目的尝试通过构建数学模型来确定蛋白质中与RNA相互作用的氨基酸位点。方法从蛋白质结构数据库(protein data bank,PDB)中收集532例蛋白质与RNA相互作用的数据,对每个氨基酸提取150个特征,并利用机器学习方法构建2个与RNA结合的蛋白质位点预测模型。结果经过在同一数据集上与其他模型比较,所构建的模型具有更好的性能。结论该预测模型的建立,为研究人员获得与RNA结合的蛋白质位点提供了较好的生物信息学支持。
Esophageal squamous cell carcinoma(ESCC)is one of the most lethal cancers worldwide.In this study,we aimed to investigate the underlying mechanisms of metastasis inhibition by miR-205 in ESCC.In microRNA(miRNA)array and quantitative RT-PCR analyses,we found that the expression level of miR-205 was significantly lower in patients with lymph node metastasis compared with that in patients without lymph node metastasis.After transfection of miR-205 mimics or inhibitors into ESCC cell lines,a significant negative correlation was observed between the expression level of miR-205 and Smad1.In luciferase reporter assays,we revealed that miR-205 inhibited the expression of SMAD1 by targeting the 30untranslated region(30-UTR)of SMAD1 mRNA in ESCC cells.Furthermore,our results showed that miR-205 suppressed the invasion and migration of ESCC cells,whereas Smad1 increased their invasion and migration.Taken together,our study demonstrates that miR-205 functions as a suppressor of tumor metastasis by regulating SMAD1expression through targeting the 30-UTR of SMAD1 mRNAin ESCC.Therefore,miR-205 may be a potential therapeutic target for miRNA-based therapy of ESCC.
Bacterial small RNAs (sRNAs) are an emerging class of regulatory RNAs of about 40-500 nucleotides in length and, by binding to their target mRNAs or proteins, get involved in many biological processes such as sensing environmental changes and regulating gene expression. Thus, identification of bacterial sRNAs and their targets has become an important part of sRNA biology. Current strategies for discovery of sRNAs and their targets usually involve bioinformatics prediction followed by experimental validation, emphasizing a key role for bioinformatics prediction. Here, therefore, we provided an overview on prediction methods, focusing on the merits and limitations of each class of models. Finally, we will present our thinking on developing related bioinformatics models in future.
An increasing data indicates that altered microRNAs(miRNAs)participate in the radiation-induced DNA damage response.However,a correlation of mRNA and miRNA profiles across the entire genome and in response to irradiation has not been thoroughly assessed.We analyzed miRNA microarray data collected from HeLa cells after ionizing radiation(IR),quantified the expression profiles of mRNAs and performed comparative analysis of the data sets using target prediction algorithms,Gene Ontology(GO)analysis,pathway analysis,and gene network construction.The results showed that the altered miRNAs were involved in regulation of various cellular functions.miRNA-gene network analyses revealed that miR-186,miR-106b,miR-15a/b,CCND1and CDK6 played vital role in the cellular radiation response.Using qRT-PCR,we confirmed that twenty-two miRNAs showed differential expression in HeLa cells treated with IR and some of these miRNAs affected cell cycle progression.This study demonstrated that miRNAs influence gene expression in the entire genome during the cellular radiation response and suggested vital pathways for further research.
HU ZhengTIE YiLü GuiXiangFU HanJiangXING RuiYunZHU JieSUN ZhiXianZHENG XiaoFei
