MP-PIPE

Computational prediction of protein-protein interaction (PPI) from protein sequence is important as many cellular functions are made possible through PPI. The Protein Interaction Prediction Engine (PIPE) software suite was developed for such predictions. The specific location of interaction is predicted by the PIPE-Sites predictor, which depends on PIPE engine. This PIPE-Sites predictor is here updated through the use of a large high-quality dataset of known PPI sites. Additionally, a similarity-weighted score had been recently developed in PIPE4 and has been proven to be more accurate for the likelihood of PPI prediction. However, PIPE-Sites are shown to be ineffective when applied to similarity-weighted score data. Thus, we here propose and evaluate a new sequence-based PPI site prediction method, named Panorama. This new method leverages similarity-weighted score data to further increase performance over two different performance metrics when evaluated on both <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{H}$</tex> . sapiens and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{S}$</tex> , cerevisiae PPI site data.

Prediction of protein-protein interaction (PPI) is one of the most challenging problems in biology. Although great progress has been devoted to the development of methodology for predicting PPIs and PIN using machine learning methods, the problem is still far from being solved since the application of most existing methods is limited. In this study, we propose a method for PPI prediction based on amino acids differences between pairs of protein sequences. 10-fold cross-validation tests based on human PPI datasets with balanced positive-to-negative ratios indicate that it performs comparably well. Therefore, our finding suggests that amino acids differences of interacting protein pairs are relevant to the prediction of PPIs and hence provide important information on sequence-based encoding schemes.

Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data, including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features, thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of nonproteolytic and noninhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task and thereby highlight limitations of computational interaction prediction methods.

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

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.

Protein interactions play an essential role in studying living systems and life phenomena. A considerable amount of literature has been published on analyzing and predicting protein interactions, such as support vector machine method, homology-based method and similarity-based method, each has its pros and cons. Most existing methods for predicting protein interactions require prior domain knowledge, making it difficult to effectively extract protein features. Single method is dissatisfactory in predicting protein interactions, declaring the need for a comprehensive method that combines the advantages of various methods. On this basis, a deep ensemble learning method called EnAmDNN (Ensemble Deep Neural Networks with Attention Mechanism) is proposed to predict protein interactions which is an appropriate candidate for comprehensive learning, combining multiple models, and considering the advantages of various methods. Particularly, it encode protein sequences by the local descriptor, auto covariance, conjoint triad, pseudo amino acid composition and combine the vector representation of each protein in the protein interaction network. Then it takes advantage of the multi-layer convolutional neural networks to automatically extract protein features and construct an attention mechanism to analyze deep-seated relationships between proteins. We set up four different structures of deep learning models. In the ensemble learning model, second layer data sets are generated with five-fold cross validation from basic learners, then predict the protein interaction network by combining 16 models. Results on five independent PPI data sets demonstrate that EnAmDNN achieves superior prediction performance than other comparing methods.

Alcohol abuse reduces response rates to IFN therapy in patients with chronic hepatitis C. To model the molecular mechanisms behind this phenotype, we characterized the effects of ethanol on Jak-Stat and MAPK pathways in Huh7 human hepatoma cells, in HCV replicon cell lines, and in primary human hepatocytes. High physiological concentrations of acute ethanol activated the Jak-Stat and p38 MAPK pathways and inhibited HCV replication in several independent replicon cell lines. Moreover, acute ethanol induced Stat1 serine phosphorylation, which was partially mediated by the p38 MAPK pathway. In contrast, when combined with exogenously applied IFN-α, ethanol inhibited the antiviral actions of IFN against HCV replication, involving inhibition of IFN-induced Stat1 tyrosine phosphorylation. These effects of alcohol occurred independently of i) alcohol metabolism via ADH and CYP2E1, and ii) cytotoxic or cytostatic effects of ethanol. In this model system, ethanol directly perturbs the Jak-Stat pathway, and HCV replication.Infection with Hepatitis C virus is a significant cause of morbidity and mortality throughout the world. With a propensity to progress to chronic infection, approximately 70% of patients with chronic viremia develop histological evidence of chronic liver diseases including chronic hepatitis, cirrhosis, and hepatocellular carcinoma. The situation is even more dire for patients who abuse ethanol, where the risk of developing end stage liver disease is significantly higher as compared to HCV patients who do not drink [1,2].Recombinant interferon alpha (IFN-α) therapy produces sustained responses (ie clearance of viremia) in 8–12% of patients with chronic hepatitis C [3]. Significant improvements in response rates can be achieved with IFN plus ribavirin combination [4-6] and pegylated IFN plus ribavirin [7,8] therapies. However, over 50% of chronically infected patients still do not clear viremia. Moreover, HCV-infected patients who abuse alcohol have extremely low response rates to IFN therapy [9], but the mechanisms involved have not been clarified.MAPKs play essential roles in regulation of differentiation, cell growth, and responses to cytokines, chemokines and stress. The core element in MAPK signaling consists of a module of 3 kinases, named MKKK, MKK, and MAPK, which sequentially phosphorylate each other [10]. Currently, four MAPK modules have been characterized in mammalian cells: Extracellular Regulated Kinases (ERK1 and 2), Stress activated/c-Jun N terminal kinase (SAPK/JNK), p38 MAP kinases, and ERK5 [11]. Interestingly, ethanol modulates MAPKs [12]. However, information on how ethanol affects MAPKs in the context of innate antiviral pathways such as the Jak-Stat pathway in human cells is extremely limited.When IFN-α binds its receptor, two receptor associated tyrosine kinases, Tyk2 and Jak1 become activated by phosphorylation, and phosphorylate Stat1 and Stat2 on conserved tyrosine residues [13]. Stat1 and Stat2 combine with the IRF-9 protein to form the transcription factor interferon stimulated gene factor 3 (ISGF-3), which binds to the interferon stimulated response element (ISRE), and induces transcription of IFN-α-induced genes (ISG). The ISGs mediate the antiviral effects of IFN. The transcriptional activities of Stats 1, 3, 4, 5a, and 5b are also regulated by serine phosphorylation [14]. Phosphorylation of Stat1 on a conserved serine amino acid at position 727 (S727), results in maximal transcriptional activity of the ISGF-3 transcription factor complex [15]. Although cross-talk between p38 MAPK and the Jak-Stat pathway is essential for IFN-induced ISRE transcription, p38 does not participate in IFN induction of Stat1 serine phosphorylation [14,16-19]. However, cellular stress responses induced by stimuli such as ultraviolet light do induce p38 MAPK mediated Stat1 S727 phosphorylation [18].In the current report, we postulated that alcohol and HCV proteins modulate MAPK and Jak-Stat pathways in human liver cells. To begin to address these issues, we characterized the interaction of acute ethanol on Jak-Stat and MAPK pathways in Huh7 cells, HCV replicon cells lines, and primary human hepatocytes.

Protein complex prediction based on maximum matching with domain–domain interaction

PRASA: An integrated web server that analyzes protein interaction types

Protein-protein interactions (PPI) play an important role in cellular processes and metabolic processes within a cell. An important task is to determine the existence of interactions among proteins. Unfortunately, existing biological experimental techniques are expensive, time-consuming and labor-intensive. The network structures of many such networks are sparse, incomplete and noisy, containing many false positive and false negatives. Thus, state-of-the-art methods for link prediction in these networks often cannot give satisfactory prediction results, especially when some networks are extremely sparse. Noticing that we typically have more than one PPI network available, we naturally wonder whether it is possible to 'transfer' the linkage knowledge from some existing, relatively dense networks to a sparse network, to improve the prediction performance. Noticing that a network structure can be modeled using a matrix model, in this paper, we introduce the well-known Collective Matrix Factorization (CMF) technique to 'transfer' usable linkage knowledge from relatively dense interaction network to a sparse target network. Our approach is to establish the correspondence between a source and a target network via network similarities. We test this method on two real protein-protein interaction networks, Helicobacter pylori (as a target network) and Human (as a source network). Our experimental results show that our method can achieve higher and more robust performance as compared to some baseline methods.

Objectives: To design a new framework to efficiently parallelize the steps of VLASPD algorithm using a hybridized apriori and fp-growth on GPU; to implement the existing and proposed framework in CUDA;to improve the performance factors like computational time, memory and CPU utilization.Methods/Statistical Analysis: This paper proposes the acceleration of Protein-Protein Interactions (PPIs) prediction on Graphics Processing Units(GPUs). A GPU can provide more processing cores and computational power in the same cost as a CPU.Findings: The frequently occurring patterns in the protein sequences can be used for PPIs prediction.The moving of the approaches from fixed length to variable length lead to computational complexity but also is found to be advantageous.Applications/Improvements:Sequence biology is since being researched by various computer engineers, the GPUs can be employed for predicting various sequence interactions like DNA-Proteins, etc. Since the GPU runs the parallel code efficiently, the methodology can be further improved if efficiently parallelized.

BackgroundWe have recently presented Schizophrenia Interactome, i.e., the network of protein-protein interactions (PPIs) of schizophrenia associated genes. PPIs predicted by our High-precision Protein-Protein Interaction Prediction model (HiPPIP) which uses machine learning to classify features of protein-pairs such as colocalization, coexpression, common molecular functions and biological processes could explain the apparent discordance between modern and historical genetic basis of Schizophrenia (published in npj Schizophrenia), and also were instrumental in discovering that OASL interacts DDX58 to activate the RIG-I immunity pathway during viral infection. These novel predicted PPIs were found to be highly accurate based on computational and experimental validations, and gave insights into possible functions of SZ genes that previously had no-known functional information. Even a single novel PPI can have enormous impact on advancement of biology, when translated effectively. How can we ensure that 500+ of these novel PPIs of schizophrenia interactome are translated effectively?MethodsWe developed a platform for Massive Open Online Discovery for Schizophrenia Research, or “MOOD for Schizophrenia Research”, that brings together trained biologists and bioinformaticians including those who are currently not affiliated with research labs (non-research students, PhDs who gave up science careers for administrative/corporate jobs or to take care of families, etc), as well as scientists who are active researchers, to hypothesize, discuss and prioritize the novel PPIs. Hypotheses are written as nanopublications with authorship credit. We are developing a number of features on the portal that allow and encourage scientists to create knowledge around the predicted PPIs and be recognized and given credit.ResultsThe first version of our website that disseminates the Schizophrenia Interactome is receiving hundreds of unique users each month. We have since developed MOOD for Schizophrenia Research and will present the key features of the website in this work. Each PPI can be viewed, researched on and written about, by participating scientists. Comprehensive information about the proteins in the PPI regarding their known functions, pathways, diseases and drug associations, is made readily available to scientists, allowing them to hypothesize the importance of the specific PPI. We present methods that we employ to promote collaboration in this work. Initially, the portal starts with a few members and it grows through referral webs (i.e. current members invite new members). The portal has a number of features that recognize and thus entice users to participate. We will also present the user feedback and participation.DiscussionPPIs are central to cellular systems. Yet less than 10% of estimated PPIs are known today. Thus, the computationally predicted PPIs which are deemed accurate, can accelerate advancement of schizophrenia biology research. The time is ripe to benefit from computer science and information technologies methods for not only discovering aspects of computational biology but to create new mechanisms to promote online collaboration to achieve big things as a summation of nanocontributions. The knowledge potential generated through this system would aid various principal investigators in well established (but ill funded) research labs by giving them access to bioinformaticians and biologists around the world who are eager be recognized for their contributions. We present here, not merely a website but a novel approach to promote collaborative research between people with heterogeneous skills and commitments by benefiting from the untapped talent of researchers around the world.

Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

Predicting protein–protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to help improve these predictions, including choosing the proper descriptors of proteins to represent these interactions, among others. In the current work, we provide a representative protein structure that is amenable to PPI classification using machine learning approaches, referred to as residue cluster classes. Through sampling and optimization, we identified the best algorithm–parameter pair to classify PPI from more than 360 different training sets. We tested these classifiers against PPI datasets that were not included in the training set but shared sequence similarity with proteins in the training set to reproduce the situation of most proteins sharing sequence similarity with others. We identified a model with almost no PPI error (96–99% of correctly classified instances) and showed that residue cluster classes of protein pairs displayed a distinct pattern between positive and negative protein interactions. Our results indicated that residue cluster classes are structural features relevant to model PPI and provide a novel tool to mathematically model the protein structure/function relationship.

Effectiveness and Efficiency: Label-Aware Hierarchical Subgraph Learning for Protein–Protein Interaction