China
Africa
Explore chapters and articles related to this topic
Nanostructured Cellular Biomolecules and Their Transformation in Context of Bionanotechnology
Published in Anil Kumar Anal, Bionanotechnology, 2018
The RNA structure is primarily similar to DNA with 3,5-phosphodiester bonds as backbone of the molecule and exists as single strand; however, RNA has a ribose sugar and uracil as pyrimidine in place of thymine. The hydroxyl group present in C2 position of ribose (Figure 2.2) increases the chemical stability of RNA and it can be cleaved into mononucleotides by an alkaline reagent, which is not possible in DNA. RNA is an unbranched single-stranded molecule that can exist in different structures by pairing of complementary bases within RNA molecule as shown in Figure 2.2. Secondary structures such as hairpins are formed by base pairing within approximately 5–10 nucleotide and stem loops structures are formed by base pairing separated by around 500 to several thousand nucleotides. Tertiary structure of RNA, pseudoknot, is formed by folding back in a hairpin and formation of a second stem—loop structure. The RNA molecules with different conformations and sizes are assigned for different functions in the cell (Lodish et al. 2000). There are three different types of functional RNAs: (1) messenger RNA (mRNA), (2) ribosomal RNA (rRNA), and (3) transfer RNA (tRNA). The small nuclear RNA exists only in eukaryotic cells and is responsible for guiding the formation of mRNA by splicing of pre-mRNA (Alberts et al. 2002).
Computation and Folding Predictions
Published in Peixuan Guo, Kirill A. Afonin, RNA Nanotechnology and Therapeutics, 2022
RNA structure prediction is very important to understand the physical mechanism of RNA functions and to design RNA-based therapies. The RNA structure prediction includes primary sequence, secondary structures, and three-dimensional (3D) structures. RNA 3D structure involves long-range tertiary interactions such as kissing interactions between the different loops; therefore, accurate evaluation of the energetic parameters for tertiary interactions is critical for 3D structure prediction. There are many programs predicting RNA 3D structures, like NAST (Jonikas et al., 2009), BARNACLE (Choi & Farach-Colton, 2003), FARFAR (Das & Baker, 2007), and others. NAST is a molecular dynamics simulation tool consisting of a knowledge-based statistical potential function applied to a coarse-grained model with a resolution of one bead nucleotide residue. Its greatest strength is to allow modeling of large RNA molecules. NAST requires secondary structure information and accepts tertiary contacts to direct the folding. When only the secondary structure information is considered, accurate prediction is limited, but when input information of tertiary contacts is taken into account, prediction accuracy can be dramatically improved. BARNACLE generates reasonable RNA-like structures using secondary structure information for small RNA molecules (<50 nt), but not for longer than 50 nt due to an increase in complexity of the probabilistic model. BARNACLE allows for efficient sampling of RNA conformations in continuous space and with related probabilities. FARFAR is only applicable to small RNA and has variable accuracy relying on the framework found successful for proteins using atomistic models and empirical potential functions.
Iron oxide/poly(3,4-ethylenedioxythiophene): poly(styrene sulfonate) glassy carbon electrode as a novel label-free electrochemical microRNA-21 sensor
Published in Instrumentation Science & Technology, 2023
Cigdem Dulgerbaki, Aysegul Uygun Oksuz
In order to characterize the selectivity of the Fe3O4/PEDOT:PSS based biosensor, hybridization was carried out between ssDNA miRNA-21 Probe and 1-Base mismatch sequence miRNA. Cyclic voltammograms were recorded for Fe3O4/PEDOT:PSS, Fe3O4/PEDOT:PSS/probe, Fe3O4/PEDOT:PSS/probe/mismatch and Fe3O4/PEDOT:PSS/probe/target as shown in Figure 5. After the miRNA probes were successively immobilized on the electrode, the CV currents were markedly reduced. Fe3O4/PEDOT:PSS increased the surface area for combining more capture probe on GCE surface. Negatively charged orthophosphate backbone of the capture probe generates an electrostatic repulsive force and creates this reduction. Moreover, this result demonstrates the immobilization of capture probe at GCE/Fe3O4/PEDOT:PSS. Following hybridization with miRNA-21, additional reducement of the current was seen. This change was due to the electrostatic repulsive force between the redox couple of Fe3O4/PEDOT:PSS and phosphate skeleton of duplex capture probe DNA-RNA structure.[23,27]
Prediction of RNA secondary structure based on stem region replacement using the RSRNA algorithm
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
Chengzhen Xu, Longjian Gao, Jin Li, Longfeng Shen, Hong Liang, Kuan Luan, Xiaomin Wu
Stem selection would occur after comparing the free energy of several structures. The main steps were as follows: First, the stem pool was generated and the stems were arranged according to length. The first stem selected was considered the root stem (denoted as SUBcurrent). If the second stem selected was compatible with SUBcurrent, it would become a part of SUBcurrent and a new stem was generated, denoted as NSUBcurrent. Otherwise, the second stem selected would be discarded and a new stem would be selected. This process would repeat until NSUBcurrent does not change. Finally, the free energy, ΔGcurrent, of NSUBcurrent was calculated to predict the RNA structure. Similarly, the predicted results of the second root stem would be obtained after reselecting the second root stem from the stem pool and repeating the aforementioned steps. As these proceeded, several results were obtained and the free energy was determined in all structures. The first stem that had the minimum free energy was deemed as the first predicted stem. The structure of this stem would not change, while all the other stems would be replaced. In addition, the aforementioned steps would be repeated to predict all the stems until the predicted structure was deemed incompatible with all the stems, at which point the predicted secondary structure of RNA molecule would be obtained. At this point, the secondary structure would be converted into a binary number to judge its compatibility, allowing the compatibility between the nested RNA structures and reducing the randomness to improve prediction accuracy without causing frequent errors as was observed in the free energy algorithm.
Design and molecular docking studies of {N1-[2-(amino)ethyl]ethane-1,2-diamine}-[tris(oxido)]-molybdenum(VI) complex as a potential antivirus drug: from synthesis to structure
Published in Journal of Coordination Chemistry, 2023
In silico molecular docking has also been applied to visualize the H-bond interactions and intermolecular interactions between 1 with the main protease of the SARS-CoV-2 (PDB ID: 7N0I). Hydrogen bonds play a vital role in stabilizing the protein–ligand complex. The H-bond donor is represented with light green mesh color while H-bond acceptor is represented with red mesh color. In 1, there is one π-hydrogen bond with a distance of 2.41 Å between NH2 of GLU-1 residue and π-electrons of 1 inside SARS-CoV-2 Mpro. There is also a π-anionic electrostatic interaction with a distance of 3.48 Å between (COO-) group of anionic TYR-115 residue and 1. The third interaction is hydrophobic, between the π-electrons of THR-100 residue and docked 1 with a distance of 4.37 Å. H-bond is a significant factor for RNA structure in biological systems and less than 2.3 Å increases the binding affinity. Most significant H-bonds of 1 with GLU-1 of amino acid residue of main protease of the SARS-CoV-2 (PDB ID: 7N0I) at a distance 2.41 Å is close to standard anti-SARS-CoV-2 reported drugs [53]. Figure 7 shows an intuitive view of the docking cavity of the total density surface representation for docked 1 inside the SARS-CoV-2 Mpro protein (PDB ID: 7N0I). Figures 7(a and b) with active site binding pocket of hydrogen bond donor and acceptor meshes are represented by pink and green colors, respectively. Figures 7(c and d) show the surface representation of hydrophobic active site binding pocket represented with blue and grey colors. The molecular docking results indicate π-interacting residues, H-bond forming residues and hydrophobic interactions between the active site of SARS-CoV-2 Mpro and 1, crucial for potential inhibition activity. [Mo(dien)O3] may be used as anti-SARS-CoV-2.