KSP: an integrated method for predicting catalyzing kinases of phosphorylation sites in proteins

Protein Kinases as Potential Targets Contribute to the Development of Agrochemicals

Using agrochemicals against pest insects, fungi, and weeds plays a major part in maintaining and improving crop yields, which helps to solve the issue of food security. Due to the limited targets and resistance of agrochemicals, protein kinases are regarded as attractive potential targets to develop new agrochemicals. Recently, a lot of investigations have shown the extension of agrochemicals by targeting protein kinases, implying an increasing concern for this kind of method. However, few people have summarized and discussed the targetability of protein kinases contributing to the development of agrochemicals. In this work, we introduce the research on protein kinases as potential targets used in crop protection and discuss the prospects of protein kinases in the field of agrochemical development. This study may not only provide guidance for the contribution of protein kinases to the development of agrochemicals but also help nonprofessionals such as students learn and understand the role of protein kinases quickly.

A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites

By denaturing proteins and promoting the formation of multiprotein complexes, protein phosphorylation has important effects on the activity of protein functional molecules and cell signaling. The regulation of protein phosphorylation allows microbes to respond rapidly and reversibly to specific environmental stimuli or niches, which is closely related to the molecular mechanisms of bacterial drug resistance. Accurate prediction of phosphorylation sites (p-site) of prokaryotes can contribute to addressing bacterial resistance and providing new perspectives for developing novel antibacterial drugs. Most existing studies focus on human phosphorylation sites, while tools targeting phosphorylation site identification of prokaryotic proteins are still relatively scarce. This study designs a capsule network-based prediction technique for p-site in prokaryotes. To address the poor scalability and unreliability of dynamic routing processes in the output space of capsule networks, a more reliable way is introduced to learn the consistency between capsules. We incorporate a self-attention mechanism into the routing algorithm to capture the global information of the capsule, reducing the computational effort while enriching the representation capability of the capsule. Aiming at the weak robustness of the model, EcapsP improves the prediction accuracy and stability by introducing shortcuts and unconditional reconfiguration. In addition, the study compares and analyzes the prediction performance based on word vectors, physicochemical properties, and mixing characteristics in predicting serine (Ser/S), threonine (Thr/T), and tyrosine (Tyr/Y) p-site. The comprehensive experimental results show that the accuracy of the developed technique is close to 70% for the identification of the three phosphorylation sites in prokaryotes. Importantly, in side-by-side comparisons with other state-of-the-art predictors, our method improves the Matthews correlation coefficient (MCC) by approximately 7%. The results demonstrate the superiority of EcapsP in terms of high performance and reliability.

Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data

Phosphoproteomic experiments routinely observe thousands of phosphorylation sites. To understand the intracellular signaling processes that generated this data, one or more causal protein kinases must be assigned to each phosphosite. However, limited knowledge of kinase specificity typically restricts assignments to a small subset of a kinome. Starting from a statistical model of a high-throughput, in vitro kinase-substrate assay, I have developed an approach to high-coverage, multi-label kinase-substrate assignment called IV-KAPhE (“In vivo-Kinase Assignment for Phosphorylation Evidence”). Tested on human data, IV-KAPhE outperforms other methods of similar scope. Such computational methods generally predict a densely connected kinase-substrate network, with most sites targeted by multiple kinases, pointing either to unaccounted-for biochemical constraints or significant cross-talk and signaling redundancy. I show that such predictions can potentially identify biased kinase-site misannotations within families of closely related kinase isozymes and they provide a robust basis for kinase activity analysis.

Proteins can pass around information inside cells about changes in the environment. This process, called intracellular signaling, helps to trigger appropriate cellular responses to environmental changes. One of the main ways information is passed to proteins is through chemical “tagging,” called phosphorylation, by enzymes called protein kinases. We can measure the phosphorylation state of practically all proteins in a cell at any moment. Starting from known cases of phosphorylation by a kinase, many computational methods have been developed to predict if the kinase might tag a certain spot on another protein or if an observed tag was attached by the kinase, with different models for each kinase. I have developed a new method that instead uses a single model to assign one or more kinases to each observed tag, built from the latest large-scale experimental data. This change in focus and unbiased training data allows my method to be significantly more accurate than past methods. I also explored useful applications for my method. For example, I used it to show that much of our knowledge about which kinase is responsible for each tag is probably inaccurately biased towards the commonly studied ones.