PhosphoChain: a novel algorithm to predict kinase and phosphatase networks from high-throughput expression data

A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction

Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI (Predicting Phage-Host Interactions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank databases and extract a set of features. Then we select high-quality negative samples based on the K-Means clustering method and construct a balanced training set. Finally, we employ a deep convolutional neural network to build the predictive model. The results indicate that PredPHI can achieve a predictive performance of 81 percent in terms of the area under the receiver operating characteristic curve on the test set, and the clustering-based method is significantly more robust than that based on randomly selecting negative samples. These results highlight that PredPHI is a useful and accurate tool for identifying phage-host interactions from sequence data.

Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources

Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein kinase and phosphatase, respectively, constitutes a key mechanism of molecular information flow in cellular systems. The protein interactions of kinases, phosphatases, and their regulatory subunits and substrates are the main part of phosphorylation networks. To elucidate the landscape of phosphorylation events has been a central goal pursued by both experimental and computational approaches. Substrate specificity (e.g., sequence, structure) or the phosphoproteome has been utilized in an array of different statistical learning methods to infer phosphorylation networks. In this chapter, different computational phosphorylation network inference-related methods and resources are summarized and discussed.

A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites

RNA 5-methylcytosine (m⁵C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m⁵C plays a vital role in RNA metabolism. Accurate localization of RNA m⁵C sites in tissue cells is the premise and basis for the in-depth understanding of the functions of m⁵C. However, the main experimental methods of detecting m⁵C sites are limited to varying degrees. Establishing a computational model to predict modification sites is an excellent complement to wet experiments for identifying m⁵C sites. In this review, we summarized some available m⁵C predictors and discussed the characteristics of these methods.

CAMK2γ antagonizes mTORC1 activation during hepatocarcinogenesis

Hepatocellular carcinoma (HCC) is one of the most deadly cancers that still lacks effective treatments. Dysregulation of kinase signaling has frequently been reported to contribute to HCC. In this study, we used bioinformatic approaches to identify kinases that regulate gene expression changes in human HCCs and two murine HCC models. We identified a role for calcium/calmodulin-dependent protein kinases II gamma isoform (CAMK2γ) in hepatocarcinogenesis. CAMK2γ^-/- mice displayed severely enhanced chemical-induced hepatocarcinogenesis compared with wild-type controls. Mechanistically, CAMK2γ deletion potentiates hepatic activation of mechanistic target of rapamycin complex 1 (mTORC1), which results in hyperproliferation of hepatocytes. Inhibition of mTORC1 by rapamycin effectively attenuates the compensatory proliferation of hepatocytes in CAMK2γ^-/- livers. We further demonstrated that CAMK2γ suppressed growth factor- or insulin-induced mTORC1 activation by inhibiting IRS1/AKT signaling. Taken together, our results reveal a novel mechanism by which CAMK2γ antagonizes mTORC1 activation during hepatocarcinogenesis.

Computational phosphorylation network reconstruction: methods and resources

The succession of protein activation and deactivation mediated by phosphorylation and dephosphorylation events constitutes a key mechanism of molecular information transfer in cellular systems. To deduce the details of those molecular information cascades and networks has been a central goal pursued by both experimental and computational approaches. Many computational network reconstruction methods employing an array of different statistical learning methods have been developed to infer phosphorylation networks based on different types of molecular data sets such as protein sequence, protein structure, or phosphoproteomics data. In this chapter, different computational network inference methods and resources for biological network reconstruction with a particular focus on phosphorylation networks are surveyed.

A high-resolution network model for global gene regulation in Mycobacterium tuberculosis

The resilience of Mycobacterium tuberculosis (MTB) is largely due to its ability to effectively counteract and even take advantage of the hostile environments of a host. In order to accelerate the discovery and characterization of these adaptive mechanisms, we have mined a compendium of 2325 publicly available transcriptome profiles of MTB to decipher a predictive, systems-scale gene regulatory network model. The resulting modular organization of 98% of all MTB genes within this regulatory network was rigorously tested using two independently generated datasets: a genome-wide map of 7248 DNA-binding locations for 143 transcription factors (TFs) and global transcriptional consequences of overexpressing 206 TFs. This analysis has discovered specific TFs that mediate conditional co-regulation of genes within 240 modules across 14 distinct environmental contexts. In addition to recapitulating previously characterized regulons, we discovered 454 novel mechanisms for gene regulation during stress, cholesterol utilization and dormancy. Significantly, 183 of these mechanisms act uniquely under conditions experienced during the infection cycle to regulate diverse functions including 23 genes that are essential to host-pathogen interactions. These and other insights underscore the power of a rational, model-driven approach to unearth novel MTB biology that operates under some but not all phases of infection.