MOTIVATION: Identifying the kinase-substrate relationships is vital to understanding the phosphorylation events and various biological processes, especially signal transductions. Although large amount of phosphorylation sites have been detected, unfortunately, it is rarely known which kinases activate those sites. Despite distinct computational approaches have been proposed to predict the kinase-substrate interactions, the prediction accuracy still needs to be improved. RESULTS: In this paper, we propose a novel probabilistic model named as PhosD to predict kinase-substrate relationships based on protein domains with the assumption that kinase-substrate interactions are accomplished with kinase-domain interactions. By further taking into account protein-protein interactions, our PhosD outperforms other popular approaches on several benchmark datasets with higher precision. In addition, some of our predicted kinase-substrate relationships are validated by signaling pathways, indicating the predictive power of our approach. Furthermore, we notice that given a kinase, the more substrates are known for the kinase the more accurate its predicted substrates will be, and the domains involved in kinase-substrate interactions are found to be more conserved across proteins phosphorylated by multiple kinases. These findings can help develop more efficient computational approaches in the future. AVAILABILITY AND IMPLEMENTATION: The data and results are available at http://comp-sysbio.org/phosd. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The protein phosphorylation modifications are important to protein activities and functions. It has been widely recognized that dysfunctional phosphorylation modifications are related to cancer. Specifically, some single amino acid variations could disrupt existing phosphorylation kinase-substrate relationships and create novel kinase-substrate relationships. Besides, numerous network-based methods have been proposed to identify meaningful disease modules, which are locally dense subnetworks. In this work, we proposed a new network clustering method to uncover disease modules, which are correlated with the specific disease, based on significance of connections instead of local density. Specially, we build a weighted tumor network of lung adenocarcinoma with kinase-substrate relationships, tissue-specific gene regulatory network, pairwise gene expression data and mutation data. With appropriate parameters decided by a machine learning method, our method identified 9 disease modules. We found that these disease modules could effectively discriminate tumor samples from normal samples. Some significantly important genes in these modules have been identified as target genes of drugs recently. Our results provide insights into the disease mechanism underlying, and help identify more target genes of drugs in the era of precision medicine.

MicroRNAs (miRNAs) are a class of small endogenous non-coding genes, acting as regulators in the post-transcriptional processes. Recently, the miRNAs are found to be widely involved in different types of diseases. Therefore, the identification of disease associated miRNAs can help understand the mechanisms that underlie the disease and identify new biomarkers. However, it is not easy to identify the miRNAs related to diseases due to its extensive involvements in various biological processes. In this work, we present a new approach to identify disease associated miRNAs based on domains, the functional and structural blocks of proteins. The results on real datasets demonstrate that our method can effectively identify disease related miRNAs with high precision.