Identification of phosphorylation sites is an important step in the function study and drug design of proteins. In recent years, there have been increasing applications of the computational method in the identification of phosphorylation sites because of its low cost and high speed. Most of the currently available methods focus on using local information around potential phosphorylation sites for prediction and do not take the global information of the protein sequence into consideration. Here, we demonstrated that the global information of protein sequences may be also critical for phosphorylation site prediction. In this paper, a new deep neural network model, called DeepPSP, was proposed for the prediction of protein phosphorylation sites. In the DeepPSP model, two parallel modules were introduced to extract both local and global features from protein sequences. Two squeeze-and-excitation blocks and one bidirectional long short-term memory block were introduced into each module to capture effective representations of the sequences. Comparative studies were carried out to evaluate the performance of DeepPSP, and four other prediction methods using public data sets The F1-score, area under receiver operating characteristic curves (AUROC), and area under precision-recall curves (AUPRC) of DeepPSP were found to be 0.4819, 0.82, and 0.50, respectively, for S/T general site prediction and 0.4206, 0.73, and 0.39, respectively, for Y general site prediction. Compared with the MusiteDeep method, the F1-score, AUROC, and AUPRC of DeepPSP were found to increase by 8.6, 2.5, and 8.7%, respectively, for S/T general site prediction and by 20.6, 5.8, and 18.2%, respectively, for Y general site prediction. Among the tested methods, the developed DeepPSP method was also found to produce best results for different kinase-specific site predictions including CDK, mitogen-activated protein kinase, CAMK, AGC, and CMGC. Taken together, the developed DeepPSP method may offer a more accurate phosphorylation site prediction by including global information. It may serve as an alternative model with better performance and interpretability for protein phosphorylation site prediction.
It is considered that insulin resistance(IR)and its signaling pathway disorder are one of pathogenesis that causes insulin target-organs/issues lesions and their slow progression. The clinical diagnosis index of IR is the homeostatic model of insulin resistance(HOMA-IR)based on fasting blood-glucose and fasting serum insulin. Furthermore, the emerging IR biomarkers including adiponectin may be the references for clinical diagnosis. The influence factors of IR are obesity, chronic microinflammation and a lack of exercise. The major signaling pathways of IR include insulin receptor substrate 1(IRS1)/phosphatidylinositiol-3-kinase(PI3 K)/serine-threonine kinase(Akt)pathway, mitogen-activated protein kinase(MAPK)pathway and Smad3 pathway. In clinics, insulin sensibility and IR could be increased and improved via promoting insulin secretion and enhancing insulin signaling activation. At present, insulin sensitizers treating IR not only have the classic thiazolidinediones and its ramifications but also have the newly discovered metformin and vitamin D. In addition, it is reported that some extracts from single Chinese herbal medicine(CHM)and Chinese herbal compound prescription such as total flavone from the flowers of Abelmoschl manihot, berberine, astragalus polysaccharides and Huang-qi decoction also have the beneficial effects in ameliorating IR. In the field of chronic kidney disease, targeting a common insulin target-organs/issues lesion, the early renal damage in diabetic mellitus, the intervention studies regarding to regulating podocyte IR signaling pathways by CHM will be one of the significant directions in the future.
Disease resistance (R) genes from wild relatives could be used to engineer broad-spectrum resistance in domesticated crops. We combined association genetics with R gene enrichment sequencing (AgRenSeq) to exploit pan-genome variation in wild diploid wheat and rapidly clone four stem rust resistance genes. AgRenSeq enables R gene cloning in any crop that has a diverse germplasm panel.