Using propensity scores to predict the kinases of unannotated phosphopeptides

NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network

Drug-target interaction (DTI) prediction is essential for new drug design and development. Constructing heterogeneous network based on diverse information about drugs, proteins and diseases provides new opportunities for DTI prediction. However, the inherent complexity, high dimensionality and noise of such a network prevent us from taking full advantage of these network characteristics. This article proposes a novel method, NGCN, to predict drug-target interactions from an integrated heterogeneous network, from which to extract relevant biological properties and association information while maintaining the topology information. It focuses on learning the topology representation of drugs and targets to improve the performance of DTI prediction. Unlike traditional methods, it focuses on learning the low-dimensional topology representation of drugs and targets via graph-based convolutional neural network. NGCN achieves substantial performance improvements over other state-of-the-art methods, such as a nearly 1.0% increase in AUPR value. Moreover, we verify the robustness of NGCN through benchmark tests, and the experimental results demonstrate it is an extensible framework capable of combining heterogeneous information for DTI prediction.

Identifying Interactions Between Kinases and Substrates Based on Protein-Protein Interaction Network

Protein phosphorylation is a kind of important post-translational modification of protein, which plays a critical role in many biological processes of eukaryote. Identifying kinase-substrate interactions is helpful to understand the mechanism of many diseases. Many computational algorithms for kinase-substrate interactions identification have been proposed. However, most of those methods are mainly focused on utilizing protein local sequence information. In this article, we propose a new computational method to predict kinase-substrate interactions based on protein-protein interaction (PPI) network. Different from existing methods, the PPI network is utilized to measure the similarities of kinase-kinase and substrate-substrate, respectively. Then, the pairwise similarities of kinase-kinase and substrate-substrate are adjusted based on the assumption that the similarities of kinase-kinase and substrate-substrate are more reliable if they are in the same cluster. Finally, the bi-random walk is used to predict potential kinase-substrate interactions. The experimental results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the case study demonstrates that it is effective in predicting potential kinase-substrate interactions.

KSIMC: Predicting Kinase⁻Substrate Interactions Based on Matrix Completion

Protein phosphorylation is an important chemical modification catalyzed by kinases. It plays important roles in many cellular processes. Predicting kinase–substrate interactions is vital to understanding the mechanism of many diseases. Many computational methods have been proposed to identify kinase–substrate interactions. However, the prediction accuracy still needs to be improved. Therefore, it is necessary to develop an efficient computational method to predict kinase–substrate interactions. In this paper, we propose a novel computational approach, KSIMC, to identify kinase–substrate interactions based on matrix completion. Firstly, the kinase similarity and substrate similarity are calculated by aligning sequence of kinase–kinase and substrate–substrate, respectively. Then, the original association network is adjusted based on the similarities. Finally, the matrix completion is used to predict potential kinase–substrate interactions. The experiment results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the relevant databases and scientific literature verify the effectiveness of our algorithm for new kinase–substrate interaction identification.