BackgroundViral infection involves a large number of protein-protein interactions (PPIs) between virus and its host. These interactions range from the initial binding of viral coat proteins to host membrane receptor to the hijacking the host transcription machinery by viral proteins. Therefore, identifying PPIs between virus and its host helps understand the mechanism of viral infections and design antiviral drugs. Many computational methods have been developed to predict PPIs, but most of them are intended for PPIs within a species rather than PPIs across different species such as PPIs between virus and host.ResultsIn this study, we developed a prediction model of virus-host PPIs, which is applicable to new viruses and hosts. We tested the prediction model on independent datasets of virus-host PPIs, which were not used in training the model. Despite a low sequence similarity between proteins in training datasets and target proteins in test datasets, the prediction model showed a high performance comparable to the best performance of other methods for single virus-host PPIs.ConclusionsOur method will be particularly useful to find PPIs between host and new viruses for which little information is available. The program and support data are available at http://bclab.inha.ac.kr/VirusHostPPI.
In the following report, thermal cycling coupled with random 10-mers as primers was used to construct randomly amplified shotgun libraries (RASLs). This approach allowed shotgun libraries to be constructed from nanogram quantities of input DNA. RASLs contained inserts from throughout a target genome in an unbiased fashion and did not appear to contain chimeric sequences. This protocol should be useful for shotgun sequencing the genomes of unculturable organisms and rapidly producing shotgun libraries from cosmids, fosmids, yeast artificial chromosomes (YACs), and bacterial artificial chromosomes (BACs).
BackgroundMotivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins. Recent computational methods for finding protein-binding sites in RNAs have several drawbacks for practical use.ResultsWe developed a new support vector machine (SVM) model for predicting protein-binding regions in mRNA sequences. The model uses sequence profiles constructed from log-odds scores of mono- and di-nucleotides and nucleotide compositions. The model was evaluated by standard 10-fold cross validation, leave-one-protein-out (LOPO) cross validation and independent testing. Since actual mRNA sequences have more non-binding regions than protein-binding regions, we tested the model on several datasets with different ratios of protein-binding regions to non-binding regions. The best performance of the model was obtained in a balanced dataset of positive and negative instances. 10-fold cross validation with a balanced dataset achieved a sensitivity of 91.6%, a specificity of 92.4%, an accuracy of 92.0%, a positive predictive value (PPV) of 91.7%, a negative predictive value (NPV) of 92.3% and a Matthews correlation coefficient (MCC) of 0.840. LOPO cross validation showed a lower performance than the 10-fold cross validation, but the performance remains high (87.6% accuracy and 0.752 MCC). In testing the model on independent datasets, it achieved an accuracy of 82.2% and an MCC of 0.656. Testing of our model and other state-of-the-art methods on a same dataset showed that our model is better than the others.ConclusionsSequence profiles of log-odds scores of mono- and di-nucleotides were much more powerful features than nucleotide compositions in finding protein-binding regions in RNA sequences. But, a slight performance gain was obtained when using the sequence profiles along with nucleotide compositions. These are preliminary results of ongoing research, but demonstrate the potential of our approach as a powerful predictor of protein-binding regions in RNA. The program and supporting data are available at http://bclab.inha.ac.kr/RBPbinding.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0386-4) contains supplementary material, which is available to authorized users.
Despite the increasing number of protein-RNA complexes in structure databases, few data resources have been made available which can be readily used in developing or testing a method for predicting either protein-binding sites in RNA sequences or RNA-binding sites in protein sequences. The problem of predicting protein-binding sites in RNA has received much less attention than the problem of predicting RNA-binding sites in protein. The data presented in this paper are related to the article entitled “PRIdictor: Protein-RNA Interaction predictor” (Tuvshinjargal et al. 2016) [1]. PRIdictor can predict protein-binding sites in RNA as well as RNA-binding sites in protein at the nucleotide- and residue-levels. This paper presents four datasets that were used to test four prediction models of PRIdictor: (1) model RP for predicting protein-binding sites in RNA from protein and RNA sequences, (2) model RaP for predicting protein-binding sites in RNA from RNA sequence alone, (3) model PR for predicting RNA-binding sites in protein from protein and RNA sequences, and (4) model PaR for predicting RNA-binding sites in protein from protein sequence alone. The datasets supplied in this article can be used as a valuable resource to evaluate and compare different methods for predicting protein-RNA binding sites.
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