scholarly journals Putative Secondary Structure at 5’UTR as a Potential Antiviral Target against SARS-CoV-2

Author(s):  
Emilio Garcia-Moran ◽  
Marta Hernández ◽  
David Abad ◽  
José M. Eiros

SARS-CoV-2 is an enveloped positive-sense single-stranded RNA coronavirus that causes COVID-19, of which the current outbreak has resulted in a high number of cases and fatalities throughout the world, even vaccine doses are being administered. The aim of this work was to scan the SARS-CoV-2 genome in search for therapeutic targets. We found a sequence in the 5’UTR (NC 045512:74-130), consisting of a typical heptamer next to a structured region that may cause ribosomal frameshifting. The potential biological value of this region is relevant through its low similarity with other viruses, including coronaviruses related to SARS-CoV, and its high sequence conservation within multiple SARS-CoV-2 isolates. We have predicted the secondary structure of the region by means of different bioinformatic tools. We have suggested a most probable secondary structure to proceed with a 3D reconstruction of the structured segment. Finally, we carried out virtual docking on the 3D structure to look for a binding site and then for drug ligands from a database of lead compounds. Several molecules that could be probably administered as oral drugs show promising binding affinity within the structured region, and so it could be possible interfere its potential regulatory role.

2021 ◽  
Author(s):  
Emilio Garcia-Moran ◽  
Marta Hernandez ◽  
David Abad ◽  
Jose Maria Eiros

Abstract SARS-CoV-2 is an enveloped positive-sense single-stranded RNA coronavirus that causes COVID-19 whose present outbreak has cost a high number of casualties throughout the world. The aim of this work was to scan the SARS-CoV-2 genome in search for new therapeutic targets. We found a sequence in the 5'UTR (NC 045512:74-130), consisting of a typical heptamer next to a structured region that may cause frameshifting. The potential biological value of this region is shown by its similarity with other coronaviruses related with SARS-CoV and its sequence conservation within isolates from SARS-CoV-2. We have predicted the secondary structure of the region by means of different bioinformatic tools. We have chosen a probable secondary structure to proceed with a 3D reconstruction of the structured segment. We carried out virtual docking on the 3D structure to look for a binding site and then for drug ligands from a database of lead compounds. Several molecules that would probably administered as oral drugs show promising binding affinity within the structured region and so it would be possible interfere the potential regulatory role of our sequence of interest.


Author(s):  
Ilaria Manfredonia ◽  
Chandran Nithin ◽  
Almudena Ponce-Salvatierra ◽  
Pritha Ghosh ◽  
Tomasz K. Wirecki ◽  
...  

SummarySARS-CoV-2 is a betacoronavirus with a linear single-stranded, positive-sense RNA genome of ∼30 kb, whose outbreak caused the still ongoing COVID-19 pandemic. The ability of coronaviruses to rapidly evolve, adapt, and cross species barriers makes the development of effective and durable therapeutic strategies a challenging and urgent need. As for other RNA viruses, genomic RNA structures are expected to play crucial roles in several steps of the coronavirus replication cycle. Despite this, only a handful of functionally conserved structural elements within coronavirus RNA genomes have been identified to date.Here, we performed RNA structure probing by SHAPE-MaP to obtain a single-base resolution secondary structure map of the full SARS-CoV-2 coronavirus genome. The SHAPE-MaP probing data recapitulate the previously described coronavirus RNA elements (5′ UTR, ribosomal frameshifting element, and 3′ UTR), and reveal new structures. Secondary structure-restrained 3D modeling of highly-structured regions across the SARS-CoV-2 genome allowed for the identification of several putative druggable pockets. Furthermore, ∼8% of the identified structure elements show significant covariation among SARS-CoV-2 and other coronaviruses, hinting at their functionally-conserved role. In addition, we identify a set of persistently single-stranded regions having high sequence conservation, suitable for the development of antisense oligonucleotide therapeutics.Collectively, our work lays the foundation for the development of innovative RNA-targeted therapeutic strategies to fight SARS-related infections.


eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Clarence Yu Cheng ◽  
Fang-Chieh Chou ◽  
Wipapat Kladwang ◽  
Siqi Tian ◽  
Pablo Cordero ◽  
...  

Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis. Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions. We report that integrating a new method called MOHCA-seq (Multiplexed •OH Cleavage Analysis with paired-end sequencing) with mutate-and-map secondary structure inference guides Rosetta 3D modeling to consistent 1-nm accuracy for intricately folded ncRNAs with lengths up to 188 nucleotides, including a blind RNA-puzzle challenge, the lariat-capping ribozyme. This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon. MCM offers a sequencing-based route to uncovering ncRNA 3D structure, applicable to functionally important but potentially heterogeneous states.


Author(s):  
Bruce A. Shapiro ◽  
Wojciech Kasprzak

Genomic information (nucleic acid and amino acid sequences) completely determines the characteristics of the nucleic acid and protein molecules that express a living organism’s function. One of the greatest challenges in which computation is playing a role is the prediction of higher order structure from the one-dimensional sequence of genes. Rules for determining macromolecule folding have been continually evolving. Specifically in the case of RNA (ribonucleic acid) there are rules and computer algorithms/systems (see below) that partially predict and can help analyze the secondary and tertiary interactions of distant parts of the polymer chain. These successes are very important for determining the structural and functional characteristics of RNA in disease processes and hi the cell life cycle. It has been shown that molecules with the same function have the potential to fold into similar structures though they might differ in their primary sequences. This fact also illustrates the importance of secondary and tertiary structure in relation to function. Examples of such constancy in secondary structure exist in transfer RNAs (tRNAs), 5s RNAs, 16s RNAs, viroid RNAs, and portions of retroviruses such as HIV. The secondary and tertiary structure of tRNA Phe (Kim et al., 1974), of a hammerhead ribozyme (Pley et al., 1994), and of Tetrahymena (Cate et al., 1996a, 1996b) have been shown by their crystal structure. Currently little is known of tertiary interactions, but studies on tRNA indicate these are weaker than secondary structure interactions (Riesner and Romer, 1973; Crothers and Cole, 1978; Jaeger et al., 1989b). It is very difficult to crystallize and/or get nuclear magnetic resonance spectrum data for large RNA molecules. Therefore, a logical place to start in determining the 3D structure of RNA is computer prediction of the secondary structure. The sequence (primary structure) of an RNA molecule is relatively easy to produce. Because experimental methods for determining RNA secondary and tertiary structure (when the primary sequence folds back on itself and forms base pairs) have not kept pace with the rapid discovery of RNA molecules and their function, use of and methods for computer prediction of secondary and tertiary structures have increasingly been developed.


2020 ◽  
Author(s):  
Samira Norouzi ◽  
Maryam Farahani ◽  
Samad Nejad Ebrahimi

Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of the drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC50<250 µM from the literature data. A 3D-QSAR model has been developed and validated to be utilized in virtual screening. Results: The best pharmacophore models have been utilized as 3D queries for virtual screening to gain promising inhibitors from a data set of thousands of natural compounds retrieved from PubChem. The hit compounds were subsequently used for molecular docking studies to investigate their affinity to the 3D structure of the SARS-CoV-2 receptors. The ADMET properties calculate for the hits with high binding affinity. Conclusion: The study outcomes can help understand the molecular characteristics and mechanisms of the binding of hit compounds to SARS-CoV-2 receptors and promising identification inhibitors that are likely to be evolved into drugs.


2019 ◽  
Author(s):  
Aminur Rab Ratul ◽  
Marcel Turcotte ◽  
M. Hamed Mozaffari ◽  
WonSook Lee

AbstractProtein secondary structure is crucial to create an information bridge between the primary structure and the tertiary (3D) structure. Precise prediction of 8-state protein secondary structure (PSS) significantly utilized in the structural and functional analysis of proteins in bioinformatics. In this recent period, deep learning techniques have been applied in this research area and raise the Q8 accuracy remarkably. Nevertheless, from a theoretical standpoint, there still lots of room for improvement, specifically in 8-state (Q8) protein secondary structure prediction. In this paper, we presented two deep learning architecture, namely 1D-Inception and BD-LSTM, to improve the performance of 8-classes PSS prediction. The input of these two architectures is a carefully constructed feature matrix from the sequence features and profile features of the proteins. Firstly, 1D-Inception is a Deep convolutional neural network-based approach that was inspired by the InceptionV3 model and containing three inception modules. Secondly, BD-LSTM is a recurrent neural network model which including bidirectional LSTM layers. Our proposed 1D-Inception method achieved 76.65%, 71.18%, 76.86%, and 74.07% Q8 accuracy respectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. Moreover, BD-LSTM acquired 74.71%, 69.49%, 74.07%, and 72.37% state-8 accuracy after evaluated on CullPdb6133, CB513, CASP10, and CASP11 datasets, respectively. Both these architectures enable the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class is very beneficial in the deep neural network. To the best of our knowledge, experiment results of the 1D-Inception model demonstrate that it outperformed all the state-of-art methods on the benchmark CullPdb6133, CB513, and CASP10 datasets.


2021 ◽  
Vol 17 (9) ◽  
pp. e1009384
Author(s):  
Abhisek Dwivedy ◽  
Richard Mariadasse ◽  
Mohammed Ahmad ◽  
Sayan Chakraborty ◽  
Deepsikha Kar ◽  
...  

Apart from the canonical fingers, palm and thumb domains, the RNA dependent RNA polymerases (RdRp) from the viral order Nidovirales possess two additional domains. Of these, the function of the Nidovirus RdRp associated nucleotidyl transferase domain (NiRAN) remains unanswered. The elucidation of the 3D structure of RdRp from the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), provided the first ever insights into the domain organisation and possible functional characteristics of the NiRAN domain. Using in silico tools, we predict that the NiRAN domain assumes a kinase or phosphotransferase like fold and binds nucleoside triphosphates at its proposed active site. Additionally, using molecular docking we have predicted the binding of three widely used kinase inhibitors and five well characterized anti-microbial compounds at the NiRAN domain active site along with their drug-likeliness. For the first time ever, using basic biochemical tools, this study shows the presence of a kinase like activity exhibited by the SARS-CoV-2 RdRp. Interestingly, a well-known kinase inhibitor- Sorafenib showed a significant inhibition and dampened viral load in SARS-CoV-2 infected cells. In line with the current global COVID-19 pandemic urgency and the emergence of newer strains with significantly higher infectivity, this study provides a new anti-SARS-CoV-2 drug target and potential lead compounds for drug repurposing against SARS-CoV-2.


2021 ◽  
Vol 4 (17) ◽  
pp. 01-07
Author(s):  
Deepa Selvi Rani ◽  
Gnana Veera Subhashini ◽  
Ambure Sharadhadevi ◽  
Emmanuel Cyril ◽  
Kumarasamy Thangaraj

Mutations in the β-MYH7 gene are one of the major causes that lead to cardiomyopathies. However, to differentiate a causative nsSNP and its impact on protein structure remains a major challenge. In the present study, we detected a missense mutation Arg723His in the head motor domain of β-MYH7 in a HCM patient, and it was absent in 207 healthy individuals. The mutant (R723H) has been found to alter an evolutionarily conserved amino acid. In addition, the mutant (R723H) was predicted pathogenic by Polyphen-2 and SIFT bioinformatic tools. Further, the superimposed 3D structure of the mutant (p.His723 homology model) with native (p. Arg723) displayed the root means square deviation (RMSD) of ~3.38A0. We know that the non-covalent interactions such as hydrophobic, electrostatic, Van der Waals, and hydrogen bonds between amino acids are at the heart of stabilizing protein structures. Here, we demonstrated how the mutant (p.His723) has disrupted a critical non-covalent interactions network at the mutation site and may contribute to the disease phenotype. Hence, our findings in the future could pave the way for developing small molecular modulators or myosin-targeted therapies for heart failure.


2020 ◽  
Author(s):  
Abhisek Dwivedy ◽  
Richard Mariadasse ◽  
Mohammed Ahmed ◽  
Deepsikha Kar ◽  
Jeyaraman Jeyakanthan ◽  
...  

Apart from the canonical fingers, palm and thumb domains, the RNA dependent RNA polymerases (RdRp) from the viral order Nidovirales possess two additional domains. Of these, the function of the Nidovirus RdRp associated nucleotidyl transferase domain (NiRAN) remains unanswered. The elucidation of the 3D structure of the RdRp from the novel coronavirus – SARS-CoV2, provided the first ever insights into the domain organisation and possible functional characteristics of the NiRAN domain. Using in silico tools, this study predicts that the NiRAN domain assumes a kinase or phosphotransferase like fold and binds GTP and UTP at its proposed active site. Additionally, using molecular docking this study predicts the binding of five well characterized anti-microbial compounds at the NiRAN domain active site and their drug-likeliness and DFT properties. In line with the current global COVID-19 pandemic urgency, this study provides a new target and potential lead compounds for drug repurposing against SARS-CoV2.


Author(s):  
Marion Adebiyi ◽  
Oludayo O. Olugbara

The influx of coronavirus in 2019 (COVID-19) from Wuhan of China has led to a global pandemic, undesirable hiatus, and recorded millions of infection cases with several deaths worldwide. The strain of COVID-19 has neither known treatments nor vaccines, but recent studies have shown that a few of its enzymes may have been considered as potential drug target. Since its influx, the virus has been well-studied, but a lot is not known about its protease yet.  The purpose of this work was to identify the binding site in-silico and present 3D structure of COVID-19 main protease (Mpro) by homology modeling through multiple alignment followed by optimization and validation. The modeling was done by Swiss-Model template library and basic local alignment search tool (BLAST). The obtained homotrimer oligo-state model was verified for reliability using structural validation software such as PROCHECK, Verify3D, MolProbity and QMEAN. The HHBlits software was used to determine the structures that matched the target sequence by evolution. Best template, 6u7h.1.A was used to build a tertiary structure for Mpro with ProMod3 3.0.0 on the Swiss-Model workspace. Self-optimized prediction method with alignment (SOPMA) was applied to compute features of the secondary structure. The verification of quality of COVID-19 structure through Ramachandran plot showed an abundance of 99.3% of amino acid residues in allowed regions while 0.1% in disallowed region. The Verify3D rated the structure a 90.87% PASS of residues having an average 3D-1D score of at least 0.2, which validates a good environment profile for the COVID-19 Mpro model. The features of the secondary structure indicated that the modeled 3D structure of Mpro contains 32.05% α-helix and 37.17% random coil with 25.92 extended strand. DeepSite algorithm elucidates the binding site area that captured local patterns in the structure and exposed the surface cavity of the binding pocket of this protein. The main result of this study suggests that blocking expression of the protein may constitute an efficient approach for transmission blockage. Hence, our thought is that Mpro of COVID-19 may be considered a potential drug target. Nevertheless, more experimental analyses, verification and validation experiments will be required as a targeted drug or vaccine design against COVID-19 virus.


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