10-3,10-2,10-1,100,101}, γ was selected from {10-3,10-2,10-1,100,101}, and λ was selected from {2-2,2-1,2021,22,23}; according to the previous research  similarity network did not utilize any known interaction information and therefore has higher predictive value. In addition, since η and µ. Fixed λ = 1, γ = 1, δ = 2, and calculate the AUPR value of the model under all possible combinations of η and µ. As shown in (A) of η becomes larger, the AUPR value of the model increases, but the overall AUPR value of the model fluctuates a little. Then, we fixed η = 5, µ = 100, γ = 1, δ = 2, and analyzed the influence of the change of λ on the AUPR value. As shown in (B) of As shown in η = 5, µ = 100, λ = 1, γ = 1, d = 100, and δ = 2 as parameters of PMDKN.Based on the above discussion, in the following study, we select aCV on all models, and lCV and pCV on SFPEL-LPI. We performed the crossover experiment using the experimental setup in Section “Experimental Settings” and used the mean of the five-fold crossover experimental results of the five random seeds as the evaluation index of the algorithm, and the parameters of these models were selected using the recommended parameters.In order to evaluate the predictive ability of PMDKN algorithm equitably, we conducted 5-fold cross validation on DATASET 1 and DATASET 2, and compared them with the following methods: SFPEL-LPI , LPLNP , LPBNI , and LKSlCV and pCV. As shown in CVp). Specifically, on DATASET 1, the AUPR values of PMDKN for lCV and pCV can reach 0.6301 and 0.4918, which is 30.92% and 49.71%, respectively, relative to SFPEL-LPI of 0.4813 and 0.3285. The AUC values of the PMDKN algorithm for lCV and pCV can reach 0.8907 and 0.7843, which are 7.52% and 17.66% higher than the 0.8284 and 0.6666 of SFPEL-LPI, respectively. The F1 value of the PMDKN algorithm for lCV and pCV can reach 0.6081 and 0.5251, which is 23.32% and 38.95% higher than the 0.4931 and 0.3779 of SFPEL-LPI, respectively. Similarly, in DATASET 2, the AUPR value and AUC value of lCV of PMDKN were higher than SFPEL-LPI, especially for pCV, the AUPR value, AUC value, and F1 value of PMDKN could reach 0.4604, 0.9019, and 0.4818, respectively, improving 281.13%, 37.78%, and 148.35% compared with the 0.1208, 0.6546, and 0.1940 of SFPEL-LPI.The prediction of new lncRNAs and new proteins are also the important criterion for evaluating the performance of the method. Among the four comparison algorithms above, only SFPEL-LPI performs the prediction of new lncRNA and new protein. Therefore, we only compare the prediction performance of SFPEL-LPI and PMDKN on Due to technical limitations, some noises may be hidden in the known lncRNA-protein interactions, such as lack of interaction information, unreal interaction information and so on. In order to test the dependence of the prediction performance of the model on the known interactions, according to the method of ip, the hit rate hit(ip) can be expressed as follows:LncRNA-protein interactions in DATASET 1 and DATASET 2 used in this paper were extracted from Npinter2.0, and the current version of Npinter has been updated to Npinter v3.0 . Comparei)Cand Cand Test , 0.6301 (on lCV), and 0.4918(on pCV) respectively; on DATASET 2, the AUPR values of PMDKN under the three experimental settings can reach 0.4808 (on aCV), 0.4794 (on lCV), and 0.4604 (on pCV) respectively, higher than other state-of-the-art methods. In the case study, 95 new proteins were predicted, and the results showed that for the top-10 candidate lncRNAs, the hit rate of PMDKN algorithm could reach 42.8%, much higher than other method. Therefore, PMDKN can be used as an effective tool for lncRNA-protein interaction prediction.In this study, we proposed a new lncRNA-protein interaction prediction model, which not only can predict the unknown interactions between lncRNAs and proteins, but also has strong prediction ability for new lncRNAs and new proteins. To fairly evaluate the predictive performance of the model, we performed three 5-fold cross-validation on the two benchmark datasets, namely, The good performance of PMDKN may have the following reasons: First, feature extraction and network construction. We extract multiple features to describe lncRNA and protein in all directions and integrate multiple infomation to construct a more accurate lncRNA (protein) similarity network, effectively avoiding the over-fitting problem that may be caused by the information deviation of a single data source. Second, the use of neighborhood information. We modified the initial lncRNA-protein interaction network to overcome the network sparsity problem, and used the adaptive neighborhood completion strategy to eliminate the errors caused by the lack of information in the latent vectors of new lncRNAs (new protein), so as to ensure the predictive ability of new proteins and new lncRNAs. Finally, the construction of the ensemble predictive model. We combine the multiple sequence features of lncRNA (protein) and the integrated similarity networks to construct the predictive model, which distinguishes positive and negative observations by setting important levels and establishes the relationship between features and potential vectors through the projection of the features, so as to improve the accuracy of model prediction.The source code and datasets used in the paper can be found in the YM and XJ designed the projection-based neighborhood non-negative matrix factorization for lncRNA-protein interaction prediction. YM and XJ designed the experiment and wrote the manuscript. TH and XJ supervised and helped conceive the study. All authors read and approved the final manuscript.This research is supported by National Key Research and Development Program of China (2017YFC0909502) and the National Natural Science Foundation of China (61532008 and 61872157).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Saccharomyces cerevisiae in the latest Munich information center for protein (MIPS) dataset. The PPIN data of S. cerevisiae in MIPS dataset includes 4,554 unique proteins in 13,528 protein–protein interactions after the elimination of the self-replicating and the self-interacting protein pairs. Using the developed FunPred 3.0 tool, we are able to achieve the mean precision, the recall and the F-score values of 0.55, 0.82 and 0.66, respectively. FunPred 3.0 is then used to predict the functions of unpredicted protein pairs  in MIPS dataset of S. cerevisiae. The method is also capable of predicting the subcellular localization of proteins along with its corresponding functions. The code and the complete prediction results are available freely at: https://github.com/SovanSaha/FunPred-3.0.git.Proteins are the most versatile macromolecules in living systems and perform crucial biological functions. In the advent of the post-genomic era, the next generation sequencing is done routinely at the population scale for a variety of species. The challenging problem is to massively determine the functions of proteins that are yet not characterized by detailed experimental studies. Identification of protein functions experimentally is a laborious and time-consuming task involving many resources. We therefore propose the automated protein function prediction methodology using in silico algorithms trained on carefully curated experimental datasets. We present the improved protein function prediction tool FunPred 3.0, an extended version of our previous methodology FunPred 2, which exploits neighborhood properties in protein–protein interaction network (PPIN) and physicochemical properties of amino acids. Our method is validated using the available functional annotations in the PPIN network of  Proteins with similar functions are more likely to interact. If the function of one protein is known then the functions of the binding un-annotated protein may either be experimentally assigned or computationally predicted . SeveralThe protein function prediction problem is characterized by several factors like the diversity of members for functional groups, the hierarchical relationships among functional classes, incomplete or missing information about proteins and their functions. Thus, it defines a complex multi-label learning problem . HierarcF-score. While executing FunPred 3.0, it has been observed that 870 PPIs out of 13,528 protein-protein interactions (PPIs), that is, ∼6.4% of the overall MIPS dataset  in MIPS dataset of Capable of prediction of subcellular localization of proteins.In one of our two earlier works, Funpred-1 , the selPPIN formed of proteins and their corresponding interactions may contain essential/non-essential proteins as well as reliable/unreliable edges. Proteins having maximum number of interconnected neighbors are considered as essential proteins while proteins having less number of interconnected neighbors are considered as non-essential ones. Presence of non-essential neighbors in the PPIN might affect the unknown protein function prediction level accuracy. Therefore, proper identification and elimination of non-essential proteins is needed to ensure the presence of maximum number of essential proteins in the PPIN. In the proposed work, detection of essential proteins is implemented by node weight. Node weight  basicallM mutually exclusive clusters based on their node weight and edge weight in the overall PPIN. Node and edge weight have been used to ensure all the most essential nodes with higher reliability are present in the cluster and get selected as test proteins.All the unique proteins are first clustered into Functional annotations are then derived from the multi-level neighborhood of an unknown protein within each cluster.In newly proposed algorithm, FunPred 3.0, first detects protein cluster and then selects all the proteins as test proteins from different predicted clusters. We have adopted the approach of forming protein cluster as mentioned in the work of More specifically, FunPred 3.0 is categorized into two sections: FunPred 3.0_Clust and FunPred 3.0_Pred.M mutually exclusive protein clusters , obtained from FunPred 3.0_Clust are considered as essential proteins and hence they are included in the test set of our proposed methodology. In the second step, FunPred 3.0_Pred predicts labels these selected unlabeled (or test) proteins using neighborhood properties and physicochemical properties of amino acids  high node and edge weight threshold, (2) medium node and edge weight threshold, (3) low node and edge weight threshold. So, we have tested FunPred 3.0 at each of the three levels to assess its performance impact.Besides predicting protein function, it has been observed that the protein subcellular localization is yet another important aspect which needs to be considered since it helps in better understanding of protein function. So, subcellular localization dataset of yeast has been obtained from UniProt database . On careIn FunPred 3.0, the subcellular localization dataset of yeast is centrally categorized under three major sections: proteins residing in nucleus (termed as nuclear proteins), proteins residing in cytoplasm (termed as cytoplasm proteins) and proteins residing in other regions (termed as interface proteins). Besides these, there is also another section termed as unpredicted localization proteins, consisting of those whose localization are not yet predicted see –6. ThereSubcellular localization dataset of yeast contains 1,679 number of unpredicted localization proteins out of which our method predicts the localization of 638 proteins successfully. Localization information for the remaining 1,041 proteins cannot be predicted because of the absence of PPIN interaction in MIPS dataset as discussed earlier. This extra added layer of biological information about subcellular localization of proteins along with the protein function prediction boost up our methodology FunPred 3.0 to the next higher level.Initially, PPIN of yeast consists of 4,554 unique proteins in 13,528 protein-protein interactions (PPINs) after the elimination of the self-replicating and the self-interacting protein pairs. After the network refinement through the execution of node and edge weight threshold, non-essential proteins along with unreliable edges get eliminated and the initial PPIN gets reduced to almost 3,174 unique proteins and 6,936 PPINs (approx.) considering three levels of thresholds from which FunPred 3.0_Clust form protein clusters to generate test set of proteins.F-score (F) values, which are calculated using the following equations:During the result analysis it is observed that proteins belonging to random functional groups like lipid metabolism, DNA Repair etc., get selected as test proteins. In FunPred 3.0_Pred, all proteins from each protein cluster formed from FunPred 3.0_Clust are considered as test proteins. The overall initial PPIN of yeast is highlighted in F-score have been retrieved since number of selected test proteins are equivalent in both the cases. The result analysis as depicted in F-score of FunPred 3.0 are reckoned as 0.55, 0.82 and 0.66, respectively under high threshold. High recall and low precision emerges out as a major characteristic of FunPred 3.0 when compared to the other methodologies except FunPred-2. It highlights the fact that most of the admissible results are successfully generated by FunPred 3.0. https://github.com/SovanSaha/FunPred-3.0.git. From F-score values than the others including its earlier version FunPred-2.The performance of FunPred 3.0 has been analyzed under different levels of thresholds of node and edge weights as highlighted in F-score than FunPred 3.0. But it returns substantially high precision values than the others as well as all our methods. DCS  and comparison with the other protein functional group prediction models show the superiority of our approach. The FunPred 3.0 software has better performance than any existing function prediction in silico method. The network structure may be pruned based on the edge weight and along with it use of physico-chemical properties lead to improved and faster functional prediction in complex and diverse protein–protein interaction networks. We would like to estimate the effectiveness of our in silico method for other organisms, such as in human protein–protein interactions with even more complex network architectures.Our results  ∩ FunPred 3.0_Pred(P2). The results of all the predicted annotations for the MIPS dataset are available at https://github.com/SovanSaha/FunPred-3.0.git. Examples of the prediction of protein function and interactions for unpredicted pairs (both unknown and missing protein pair) have been shown in The initial results motivate us to predict the subcellular localization and unpredicted protein pair functions (missing/unknown functions) for 870 PPIs extracted from MIPS dataset. A protein can perform multiple functions in isolation. It may also perform some specific functions while interacting with one protein while perform some other specific functions while reacting with other proteins. But considering the fact that a protein often shares similar functions with proteins that interact with it , each prhttps://github.com/SovanSaha/FunPred-3.0.git.Summarizing, 767 unpredicted protein pair functions (511 unknown protein pair functions and 256 missing protein pair functions) in the MIPS dataset could be predicted using our FunPred 3.0 algorithm. Our approach failed to predict 103 unpredicted protein pairs since they have less number of acceptable neighbors. Simultaneously our methodology also performs very well in predicting subcellular localization of proteins as discussed earlier in the methodology section earlier. All the datasets and supplementary files are also freely available at https://github.com/SovanSaha/FunPred-3.0.git) for non-commercial research.FunPred 3.0 proved to be an improved and advanced version of our previous methodology FunPred-2. The enhanced performance of FunPred 3.0 is due to the use of node weight, edge weight and physicochemical properties of proteins in the prediction pathway of test set of proteins. It should be highlighted here that FunPred 3.0 incorporates the most essential features classified through four classifiers: XGBoost, Random Forest, Extra Tree and RFE. RFE which indeed plays an important role in improving the performance of the proposed methodology. This method does not consider dynamic PPIN and integration of other multiple types of data like domain , 2011b e10.7717/peerj.6830/supp-1Supplemental Information 1Information about Protein-Protein interaction network, Protein pair function prediction, MIPS database and physico-chemical properties.Click here for additional data file.


Top-down mass spectrometry has unique advantages in identifying proteoforms with multiple post-translational modifications and/or unknown alterations. Most software tools in this area search top-down mass spectra against a protein sequence database for proteoform identification. When the species studied in a mass spectrometry experiment lacks its proteome sequence database, a homologous protein sequence database can be used for proteoform identification. The accuracy of homologous protein sequences affects the sensitivity of proteoform identification and the accuracy of mass shift localization.Escherichia coli K12 MG1655, and evaluated its performance using an Escherichia coli K12 MG1655 proteome database and a homologous protein database. The number of identified spectra with the homologous database was about half of that with the Escherichia coli K12 MG1655 database. We also tested TopPIC on a top-down mass spectrometry data set of human MCF-7 cells and obtained similar results.We tested TopPIC, a commonly used software tool for top-down mass spectral identification, on a top-down mass spectrometry data set of Experimental results demonstrated that TopPIC is capable of identifying many proteoform spectrum matches and localizing unknown alterations using homologous protein sequences containing no more than 2 mutations.The online version of this article (10.1186/s12859-018-2462-1) contains supplementary material, which is available to authorized users. Top-down mass spectrometry (MS) has become a widely-used technology for proteoform identification because it has unique advantages in analyzing modified proteoforms . In the Database search is routinely used for spectral identification by top-down tandem mass spectrometry (MS/MS). In this approach, experimental MS/MS spectra are searched against theoretical spectra generated from database protein sequences to find high scoring proteoform spectrum matches (PrSMs). Many studies –7 have bWhen top-down MS/MS is used to analyze a species whose proteome database is unavailable, a homologous proteome database can be searched against for spectral identification. A homologous sequence contains mutations compared with the corresponding sequence of the species being studied. Alterations, such as PTMs and mutations, in a proteoform introduce mass shifts to peaks in its MS/MS spectra Fig. . A low sEscherichia coli K12 MG1655 showed that TopPIC achieved high sensitivity in proteoform identification and high accuracy in mass shift localization when homologous protein sequences contain no more than 2 mutations.TopPIC is a commonly used software tool for the identification and characterization of proteoforms with unknown alterations by top-down MS/MS. In this paper, we present a method for proteoform identification by top-down MS using homologous protein sequences when the species being studied lacks a proteome database. We also study how mutations in homologous database protein sequences affect the performance of TopPIC on proteoform identification. Experimental results on a top-down MS/MS data set from Escherichia coli (EC) and the second was from MCF-7 cells.Two top-down MS/MS data sets were used to evaluate the performance of TopPIC and how mutations in database protein sequences affect the sensitivity and accuracy of proteoform identification: the first was from ∘C with shaking. Cells were harvested at 4 ∘C. Cell pellets were lysed by using 0.1 mM zirconia/silica beads. Cell debris and beads were removed by centrifugation, and the clarified cell lysate was subject to ultracentrifugation at 4 ∘C for sub-cellular fractionation. EC protein was separated by reverse phase liquid chromatography (RPLC) on a Waters NanoAquity system with a custom packed C5 column and analyzed by an LTQ Orbitrap Velos mass spectrometer. Parent spectra were collected at a 60,000 resolution and the top 4 ions in each MS spectrum were selected for MS/MS analysis, in which the resolution was 60,000 and the alternating fragmentation mode was used. A total of 2027 collisional-induced dissociation (CID) and 2027 electron-transfer dissociation (ETD) MS/MS spectra were generated. The raw file of the EC data was converted into an mzXML file using msconvert in ProteoWizard (version 2.0) [The EC data set was published in . EC K12 ion 2.0) , and theion 2.0)  with defThe soluble MCF-7 intact proteins were separated using the bead-beating based cell lysis approach followed by a filter-based desalting step. A home-made long column was directly coupled on an LTQ Orbitrap Velos Pro mass spectrometer with a customized ion source. The MS/MS data were collected with data dependent CID and a resolution of 60,000. A total of 5310 MS/MS spectra were collected. The raw file of the MCF-7 data was converted into an mzML file using msconvert, and the mzML file was deconvoluted using TopFD in TopPIC suite  with defA proteome database of EC K12 MG1655 and a proteome database of EC ISC11 were used in the EC data analysis. The two proteome databases were downloaded from the UniProt database (version May 2016) . The twoThe design of the EC data analysis is shown in Fig. In TopPIC, a shuffled decoy database was concatenated to the target database, a 1% spectrum-level false discovery rate (FDR) was used for filtering identifications. The error tolerances for precursor and fragment masses were set to 15 parts-per-million (ppm), no fixed modifications were used, and the other parameters were set to default values. All the parameters are shown in Additional file We evaluated the accuracy of the identifications reported by the ISC11 database search as well as the accuracy of the localization of mass shifts in these identifications. In the evaluation, spectral identifications reported by the K12 database search were used to find “correct identifications” for the ISC11 database search.Q be a query spectrum, and FK12 the best scoring proteoform identified from the K12 database  We employ BLAST-P (version 2.3.31) [SK12 against the ISC11 database to find the best ISC11 homologous sequence, denoted by PISC11. The K12 protein segment SK12 may be aligned with only a subsequence of PISC11. (2) The R function “pairwiseAlignment” in package “Biostrings” [SK12 and PISC11. The subsequence of PISC11 in the alignment, denoted by homologous protein segment of Q. The match between the query spectrum Q and the homologous protein segment Q. We compare SISC11 to evaluate the accuracy of the ISC11 proteoform FISC11. If SISC11 and SISC11 and Below we describe the method for mapping a K12 identification to an ISC11 identification. Let  2.3.31)  to searcstrings”  is used E-value cutoff of 0.01 in BLAST-P was used to filter identifications. Other parameters in BLAST-P were set to default values. In the function “pairwiseAlignment”, the penalty for a gap extension was set to 4, the penalty for a gap opening was set to 10, and all the other parameters were set to default values.In the analysis of the EC data, an SSHIFT of amino acids in the ISC11 protein segment as the possible positions of the mutations for the unknown mass shift. If SSHIFT covers the positions of all the substitutions, insertions and deletions, we say the reported subsequence SSHIFT is correct. (2) The K12 proteoform has an unknown mass shift compared with the unmodified K12 protein segment. In this case, the unknown mass shift in the ISC11 proteoform is the sum of the mass shifts of all the substitutions, insertions and deletions between the K12 protein segment and the ISC11 protein segment as well as the unknown mass shift in the K12 proteoform. If SSHIFT covers the positions of all the substitutions, insertions and deletions as well as those of the mass shift, we say the reported subsequence SSHIFT is correct.When a PrSM reported from the ISC11 database search correctly identifies the protein segment, we further study the accuracy of its mass shift localization. There are two types of unknown mass shifts in reported matches between spectra and ISC11 proteoforms: (1) The K12 proteoform of the query spectrum does not have any unknown alternations, and the K12 protein segment is the same as the K12 proteoform. In this case, the unknown mass shift in the ISC11 proteoform is the sum of the mass shifts of all the substitutions, insertions and deletions between the K12 protein segment and the ISC11 protein segment. TopPIC reports a subsequence E-value cutoff of 0.01. BLOSUM62 was used as the similarity matrix and other parameters were set to default values in BLAST-P. Of the 4312 K12 protein sequences, 3769 were aligned to homologous ISC11 protein sequences. BLAST-P reported a sequence identity  for each K12 and ISC11 protein sequence pair. The histogram of the reported sequence identities is given in Fig. %) have an identity no less than 90%, and 960 (25.5%) have an identity between 80% and 90%. The sequence similarities are high for many reported homologous sequence pairs, making it possible to search the EC top-down MS data against the ISC11 database for proteoform identification.We analyzed the similarity between protein sequences in the K12 database and their homologous sequences in the ISC11 database. We used BLAST-P to search each protein sequence in the K12 database against the ISC11 database to find its homologous sequence with an %) spectra and missed 946 spectra identified by the K12 database search  between them. Of the 1792 segment pairs, a total of 1126 have no more than 4 mutations: 328 segment pairs are the same, 536 segment pairs contain only one or two mutations, and 262 segment pairs contain three or four mutations. When the homologous protein segment of a query spectrum contains 5 or more mutations, software tools for spectral identification often fail to identify it using homologous sequences. As a result, we focused on only the 1126 spectra with no more than 4 mutations in segment pairs, and these spectra are referred to as the high identity spectrum (HIS) set.For each of the 1895 identified K12 PrSMs, we obtained its K12 proteoform and K12 protein segment, and searched the K12 protein segment against the ISC11 database to find its homologous protein segment.  of the spectra identified by the human database search.  and 557 each with one unknown mass shift (mass shift group).%) of the 569 spectra in the perfect group and 375 (67.32%) of the 557 spectra in the mass shift group. In the perfect group, the ISC11 database search identified the correct protein for 444 spectra, and the correct protein segment for 438 spectra.  based on the numbers of mutations between their K12 segments and homologous protein segments. (See “Gi (0≤i≤4) contained the spectra corresponding to protein segment pairs with i mutations. The 557 spectra in the mass shift group were divided into 5 subgroups H0, H1, H2, H3, H4  similarly. Let nt be the number of spectra in a subgroup. Let np and ns be the numbers of spectra in the subgroup with correct protein identifications and correct protein segment identifications in the ISC11 database search, respectively. We define the correct protein (CP) rate and the correct segment (CS) rate as the ratios We investigated how the numbers of mutations in homologous sequences affect the sensitivity of spectral identification when the homologous ISC11 database was used. The 569 spectra in the perfect group were divided into 5 subgroups % to 18.8%, and that for the mass shift group drops from 92.7% to 22.2%. The CS rates are above 79% for G1, G2, and H1, showing that a good sensitivity can be obtained when homologous sequences have high similarity (no more than 2 mutations). The CS rates are below 25% for G3 and G4. In general, the mass shift subgroups have lower CS rates than the perfect subgroups because of the existence of unknown mass shifts in identified proteoforms. In the 10 subgroups, the CS rates are similar to the CP rates, demonstrating that the ISC11 database search can identify the correct protein segment in most cases when the correct protein is identified.As the number of mutations per protein segment pair increases from 0 to 4, the CS rate for the perfect group drops from 99.0G1,…,G4,H1,…,H4 for the evaluation of mass shift localization. A total of 248 PrSMs were selected from the perfect group, and 235 from the mass shift group. The mass shifts were correctly localized in 132 (53.2%) of the 248 PrSMs in the perfect group and in 35 (14.9%) of the 235 PrSMs in the mass shift group Additional file 2Identifications from the EC data set. (XLSX 583 kb)Additional file 3Identifications from the MCF-7 data set. (XLSX 351 kb)


Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein−protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment. In order to study protein complexes, new experimental assays have been recently developed to reconstruct protein−protein interactions, protein complex composition, and stoichiometry coefficients within HeLa cell line.3 On the other hand, computational methods can now identify protein complexes from interaction networks.4 However, these methods cannot provide quantitative prediction of protein complexes yet. Solving this task for the whole proteome is complicated by the large scale of this system and requires high computational power. Computational approaches that aims to predict such information requires finding the right abstraction level to model protein complex formation. High resolution simulations can provide detailed information of a limited set of protein complexes,6 but cannot be applied to large-scale scenarios. To solve these issues, we previously designed SiComPre, a method to perform stochastic simulations of protein complexation and decomplexation reactions.1 Models were generated from a priori information: protein−protein interactions, domain−domain interactions, GO functional terms, and protein abundances. In a stochastic simulation  we follow each individual molecule and track its binding and unbinding reactions as it diffuses through well-mixed subvolumes, where these reactions can occur. Domains were used as binding sites for a protein and in the case of a PPI the proteins that do not have any interacting domains, we added a pair of interacting fictitious domains only if the two proteins have a common “biological function” according to Gene Ontology (GO).8 Space is modeled as a two-dimensional grid where each lattice is a subvolume and proteins can diffuse across them at each timepoint according the Flick’s law. Between two timepoints an instance of Gillespie’s algorithm is executed for each subvolume which simulates complexation and decomplexation reactions between proteins present in the same subvolume. This approach allows us to simulate the competitive binding to a protein binding site and predict protein complexes specific for a condition corresponding to the protein abundance data used as input of the model. Finally, simulation results are postprocessed to obtain qualitative and quantitative prediction of protein complexes.Most proteins are biologically active only when they serve as part of a protein complex and their identification is important to understand cellular functions. Protein complex assembly is limited by multiple constraints that need to be considered for protein complex predictions. A pair of interacting proteins has to be expressed in the same cell, within the same subcellular compartment at the same time. Moreover, high abundance of a protein can titrate away some or all of its binding partners, which will not be capable of interacting with other molecules. Binding sites are also a finite resource of a protein, limiting the number of possible simultaneous interactions. Even when multiple binding sites are available, it is not always possible for a protein to have many simultaneous interactions, since a bounded protein might make other binding sites inaccessible due to its size. Furthermore, cellular functions are dependent on the quantities of protein complexes,In the first version of the method, the simulation space was not corresponding to any biological meaning, each protein could have unlimited simultaneous binding partners and we could not provide a software tool for users without advanced computational skills. Here we present SiComPre 1.0, a software tool based on an updated version of our previously defined method. SiComPre 1.0 has an easy-to-use graphical user interface allowing the customization of all parameters and setting of the simulations. Furthermore, it provides a tool to compare complexomes simulated with various settings, enabling context-dependent prediction of protein complexes. Finally, we optimized the core simulation algorithm to increase computational performance on GPU.The goals of SiComPre 1.0 are to provide an accessible interface to all its features, provide the capability to compare results to investigate the effects of context-specific proteomics data on predictions and to improve the scores in predicting protein complexes.1 the SiComPre 1.0 simulate stochastic complexation, decomplexation and diffusion of proteins in a 2D space, dynamically generating a list of complexes , which show quite robust distribution of the various complexes, independent of initial condition and simulated time (after a transient period). To obtain both a qualitative and quantitative prediction, the software refines the prediction in order to generate a list of refined complexes (RCs) together with their abundances  could give a prediction of the distribution of the various alternative forms.Like in our previous method,ces Fig. . SiComProns Fig. , which mons Fig. . For thi17 into a format directly used as input for SiComPre 1.0  as well as select parameters for model generation and simulation  and the score of the given predicted complex against a reference database, with the best-matching known complex.We also added a functionality to automatically compare results of multiple sets of simulations. This process identifies clusters of structurally similar complexes across multiple simulation results (e.g. generated from different inputs) and report variations in their abundances (Supplementary Text 18), users can generate networks of the predicted protein complexes containing info on the abundance of proteins participating in a given complex (how many times a protein appears in a simulated complex that has the selected RC as best matching), with some proteins appearing in multiple copies in a given complex. The exported file also gives information on the specificity of proteins in a complex as defined above.Another key feature of SiComPre 1.0 is the possibility to analyze the specificity of proteins constituting protein complexes. Users can export a list of protein complexes, together with the abundance of each protein within a given complex and their total abundance. The combination of these abundances gives a specificity measure (the fraction of proteins in the whole cell that are found in simulated complexes matching the selected RC) of each protein to each protein complex, which can be used to design the best baits for any protein complex  and accuracy.23 protein localization and molecular function from MIPS24 and protein abundances from Ghaemmaghami et al.25 Protein domains association are retrieved from SMART26 and IDDI27 was used as DDI dataset. We first tested new features of SiComPre 1.0 on the limit of the total possible binding to each protein and the effects of incorporating compartmental localization data on each protein (Supplementary Text 21) when there was an overlap score >0.25. According to the optimal composite score values,28 we found that 4096 subvolumes and a binding limit of 10 simultaneous partners give the best results  than with data on compartments  of them were associated to ribosomal proteins in GO, suggesting that ribosomal proteins do not interact with each other directly, rather they interact through ribosomal RNAs (rRNAs). Therefore, we added protein−rRNA binding information29 to the simulation  to 1036 proteins. As expected, a large portion .1With SiComPre 1.0 it is possible to predict the abundance of protein complexes in a cell by merging similar SCs into RCs and count the overlapping SCs for each RC. These results can be exported as CSV file together with other features . Separately, we validated our quantitative predictions against the limited protein complex abundance data from the literature.30 Although expression of many proteins is post-transcriptionally regulated, this is helpful to extend the applicability of SiComPre 1.0 to a larger spectrum of conditions. Nowadays gene expression quantification is available for multiple cell types and organisms obtained from different experimental conditions and also at the single-cell level.31 Furthermore, these data have typically a larger coverage compared to proteomics data. This method of using mRNA abundances instead of protein abundances led to a decrement in the qualitative matching composite score to 2.0, but at the same time the quantitative predictions increase to a Pearson’s correlation of 0.94 , with the reduction of protein abundances to the square root of measured values.33 is to induce prkaa1, scaffold protein for the formation of AMPK complex, whose functions are to regulate cellular energy, metabolism and it acts as a regulator of cell polarity by remodeling actin cytoskeleton. To exploit the potential of SiComPre 1.0 in predicting variation in the complexome upon perturbation, we compare protein complexes predicted from protein abundance data on mouse liver before and after metformin treatment. SiComPre 1.0 cannot model post-translational modifications, but it can give an insight about which protein complexes might be affected upon perturbation by introducing a new molecule into the system. We ran a set of three simulations with a total of six timepoints for each of the following protein abundance setups: (i) normal liver protein abundances, (ii) post metformin treatment liver protein abundances and (iii) normal liver protein abundances, plus metformin with its molecular bindings, retrieved from STITCH.34 We compared these simulations in two different ways to test how well SiComPre can predict the effect of metformin on the liver complexome. We compared the simulated complexome of the normal liver (i) to the experimentally perturbed system , and (ii) to the system, where in silico we added only metformin with its interactions to the normal complexome. Protein abundances data came from MOPED,17 PPI from Biorgrid,35 DDI from IDDI,27 protein domain from SMART,26 protein localization and protein function from GO.8 We used different input resources to highlight that SiComPre 1.0 can handle various input formats. In the supplement we provide a series of scripts that can help users to transform downloaded data from various online resources into a format readable by SiComPre 1.0 . The primary action, according to Drugbank,37 In addition, the observed relationship between metformin and histone levels have been identified earlier.38 Furthermore, SiComPre 1.0 also suggests that metformin affects cell motility and the cytoskeleton, as shown in PC3 and DU145 prostate cancer cells.39 The predicted complexes responsible for this overlapping pattern can be matched to the chaperonin containing TCP1 complex, p97-Ufd1-Npl4-IP3 receptor complex, Smcb-Smcd-PW29 complex, Xin-Cdh2-Ctnnb1-Ctnnd1 complex, and Kif3-cadherin-catenin complex.Heatmap of protein complex abundances show that the experimental metformin treatment caused a large effect on the abundance of proteins and as a result on the abundance of protein complexes Fig. , while t19 Multiple PPI datasets were tested and we found that the Human Integrated Protein−Protein Interaction Reference, called Hippie40 was performing better than any other dataset with both SiComPre 1.0 and ClusterOne (Supplementary Text S5). Using this dataset as the PPI input dataset, SiComPre 1.0 was able to identify a total of 56% of all the complexes collected in the CORUM reference dataset.22 The tissue with the highest percentage of matched complexes was the adult testis (29%), while the data from adult heart tissue gave the lowest matching (15%) to protein complexes in CORUM. In comparison, ClusterOne20 could predict only 10% of these complexes, highlighting that SiComPre 1.0 and the incorporation of protein abundance data could help better predictions  and DNA binding (GO:0003677) was more abundant in B cells, frontal lobe and adult monocytes, while the subunit involved in threonine-type endopeptidase and peptidase activity (GO:0004298) was more abundant in TH cells, testis, and adult ovary  as the complex with the highest variability in its abundance. It was highly abundant in adult B and TH cells and in fetal ovarian cells. To investigate how the complexome of different cell types differ from each other we performed a principal component analysis based on SiComPre 1.0-predicted complex abundances  that can be used to drive proteomic experiments and to reveal new drug targets. To investigate competing binding events, the tool allows users to investigate what complexes are formed in a limited set of proteins and it also can predict the quantities of the competing complexes. SiComPre 1.0 can be also used to test changes in the complexome for perturbations where protein abundances change within cells .The accuracy of qualitative and quantitative predictions is interdependent. We cannot have a good quantitative prediction with a wrong qualitative prediction Fig. . FurtherEscherichia coli cells under 22 different growth conditions49 and data generated on further tissue-dependent expressions in multiple organisms.51 It was also shown how to quantify proteins at the single-cell level.52 These data can be translated into single-cell complexome prediction with SiComPre 1.0 to explore cell-to-cell variability in terms of protein complexes. In general, SiComPre 1.0 allows users to quickly analyze quantitative proteomics data and predict differences in the complexome. This could lead to useful insights to design further functional experiments.With the advent of new experimental techniques, novel context-specific data are becoming available. These provide an exciting opportunity for SiComPre 1.0 users to study how protein complexes vary in different conditions. For instance, Schmidt et al. recently quantified the absolute protein abundance in SiComPre 1.0 is still not able to dynamically capture the expression and degradation of proteins over time as this will require a more computationally intensive simulation, making the analysis of human data nearly impossible. However, users with time-dependent data can use such data as input of SiComPre 1.0 to obtain protein complex predictions for multiple time points and compare how the complexome changes in time. Currently, predictions are based on approximated, fixed parameters for all proteins , but as these become available the software already allows to use individually measured rates for each protein and interaction.21 We considered a successful prediction on protein complexes with an overlap >0.25, where the overlap score for a predicted complex p compared to a reference complex r is defined as follows:Predicted protein complexes were compared to a reference dataset.A combination of different measurement scores has been utilized to assess SiComPre performance against other tools.Recall: the fraction of successfully predicted protein complexes in the reference dataset.ω is the threshold value, P is the set of simulated complexes and B of reference complexes.Sensitivity: the fraction of proteins in reference protein complex i which are found in a predicted complex j, the real complex-wise sensitivity is the maximal fraction of proteins of complex i by its best-matching simulated complex complex-wise sensitivity is the weighted average of real complex-wise sensitivity over all complexesPositive predictive value: the number of proteins in a predicted complex j that belong to a real complex i over the total number of proteins of predicted complex j assigned to all complexes, Accuracy: geometric accuracy is the geometrical mean of Sn and PPV, MMR: is a measure proposed by Nepusz et al.20 which tries to find the better correspondence between simulated complexes and reference complexes. This value can be calculated as maximum bipartite matching in weighted graph, where nodes corresponding to a predicted complex are connected with an edge to a node representing a real complex where its weight is the overlap value between the complexes.53 Next, small adjustments were adopted to better fit the size to the number of proteins in the relative compartment. The input data used for the experiment is located and described in the “Data” directory of SiComPre 1.0.All the simulations have been performed with SiComPre 1.0 GUI. For all simulations grid dimension was 64 × 64 SVs and SiComPre was executed with default parameters. Only for human and mouse simulations, cell volume was set to 1000, because protein abundances were expressed in molecules/million. For yeast simulation, maximum concentration of protein in one SV was set to 150, while in human the value used was 1500. Size of compartments has been chosen according to the square root of the volume reported for liver cells.i in a complex c is defined as i in a complex c, while Ai is the total abundance of the protein i in the system.The specificity for a protein 36Statistical analyses have been performed in R and Python. Function enrichment analysis was performed with DAVID.Supplementary Text 1


Relationships between bio-entities  constitute a significant part of our knowledge. Most of this information is documented as unstructured text in different forms, such as books, articles and on-line pages. Automatic extraction of such information and storing it in structured form could help researchers more easily access such information and also make it possible to incorporate it in advanced integrative analysis. In this study, we developed a novel approach to extract bio-entity relationships information using Nature Language Processing (NLP) and a graph-theoretic algorithm.Our method, called GRGT , not only extracts the pairs of terms that have certain relationships, but also extracts the type of relationship (the word describing the relationships). In addition, the directionality of the relationship can also be extracted. Our method is based on the assumption that a triplet exists for a pair of interactions. A triplet is defined as two terms (entities) and an interaction word describing the relationship of the two terms in a sentence. We first use a sentence parsing tool to obtain the sentence structure represented as a dependency graph where words are nodes and edges are typed dependencies. The shortest paths among the pairs of words in the triplet are then extracted, which form the basis for our information extraction method. Flexible pattern matching scheme was then used to match a triplet graph with unknown relationship to those triplet graphs with labels  in the database.We applied the method on three benchmark datasets to extract the protein-protein-interactions (PPIs), and obtained better precision than the top performing methods in literature.We have developed a method to extract the protein-protein interactions from biomedical literature. PPIs extracted by our method have higher precision among other methods, suggesting that our method can be used to effectively extract PPIs and deposit them into databases. Beyond extracting PPIs, our method could be easily extended to extracting relationship information between other bio-entities. Relationships among different biological terms such as genes, proteins, diseases, small molecules, pathways, and gene ontology (TO) terms  form the backbone of our knowledge. Bio-entity relationships such as protein-protein interactions (PPIs) are indispensable for understanding of complex diseases, biological processes, and guiding drug discoveries . Human aHowever, human annotation can be very time and resource consuming, and keeping pace with the ever increasing amount of biomedical publications has become more and more difficult. As a result, computational methods have been designed to extract bio-entity relationships automatically from the literature, and used to assist scientists in their efforts to build databases using manual annotation approach –48. MostSimple rules, such as co-occurrence, were used in the early efforts of PPI extraction. Co-occurrence assumes that two proteins likely interact with each other if they co-occurred in the same sentence/abstract , 71. The... [. [Recently, machine learning solutions have been proposed to extract PPI information automatically. By learning the language rules from annotated texts, machine learning techniques can perform better than other methods in terms of both decreasing the false-positive rate and increasing the coverage –69. Huan...  used a d... [.  used a kAlthough extensive studies have by far been carried out, existing methods only achieved partial success in small datasets , 67, 73 A machine learning based PPI extraction method was developed by Chowdhary et al. . In thisThus far, there have been few methods that extract both the protein names and the interaction words at the same time. However, only the protein names are insufficient to understand PPIs. As a result, there is an urgent need to extract the PPI triplet (two different protein names and one interact word) in order to reveal how the proteins are interacted .interacts. There are five PPI triplets , and one single sentence contains only one triplet; In practice, however, a PPI triplet ordered as (interaction word --- protein1 --- protein2) may occur, and for each sentence, multiple distinguished triplets may exist as well. In most cases, there is only one triplet that describes the true PPI. For example, the sentence in Fig. Recently Natural Language Processing (NLP) techniques have been utilized in many machine learning approaches –66 to paBui et al. has developed a hybrid approach for extracting PPIs . The metA comprehensive benchmark was developed for Kernel based PPI extraction methods by Tikk et al. . In the In this paper, we propose a method based on NLP and automatically learn rules/patterns to extract the PPI triplets from sentences. We then classify them as true or false with probabilities based on whether the interaction words correctly describe the interaction relationship between the two participant protein names.Our method, GRGT, utilized the grammatical relationship among each Protein-Protein-Interaction triplet extracted by natural language processing (NLP) techniques and a graph theorem algorithm  as feature to build a classifier. A dictionary of protein names and interaction words with their morphemes were built based on our previous study . All int“The first PDZ domain of PAR3alpha is considered to interact with PAR6.,” was parsed to have a relationship graph showing in Fig. nn, nsubj, etc. are typed dependencies defined in [The sentence harboring the PPI triple was first tokenized, so that each word took their own tag as an independent component. The tokenized sentence was then parsed using Stanford Sentence Parser to obtain the grammatical relationships among all the words. For example, the sentence, fined in .Fig. 2Grdep), which has the following types: auxiliary (aux), argument (arg), coordination (cc), conjunct (conj), expletive (expl), modifier (mod), parataxis (parataxis), punctuation (punct), referent (ref) and semantic dependent (sdep). Each of the above types may have subtypes themselves. For example, arg has subtypes: agent (agent), complement (comp) and subject (subj), where subj has nominal subject (nsubj) and clausal subject (csubj) as its subtypes. For example, “domain” is nsubj of “interact”  as the minimal sub-graph containing the triplet. Dijkstra’s shortest paths algorithm was adopted to find the shortest path  interact with P2 (PAR6).” The information in this graph is all the information we need to know to infer the interaction between PAR3alpha and PAR6. In fact, for two triplets with only altered protein names but exactly the same GRGT, these two triples are equivalent in the sense of grammatical relations; thus, they shall be classified as the same category. Although the direct feature, exact GRGT, is quite specific and the classification based on only these exact GRGTs are of very high precision. It sacrifices the generalizability a lot: the pattern of a new GRGT of PPI triplet has to match the training true patterns exactly to be considered as a true PPI. To introduce more general GRGTs, we could relax the subgraph. For example, in the subgraph above, we allowed the domain to vary from annotated samples  in all triplets that have this feature.The directions of the sub-graph can also be inferred at the same time, since the information of the direction of the true patterns can also be annotated.“domain of P1 interact with P2” as the annotated training sample to demonstrate how the decision tree works. The procedure is shown below:“domain of P1 interact with P2” is a match, and the probability of triplet “P1-interact-P2” being true is assigned as the probability of this feature being associated with true PPIs. However, “motif of P1 interact with P2” does not match the feature, thus should be passed to the next level.The first level of the decision tree will be the exact feature in Fig. “motif of P1 interact with P2” is a match; therefore, the probability of this triplet P1-interact-P2 classified as true triplet is the associated probability of the feature. However, the sentence “motif of P1 associates with P2” does not have a feature in this level since the interaction word is different. Therefore, it is passed to the next level.The second level is the relaxed graph as shown in Fig. “motif of P1 associates with P2” is a match in level 3, although it is not a match in level 1 or 2. Therefore the triplet P1-associates-P2 is given the probability being true as the probability of the feature being true. If a sentence fails to match the pattern in this level (in practice there may be much more levels), we mark the triplet contained in this sentence as a false triplet.The third level, as described in Fig. A simple decision tree Fig.  was usedTable Table Recently, several studies introduced deep learning methods for PPI extraction –83. We ansubj  can be reduced to subj (subject) or even further to arg (argument). We need to balance recall and precision rate while doing this, as simplification would improve the recall rate, but with a cost of lowered precision rate. Some more experiments can be performed on various ways of reducing the exact patterns, and on how to combine the new relaxed patterns with our existing patterns by designing different decision trees to achieve better performance.To further improve the performance of GRGT, the patterns can be simplified further so that more true triplets can be matched if they are similar to true patterns, but not exactly the same. The hierarchical structure of the typed dependencies can be used for this purpose. For example, We further analyzed the extracted patterns (subgraphs), and in Table This method can be used to extract other relationships as well, as long as the triplet is well defined and the library for terms and interaction words are given.Consistent with literature, Table In this work, we developed a new NLP-based method, GRGT, for extracting the protein-protein interactions from biomedical literature. The performance of GRGT was demonstrated by comparing with top performing methods using benchmark datasets. GRGT obtained better precision, indicating that researchers can use PPIs extracted by GRGT as prior knowledge to guide experiment design with high confidence. We believe that GRGT will be a very useful tool for PPI-extraction task.


Detecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems.In this study, we present a novel algorithm, named Improved Flower Pollination Algorithm (IFPA), to identify protein complexes in multi-relation reconstructed dynamic PPI networks. Specifically, we first introduce a concept called co-essentiality, which considers the protein essentiality to search essential interactions, Then, we devise the multi-relation reconstructed dynamic PPI networks (MRDPNs) and discover the potential cores of protein complexes in MRDPNs. Finally, an IFPA algorithm is put forward based on the flower pollination mechanism to generate protein complexes by simulating the process of pollen find the optimal pollination plants, namely, attach the peripheries to the corresponding cores.The experimental results on three different datasets  show that our IFPA algorithm is more superior to some representative methods in the prediction of protein complexes.Our proposed IFPA algorithm is powerful in protein complex detection by building multi-relation reconstructed dynamic protein networks and using improved flower pollination algorithm. The experimental results indicate that our IFPA algorithm can obtain better performance than other methods. Understanding biological processes is an important task in the living organisms. Proteins are vital components in many biological processes, such as metabolism, signaling, transportation and so on. Biological functions are performed by protein complexes composed of proteins interacted with each other, rather than by individual proteins , 2. DeteAs one of the earliest computational methods to predict protein complexes, the Molecular Complex Detection (MCODE)  weightedIt is well known that Gavin et al.  introducIn general, the judgment of interactions between two proteins is implemented by using experimental methods. Unfortunately, these methods are not always dependable  and it mSince the flower pollination algorithm (FPA)  has showThe remaining part of this paper is organized as follows. Section 2 (Methods) elucidates our proposed new algorithm called IFPA. Section 3 (Results and Discussion) provides the exhaustive analysis and descriptions for the experiments. Finally, Section 4 (Conclusions) is the summary of this study.The PPI network can be represented generally as an undirected graph, and the proteins are treated as nodes and the interactions are considered as edges. Here, the static PPI networks are converted into the multi-relation reconstructed dynamic PPI networks. And then we apply IFPA to add attachments to the appropriate cores based on the core-attachment structure.The availability of gene expression data enables researchers to reveal the dynamics of molecular networks and improve the identification of protein complexes –24. Hencv is considered to be active in DPSN if its gene expression value is not less than the active threshold Active_Th(v):μ(v) is the algorithmic mean of gene expression values of v over times 1 to n and σ(v) is the standard deviation of its gene expression values. For each protein, three-sigma method is used to calculate the active threshold Active_Th(v). A original PPI network can be described as an undirected graph G, where V denotes node set that are proteins and E presents edge set that are their connections. And the dynamic PPI network can be represented as Gt  at timestamp t . At a certain time point, if two proteins vi and vj are active and interact with each other in the original static PPI network, then there is a connection between protein vi and vj in a DPSN. After that, twelve dynamic PPI subnetworks are constructed from the original static PPI network.First, we use three-sigma method  in orderDefinition 1  Essential proteins are indispensable for the survival of an organism. Then we can believe that the interaction between two essential proteins is also necessary. Hence, a concept based on essential protein is extended to measure the essentiality between two proteins, and the essentiality values are considered as their weights.Moreover, integrating heterogeneous data source into a single network can enhance the reliability of networks, which inspires us that assigning the suitable weights to edges can strengthen the confidence of interactions, and the implementation will be discussed in the following. Figure vi and vj, the edge between them is considered as an essential edge if both of vi and vj are the essential proteins, similarly, the edge between them is considered as an uncertain edge if vi or vj is the essential protein, and the edge between them is considered as a nonessential edge if neither of vi and vj is the essential protein. Only the essential edges are taken into account to reconstruct the networks here. And eeij is the essential edge between vi and vj, the co-essentiality between these two proteins can be represented as follows.ESSij denotes the weight value of essential edge which equals to one and sum(ESSj) denotes the sum of the weight values of a column.Definition 2  Given two interacting proteins vi and vj, the interaction between them will be more reliable if vi and vj exist in same subcellular location, its co-localization is defined by the following equation.Before giving the concept of co-essentiality, we first elaborate the definition of an essential edge. Given two proteins SCLi| and |SCLj| are the number of subcellular location of proteins vi and vj, respectively.Definition 3 (Co-annotation) Given two interacting proteins vi and vj, they have the similar function if there are some common GO annotations between vi and vj, its co-annotation is calculated as follows.GOi| and |GOj| are the number of GO annotations of proteins vi and vj, respectively.Definition 4 (Co-cluster) Given two interacting proteins vi and vj, its co-cluster is measured by using the edge clustering coefficient (ECC) [nt (ECC)  as folloZij represents the number of triangles built on edge , |Ni| and |Nj| are the degrees of protein vi and vj, respectively.vi and vj is stands for as follows.Multiple relation defined above are used to weight the networks. The multi-relation value between W to upgrade the credibility of interactions. For an edge, its normalized W value NW is expressed by the following formula.num(multi-relation) is the total number of the network relations, i.e., the four kinds of relations including coessentiality, colocalization, coannotation, cocluster and the networks are reconstructed by mixing them. Eventually, the dynamic PPI subnetworks (DPSNs) are switched into the multi-relation reconstructed dynamic PPI networks (MRDPNs).These multi-relation values are regarded as the weights of edges Definition 5 (Weighted Degree) The proteins with weighted degree greater than average weighted degree are sorted in descending order as the candidate core set CC. The weighted degree of a protein i in the MRDPN is the number of interactions in which this protein is involved, which can be expressed as follows.As we all know that protein complex core should be a densely connected subgraph in the PPI network. Thus, we pick the seed proteins in the first stage, and extend seed proteins to the cores in the second stage.CC be a seed protein which plays an irreplaceable role in protein complex. The neighbors of the seed protein are inserted into a core set when the condition that the density of core set is greater than a given threshold DT is satisfied. The threshold DT will be discussed in the next section.CS can measure how close the core is, and its definition is as follows.Definition 6 (Density) The density of core set Let first node in the candidate core set CS| denotes the number of nodes in core set. Initially, core set CS contains one seed protein i. A neighbor of seed protein is added to the core set if adding it can make the Density(CS) greater than the threshold DT. This process is repeated until all neighbors of seed protein are sought and the predicted core is generated. Once a complex core is completed, all nodes in it will be labeled with “1” and cannot be extended into any other complex cores. This process will stop when the CC is empty.Since the core plays a central role, the periphery plays a supporting role. The key idea behind our presented IFPA algorithm is to utilize the pollination mechanism to mimic the process of pollen falling on suitable flowers, which is completely different from other general methods. In this subsection, we first give a brief introduction to the flower pollination algorithm (FPA) , and theFPA is a nature-inspired optimization algorithm that comprises two main patterns, that is global pollination and local pollination. The global pollination can be represented as:i at iteration t, and G is the current best solution. The parameter L is the strength of the pollination, namely a step size, we use a Lévy flight to represent that insects move over a long distance with various distance steps. That is, L is greater than 0 and obeys the Lévy distribution:Γ(λ) is the standard gamma function. The local pollination can be defined as:Ψ obeys the uniform distribution of 0 to 1.Thr denotes a threshold and it is set as 0.2 here. The function of randperm is to return an integer from one to Num which means to find new core sequence number, and Num is the number of cores and the value of d is one.Then, we use IFPA which is an advanced version of FPA algorithm to find the closest cores for peripheries, which is equivalent to finding the most satisfactory flowering plants for pollen. The workflow of IFPA algorithm is shown in Fig. Definition 7 (Pollination Priority) As a part of an entire protein complex, the attachments maintain relatively close relationship with the core, we call this relationship as pollination priority. The “pollination priority” of a pollen to its core set CS is represented as follows.u is the protein in core set CS. The pollination priority depends on the affinity between the pollen and the flowers. The closer the relationship between pollen and a flower, the higher priority it pollinates on this flower. In the update procedure, if the pollen can find a flower that makes the value of pollination priority better, then the pollen falls on this flower, otherwise, the pollen finds a new flower to pollinate.Finally, we further merge all the candidate protein complexes mined in twelve subnetworks and filter highly overlapping complexes, as our final predicted protein complexes. Algorithm 1 outlines the implementation process of our IFPA method.Three popular datasets, i.e., DIP , MIPS 2 and Kroghttp://sequence.stanford.edu/group/yeast_deletion_project). CYC2008 [The protein subcellular localization dataset is downloaded from the COMPARTMENTS database . There a CYC2008  is used The most commonly used evaluation metrics are used in our experiments and their specific definitions are described below.Definition 8 (Overlapping Score) Given a predicted protein complex P and a known protein complex K, the Overlapping Score (OS) between P and K is defined as follows.VP ∩ VK| is the number of common proteins in the predicted protein complex P and the known protein complex K, |VP| is the size of the predicted complex and |VK| is the size of the known complex. If OS ≥ 0.2, we consider that the predicted complex matches with the real one.Definition 9  Sensitivity (Sn), Specificity (Sp) and F-measure are represented as follows.TP is the number of the predicted complexes which are matched with the known complexes, FN is the number of known complexes which are not matched with any predicted complexes, and FP is the number of the predicted complexes which are not matched with any known complexes. F-measure is a comprehensive metric combined sensitivity and specificity.Definition 10  In order to estimate the statistical significance of detected protein complexes, the researchers annotate their biological functions by using p-value. Given a predicted protein complex, the p-value [k is the number of proteins of the functional group in the complex, N is the size of the whole PPI network, C is the size of a protein complex and F is the size of a functional group in the network. And the p-value is defined as follows. p-value  is the pp-value is used to evaluate the biological relevance of the predicted protein complexes. Generally, the protein complex is considered to be meaningless if its p-value is greater than 0.01. And for a complex, the smaller its p-value is, the more biological significance it has.The DT decides whether a protein could be merged into the current core. How to choose a relatively suitable DT should be carefully considered to achieve better performance of our IFPA algorithm. Thus, varying DT from 0.1 to 0.9 with the interval 0.05, we calculate F-measure to observe the effect of the variation of DT on the performance of our IFPA algorithm, so as to choose the relatively appropriate DT, as shown in Fig. F-measure obtains the better value when DT is set as 0.25 based on the both DIP and MIPS datasets and when DT is set as 0.15 based on the Krogan dataset, which means that IFPA is more effective. For this reason, the density threshold DT is set as 0.25 in DIP and MIPS datasets and 0.15 in Krogan dataset.The density threshold Sn, Sp and F-measure based on DIP, MIPS and Krogan datasets, respectively. Apparently, IFPA algorithm yields the best F-measure in comparison with other existing methods in all datasets, which means that our IFPA method remarkably outperforms other methods. Besides, in Table PC denotes the total number of detected protein complexes, MPC is the number of detected protein complexes which were matched, MKC represents the number of matched known protein complexes, Perfect denotes OS = 1 which means that the predicted protein complexes are perfectly matched with the known protein complexes. As shown in Table Perfect both in DIP and MIPS datasets.To prove the validity of our proposed algorithm, we compare IFPA algorithm with MCODE, MCL, ClusterONE, CSO, COACH and CORE on three different PPI networks. Figures p-value is less than 0.01. In order to confirm the biological significance of detected protein complexes, the p-value is calculated by using the tool GO::TermFinder (https://www.yeastgenome.org/cgi-bin/GO/goTermFinder.pl). We randomly select some predicted protein complexes to calculate their p-value concerning Biological Process ontologies based on Krogan dataset, as shown in Table p-value and it demonstrates that the protein complexes predicted by our IFPA method have strong biological significance. The predicted complexes with strong biological significance can provide help for biology researches to some extent.Many of our detected protein complexes have a good match with the known protein complexes. We consider a detected complex to be biologically significant if its Figure Identification of protein complexes from PPI networks is distinctly important in proteomics. In this study, a flower pollination mechanism-based method is proposed to detect protein complexes in multi-relation reconstructed dynamic protein networks. To begin with, we build multi-relation reconstructed dynamic protein networks. Then, according to the core–periphery structure, we group the closely connected proteins as the cores and apply IFPA algorithm to attach peripheries to the optimal cores to form the predicted protein complexes. IFPA algorithm has been carried out on three different multi-relation reconstructed dynamic PPI networks and the experimental results demonstrate that our IFPA algorithm can obtain better clustering performance compared with other methods in most cases. The protein complexes we detected are likely to help the biologists gain some useful biological insights.


In support of reproducible research, data are tied to Ensembl releases and are kept separately from the software. Premade data packages are available for a variety of genomes and Ensembl releases. Three examples demonstrate typical use cases of this software.Bioinformatics research frequently involves handling gene-centric data such as exons, transcripts, proteins and their positions relative to a reference coordinate system. The ensembldb is part of Bioconductor (https://bioconductor.org/packages/ensembldb).Bioinformatics online. When the human genome was released as a first draft  genomic coordinates for all genes, transcripts and exons of a species and their relation to each other; (ii) general metadata information such as gene and transcript biotypes, NCBI Entrez gene IDs; and (iii) protein annotations including amino acid sequences, positions of protein domains within these . It is based on our AnnotationFilter classes, available as a separate Bioconductor package to encourage usage beyond ensembldb. This filtering framework can be classified into two main groups: one to query arbitrary textual information, such as gene symbols or UniProt accession numbers, and the other to handle positional information of genes, exons, transcripts and protein domains. Filters can be combined with logical expressions to create tailored queries and retrieve only specific data from the databases. This is particularly useful for visualizing transcript models from certain genomic regions: ensembldb facilitates plotting with Bioconductor packages ggbio  returns the genomic annotations for three genes: SIM2, a master regulator of neurogenesis thought to contribute to some phenotypes of Down syndrome  on the human genome (version GRCh38) and is readily converted by ensembldb to position 294 on the respective protein given by Ensembl ID ENSP00000451605 using the command genomeToProtein), edb) with edb as defined in the first example. We furthermore find that this protein is encoded by the MC1R gene issuing the AnnotationDbi-compatible query command select.Another hallmark of proteins$protein_sequence, we get the protein sequence for the selected ID to find on position 294 an aspartic acid (‘D’), which is in agreement with the reference amino acid of variant Asp294His (Our annotation packages also contain protein sequence information. Thus, with the call Expanded code with descriptions and results for these two examples is provided as ensembldb can be easily integrated into genome analysis pipelines. An extended example is given in the Finally, by providing gene annotations and positional information of exons on the genome and supporting the standard Bioconductor interfaces for data retrieval, ensembldb, which utilizes annotation resources from Ensembl and integrates them into Bioconductor. The separation of source code and annotation data facilitates reproducible research by allowing ensembldb to access any set of annotations published in the past. With an extensive filtering system, searches can be customized to meet very specific requirements and powerful coordinate mapping functions enable conversion of coordinates between proteins, transcripts, and the genome. Providing protein and protein domain annotations along with genome-centered annotations makes ensembldb also an asset for any post-genome data analysis that aims to combine data from these various domains.Here we have described the Bioconductor package EnsDb annotation databases for all Ensembl vertebrates and plan to provide future continuous support for them via AnnotationHub.For each new Ensembl release, we create Conflict of Interest: none declared.btz031_Supplementary_InformationClick here for additional data file.