1.
An evolving tale of two interacting RNAs-themes and variations of the T-box riboswitch mechanism.
Suddala, KC, Zhang, J
IUBMB life. 2019;(8):1167-1180
Abstract
T-box riboswitches are a widespread class of structured noncoding RNAs in Gram-positive bacteria that regulate the expression of amino acid-related genes. They form negative feedback loops to maintain steady supplies of aminoacyl-transfer RNAs (tRNAs) to the translating ribosomes. T-box riboswitches are located in the 5' leader regions of mRNAs that they regulate and directly bind to their cognate tRNA ligands. T-boxes further sense the aminoacylation state of the bound tRNAs and, based on this readout, regulate gene expression at the level of transcription or translation. T-box riboswitches consist of two conserved domains-a 5' Stem I domain that is involved in specific tRNA recognition and a 3' antiterminator/antisequestrator (or discriminator) domain that senses the amino acid on the 3' end of the bound tRNA. Interaction of the 3' end of an uncharged but not charged tRNA with a thermodynamically weak discriminator domain stabilizes it to promote transcription readthrough or translation initiation. Recent biochemical, biophysical, and structural studies have provided high-resolution insights into the mechanism of tRNA recognition by Stem I, several structural models of full-length T-box-tRNA complexes, mechanism of amino acid sensing by the antiterminator domain, as well as kinetic details of tRNA binding to the T-box riboswitches. In addition, translation-regulating T-box riboswitches have been recently characterized, which presented key differences from the canonical transcriptional T-boxes. Here, we review the recent developments in understanding the T-box riboswitch mechanism that have employed various complementary approaches. Further, the regulation of multiple essential genes by T-boxes makes them very attractive drug targets to combat drug resistance. The recent progress in understanding the biochemical, structural, and dynamic aspects of the T-box riboswitch mechanism will enable more precise and effective targeting with small molecules. © 2019 IUBMB Life, 2019 © 2019 IUBMB Life, 71(8):1167-1180, 2019.
2.
RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.
Shen, WJ, Cui, W, Chen, D, Zhang, J, Xu, J
Molecules (Basel, Switzerland). 2018;(3)
Abstract
RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at http://bmc.med.stu.edu.cn/RPiRLS.