One goal of structural biology is to understand how a protein’s 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex into its constituent atom-atom interactions is typically sufficient to rapidly predict ligand affinity with high accuracy , a fact that is exploited to support structure-based drug design. We recently found that protein-DNA/RNA affinity can also be predicted with high accuracy using extensions of existing techniques, but protein-protein affinity could not be predicted with >60% correlation, even when the protein-protein complex was available.t test and nonparametric Mann–Whitney U test.X-ray and NMR structures of protein-protein complexes, their associated binding affinities and experimental conditions were obtained from different binding affinity and structural databases. Statistical models were implemented using a generalized linear model framework, including the experimental conditions as new model features. We evaluated the potential for new features to improve affinity prediction models by calculating the Pearson correlation between predicted and experimental binding affinities on the training and test data after model fitting and after cross-validation. Differences in accuracy were assessed using two-sample p = 4.32x10−3). In addition, incorporating information on the experimental conditions under which affinities were measured  had significant effects on prediction accuracy. We also highlight a number of potential errors in large structure-affinity databases, which could affect both model training and accuracy assessment.Here we evaluate a range of potential factors that may interfere with accurate protein-protein affinity prediction. We find that X-ray crystal resolution has the strongest single effect on protein-protein affinity prediction. Limiting our analyses to only high-resolution complexes (≤2.5 Å) increased the correlation between predicted and experimental affinity from 54 to 68%  contains supplementary material, which is available to authorized users. Proteins are involved in the majority of chemical reactions that take place within living cells, making them essential for all aspects of cellular function. Proteins never work in isolation; their functional repertoire is determined by how they interact with various small-molecule, DNA/RNA, protein or other ligands. Ligand affinity is largely determined by a protein’s 3-dimensional structure, which determines the spatial conformation of attractive and repulsive forces between the protein and a potential ligand –3. The aHigh-throughput prediction of protein-ligand affinity is typically conducted using a fast statistical “scoring function” that decomposes binding affinity into component atom-atom interaction terms representing the attractive and repulsive forces acting across the protein-ligand complex , 8. AlthMany scoring functions are capable of using only the atomic interactions extracted from crystal structures to rapidly predict protein-small molecule affinities with >70% correlation, which is commonly considered adequate to support structure-based drug design , 15–21. Accurate prediction of protein-protein interactions is a major goal of computational structural biology, and many approaches have been examined to improve the accuracy of protein-protein affinity prediction . The str10-transformed dissociation constants, pKds) were obtained from PDBbind [X-ray and NMR structures of protein-protein complexes and their associated binding affinities , assuming a Gaussian error distribution with logarithmic link function. We used the GLMULTI genetic algorithm to generate 500 candidate models  and selected the best-fit model for each training dataset using the Akaike information criterion (AIC).r2) between predicted and experimental binding affinities on the training data after model fitting, which represents the ‘best-case’ possible accuracy.We evaluated the potential for new features to improve affinity prediction models by calculating the Pearson correlation  and root mean squared deviation (RMSD) between predicted and experimentally-determined binding affinities on the unseen testing data and report the average r2 and RMSD of each model over the 100 replicates.We then used cross-validation to estimate the expected accuracy of each trained statistical model when applied to new data and to evaluate possible model over-fitting to training data , 14. Fort test, assuming unequal variances, and the nonparametric Mann–Whitney U test. For evaluating the effects of dataset subsampling on predictive accuracy, we used Fisher’s z-transformation, which incorporates a correction for comparing results obtained on a subsample to results from the full dataset (33). In addition, we performed 1000 replicates of random subsampling to evaluate the expected effect of subsampling on predictive accuracy.Differences in accuracy were assessed using the parametric two-tailed, two-sample Statistical prediction of protein-protein binding affinity relies on information extracted from large structure-affinity databases , 35. AccWe found that nearly 2/3 of the protein-protein complexes in PDBbind had ambiguous affinity measurements or multiple ligands, making it difficult to confidently assign affinity information to specific components of the structural complex  to the unbound (apo) structures and has been successfully applied for predicting binding affinity in a small dataset of 17 protein-protein complexes [We also evaluated the relationship between binding affinity and structural changes caused by protein-protein binding by examining the change in conformational entropy upon complex formation. This can be roughly calculated by comparing the structure of the bound complex  and changes in the accessible-to-solvent surface area upon complex formation. Although RMSD was not correlated with experimental binding affinity , we did observe a significant correlation between binding affinity and the change in accessible-to-solvent area caused by formation of the protein-protein binding interface, suggesting that this parameter may be useful for improving affinity prediction  and the filtered PDBBind database  exhibit correct folding, have very small numbers of incorrect rotamers and present accurate surface loops. In contrast, low-resolution structures (>3.5 Å) are more likely to result in folding errors or incorrectly-modeled surface loops , 46. We r2 = 0.11, p = 5.26x10−3; Additional file r2 = 0.64 vs. 0.65; Fisher one-tailed z = 0.16, p = 0.87; Fig. We did observe a weak but significant correlation between crystal resolution and the difference between predicted and experimental binding affinities  improved the correlation between predicted and experimental affinities on training data from 0.64 to 0.85  between predicted and experimental affinities, compared to the original dataset , these results suggest that incorporating parameters describing the experimental conditions used to measure protein-protein binding affinity may be important for training affinity-prediction models. The lack of information describing binding assay conditions in large structure-affinity databases may impose a limit on the accuracy of statistical models constructed from these databases.Protein-protein affinity can be measured by a variety of approaches, some of which may more strongly impact affinity prediction than others. Common technologies used in the protein-protein affinity benchmark database  are Isotp = 1.16x10−4), whereas ITC was weakly negatively correlated with affinity . Similarly, RMSD decreased from 2.40 to 2.10 when temperature was incorporated . Finally, RMSD decreased from 2.36 to 1.65 when binding assay method was included as a model parameter .Incorporating binding assay conditions in our statistical model also resulted in a significant decrease in RMSD between predicted and experimentally-determined affinities Fig. . Adding Overall, these results suggest that incorporating information about the experimental conditions used to measure protein-protein affinity can have a strong effect on the predictive accuracy of statistical models. Detailed experimental conditions are generally not incorporated into large-scale structure-affinity databases, which may place a practical upper bound on the accuracy of statistical affinity prediction.rd worst prediction made by our statistical model, with a predicted pKd based on the mutant structure of 7.06 vs. an experimental pKd of the wild-type protein of 11.49. It is not clear whether this mis-prediction is due to a poor statistical fit to this complex or to differences between the binding affinities of the mutant vs. wild-type proteins.Manual examination of specific examples in the protein-protein affinity benchmark database  revealedTo evaluate the potential effects of this mismatch on predicted binding affinity accuracy, we generated the wild-type structure of NuiA using homology modeling  and re-eAlthough the significance of the improvement in this single ‘case study’ cannot be evaluated statistically—and is likely to depend on the specific scientific question being considered—this result does suggest that small differences between the crystalized protein-protein complex and the complex whose binding affinity is measured—in this case a single amino acid mutation—may have a measurably negative affect on the accuracy of affinity prediction. The extent of similar errors in large-scale structure-affinity databases is unknown.th worst prediction made by the statistical model . Although the extent to which the presence/absence of the MES cofactor may have affected affinity prediction is unclear, that the crystalized complex does not correspond to the complex assayed in the experimental affinity measurement raises concerns about the accuracy of this database entry.Missing information about key affinity-determining factors from either the crystallization or affinity experiments could also affect affinity prediction accuracy. In one example, the crystal structure of GTP-Bound Rab4Q67L GTPase in complex with the central Rab binding domain of Rabenosyn-5 (PDB ID 1Z0K) has an additional cofactor, 2-(N-morpholino)-ethanesulfonic acid or MES, which was not present in the binding assay . This coOther examples of missing information likely to affect affinity prediction could be manually identified from the affinity benchmark database , many ofThese potential database errors were identifiable due to the amount of detail provided in small curated databases like the protein-protein affinity benchmark . HoweverWe also identified a handful of cases in which binding affinity values were incorrectly entered in the PDBbind database. For example, the binding affinity (pKd) assigned to Human prolactin (hPRL) in complex with its receptor is 0.67 in PDBbind, whereas the experimentally-determined binding affinity from the literature is >5.65, depending on pH . SimilarThe accuracy of machine learning and other statistical prediction methods depends on having a large quantity of high-quality training data. Errors in the training data can impair the inferred model’s predictive performance , whereasWe have shown that limiting training data to high-resolution crystal structures—easily extracted from structural information—can dramatically improve affinity prediction. However, we are cautious that the resulting reduction in breadth of training data may limit the generalizability of inferred models to new problems, particularly complex structural interactions that may not crystalize at high resolution due to inherent flexibility.We have also shown that incorporating information about the experimental conditions used to measure binding affinity may be important for producing accurate affinity predictions from structural data, probably due to their effects on resulting affinity measurements. Unfortunately, most large structure-affinity databases do not include detailed experimental information, and databases that do include this information appear to have at least some examples of dramatic mismatches between crystallographic and affinity-measurement conditions. The extent to which these types of potential errors are present in large-scale databases is not known, making it difficult to assess the general impact of these potential problems on affinity prediction.Although careful manual curation can be used to develop high-quality structure-affinity databases, this approach is unlikely to scale up to the number of structures required for training robust, generalizable predictive models. A possible computational approach to building high-quality, large-scale structure-affinity databases would be to extract detailed information about crystallographic and affinity-measurement conditions directly from scientific literature using text-mining approaches –75, althAdditional file 1:Appendix with Supplementary Material. Appendix, including text, tables, and figures for supplementary data. (PDF 1564 kb)


Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ The predictions of biomolecular functions and properties from biomolecular structures are of fundamental importance in computational biophysics. The structural and biological complexities of biomolecules and their interactions hinder successful predictions. Machine learning has become an important tool for such predictions. Recent advances in deep learning architectures, particularly convolutional neural network (CNN), have profoundly impacted a number of disciplines, such as image classification and voice recognition. Though CNN can be directly applied to molecular sciences by using a three-dimensional (3D) image-like brute-force representation, it is computationally intractable when applied to large biomolecules and large datasets. We propose a topological strategy to significantly reduce the structural and biological complexity of biomolecules and provide an efficient topology based CNN architecture. Element-specific persistent homology, a new algebraic topology, has been developed to cast biomolecules in a multichannel image-like representation suitable for CNN. The power of the proposed topology based neural network (TopologyNet) is further enhanced by auxiliary descriptors and a multi-task deep learning architecture. It has been demonstrated that TopologyNet framework outperforms other methods in the predictions of protein-ligand binding affinities and mutation induced protein stability changes. PLOS Computational Biology Methods paper.This is a Understanding the structure-function relationships of biomolecules is fundamentally important in computational biophysics and experimental biology. As such, methods that can robustly predict biomolecular properties, such as protein-ligand binding affinity and protein stability change upon mutation from three-dimensional (3D) structures are important tools to help us understand this relationship. Numerous approaches have been developed to unveil the structure-function relationship. Physics based models make use of fundamental laws of physics, i.e., quantum mechanics, molecular mechanics, continuum mechanics, multiscale modeling, statistical mechanics, thermodynamics, etc, to investigate structure-function relationships and predict function from structure. Physical methods provide important insights and are indispensable for understanding the relationships between protein structure and function.The exponential growth of biological data has set the stage for data-driven discovery of structure-function relationships. Indeed, the Protein Data Bank (PDB) has accumulated near 130,000 tertiary structures. The availability of 3D structural data enables knowledge based approaches to offer complementary and competitive predictions of structure-function relationships. Recent advances in machine learning algorithms have made data driven approaches more competitive and powerful than ever. Arguably, machine learning is one of the most important developments in data analysis. Machine learning has become an indispensable tool in biomolecular data analysis and prediction. Virtually every computational problem in computational biology and biophysics, such as the prediction of solvation free energies, protein-ligand binding affinities, mutation impacts, pKa values, etc, has a class of knowledge based approaches that are either parallel or complementary to physics based approaches. The ability to recognize nonlinear and high-order interactions among features as well as the capability of handling data with underlying spatial dimensions hierarchically has lead to breakthroughs in deep convolutional neural networks in image processing, video, audio and computer vision . LikewisGiven multiple learning tasks, multi-task learning (MTL)  providesFor complex 3D biomolecular data, the physical features used in machine learning vary greatly in their nature. Typical features are generated from geometric properties, electrostatics, atom types, atomic partial charges, and graph theory based properties . Such matopology-function relationships exist in many biomolecular systems.Most theoretical models for the study of structure-function relationships of biomolecules are based on geometric modeling techniques. Mathematically, these approaches exploit local geometric information, i.e., coordinates, distances, angles, areas, and sometimes curvatures  for the Topology offers entirely different approaches and could provide significant simplification of biomolecular data –24. The Persistent homology is a relatively new branch of algebraic topology that embeds multiscale geometric information in topological invariants to achieve an interplay between geometry and topology. It creates a variety of topologies of a given object by varying a filtration parameter, such as the radii of balls centered at the nodes or the level set of a surface function. As a result, persistent homology can capture topological structures continuously over a range of spatial scales. Unlike commonly used computational homology which results in truly metric free representations, persistent homology embeds geometric information in topological invariants, e.g., Betti numbers so that “birth” and “death” of isolated components, circles, rings, voids or cavities can be monitored at any geometric scale by topological measurements. In the past decade, persistent homology has been developed as a new multiscale representation of topological features. The 0-th dimensional version was originally introduced for computer vision applications under the name “size function” , 27. PerPersistent homology has been applied to computational biology –48, in tThe objective of the present work is to introduce a new framework for the structure based biomolecular property predictions using element-specific persistent homology, and convolutional and multi-task neural networks. In this framework, element-specific persistent homology reduces geometric and biological complexities and provides a sufficient and structured low level representation for neural networks. Given this representation, convolutional neural networks can then learn from data to extract high level representations of the biomolecular systems, while retaining the spatial relationships, and construct mappings from these representations to the target properties. For the prediction problems whose available datasets are small, multi-task learning by jointly learning the related prediction problems with larger available datasets helps to extract a proper high level representation for the target applications. The element-specific treatment is inspired by the RF-score method  for bindProtein-ligand binding is a fundamental biological process in cells and involves detailed molecular recognition, synergistic protein-ligand interaction, and may involve protein conformational changes. Agonist binding is crucial to receptor functions and typically triggers a physiological response, such as transmitter-mediated signal transduction, hormone and growth factor regulated metabolic pathways, stimulus-initiated gene expression, enzyme production, cell secretion, etc. Understanding protein-ligand interactions has been a fundamental issue in molecular biophysics, structural biology and medicine. A specific task in drug and protein design is to predict protein-ligand binding affinity from given structural information  Protein-et al  is divided into n equal length subintervals and the patterns are characterized on each subinterval. The description vectors are defined asbj, dj are birth and death of bar j. The three types of representation vectors are computed for sets of Betti-1 and Betti-2 bars. For Betti-0 bars, since their birth positions are uniformly 0, only Vd needs to be addressed. To characterize pairwise interactions between atoms, it is convenient to simply use pairwise distance information between atoms. The corresponding image-like representation, denoted by Vr, can be constructed similarly to Vd by substituting the set of barcodes by a collection of distances between the atom pairs of interest. It should be noted that Vr is not equivalent to Vd in most simplicial complex setups. Generally speaking, Vr also reflects the 0th order topological connectivity information. It is used as the characterization of 0th order connectivity of the biomolecules in the applications shown in this work. Finally, we let Xs denote all the feature vectors for the sth sample and let Ys denote the corresponding target value.The TFs and ESTFs are originally stored as collections of barcodes denoted by Vb, Vd, and Vp, can be viewed as one-dimensional (1D) images. Each subinterval in the filtration axis represents a digit (or pixel) in the 1D-image-like representation. Such a treatment of topological features describes the topological information with appropriately chosen resolution of L/n. Meanwhile, the chemical information in the ESTFs of m channels to describe different element type, protein mutation status , topological dimension , and topological event . Each element in the 1D-image-like representation is standardized to have zero mean and unit variance among the data sets. This 1D-image-like topological representation can be easily transferred among problems such as protein-ligand binding affinity modeling and prediction of protein stability change upon mutation. Traditional machine learning approaches require manual extraction of features for each domain of application. When the convolutional neural network is applied, the convolution layers identify local patterns of atomic interactions and the fully connected layers then extract higher level descriptions of the system by combining local patterns at various distance scales.To feed the outputs of TFs into the convolutional neural network, the barcodes are transformed to a 1D-image-like representation with multiple channels. Topological feature vectors, ai and aj asd is Euclidean distance between two atoms and A(⋅) denotes the affiliation of an atom which is either a protein or a ligand.In computation, the binding affinity, or alternatively the binding free energy, can be modeled via an energy cycle as shown in Vd providing 36 channels. The birth (Vb), death (Vd), and persistence (Vp) for Betti-1 and Betti-2 barcodes are computed for carbon atoms and all heavy atoms of the protein and the protein-ligand complex which results in 24 channels. The difference between the characterization of the protein and the protein-ligand complex accounts for another 12 channels. Thus, we have a total of 72 channels. Here, 0-dimensional TFs describe intramolecular interactions between the protein and ligand. All heavy atom TFs delineate the geometric effect of protein-ligand binding. The TFs of carbon atoms account for hydrophobic effects and also implicitly reflect the solvation effects. The distance scale interval,  Å is divided into bins of length 0.25 Å.The ESTFs used in this application are summarized in Modeling protein folding free energy change upon mutation basically involves the unfolded states and folded structures of the mutant and the wild type as shown in The preprocessed multichannel topological image is standardized with mean 0 and standard deviation 1 for use in the convolutional neural network. A convolutional neural network with a few 1D convolution layers, followed by several fully connected layers, is used to extract higher level features from multichannel topological images and to perform regression with the learned features. An illustration of the convolutional neural network structure is shown in n × m second order tensor V, where n is the number of topological feature pixels and m is number of channels for each pixel. In this approach, n corresponds to the radius filtration dimension of the biomolecular topological analysis and m corresponds the number of representation vectors used which are defined in w, a convolutional filter F can be represented by a w × m second order tensor. By moving the window of size w along the radius filtration direction of V, a sequence of Nf second order tensors, which are subtensors of V, are obtained and can be concatenated to form an Nf × w × m third order tensor T. The filter F operated on T results in a first order tensor TijkFjk by tensor contraction. Concatenating the outputs of nf filters gives an Nf × nf second order tensor. Generally speaking, a 1D convolution layer takes an n × m tensor and outputs an Nf × nf tensor.Consider an L2 minimization does not work. One popular approach of training a neural network is the stochastic gradient decent (SGD) method. Let Θ be the parameters in the network and i to Θi+1 from step i to step i + 1 asτ is the learning rate, Xs and Ys are the input and target of the sth sample of the training set. In practice, the training set  is often split into mini-batches {}s∈S. SGD method then goes through each mini-batch instead of going through only one example at a time. When the landscape of the objective function is like a long steep valley, momentum is added to accelerate convergence of the algorithm. The updating scheme can therefore be changed toη ≤ 1 is a scalar coefficient for the momentum term.Feedforward neural networks are usually trained by backpropagation where the error of the output layer is calculated and is propagated backward through the network to update its weights. For structured neural networks, conventional Neural networks with several convolution layers and fully connected layers possess a large number of degrees of freedom which can easily lead to overfitting. The dropout technique is an easy way of preventing network overfitting . During In addition to dropout technique which regularizes each individual model, bagging is a technique to combine the output of several models trained separately by averaging to reduce generalization error. This is based on the assumption that models with randomness in the training process likely make different errors on testing data. Generally, bagging trains different models on different subsets of the training set. Specifically, as neural networks have relatively high underlying randomness caused by factors including the random weights initialization and the random mini-batch partition, it can benefit from bagging even if the individual models are trained on the same dataset. In this work, bagging of neural network models trained individually with the same architecture and training dataset is used.Deep learning architecture also allows the use of non-image-like features together with image or image-like features. In this work, additional auxiliary features, which are important to mutation analysis, are incorporated after the convolution layers as shown in We construct a multi-task multichannel topological convolutional neural network (MM-TCNN) architecture to carry out simultaneous training and prediction. The common topological attributes and underlying physical interactions in features provide a basis for multi-task predictions. Because the deep neural networks are jointly trained from multiple prediction tasks, we expect the networks to generate robust high-level representations from low level TFs for prediction problems. We also expect that the refined representation would lead to prediction models with improved generalized performance. From the proposed deep learning models, we hope to gain insights into how the nonlinear and nonlocal interactions among topological features impact various prediction tasks, which could further lead to better understanding towards the interactions among biomolecular prediction tasks. Finally, tasks with insufficient training data sets will be more likely to benefit from the information collected from tasks with large training sets in a multi-task learning framework. In the present mutation analysis, there are two data sets. The mutation data of the large data set for globular proteins are more reliable, while those of the small data set for membrane proteins are noisy and less reliable due to the fact that the current technologies for membrane protein mutagenesis experiments are immature. The prediction for membrane proteins benefits from joint learning with the prediction for globular proteins. The coupling of the two predictions through a neural network is shown in Sj is the set of parameters for the jth task of the shared layers, ΘBj is the set of parameters for the jth branch of neurons dedicated for the jth task, and  are training data for the jth task. Here N sets of parameters. Finally jth loss function. In this work, we force the shared layers of the two problems to be the same and the regularization of the network is realized using dropout.The general objective function to minimize for multi-task learning through neural networks can be decomposed into training loss, similarity penalty for shared layers, and regularization term asDue to the complexity of the network for the mutation example with auxiliary features, a brief parameter search is performed using Hyperopt  with onlIn the protein-ligand binding affinity predictions, we repeatedly train 100 single neural networks individually. To test the performance of bagging of the models, we randomly select 50 trained models from the 100 individually trained networks and output the average value of the outputs from the 50 selected models as the prediction. The performance is then computed for the bagging. This process is repeated 100 times and both median and best results are reported.In the mutation induced protein stability predictions, we use the same procedure used in the protein-ligand binding prediction, for the “S350” task, where the training and testing split is predefined. In the case of cross validation, 10 sets of 5-fold splits are generated randomly and 20 single models are generated for each split. The average prediction is taken over the 20 models within each split and the median result of the 10 splits is reported. Bagging of only 20 models is performed here because it is not valid to do bagging of predictors on different cross validation splits. The bagging of 50 models will result in 50x10x5(five folds) = 2500 training processes which is too computationally expensive. Details of the network architectures of the three examples can be found in Multichannel topological convolutional neural network.Dionysus software  with CGAS1 TextHandcrafted auxiliary features for prediction of protein folding free energy change upon mutation.(PDF)Click here for additional data file.S2 TextDetailed architectures and parameters of the neural networks introduced in this work.(PDF)Click here for additional data file.S1 CodeSource code for the generation of 1D image-like topological features for the binding problem.(ZIP)Click here for additional data file.S2 CodeSource code for the generation of 1D image-like topological features for the mutation problem.(ZIP)Click here for additional data file.


Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method. Relating proteins with Long non-coding RNAs (lncRNAs) is a tremendous and meaningful task in human health with applications in understanding lncRNA mechanisms, diagnosis and therapy7. In general, lncRNAs exert functions by interfacing with corresponding RNA-binding proteins. Thus, identifying lncRNA interacted proteins is significant to understand complex functions of lncRNA and molecular mechanism9 of disease progression and cellular circuitry11.Long non-coding RNAs (lncRNAs) are becoming critically important for the understanding of life sciences. Studies have indicated that lncRNAs play critical roles in many important biological processes such as chromatin modificationet al.12 proposed a method to predict ncRNA-protein interactions in 2011 and a approach named RPISeq was presented by Muppirala et al.13 at the same year, which was constructed by using the features derived from protein and RNA sequences. They trained Random Forest (RF) and Support Vector Machine (SVM) classifiers using 3-mer and 4-mer conjoint triad features for amino acid and nucleotide sequences, respectively14. Bellucci et al. created a method named catRAPID15 by exploiting the physicochemical properties including secondary structure, hydrogen bonding and van der Waals propensities. Wang et al. proposed an approach based on Naíve Bayes (NB) and Extended NB (ENB) classifiers using the similar data and features supported in Muppirala et al.’s work16. In 2013, IncPro17 was created by Lu et al. using three types of classical protein secondary structures, hydrogen-bond and Van der Waals propensities, and six types of RNA secondary structures (RSS).Since experimental methods to detect lncRNA-protein interactions are time-consuming and costly, several computational approaches have been reported for predicting RNA-binding proteins (RBPs). For example, Pancaldi 18 and drug-target interaction prediction and some of them have achieved good performances. One of the most commonly used approach is guilt-by-association (GBA)19, which provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. New emerged methods, including the Katz method20, Combining dATa Across species using Positive-Unlabeled Learning Techniques(CATAPULT)19, Random Walk with Restart (RWR)21, and LncRNA-protein Interaction prediction based on Heterogeneous Network model (LPIHN)22, have extended the association from just direct protein interactions to more distant connections in various ways. The KATZ measure20 is a weighted sum of the number of paths in the network that measures the similarity of two nodes. CATAPULT19 is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. RWR21 is a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks and it add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves. LPIHN22 is a network-based method by implement a random walk on a heterogeneous network. PRINCE is a global method based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. Compared with LPIHN and RWR, PRINCE propagates information in a smaller network but contains more connotative meaning when build the initial probability values and has made great performance in gene prioritization23 and disease identification24.Nevertheless, all of these methods only focus on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. On the other hand, biological network-based methods was already widely used in many types of studies, such as disease gene prioritization25. The basic idea is that similar objects are more likely to be related to some other objects. Considering the relatedness of heterogeneous objects is path-constrained, HeteSim gives a uniform and symmetric measure for arbitrary paths to evaluate the relatedness of heterogeneous object pair (same or different types) with one single score. Due to the relevance path not only captures the semantics information but also constrains the walk path, the score is also a path-based similarity measure.However, many existing network-based methods simply view objects in heterogeneous networks as the same type and do not consider the subtle semantic meanings of different paths. In this paper, we adopt a method named HeteSim, which is a path-based measure to calculate the relevance between objects in heterogeneous networkAn example of HeteSim score is illustrated in Fig. . The numHere, we propose a method named PLPIHS Fig.  to predi26 is implemented on the verified lncRNA-protein associations to evaluate the performance of LPIHN22. We leave a known lncRNA-protein pair in turn as the test sample and all the other known lncRNA-protein pairs are regarded as training samples. In order to improve the accuracy of PLPIHS, we remove all connected lncRNAs and proteins while in each validation round. Receiver Operating Characteristic(ROC) curve27 is used to evaluate the prediction performance, which plots true-positive rate  versus false-positive rate  at different rank cutoffs. When varying the rank cutoffs of successful prediction, we can obtain the corresponding TPR and FPR. In this way, ROC curve is drawn and the area under the curve(AUC) is calculated as well. For a rank threshold, sensitivity(SEN)28 and specificity(SPE)29 are defined as follow:LOOCV30, are calculated as follows:TN and TP represent the number of negative sites and positive sites that are correctly predicted. FP and FN represent the number of positive sites and negative sites that are wrongly predicted. Meanwhile, some common used measurements, namely, accuracy(ACC), precision(PRE), Mathew correlation coefficient(MCC) and F1-ScoreThese measurements are also used to assess the capability of PLPIHS during the preprocessing procedure.In this paper, we only have two kinds of objects, lncRNA and protein. Thus, the paths from a lncRNA to a protein in our heterogeneous network with length less than six is listed in Table β is used to control the influence of longer paths. The longer the path length is, the smaller the inhibiting factor is. Path length equals 3 matches with constant β, path length equals 4 matches with constant β*β and path length equals 5 matches with constant β*β*β. Table β has tiny impact on the final results and β = 0.2, 0.4 and 0.7 achieved the best AUC score and the others are not far behind yet.The constant factor To further verify the dependability of our method, we compare the three networks of different connectivity density under different cutoff value 0.3, 0.5 and 0.9 (see lncRNA-Protein associations). The results are shown in Fig. α = 0.9, c = −15, d = log(9999) and the number of propagation iteration is ten. The parameters of LPIHN are consistent with the original implementation as well . For the RWR method, there is only one restart probability r and it’s effects is very slight, which is proved by experiments. The parameter r is set as 0.5 in this comparison.To evaluate the performance of the proposed PLPIHS, we compare it with three state-of-the-art methods: PRINCE, LPIHN and RWR. The parameters of PRINCE is set as follow: In order to calculate the performance of the different methods, we use a leave-one-out cross validation procedure. We extract 2000 lncRNA-protein associations from the 0.9 network as positive samples, the same number of negative samples are chosen randomly from the 0.3 network as well, avoiding the error caused by imbalance dataset. The gold set which containing 185 lncRNA-protein interactions downloaded from NPinter database has been included in positive pairs as well. In the lncRNA protein prioritization, each lncRNA-protein interaction is utilized as the test set in turn and the remaining associations are used as training data. The whole experiment will be repeated 4000 times to testing each lncRNA-protein pairs in the dataset. ROC curve is drawn based on true positive rate (TPR) and false positive rate (FPR) at different thresholds. The AUC score is utilized to measure the performance. AUC = 1 demonstrates the perfect performance and AUC = 0.5 demonstrates the random performance.The ROC curve of PLPIHS, LPIHS, PRINCE and RWR are plotted in Fig. For further validation, we also randomly selected 2000 lncRNA-protein associations from the rest of positive samples in 0.9 network and the same number of negative interactions are picked out from the remaining negative samples of 0.3 network to generate the independent test data set. Since the existing network based methods is not suitable for independent test, we only evaluate the performance for the proposed PLPIHS. The independent test results are shown in Fig. By applying the proposed PLPIHS method, novel candidate lncRNA-related proteins are predicted using LOOCV. We applied PLPIHS onto the 2000 known lncRNA-protein associations, which includes 1511 lncRNAs and 344 proteins to infer novel lncRNA-protein interactions. As a result, an area under the ROC curve of 0.9669, 0.9705 and 0.9703  is examined. MALAT1 is a long non-coding RNA which is over-expressed in many human oncogenic tissues and regulates cell cycle and survival34. We construct the interaction networks of lncRNA MALAT1 by using the prediction results of these four methods  and regulates cellular levels of phosphorylated forms of SR proteins36. And it is also as target of TARDBP to play the biological performance and found that TDP-43 bound to long ncRNAs in highly sequence-specific manner in tissue from subjects with or without FTLD-TDP, the MALAT1 ncRNA recruits splicing factors to nuclear speckles and affects phosphorylation of serine/arginine-rich splicing factor proteins38. All these results indicate that our proposed method is effective and reliable in identifying novel lncRNA-related proteins.We manually check the top 10 proteins in the ranked list under 0.5 network Table .Three of13. Only a small number of lncRNA-protein interactions have been well-characterized. Computational methods can be helpful in suggesting potential interactions for possible experimentation25. In this study, we use HeteSim measure to calculate the relevance between lncRNA and protein in a heterogeneous network. The importance of inferring novel lncRNA-protein interactions by considering the subtle semantic meanings of different paths in the heterogeneous network have been verified39. We first construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Then, we use the HeteSim measure to calculate a score for each lncRNA-protein pairs in each path. Finally, a SVM classifier is used to combine the scores of different paths and making predictions. We compare the proposed PLPIHS with PRINCE, RWR and LPIHN and find that PLPIHS obtain an AUC score of 0.9679 in 0.3 network, which is significantly higher than PRINCE, RWR and LPIHN . We also compare the performance of these four methods in networks of different connectivity density. As a result, PLPIHS outperforms the other method across all the networks. Moreover, when analysing the predicted proteins interacted with lncRNA MALAT1, PLPIHS successfully predicts 63 out of 68 associations, while PRINCE, RWR and LPIHN retrieve much lower interactions of 57, 57 and 53, respectively. And the top-ranked lncRNA-protein interactions predicted by our method are supported by existing literatures. The results highlight the advantages of our proposed method in predicting possible lncRNA-protein interactions.LncRNAs are involved in a wide range of biological functions through diverse molecular mechanisms often including the interaction with one or more protein partners9. A total of 15941 lncRNA genes and 20284 protein-coding genes are extracted. To obtain genome-wide lncRNA and protein-coding gene associations, we combine three sources of data:40. Three preprocessed co-expression datasets  including pre-calculated pairwise Pearson’s correlation coefficients for human were collected from COXPRESdb. The correlations are calculated as follows:C is the overall correlation between gene l (lncRNA) and protein-coding gene p, Cd is the correlation score between l and p in dataset d, D is the number of gene pairs (l and p) with positive correlation scores. Gene pairs with negative correlation scores are removed.Co-expression data from COXPRESdb41 and GEO42. We obtained the co-expresionn data from the work of Jiang et al.43. RNA-Seq raw data of 19 human normal tissues are obtained from ArrayExpress (E-MTAB-513) and GEO (GSE30554). TopHat and Cufflinks with the default parameters are used to calculate the expression values. Pearson’s correlation coefficients are used to evaluate the co-expression of lncRNA-protein pairs.Co-expression data from ArrayExpress34 in April 2016 and then filter the lncRNAs and their interaction proteins, by restricting the organism of lncRNAs to “Homo sapiens”. The score I is 1 if there is an interaction between lncRNA l and protein p, otherwise the score is 0.lncRNA-protein interaction data. We download known lncRNA-protein interaction dataset from Npinter 3.0All human lncRNA genes and protein-coding genes are downloaded from the GENCODE Release 2444 , including the expression profiles of 90,062 lncRNA in 24 human tissues or cell types. Pearson’s correlation coefficient between the expression profiles of each pair of lncRNAs is calculated as the similarity.The lncRNA expression profiles are obtained from NONCODE2016 database45, which contains weighted protein interactions derived from computational prediction methods, high-throughput experiments, and text mining. The confidence scores are computed by combining the probabilities from the different evidence channels, correcting for the probability of randomly observing an interaction.We obtain the protein-protein interaction (PPI) data from STRING database V10.039. Further, the HeteSim score has some good properties , which have achieved positive performance in many studies25. In this study, we use HeteSim scores to measure the similarities between lncRNAs and proteins.The HeteSim measure is a uniform and symmetric relevance measure. It can be used to calculate the relatedness of objects with the same or different types in a uniform framework, and it is also a path-constrained measure to estimate the relatedness of object pairs based on the search path that connects two objects through a sequence of node typesDefinition 1 Transition probability matrix39 L and P are two kinds of object in the heterogeneous network, (ILP)n*m is an adjacent matrix between L and P, then the normalized matrix of ILP along the row vector is defined asDefinition 2 Reachable probability matrix39 In a heterogeneous network, the reachable probability matrix Pi belongs to any objects in the heterogeneous network, can be expressed asSplit the path into two parts. When the length n of path Achieve the transition probability matrix and reachable probability matrix under the path Calculate the HeteSim score:An example of calculating HeteSim score is indicated in Fig. Split the path ILT and ITP below, which means the interactions between lncRNAs and proteins, we can obtain the transition probability matrix TLT and TTP by normalizing the two matrix along the row vector. Actually, the reachable probability matrix for path TLT and TTP.Given the adjacent matrix Calculate the HeteSim score for each pair in the network. and the hetesim score matrix are displayed below. i.e.,Based on the definitions above, the steps of calculating HeteSim scores between two kinds of objects (lncRNA and protein) can be presented as follows:Among a heterogeneous network, different paths can express different semantic meanings. For instance, a lncRNA and a protein is connected via ‘lncRNA-lncRNA-protein’ path or ‘lncRNA-protein-protein’ path representing totally different meanings. The former means that if a lncRNA is associated with a protein, then another lncRNA similar to the lncRNA will be potential associated with the protein. The latter shows that if a protein associated with a lncRNA, then another protein interacted with the protein will be likely associated with the lncRNA. Therefore, the potential information hidden in each path is extraordinary essential to be taken into account during prediction.β to inhibit the influence of longer paths.The longer the path length is, the smaller the inhibiting factor is. Finally, a SVM classifier is built with these scores to predict potential lncRNA-protein associations. On the account of the HeteSim measure is based on the path-based relevance framework39, it can effectively dig out the subtle semantics of each paths.The PLPIHS framework is illustrated in Fig.


Identifying interactions between proteins in a complex is essential to analyze their role in various cellular activities and structuralstability. Therefore, effective algorithms and computer programs are required for the better understanding of various interactionsexist at the protein-protein interface. The protein inter-chain interaction (PICI) server was developed to identify these interactionsites and various interactions that contribute to the specificity and strength of the protein complex by using Protein InteractionIdentification Algorithm (PIIA). The multi-parametric approach of this algorithm involves the interface area between the subunits,the atomic coordinate distance, the linearity of bonds, the orientation of aromatic side-chains, and physicochemical properties ofthe residues. Particular advantages of PICI include its ability to identify various strong and weak interactions, including thosewhich have not been considered before by any other server. Representation of interface data in text tables, 3D interaction visualizer,colored sequence viewer, and a dynamic colored graphical matrix display makes it distinct from similar web servers. Users cananalyze interactions within multi-chain proteins by their PDBids or by uploading a structure atomic coordinate file and candownload the result in plain text or XML format.http://pici.bicpu.edu.in/PICI is freely accessible at  Analyzing interactions within protein complexes are crucial togain a better understanding of their role in signaling pathways,enzyme regulation, receptor binding, antigen recognition, andseveral other cellular processes, hence are prime targets fordrug design. Earlier studies have described the principlesgoverning the interactions and factors that influence theformation of protein complexes . Many paPICI computes all strong and weak interactions using atomiccoordinate data structure files (PDB format) with variousdistances and angle parameters. The data flow and processingof the web server pass through four stages: (i) Input, (ii) Preprocessing,(iii) Processing with PIIA, and (iv) Output . For calPIIA calculates nine different types of interactions betweenneighboring chains based on (i) the interface area, (ii) theatomic coordinate distance, (iii) the linearity of bonds, (iv) theorientation of aromatic side chains, and (v) the physicochemicalproperties of the residues. The integration of these methodsenables PICI to offer efficient identification of location andtypes of interactions with greater reliability of results. Defaultcutoff distance and angle for calculations are shown in (a) For NMR structures, only the first model is considered; (b)Cysteine gamma sulfur atoms which participate in disulfidebridges are not considered as proton donors while calculatinghydrogen bonds; (c) Backbone nitrogen atoms  are excluded from proton acceptors due to delocalizationof lone pair electrons with the neighboring carbonyl group; (d)Hydrophobicities of residues are based on solvent transfer freeenergies from octanol to water . (e) BotPICI web interface is developed with a set of PHP scripts andJavaScripts to display the generated data interactively byresponsive HTML web pages with CSS, which work on allmajor web browsers. PICI takes a protein structure file in PDBformat as input. After pre-processing and processing the inputdata, it displays the output in an interactive result webpage. Onthe result viewer page, each button on the top panelcorresponds to specific interactions and shares a common webpage composed of three panels  to facilitate the analysis of theinformation . PICI alThe structure visualization panel dynamically displaysinteracting residues in the JSmol structure viewer which iscompatible with all browser types. It allows users to visualizeatomic details of all interacting residues at the interface.Amino acid sequences of the multi-chain protein are shownand residues are highlighted for each interaction. The singleletter code residues are colored by their physicochemicalproperties such as green for hydrophobic, yellow for neutral,red for negatively charged, and blue for positively chargedresidues. The interacting residues in the sequence aredynamically highlighted for each type of interaction. BLASTlinks are generated for each sequence to perform sequencesimilarity search on PDB or UniProt database.PICI also generates a dynamic matrix of interacting residues inthe selected chain to other chains with colored compartments. Each coPICI web-server will serve as a useful resource for thestructural bioinformatics and the proteomics community toperform analysis of protein structures based inter-chaininteractions. Although there are some existing web serversdoing similar jobs such as PIC , PDBsum


Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers.In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far.We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art. Many proteins carry out their biological functions by interacting with other proteins to form multiprotein structures, called protein complexes , which aSeveral high-throughput methods have supplied us with large datasets of protein-protein interactions (PPIs) , 8, suchAnother class of methods focuses specifically on the prediction of heterodimers, using either random walks on PPI networks, such as the Repeated Random Walks (RRW) method  and the To improve the prediction accuracy for heterodimers, Ruan et al.  proposedYong et al.  proposedNote that Yugandhar et al.  applied pairs of proteins as interacting or not, we exploit the notion of pairwise kernels to extend kernels between individual proteins to kernels between pairs of proteins, investigating in particular the metric learning pairwise kernel (MLPK) and tensor product pairwise kernel (TPPK), as explained in [In this paper, our goal is to further improve the prediction accuracy for heterodimers. We investigate combination kernels to encode the domain composition of proteins involved in a complex since the one used in Ruan et al.  was veryained in  and in tBesides, we consider that various sources of information may contribute to an accurate predictor. The combination of various sources can be divided into three situations: (1)various types of features with a single kernel; (2)one type of features with multiple kernels; (3)various types of features with multiple kernels. We test all the three situations and show only significant results in our computational experiments. On various types of features, besides the protein-protein interaction (PPI) and domain properties, we also try to use phylogenetic profile property. The reason is that two proteins that are both present or absent in the same genome are likely to have related functions. Moreover, protein subcellular localization property is considered as well. As proteins must be localized at their appropriate subcellular compartment to perform their function, proteins in the same location may have similar functions. On multiple kernels, we employ Min kernel and its two normalization forms, MinMax kernel and Scaled Min kernel, as well as two pairwise kernels, MLPK and TPPK.Then, we employ C-Support Vector Classification (C-SVC), carry out ten-fold cross-validation and calculate the average precision, recall, and F-measures. The computational experiments show that using Min kernel improves the prediction performance, and the combinations of multiple kernels outperform single Min kernel, therefore is superior to  and otheThe rest of paper is organized as follows: “f(x) to predict if a pair x of proteins in the test set can form a heterodimer (f(x)≥0) or not (f(x)<0). The definition of positive examples and negative examples are the same as [f(x) from a training set ,…,, where each yi∈{−1,1} indicates whether the pair can form a complex or not, we employ a C-support vector classification (C-SVC) classifier, with balanced loss penalty to compensate for the fact that the numbers of positive examples and negative examples are very unbalanced.We formulate the problem of heterodimer prediction as a supervised binary classification problem. Given a set of pairs of proteins that known to form heterodimers (positive examples), and pairs of proteins that do not form heterodimers (negative examples) as training data, we learn a function  same as . To learWe explain multiple kernels involving properties of PPI, domain, phylogenetic profile, and subcellular localization in this section.ψ for a pair of proteins Pi, Pj is defined as For the PPI and domain properties, we follow the work in , featurewij denotes the weight of the interaction between Pi and Pj. These are novel features proposed by Ruan et al., and the detailed descriptions of each feature can be found in [where found in .There is another method involving domain property proposed in , called Dj in proteins. We define a feature space mapping ϕdom for protein Pi so that the j-th element of ϕdom(Pi) is the number of domains of Dj in P. For example, in Fig. Pi with domains D1,D1,D3,D4 and the right side is the corresponding feature space mapping ϕdom(Pi) with values  representing 2 D1s, 0 D2, 1 D3, 1 D4, 0 D5, and so on, included in protein Pi. The dimension of ϕdom(Pi) is the total number of distinct domains contained in the whole proteins.Suppose that there are several domains KC for two pairs of proteins,  and , is defined as The formulation of Domain Composition kernel δ(S)=1 if S holds, otherwise 0. It should be noted that the Domain Composition kernel is actually defined for pairs of two or more proteins.where In this study, we focus on replacing Domain Composition kernel with more promising combination kernels. Before presenting combination kernels, we first continuously introduce other properties.The phylogenetic profile of a protein is a vector that describes the presence or absence of homologs in organisms. It has been studied that proteins having similar profiles strongly tend to be functionally linked , and it m entries, where m corresponds to the number of genomes . We indicate the presence of a homolog to a given protein in the j-th genome with an entry of unity at the j-th element. If no homolog is found, the element is zero.To represent the subset of organisms that contain a homolog, we constructed a phylogenetic profile for each protein. This profile is a vector with Pi) is encoded by 2, 717 genomes, which consist of eukaryotes, bacteria and archaea. Proteins coded by the j-th genome are defined as including a homolog of a protein Pi if they align to the protein Pi with a score that is deemed statistically significant.We compute phylogenetic profiles for the 5, 497 proteins encoded by the genome from KEGG OC , a novelϕphylo for protein Pi so that the j-th element of ϕphylo(Pi) describes whether or not the j-th genome contains Pi. For example, in the genomes, P1 exists in EC and BS but not in SC, so for the phylogenetic profile of protein P1, elements of EC and BS are 1, and SC is 0.In Fig. ϕlocal(Pi) containing subcellular localization information for each protein Pi. The size of feature space is the sum of unique localizations for all proteins in our experiments, with elements 1 and 0, each represents whether or not the corresponding protein exists in the location   and TensKMLPK between pairs  and  is defined as Vert et al.  presentsx1,x2) and another pair  is done through comparing the feature space of pair K+K and that of pair K+K. In other words, MLPK compares pairs through the differences between their elements in the feature space.The rationale behind MLPK is that the comparison between a pair , as well as two pairwise kernels between protein pairs, MLPK kernel and TPPK kernel. We therefore consider all possible combinations (3×2=6) of these kernels.P1, P2) and , we have the following combinations. For two protein pairs (K(ϕ(Pi),ϕ(Pj)) denotes Min kernel or one of its normalized versions in the two equations. That is to say, we plug Min kernel and its normalized versions into Eqs. (ϕ(Pi) can be any one of ϕdom(Pi), ϕphylo(Pi) and ϕlocal(Pi).where nto Eqs.  and 9),,K(ϕ(Pi),ψ (Eq. (Then we combine the feature space mapping ψ (Eq. ) with thα is a constant, and K is either of the 6 combination kernels. We call Kcomb using KMin “Min-MLPK kernel”, using KMinMax “MinMax-MLPK kernel”, using Knorm “Normalized Min-MLPK kernel”, respectively. Similarly, when applying TPPK kernel, we just need to replace “MLPK” with “TPPK” for their names.where The study  pointed Kcomb using KM and KT “MinMax-MLPK-TPPK kernel”.We call C-Support Vector Classification (C-SVC) [f(x)=w⊤x that best separates positive examples from negative ones by solving the optimization problem: We use the  (C-SVC) , 39 formC+ and C− are regularization parameters for positive and negative examples, respectively. Instead of representing explicitly each pair of proteins by a vector of descriptors x∈Rp, we will use positive definite kernels K in which case the C-SVC classifier takes the form α∈Rn is the solution of the dual problem: where K is the n×n Gram matrix with entries Kij=K and 1 is the n-dimensional vector of ones. For implementation of C-SVC, we used libsvm (version 3.11) [where on 3.11) .In order to compare our proposed method with the method in , we usedhttp://wodaklab.org/cyc2008/.Also, we prepared the same dataset from CYC2008 [We defined a positive example as a pair of proteins included in WI-PHI as well as a heterodimer included in CYC2008. A negative example was defined as a pair of proteins included in WI-PHI, which meanwhile should not be any heterodimer but be a subset of some other complexes in CYC2008. As a result, we had 152 positive examples and 5345 negative examples.We chose the following three measures to evaluate our performance. Precision describes the rate of correctly predicted positive examples to all positively predicted examples, and recall describes the rate of correctly predicted positive examples to all positive examples. Both of them indicate the effectiveness of the method from different aspects. F-measure is defined as their harmonic mean, which was used for evaluating the balance of precision and recall since it is insufficient to evaluate by any single one of them.They are defined as TP,FP, and FN represent the numbers of correctly predicted positive examples, incorrectly predicted positive examples, and incorrectly predicted negative examples, respectively.where We present below a comparison of our proposed combination kernels and the best existing method , which iC-SVC with varying mixing parameter α = 0.0, 0.1, 0.2,..., 2.0. and regularization parameters C− = 0.1, 0.2, ⋯, 2.0, C+ = 3.0, 3.5, ⋯, 6.0. The best result was obtained for α = 0.5, C− = 1.0 and C+ = 4.0. In their experiments, they found that the results almost did not change while C− varied. Therefore, in this study, we kept the value of C− as 1.0, and set other parameters around the best value: α = 0.0, 0.1, 0.2,..., 1.0, and C+ = 3.5, 4.0, 4.5. By performing 10-fold cross-validation each time and taking the average of precision, recall, and F-measure, we used the same experimental procedure to compare the performance of Min kernel and its normalization forms, as well as kernels combining MLPK, TPPK and their summation MLPK + TPPK. The results are shown in Figs. In , they emC+ = 4.5 and α = 0.3, Normalized Min-MLPK kernel attains the best F-measure 0.686, compared with 0.631 in [C+ = 4.0, C− = 1.0 and α = 0.0, 0.1, 0.2,..., 1.0. The best two results 0.678 and 0.675 were obtained by the Normalized Min-MLPK kernel when α = 0.4 and 0.3, respectively. The third best result 0.673 was obtained by MinMax-MLPK kernel when α = 0.4.When 0.631 in . Figure α, as well as all min kernels and its normalization forms, while TPPK-combined kernels are similar with Domain Composition kernel, even a little lower at some points. It demonstrates that converting to MLPK pairwise kernel indeed leads to better prediction performance.These three figures indicate that all the MLPK-combined kernels outperform previously proposed Domain Composition kernel for every value of The better performance of MLPK compared to TPPK implies that protein pairs in the training set are similar to other pairs, but not similar to each other. This observation is not surprising because the composition of domains in given protein pairs and known heterodimers (protein pairs) are expected similar, while they do not have to be similar with each other. That is also the reason why Ruan et al. proposed Domain Composition kernel in . It alsoα changes. So it suggests that, unfortunately, localization property has no contribution to predicting heterodimers. This is surprising at first since two proteins could form a complex only if they are co-localized. However, the localization data is somewhat not complete because not all of yeast proteins are assigned localization, and many proteins are assigned to multiple locations. As a result, the information turns out to be not useful because only a small part of protein pairs share exactly the same localization.We also show the results of subcellular localization property and phylogenetic profile property in Figs. For phylogenetic profile property, it performs better than  at many Table We applied multiple combination kernels based on various types of information, such as protein protein interaction, domain, subcellular localization, and phylogenetic profile to predicting heterodimers. We combined Min kernel  with the information above and a pairwise kernel (MLPK or TPPK) by plugging. To evaluate our proposed method, we performed ten-fold cross-validation computational experiments for the combination kernels. The results suggest that our proposed method improved the performance of our previous work, which had been the best existing method so far. In particular, the Normalized Min-MLPK has the best performance.We indicated that for the problem of predicting heterodimeric protein complexes, multiple combination kernels have better performance than single kernel, and proved that MLPK-combined kernels nearly always have better prediction performance than TPPK-combined kernels. In addition, our results suggest that the information of PPI and domain is more meaningful and promising than subcellular localization and phylogenetic profile on this problem. Furthermore, we could give a conclusion that the information of subcellular localization has nearly no influence on prediction of heterodimers.An interesting perspective for future research is to design a new kernel based on the neighboring topological structure and weight-labeled edge information, or extract the useful sequence information of protein complexes by deep learning to solve this problem.


Penicillium expansum, the causal agent of blue mold, is one of the most prevalent post-harvest pathogens, infecting a wide range of crops after harvest. In response, crops have evolved various defense systems to protect themselves against this and other pathogens. Penicillium–crop interaction is a multifaceted process and mediated by pathogen- and host-derived proteins. Identification and characterization of the inter-species protein–protein interactions (PPIs) are fundamental to elucidating the molecular mechanisms underlying infection processes between P. expansum and plant crops. Here, we have developed PCPPI, the Penicillium-Crop Protein–Protein Interactions database, which is constructed based on the experimentally determined orthologous interactions in pathogen–plant systems and available domain–domain interactions (DDIs) in each PPI. Thus far, it stores information on 9911 proteins, 439 904 interactions and seven host species, including apple, kiwifruit, maize, pear, rice, strawberry and tomato. Further analysis through the gene ontology (GO) annotation indicated that proteins with more interacting partners tend to execute the essential function. Significantly, semantic statistics of the GO terms also provided strong support for the accuracy of our predicted interactions in PCPPI. We believe that all the PCPPI datasets are helpful to facilitate the study of pathogen-crop interactions and freely available to the research community.Database URL: http://bdg.hfut.edu.cn/pcppi/index.html Penicillium expansum is a devastating phytopathogen that causes decay in crop plants during post-harvest handling and storage. This pathogen has a broad and variable host range, including apple, citrus fruit, pear, tomato, kiwifruit, maize and rice  have been reported to be involved in the initial signaling cascades that stimulate the growth of P. expansum on crops , and described it here. PCPPI features a user-friendly interface for database search, information browse and interaction visualization. This resource and related documents are freely accessible at http://bdg.hfut.edu.cn/pcppi/index.html.Recently, several genes         , KIR (ftp://bdg.hfut.edu.cn/kiwi/Actinidia_chinensis), maizeGDB (http://ftp.maizegdb.org/MaizeGDB/FTP/maize_proteome/), GDR (ftp://ftp.bioinfo.wsu.edu/species/Pyrus_communis/Pcommunis-draft_genome.v1.0/genes/), EnsemblPlants , GDR (https://www.rosaceae.org/node/4230473) and Solgenomics , respectively. Additionally, the interactome datasets of pathogen–host interactions were obtained from the HPIDB database   . The proquences) , kiwifruquences) , pear (Pquences) , rice (Oquences) , strawbequences)  and tomaquences)  were obtE-value cutoff 1E-5  the basic information, including protein name/synonyms, protein sequences, nucleotide sequences of the corresponding genes, MW (molecular weight) and theoretical PI, is annotated referring to UniProt Knowledgebase and GenBank; (ii) the biological function of proteins, such as the Pfam domain  and genehttp://visjs.org/) is embedded within the web pages to display the predicted PPI datasets. Links, including Browse, Download and Documentation, are also provided in the navigation tool bar located on the top right of each web page.Currently, PCPPI operates on a Linux, Apache, MySQL and PHP (LAMP) stack. MySQL is used for storage, maintenance, and operation of the datasets. The front-end user interface is implemented in PHP, which is a popular scripting language designed for dynamic web development. A well-defined and packaged JavaScript called jQuery is used to improve website performance and enhance user experience. Also, the online BLAST program is employed for homology search and the Vis.js library  or the keywords of gene/protein names. The full name and abbreviation are both feasible, where they are automatically normalized with synonyms. The species should be firstly specified by choosing their respective names in dropdown list.P. expansum should be typed in the left text box, and the identifier of proteins in crops should be entered in the right box. Records will be found only if these two proteins interact with each other.The paired search options are accession number centric. Users can find the potential protein–protein interactions with the identifiers. The identifier of proteins in The batch search option allows users to input a list of protein identifiers, or upload a file. Before submitting, the algorithms should be specified by selecting the setting ‘Show only pair wise interactions between the specified proteins’ or ‘Show all interactions involving specified proteins’.The Paired Search and Batch Search are provided in the Advanced Search column.P. expansum), Protein name (in P. expansum), Protein ID (in crops), Protein name (in crops), DDI annotation , pipes (two interactions) and hubs (multiple interactions) of different sizes  according to our previous work . As. AsP. exFurthermore, the proteins in free ends and major hubs were assigned to molecular function and biological process categories according to the GO annotation from the Gene Ontology Consortium . The resGenerally, conserved domains are the main functional and structural units of proteins and play important roles in PPIs by forming interactions in diverse ways (heterotypic or homotypic) . DDIs, aP. expansum and tomato, and then used as the testing data . Therefore, the significant overlap of the identified GO terms between P. exponsum and crops greatly support the quality of the predictions generated in PCPPI as well.Furthermore, the above analysis of GO annotations has presented the enriched biological themes and functional proteins that clustered in vidually . Therefovidually . The higP. expansum on crop plants. Identifying these interactions is helpful to elucidate the molecular mechanisms underlying fungal pathogenicity, signal transduction and metabolic pathways. These datasets could be used for both theorists and experimentalists to reassemble protein complexes, expand existing pathways, as well as discover potential new antibiotic targets. In order to provide up-to-date data on protein interactions, we will continue to collect and incorporate the latest datasets of PPIs and DDIs, and develop new methods to predict potential protein interactions. Moreover, we will reiterate the process of database structure and user interface design to enhance the web content and functionality.In summary, PCPPI is the first web database providing comprehensive information about predicted PPIs involved in the pathogenicity and virulence of Database Online.National Natural Science Foundation of China [31461143008 and 31471157]; the Joint NSFC-ISF Research Program, jointly funded by the National Natural Science Foundation of China and the Israel Science Foundation [1936/14]; Fundamental Research Funds for the Central Universities [JZ2016HGBZ1017]; National Science Fund for Distinguished Young Scholars [30825030]; Advanced Program of Doctoral Fund of Ministry of Education of China [20110181130009]; Key Project from the Government of Sichuan Province [2013NZ0014]; Key Project from the Government of Anhui Province [1401032006]. Funding for open access charge: Joint NSFC-ISF Research Program, jointly funded by the National Natural Science Foundation of China and the Israel Science Foundation [1936/14].Conflict of interest. None declared.Supplementary DataClick here for additional data file.


INSR gene based on different computational methods. Analysis of INSR was initiated with PROVEAN followed by PolyPhen and I-Mutant servers to investigate the effects of 57 nsSNPs retrieved from database of SNP (dbSNP). A total of 18 mutations that were found to exert damaging effects on the INSR protein structure and function were chosen for further analysis. Among these mutations, our computational analysis suggested that 13 nsSNPs decreased protein stability and might have resulted in loss of function. Therefore, the probability of their involvement in disease predisposition increases. In the lack of adequate prior reports on the possible deleterious effects of nsSNPs, we have systematically analyzed and characterized the functional variants in coding region that can alter the expression and function of INSR gene. In silico characterization of nsSNPs affecting INSR gene function can aid in better understanding of genetic differences in disease susceptibility.Single-nucleotide polymorphisms (SNPs) associated with complex disorders can create, destroy, or modify protein coding sites. Single amino acid substitutions in the insulin receptor (INSR) are the most common forms of genetic variations that account for various diseases like Donohue syndrome or Leprechaunism, Rabson-Mendenhall syndrome, and type A insulin resistance. We analyzed the deleterious nonsynonymous SNPs (nsSNPs) in INSR gene.The insulin receptor (INSR) is a tyrosine kinase-specific transmembrane receptor that is activated by insulin, insulin growth factor I, and insulin growth factor II . Metabolβ thalassemia, and cystic fibrosis, result from SNPs [Single-nucleotide polymorphisms (SNPs) are the most common form of human genetic variations and nearly half a million of SNPs reside in the exons of the human genome. Among these SNPs, “nonsynonymous SNPs (nsSNPs)” can alter the amino acid residues and contribute to functional diversity in encoded proteins in the human population. The genomic distribution of SNPs is not obviously homogenous. In general, SNPs occur in noncoding regions more frequently than in coding regions . Geneticrom SNPs –12. For rom SNPs –15. For rom SNPs . The con in silico which may have an impact on the structural integrity of human INSR protein and are involved in several genetic diseases. Knowledge of in silico analysis of SNPs will play a major role in the understanding of the genetic basis of several complex genetic human diseases. Furthermore, the genetics of human phenotypic diversity could also be implied by establishing the functions of these SNPs. Using laboratory techniques, it is still a major obstacle to identify the functional SNPs in a disease-related gene. However, with recent advancements in the “in silico” technique and procedures, it is now possible to carry out research investigations without the need for extensive lab work. The main focus of this work is to investigate the SNPs genetic variations in the human INSR gene and their possible effects on structure and functions of INSR using bioinformatics and computational algorithms.In this study, we identified the nsSNPs' deleterious mutations INSR gene was collected from Online Mendelian Inheritance in Man (OMIM) and Entrez Gene on National Center for Biological Information (NCBI) web sites. The SNPs information (protein accession number and SNP ID) of the INSR gene was retrieved from the NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp/).The data of humanhttp://provean.jcvi.org/index.php), I-Mutant 3.0 (http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi), and PolyPhen (http://genetics.bwh.harvard.edu/pph2/) were used in this study.A sequence based predictor estimates the effect of protein sequence variation on protein function. Many web servers are available to predict the effect of single amino acid variations on protein stability and protein binding efficiency. PROVEAN  or is predicted to be “neutral” if the score is above the threshold .PolyPhen version 2 predicts the influence of amino acid substitution on the structure and function of proteins by using the specific empirical rules. Protein sequence, database ID/accession number, amino acid position, and amino acid variant details are the input options for PolyPhen . The tooI-Mutant 2.0 and I-Mutant 3.0 are based on Support Vector Machine (SVM) algorithm to predict the stability of the protein due to single amino acid variations. It can predict protein stability changes by using protein sequence or structure. It has an overall accuracy of 77% when prediction is based on protein sequence. I-Mutant 2.0 and I-Mutant 3.0 predict the DDG values as a regression estimator and the sign of the stability change. I-Mutant 3.0 furthermore classifies mutations into three categories: neutral mutation (−0.5 ≤ DDG ≤ 0.5), large decrease (≤ −0.5), and large increase (>0.5) , 20.http://www.ebi.ac.uk/pdbsum/) was used to find the proteins related to the INSR. PDBsum provides an at-a-glance overview of every macromolecular structure deposited in the Protein Data Bank (PDB). It performs a FASTA search against all sequences in the PDB to obtain a list of the closest matches [The EMBL-EBI web-based tool PDBsum  was implemented for evaluating the quality and validation of the refined 3D structural models. The SAVES integrates PROCHECK, PROVE, and ERRAT software programs to check overall quality of the 3D models obtained from the PYMOL mutagenesis tool. Structure refinement was carried out using KoBaMIN which is based on knowledge based potential refinement for proteins protocol .The approach called Mutation Cutoff Scanning Matrix (mCSM) uses the concept of graph-based structural signatures to study and predict the impact of single-point mutations on protein stability and protein-protein and protein-nucleic acid affinity. The mCSM encodes distance patterns between atoms to represent protein residue environments . in silico approaches used in this study is shown in The predicted structures were viewed in University of California San Francisco (UCSF) Chimera. It is a computationally intensive program for visualization of molecular models and it provides an interactive interface for the user for analyzing the models and model related data. It provides a platform for analyzing sequence alignments, generating homology models, molecular docking, viewing various density models, and also comparing different models by superimposition . The mut INSR genes have been renamed. We carefully cross-examined the databases and removed those old and invalid SNPs. At dbSNP, INSR gene contains data for 4967 SNPs. Out of 4967 SNPs, only 57 were nsSNPs in the coding region  and “neutral” above it. A  .txt file containing “db SNP rsIDs” of all 57 nsSNPs was submitted to the “dbSNP rsIDs” page to calculate the PROVEAN score. Out of 57 nsSNPs, PROVEAN predicted 24 as deleterious and 33 as neutral . Among tPolyPhen identifies homologues of the input sequences via BLAST and calculates PSIC scores for every variant and estimates the difference between the variant scores; the difference of 0.339 is detrimental. There are certain empirical rules applied to the sequences and the accuracy is approximately 82% with a chance of 8% false-positive prediction. The protein accession number of INSR (P06213) and the amino acid substitutions corresponding to each of the 57 nsSNPs were submitted separately. I-Mutant is a neural network based routine tool used in the analysis of protein stability alterations by considering the single-site mutation. I-Mutant also provides the scores for free energy alterations, calculated with the FOLD-X energy based web server. By assimilating the FOLD-X estimations with those of I-Mutant, about 93% precision can be achieved. We have considered a threshold of −1.5 Kcal/mol to predict a SNP to be destabilized. Forty-six nsSNPs were considered as destabilized with DDG values by I-Mutant . Finallyhttp://www.bioinf.org.uk/saap/db/) is offline due to essential maintenance. Thus, we were unable to map the deleterious nsSNPs into protein structure through SAAP. Mapping the deleterious nsSNPs into protein structure information was performed through the LS-SNP/PDB server.By using the EMBL-EBI web-based tool PDBsum, the INSR protein structures were searched. Two related protein structures, namely, 2HR7 and 4IBM, were found to share 100% amino acid sequence similarity. The single amino acid polymorphism (SAAP) database server  and the mutant modeled structures. The KoBaMIN web server uses a force field for energy minimization. The total energy for all the mutant and native models after minimization is listed in The effects of the nsSNPs on protein stability were computed with FOLD-X by mCSM server which uses an empirical energy equation to calculate the Gibbs free energy DDG. The empirical energy terms consider the location and type of a substituted residue. The mCSM is a structure based prediction tool. Two different analysis protocols were utilized to obtain maximum information over the effect of the single amino acid substitutions: (1) all the nsSNPs were considered singularly and their effect on the protein stability and interaction potential was determined; (2) the nsSNPs were considered according to the allelic sequences. Initially, all the structures were minimized and obtained a stable protein stability value. Then the structures for each single amino acid variation were generated using the Build Model feature of FOLD-X 3.0. Finally, the effect of each single amino acid variation on the protein stability of INSR was determined using the analyzed complex features. The mutation was considered as destabilizing and stabilizing when the DDG was >0 and <0, respectively. In this prediction method, all the mutant structures ultimately derived from the PROVEAN, PolyPhen, and I-Mutant programs were finally submitted to the mCSM server to predict mutant structure's protein stability upon mutation. The mCSM predicted all structures as “Destabilizing” including two as “Highly Destabilizing” . INSR can manifest several insulin-resistant syndromes like Leprechaunism, Rabson-Mendenhall syndrome, and type A insulin resistance [ in silico analysis approaches to predict the functional effects of nsSNPs on genes such as G6PD, BARF, and PTEN [ in silico strategy to analyze and predict the functional effects of SNPs on INSR.The SNP insistance , 31. Diasistance . Numerouand PTEN , 34. The in silico methods based on the combination of two distinctive approaches which are sequence and structural based approaches. In comparison with the structure based methods, sequence based prediction methods are one step ahead because they can be applied to any proteins with known relatives, whereas structure based approaches are not feasible to implement for proteins with unknown 3D structures. Software programs and servers that integrate both sequence and structure resources have advantage of being able to assess the authenticity of the predicted results by cross-referencing the results from both methods. Most computational methods utilize this information for the prediction and analyses of deleterious nsSNPs, among which PROVEAN and PolyPhen algorithms are the main representatives. Considering normalized probability score below −2.5 in PROVEAN and a PSIC score 1.5 in PolyPhen as deleterious, 24 and 21 of amino acid substitutions were predicted to have functional impact on INSR gene. The variation in prediction score of PROVEAN and PolyPhen is mainly because of the difference in sequence alignment and the values used to classify the variants. Significant similarity was observed between the results obtained by PROVEAN and PolyPhen. PROVEAN and PolyPhen in predicting the effect of nsSNPs on protein function might be suitable in silico approach [We used differentapproach .In order to predict the impact of nsSNPs on protein structure, I-Mutant 3.0 was used which evaluated the stability change upon single-site mutation. I-Mutant 3.0 was ranked as one of the most reliable predictors based on the work performed by Khan and Vihinen . Based oEach mutation was considered individually to study the inherent effect of the SNP. In addition, the allelic sequences were analyzed to investigate if the polymorphisms neutralized each other by occurring simultaneously as an act of preservation of function by nature. The mCSM was used to analyze the effects of single amino acid variations on the structure and stability of the protein.Our results indicated that all of the 13 mutant structures of 2HR7 and 4IBM were predicted as “Destabilizing” which signified our results found by PROVEAN and PolyPhen. Among all the destabilized mutant structures, two mutants were labelled as “Highly Destabilizing” which were rs1051691 and rs52800171 in their I448T and W1220L positions, respectively, which suggested that these polymorphisms should be considered as a potential target for future experiments. If a single amino acid variation shows a change in protein stability or protein-protein interaction, it should give comparable values with the sign reversal for the reverse mutation. This would indicate that the prediction of the effect of the single amino acid variation on the protein structure or protein-protein interaction might be substantial. INSR gene and several diseases like insulin-resistant syndromes such as Leprechaunism, Rabson-Mendenhall syndrome, and type A insulin resistance. The present study concludes that 13 nsSNPs especially rs1051691 and rs52800171 decreases protein stability and are not tolerated or may result in loss of function. Their presence in the INSR increases the possibility of altered transcriptional and cell cycle regulation and INSR mediated diseases. Therefore, the probability of their involvement in disease predisposition increases. Thus, for further analysis, these mutations should be given priority to obtain detailed information on their effects. In order to confirm the structures modeled in this study, the actual structures should be determined by X-ray crystallography or nuclear magnetic resonance spectroscopy. We anticipate that the results obtained from our analysis would pave the way for providing useful information to the researchers and can play an important role in bridging the gap between biologists and bioinformaticians.This study shows a correlation between SNPs in the