Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, Kolkata, India.
Division of Vaccine-Discovery, La Jolla Institute for Immunology, San Diego, California, USA.
RNA Biol. 2021 Aug;18(8):1152-1159. doi: 10.1080/15476286.2020.1836455. Epub 2020 Oct 25.
Bacterial small-RNA (sRNA) sequences are functional RNAs, which play an important role in regulating the expression of a diverse class of genes. It is thus critical to identify such sRNA sequences and their probable mRNA targets. Here, we discuss new procedures to identify and characterize sRNA and their targets via the introduction of an integrated online platform 'PresRAT'. PresRAT uses the primary and secondary structural attributes of sRNA sequences to predict sRNA from a given sequence or bacterial genome. PresRAT also finds probable target mRNAs of sRNA sequences from a given bacterial chromosome and further concentrates on the identification of the probable sRNA-mRNA binding regions. Using PresRAT, we have identified a total of 66,209 potential sRNA sequences from 292 bacterial genomes and 2247 potential targets from 13 bacterial genomes. We have also implemented a protocol to build and refine 3D models of sRNA and sRNA-mRNA duplex regions and generated 3D models of 50 known sRNAs and 81 sRNA-mRNA duplexes using this platform. Along with the server part, PresRAT also contains a database section, which enlists the predicted sRNA sequences, sRNA targets, and their corresponding 3D models with structural dynamics information.
细菌小 RNA(sRNA)序列是具有功能的 RNA,在调控各类基因表达中发挥重要作用。因此,识别这些 sRNA 序列及其可能的 mRNA 靶标至关重要。在这里,我们通过引入一个集成的在线平台“PresRAT”来讨论识别和表征 sRNA 及其靶标的新方法。PresRAT 使用 sRNA 序列的一级和二级结构属性来预测给定序列或细菌基因组中的 sRNA。PresRAT 还可以从给定的细菌染色体中找到 sRNA 序列的可能靶标 mRNA,并进一步集中于鉴定可能的 sRNA-mRNA 结合区域。使用 PresRAT,我们从 292 个细菌基因组中总共鉴定出 66209 个潜在的 sRNA 序列,从 13 个细菌基因组中鉴定出 2247 个潜在的靶标。我们还实施了一种构建和细化 sRNA 和 sRNA-mRNA 双链区 3D 模型的方案,并使用该平台生成了 50 个已知 sRNA 和 81 个 sRNA-mRNA 双链区的 3D 模型。除了服务器部分,PresRAT 还包含一个数据库部分,其中列出了预测的 sRNA 序列、sRNA 靶标及其相应的 3D 模型,包括结构动力学信息。