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 expres- sion. 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 limita- tions of each class of models. Finally, we will present our thinking on developing related bioinformafics models in future.
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。
