Non-interacting Protein Pairs Research Articles

Imaging the Raf-MEK-ERK Signaling Cascade in Living Cells.

Conventional biochemical methods for studying cellular signaling cascades have relied on destructive cell disruption. In contrast, the live cell imaging of fluorescent-tagged transfected proteins offers a non-invasive approach to understanding signal transduction events. One strategy involves monitoring the phosphorylation-dependent shuttling of a fluorescent-labeled kinase between the nucleus and cytoplasm using nuclear localization, export signals, or both. In this paper, we introduce a simple method to visualize intracellular signal transduction in live cells by exploring the translocation properties of PKC from the cytoplasm to the membrane. We fused bait protein to PKC, allowing the bait (RFP-labeled) and target (GFP-labeled) proteins to co-translocate from the cytoplasm to the membrane. However, in non-interacting protein pairs, only the bait protein was translocated to the plasma membrane. To verify our approach, we examined the Raf-MEK-ERK signaling cascade (ERK pathway). We successfully visualized direct Raf1/MEK2 interaction and the KSR1-containing ternary complex (Raf1/MEK2/KSR1). However, the interaction between MEK and ERK was dependent on the presence of the KSR1 scaffold protein under our experimental conditions.

KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.

Aberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder. KSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low-dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder's generalization capability on four different datasets and compare its performance with other state-of-the-art prediction models. We employ KSFinder to predict substrates of 68 "dark" kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates. KSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥0.7 (nine at >0.9, six at 0.8-0.9, and two at 0.7-0.8). The evaluation of 93,593 negative predictions (probability ≤0.3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis. KSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates.

DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions.

Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper proposes DensePPI, a novel deep convolution strategy applied to the 2D image map generated from the interacting protein pairs for PPI prediction. A colour encoding scheme has been introduced to embed the bigram interaction possibilities of Amino Acids into RGB colour space to enhance the learning and prediction task. The DensePPI model is trained on 5.5 million sub-images of size 128×128 generated from nearly 36,000 interacting and 36,000 non-interacting benchmark protein pairs. The performance is evaluated on independent datasets from five different organisms; Caenorhabditis elegans, Escherichia coli, Helicobacter Pylori, Homo sapiens and Mus Musculus. The proposed model achieves an average prediction accuracy score of 99.95% on these datasets, considering inter-species and intra-species interactions. The performance of DensePPI is compared with the state-of-the-art methods and outperforms those approaches in different evaluation metrics. Improved performance of DensePPI indicates the efficiency of the image-based encoding strategy of sequence information with the deep learning architecture in PPI prediction. The enhanced performance on diverse test sets shows that the DensePPI is significant for intra-species interaction prediction and cross-species interactions. The dataset, supplementary file, and the developed models are available at https://github.com/Aanzil/DensePPI for academic use only.

ProtInteract: A deep learning framework for predicting protein–protein interactions

Proteins mainly perform their functions by interacting with other proteins. Protein–protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein–protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein’s primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein’s primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein’s amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.

Protein–protein interaction and non-interaction predictions using gene sequence natural vector

Predicting protein–protein interaction and non-interaction are two important different aspects of multi-body structure predictions, which provide vital information about protein function. Some computational methods have recently been developed to complement experimental methods, but still cannot effectively detect real non-interacting protein pairs. We proposed a gene sequence-based method, named NVDT (Natural Vector combine with Dinucleotide and Triplet nucleotide), for the prediction of interaction and non-interaction. For protein–protein non-interactions (PPNIs), the proposed method obtained accuracies of 86.23% for Homo sapiens and 85.34% for Mus musculus, and it performed well on three types of non-interaction networks. For protein-protein interactions (PPIs), we obtained accuracies of 99.20, 94.94, 98.56, 95.41, and 94.83% for Saccharomyces cerevisiae, Drosophila melanogaster, Helicobacter pylori, Homo sapiens, and Mus musculus, respectively. Furthermore, NVDT outperformed established sequence-based methods and demonstrated high prediction results for cross-species interactions. NVDT is expected to be an effective approach for predicting PPIs and PPNIs.

Predicting Primary Sequence-Based Protein-Protein Interactions Using a Mercer Series Representation of Nonlinear Support Vector Machine

The prediction of protein-protein interactions (PPIs) is essential to understand the cellular processes from a medical perspective. Among the various machine learning techniques, kernel-based Support Vector Machine (SVM) has been commonly employed to discriminate between interacting and non-interacting protein pairs. The main drawback of employing the kernel-based SVM to datasets with many features, such as the primary sequence-based protein-protein dataset, is the significant increase in computational time of training stage. This increase in computational time is mainly due to the presence of the kernel in solving the quadratic optimisation problem (QOP) involved in nonlinear SVM. In order to fix this issue, we propose a novel and efficient computational algorithm by approximating the kernel-based SVM using a low-rank truncated Mercer series as well as desired. As a result, the QOP for the approximated kernel-based SVM will be very tractable in the sense that there is a significant reduction in computational time of training and validating stages. We illustrate the novelty of the proposed method by predicting the PPIs of “S. Cerevisiae” where the protein features extracted using the multiscale local descriptor (MLD), and then we compare the predictive performance of the proposed low-rank approximation with the existing methods. Finally, the new method results in significant reduction in computational time for predicting PPIs with almost as accuracy as kernel-based SVM.

Predicting protein-protein interactions using high-quality non-interacting pairs

BackgroundIdentifying protein-protein interactions (PPIs) is of paramount importance for understanding cellular processes. Machine learning-based approaches have been developed to predict PPIs, but the effectiveness of these approaches is unsatisfactory. One major reason is that they randomly choose non-interacting protein pairs (negative samples) or heuristically select non-interacting pairs with low quality.ResultsTo boost the effectiveness of predicting PPIs, we propose two novel approaches (NIP-SS and NIP-RW) to generate high quality non-interacting pairs based on sequence similarity and random walk, respectively. Specifically, the known PPIs collected from public databases are used to generate the positive samples. NIP-SS then selects the top-m dissimilar protein pairs as negative examples and controls the degree distribution of selected proteins to construct the negative dataset. NIP-RW performs random walk on the PPI network to update the adjacency matrix of the network, and then selects protein pairs not connected in the updated network as negative samples. Next, we use auto covariance (AC) descriptor to encode the feature information of amino acid sequences. After that, we employ deep neural networks (DNNs) to predict PPIs based on extracted features, positive and negative examples. Extensive experiments show that NIP-SS and NIP-RW can generate negative samples with higher quality than existing strategies and thus enable more accurate prediction.ConclusionsThe experimental results prove that negative datasets constructed by NIP-SS and NIP-RW can reduce the bias and have good generalization ability. NIP-SS and NIP-RW can be used as a plugin to boost the effectiveness of PPIs prediction. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=NIP.

Genomic data integration systematically biases interactome mapping.

Elucidating the complete network of protein-protein interactions, or interactome, is a fundamental goal of the post-genomic era, yet existing interactome maps are far from complete. To increase the throughput and resolution of interactome mapping, methods for protein-protein interaction discovery by co-migration have been introduced. However, accurate identification of interacting protein pairs within the resulting large-scale proteomic datasets is challenging. Consequently, most computational pipelines for co-migration data analysis incorporate external genomic datasets to distinguish interacting from non-interacting protein pairs. The effect of this procedure on interactome mapping is poorly understood. Here, we conduct a rigorous analysis of genomic data integration for interactome recovery across a large number of co-migration datasets, spanning diverse experimental and computational methods. We find that genomic data integration leads to an increase in the functional coherence of the resulting interactome maps, but this comes at the expense of a decrease in power to discover novel interactions. Importantly, putative novel interactions predicted by genomic data integration are no more likely to later be experimentally discovered than those predicted from co-migration data alone. Our results reveal a widespread and unappreciated limitation in a methodology that has been widely used to map the interactome of humans and model organisms.

IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are characterized by the lack of a fixed tertiary structure and are involved in the regulation of key biological processes via binding to multiple protein partners. IDPs are malleable, adapting to structurally different partners, and this flexibility stems from features encoded in the primary structure. The assumption that universal sequence information will facilitate coverage of the sparse zones of the human interactome motivated us to explore the possibility of predicting protein-protein interactions (PPIs) that involve IDPs based on sequence characteristics. We developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs. Consideration of both sequence determinants specific for conformational organizations and the multiplicity of IDP interactions in the training phase ensured a reliable approach that is superior to current state-of-the-art methods. By applying a strict evaluation procedure, we confirm that our method predicts interactions of the IDP of interest even on the proteome-scale. This service is provided as a web tool to expedite the discovery of new interactions and IDP functions with enhanced efficiency.

PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.

Protein–protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the Saccharomyces cerevisiae dataset. A high accuracy of 94.56%, with 94.79% sensitivity at 94.36% precision, was obtained. The experimental results illustrated that the proposed approach can extract the most significant features from each protein sequence and can be a bright and meaningful tool for the research of proteomics.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel

BackgroundSince 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.ResultsIn 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.ConclusionsWe 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.

Non-interacting proteins may resemble interacting proteins: prevalence and implications

The vast majority of proteins do not form functional interactions in physiological conditions. We have considered several sets of protein pairs from S. cerevisiae with no functional interaction reported, denoted as non-interacting pairs, and compared their 3D structures to available experimental complexes. We identified some non-interacting pairs with significant structural similarity with experimental complexes, indicating that, even though they do not form functional interactions, they have compatible structures. We estimate that up to 8.7% of non-interacting protein pairs could have compatible structures. This number of interactions exceeds the number of functional interactions (around 0.2% of the total interactions) by a factor 40. Network analysis suggests that the interactions formed by non-interacting pairs with compatible structures could be particularly hazardous to the protein-protein interaction network. From a structural point of view, these interactions display no aberrant structural characteristics, and are even predicted as relatively stable and enriched in potential physical interactors, suggesting a major role of regulation to prevent them.

Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.

Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF) algorithm combined with Autocovariance (AC) features extracted from the Position-Specific Scoring Matrix (PSSM). More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs of Yeast, H. pylori, and independent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems.

Computational prediction of virus-human protein-protein interactions using embedding kernelized heterogeneous data.

Pathogenic microorganisms exploit host cellular mechanisms and evade host defense mechanisms through molecular pathogen-host interactions (PHIs). Therefore, comprehensive analysis of these PHI networks should be an initial step for developing effective therapeutics against infectious diseases. Computational prediction of PHI data is gaining increasing demand because of scarcity of experimental data. Prediction of protein-protein interactions (PPIs) within PHI systems can be formulated as a classification problem, which requires the knowledge of non-interacting protein pairs. This is a restricting requirement since we lack datasets that report non-interacting protein pairs. In this study, we formulated the "computational prediction of PHI data" problem using kernel embedding of heterogeneous data. This eliminates the abovementioned requirement and enables us to predict new interactions without randomly labeling protein pairs as non-interacting. Domain-domain associations are used to filter the predicted results leading to 175 novel PHIs between 170 human proteins and 105 viral proteins. To compare our results with the state-of-the-art studies that use a binary classification formulation, we modified our settings to consider the same formulation. Detailed evaluations are conducted and our results provide more than 10 percent improvements for accuracy and AUC (area under the receiving operating curve) results in comparison with state-of-the-art methods.

Fuzzy support vector machine model to predict human death domain protein–protein interactions

Proteins have crucial importance in every living system, and Protein–Protein Interactions (PPIs) play a pivotal role in regulation of virtually all biological processes such as DNA transcription, replication, metabolic cycles and signaling cascades. The PPIs play an important role in the complex process of cell death which mainly occurs via apoptosis and necrosis in eukaryotic cells. Apoptosis is an orderly cellular suicide program critical for the development and homeostasis of multicellular organism. Failure to control apoptosis can have catastrophic consequences. The cascades of amazing reactions carried out by proteins such as Caspase, CARD, NLRP, NOD, FADD, DEDD, POP, Myd88 etc. play important role in the process of cell death. The high throughput experimental methods for determining PPIs are time consuming, expensive and are generating huge amount of PPIs data. Therefore, there is need to develop computational methods to efficiently and accurately predict PPIs. In this work, an attempt has been made to develop a fuzzy support vector machine (F-SVM) model for predicting death domain (DD) PPIs based on sixteen physicochemical, biochemical and structural features of amino acids which are monomers of proteins. First, the protein primary sequences are encoded into sequential features represented by descriptors. Then, the Support Vector Machine and Sequential Minimal Optimization of WEKA software are employed to classify interacting and non-interacting protein pairs. The performance of SVM and F-SVM with various kernel functions has been evaluated and it was observed that libSVM with Polynomial kernel was found to be best with accuracy of 77.94 % via F-SVM which is optimum model in predicting human DD-PPIs. Validation is performed by tenfold cross-validation technique. The F-SVM performance measure is 2.94 % higher than SVM in terms of accuracy with the use of custom designed fuzzy membership function. The results obtained are in agreement with available experimental data. Such models can be useful in providing PPI information of DD proteins which can be useful in understanding the molecular mechanisms involved in death of cells taking place due to aging, programmed cell death and various diseases. It may through some light on the study of cancerous cell and gerontology.

Sequence co-evolution gives 3D contacts and structures of protein complexes.

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

Predicting Protein-Protein Interaction by the Mirrortree Method: Possibilities and Limitations

Molecular co-evolution analysis as a sequence-only based method has been used to predict protein-protein interactions. In co-evolution analysis, Pearson's correlation within the mirrortree method is a well-known way of quantifying the correlation between protein pairs. Here we studied the mirrortree method on both known interacting protein pairs and sets of presumed non-interacting protein pairs, to evaluate the utility of this correlation analysis method for predicting protein-protein interactions within eukaryotes. We varied metrics for computing evolutionary distance and evolutionary span of the species analyzed. We found the differences between co-evolutionary correlation scores of the interacting and non-interacting proteins, normalized for evolutionary span, to be significantly predictive for proteins conserved over a wide range of eukaryotic clades (from mammals to fungi). On the other hand, for narrower ranges of evolutionary span, the predictive power was much weaker.

The Development of a Universal In Silico Predictor of Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation.

Understanding Protein–Protein Interactions Using Local Structural Features

Protein–protein interactions (PPIs) play a relevant role among the different functions of a cell. Identifying the PPI network of a given organism (interactome) is useful to shed light on the key molecular mechanisms within a biological system. In this work, we show the role of structural features (loops and domains) to comprehend the molecular mechanisms of PPIs. A paradox in protein–protein binding is to explain how the unbound proteins of a binary complex recognize each other among a large population within a cell and how they find their best docking interface in a short timescale. We use interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation of this classification suggests that the balance between favoring and disfavoring structural features determines if a pair of proteins interacts or not. Our results are in agreement with previous works and support the funnel-like intermolecular energy landscape theory that explains PPIs. We have used these features to score the likelihood of the interaction between two proteins and to develop a method for the prediction of PPIs. We have tested our method on several sets with unbalanced ratios of interactions and non-interactions to simulate real conditions, obtaining accuracies higher than 25% in the most unfavorable circumstances.

Inferring homologous protein-protein interactions through pair position specific scoring matrix

BackgroundThe protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species.ResultsIn this study, we proposed "pair Position Specific Scoring Matrix (pairPSSM)" to identify homologous PPIs. The pairPSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pairPSSM were 0.42 and 0.31, respectively. Moreover, we applied pairPSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli).ConclusionsOur pairPSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pairPSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pairPSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.