Rice is an important crop throughout the world and is the staple food for about half the world's population. For better breeding and improved production, we need to know the function of rice molecules which facilitate their function through interactions with each other. The database of interacting proteins in Oryza sativa (DIPOS) provides comprehensive information of interacting proteins in rice, where the interactions are predicted using two computational methods, i.e., interologs and domain based methods. DIPOS contains 14614067 pairwise interactions among 27746 proteins, covering about 41% of the whole Oryaza sativa proteome. Furthermore, each interaction is assigned a confidence score which further enables biologists to sort out the important proteins. Biological explanations of pathways and interactions are also provided based on the database. Public access to the DIPOS is available at http://csb.shu.edu.cn/dipos and http://ppi.riceresearch.info.

Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive.In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes.The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.

The fungal pathogen Fusarium graminearum (telomorph Gibberella zeae) is the causal agent of several destructive crop diseases, where a set of genes usually work in concert to cause diseases to crops. To function appropriately, the F. graminearum proteins inside one cell should be assigned to different compartments, i.e. subcellular localizations. Therefore, the subcellular localizations of F. graminearum proteins can provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. Unfortunately, there are no subcellular localization information for F. graminearum proteins available now. Computational approaches provide an alternative way to predicting F. graminearum protein subcellular localizations due to the expensive and time-consuming biological experiments in lab.In this paper, we developed a novel predictor, namely FGsub, to predict F. graminearum protein subcellular localizations from the primary structures. First, a non-redundant fungi data set with subcellular localization annotation is collected from UniProtKB database and used as training set, where the subcellular locations are classified into 10 groups. Subsequently, Support Vector Machine (SVM) is trained on the training set and used to predict F. graminearum protein subcellular localizations for those proteins that do not have significant sequence similarity to those in training set. The performance of SVMs on training set with 10-fold cross-validation demonstrates the efficiency and effectiveness of the proposed method. In addition, for F. graminearum proteins that have significant sequence similarity to those in training set, BLAST is utilized to transfer annotations of homologous proteins to uncharacterized F. graminearum proteins so that the F. graminearum proteins are annotated more comprehensively.In this work, we present FGsub to predict F. graminearum protein subcellular localizations in a comprehensive manner. We make four fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can solve imbalance problem and avoid false positive results. Second, we design an ensemble classifier which employs feature selection to further improve prediction accuracy. Third, we use BLAST to complement machine learning based methods, which enlarges our prediction coverage. Last and most important, we predict the subcellular localizations of 12786 F. graminearum proteins, which provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus.

The fungal pathogen Fusarium graminearum (telomorph Gibberella zeae) is the causal agent of several destructive crop diseases. Identifying interactions among F. graminearum proteins and understanding their functions can provide insights into pathogenic mechanisms underlying F. graminearum−host interactions. F. graminearum protein−protein interaction (FPPI) database provides comprehensive information of protein−protein interactions (PPIs) of F. graminearum predicted based on both interologs from several PPI databases of seven species and domain−domain interactions experimentally determined based on protein structures. FPPI contains 223 166 interactions among 7406 proteins for F. graminearum. To the best of our knowledge, it is the first PPI map for this destructive fungus, which is thereby expected to shed light on biological functions of F. graminearum proteins. The predicted interactome covers about 52% of the whole F. graminearum proteome, and each interaction is assigned a score as the confidence for the predicted PPI. In particular, we constructed a core PPI data set with high confidence that consists of 27 102 interactions and 3745 proteins. To verify the reliability of the predicted interactome, we conducted yeast two-hybrid experiments over 3 randomly selected predictions from the core PPI data set, among which one pair of proteins was confirmed to indeed interact with each other, thereby proving high confidence on the core PPI data set. In addition, FPPI contains other functional information for F. graminearum genes, including homologues in other species deposited in different databases and the inferred functional characteristics, and so on. We further constructed an intuitive query interface for the database that provides easy access to the important features of proteins. In summary, FPPI is a rich source of information for system-level understanding of gene functions and biological processes in F. graminearum. Public access to the FPPI database is available at http://csb.shu.edu.cn/fppi.