Bioinformatics prediction of siRNAs as potential antiviral agents against dengue viruses

Multi-epitope vaccine candidate design for dengue virus

Dengue Fever (DF) is a vector-borne neglected viral disease with a high burden in the sub-tropics of Asia and Africa. Aedes aegypti is responsible for 90% of cases in the global burden of disease. The primary goal of the treatment is to eliminate the virus from the bloodstream of affected individuals. A successful dengue vaccine must elicit both neutralizing antibodies and cell-mediated immunity and there is no vaccine to date to prevent DF. A multi-epitope vaccine composed of a series of or overlapping peptides is, therefore, an ideal approach for the prevention and treatment of pathogenic organisms. An immunoinformatics approach was employed to design a theoretical multi-epitope vaccine candidate. This vaccine candidate consists of linear B-cell epitope, TH cells epitope and CTL of reported potential vaccine candidates. These epitopes were linked together with suitable linkers and adjuvant at the N terminal and C terminal. The 3D Structure of the vaccine was modeled, refined and validated using computational tools. Protein-protein docking of vaccine candidates with TLR3 protein results in efficient binding. Immune stimulation of vaccine candidates predicted high levels of IgG and IgM. This candidate vaccine should be validated experimentally using suitable in-vivo and in-vitro studies to use in dengue fever virus elimination programmes.

Oligonucleotide-Based Approaches to Inhibit Dengue Virus Replication

Dengue fever is one of the most common viral infections affecting humans. It is an expanding public health problem, particularly in tropical and subtropical regions. No effective vaccine or antiviral therapies against Dengue virus (DENV) infection are available. Therefore, there is a strong need to develop safe and effective therapeutic strategies that can reduce the burden and duration of hospitalizations due to this life-threatening disease. Oligonucleotide-based strategies are considered as an attractive means of inhibiting viral replication since oligonucleotides can be designed to interact with any viral RNA, provided its sequence is known. The resultant targeted destruction of viral RNA interferes with viral replication without inducing any adverse effects on cellular processes. In this review, we elaborate the ribozymes, RNA interference, CRISPR, aptamer and morpholino strategies for the inhibition of DENV replication and discuss the challenges involved in utilizing such approaches.

Antivirals Against Coronaviruses: Candidate Drugs for SARS-CoV-2 Treatment?

Coronaviruses (CoVs) are a group of viruses from the family Coronaviridae that can infect humans and animals, causing mild to severe diseases. The ongoing pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents a global threat, urging the development of new therapeutic strategies. Here we present a selection of relevant compounds that have been described from 2005 until now as having in vitro and/or in vivo antiviral activities against human and/or animal CoVs. We also present compounds that have reached clinical trials as well as further discussing the potentiality of other molecules for application in (re)emergent CoVs outbreaks. Finally, through rationalization of the data presented herein, we wish to encourage further research encompassing these compounds as potential SARS-CoV-2 drug candidates.

Inhibition of dengue virus infection by small interfering RNAs that target highly conserved sequences in the NS4B or NS5 coding regions

Dengue fever is one of the most common viral infections in the world. Although a vaccine against dengue virus (DENV) has been approved in several countries, this disease is still considered a public health priority worldwide. The ability of three small interfering RNAs (FG-siRNAs) targeting conserved sequences in the NS4B and NS5 regions of the DENV genome to inhibit DENV replication was tested in vitro in both Vero and C6/36 cells. The FG-siRNAs were effective against DENV-1, -3, and -4, but not DENV-2. A fourth siRNA specifically targeting the NS5 region of the DENV-2 genome (SG-siRNA) was designed and tested against two different DENV-2 strains, showing high levels of inhibition in both mammalian and insect cells.

Potential siRNA Molecules for Nucleoprotein and M2/L Overlapping Region of Respiratory Syncytial Virus: In Silico Design

Background

Human respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract disease in the pediatric population, elderly and in immunosuppressed individuals. Respiratory syncytial virus is also responsible for bronchiolitis, pneumonia, and chronic obstructive pulmonary infections in all age groups. With this high disease burden and the lack of an effective RSV treatment and vaccine, there is a clear need for discovery and development of novel, effective and safe drugs to prevent and treat RSV disease. The most innovative approach is the use of small interfering RNAs (siRNAs) which represent a revolutionary new concept in human therapeutics. The nucleoprotein gene of RSV which is known as the most conserved gene and the M2/L mRNA, which encompass sixty-eight overlapping nucleotides, were selected as suitable targets for siRNA design.

Objectives

The present study is aimed to design potential siRNAs for silencing nucleoprotein and an overlapping region of M2-L coding mRNAs by computational analysis.

Materials and Methods

Various computational methods (target alignment, similarity search, secondary structure prediction, and RNA interaction calculation) have been used for siRNA designing against different strains of RSV.

Results

In this study, seven siRNA molecules were rationally designed against the nucleoprotein gene and validated using various computational methods for silencing different strains of RSV. Additionally, three effective siRNA molecules targeting the overlapping region of M2/L mRNA were designed.

Conclusions

This approach provides insight and a validated strategy for chemical synthesis of an antiviral RNA molecule which meets many sequence features for efficient silencing and treatment at the genomic level.

RNAi: antiviral therapy against dengue virus

Dengue virus infection has become a global threat affecting around 100 countries in the world. Currently, there is no licensed antiviral agent available against dengue. Thus, there is a strong need to develop therapeutic strategies that can tackle this life threatening disease. RNA interference is an important and effective gene silencing process which degrades targeted RNA by a sequence specific process. Several studies have been conducted during the last decade to evaluate the efficiency of siRNA in inhibiting dengue virus replication. This review summarizes siRNAs as a therapeutic approach against dengue virus serotypes and concludes that siRNAs against virus and host genes can be next generation treatment of dengue virus infection.

siRNAs encapsulated in recombinant capsid protein derived from Dengue serotype 2 virus inhibits the four serotypes of the virus and proliferation of cancer cells

siRNA delivery potential of the Dengue virus capsid protein in cultured cells was recently reported, but target knockdown potential in the context of specific diseases has not been explored. In this study we have evaluated the utility of the protein as an siRNA carrier for anti Dengue viral and anti cancer applications using cell culture systems. We show that target specific siRNAs delivered using the capsid protein inhibit infection by the four serotypes of Dengue virus and proliferation of two cancer cell lines. Our data confirm the potential of the capsid for anti Dengue viral and anti cancer RNAi applications. In addition, we have optimized a fermentation strategy to improve the yield of Escherichia coli expressed D2C protein since the reported yields of E. coli expressed flaviviral capsid proteins are low.

VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses

BACKGROUND

Selection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed "VIRsiRNApred" a support vector machine (SVM) based method for predicting the efficacy of viral siRNA.

METHODS

In the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T1380) and validation (V345) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development.

RESULTS

During 10-fold cross validation on T1380 using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V345 independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree.

CONCLUSION

VIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/.