DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning / 基因组蛋白质组与生物信息学报·英文版

Protein nitration and nitrosylation are essential post-translational modifications (PTMs) involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases. Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65 for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively, demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%-42% improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http://deepnitro.renlab.org.

Artesunate and betulilic acid block lipopolysaccharide-induced angiogenesis and hyperplasia in mice / 中国药学杂志

OBJECTIVE: To disclose the pathological mechanism of bacterial LPS-induced angiogenesis and hyperplasia in tumorlike hyperplasia and the pharmacological mechanism underlying artesunate and betulilic acid blocking tumor-like hyperplasia. METHODS: The tumor-like hyperplasia models were established for mouse hypodermis and articular synovium using LPS or LPS-containing complete Fround's adjuvant (CFA). The morphological features (inflammatory grades) were described and the histochemical signatures (angiogenesis, hyperplasia, and inflammatory infiltration) were examined. The serial concentrations of nitric oxide (NO), blood oxygen saturation (SpO2) and 3-nitrotyrosine (3NT) were quantitatively determined by biochemical, physiological and immunological procedures, and the expression levels of inducible nitric oxide synthase (iNOS), hypoxia-induced factor 1α (HIF-1α) and vascular endothelial growth factor (VEGF) were analyzed by the immunohistochemical method. RESULTS: Both LPS and CFA can lead to the inflammatory phenotypes and abnormal hyperplasia-related microscopic alterations. They enable the elevation of NO, decline of SpO2, and increase of 3NT (protein nitration). The considerable upregulation of iNOS, HIF-1α and VEGF are accompanied by angiogenesis and hyperplasia. While a NO donor compound replicates such pathogenesis, a NO synthesis inhibitor antagonizes this effect. Artesunate and betulilic acid down-regulate iNOS, HIF-1α and VEGF, decrease NO, and increase SpO2, thereby leading to the melioration or interruption of aberrant inflammatory angiogenesis and hyperplasia. CONCLUSION: LPS-triggered iNOS overexpression and potent NO burst may represent the direct drivers for angiogenesis and hyperplasia. Due to repression of the pathogenic process of tumor-like hyperplasia, artesunate and betulilic acid possess the prophylactic and therapeutic potentials.