Glycans are carbohydrate modifications typically found on proteins or lipids, and can act as ligands for glycan-binding proteins called lectins. Glycans and lectins play crucial roles in the function of cells and organs, and in the immune system of animals and humans. Viral pathogens use glycans and lectins that are encoded by their own or the host genome for their replication and spread. Recent advances in glycobiological research indicate that glycans and lectins mediate key interactions at the virus-host interface, controlling viral spread and/or activation of the immune system. This review reflects on glycan–lectin interactions in the context of viral infection and antiviral immunity. A short introduction illustrates the nature of glycans and lectins, and conveys the basic principles of their interactions. Subsequently, examples are discussed highlighting specific glycan–lectin interactions and how they affect the progress of viral infections, either benefiting the host or the virus. Moreover, glycan and lectin variability and their potential biological consequences are discussed. Finally, the review outlines how recent advances in the glycan–lectin field might be transformed into promising new approaches to antiviral therapy.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1111/1574-6976.12052

Bitter-sweet symphony: glycan-lectin interactions in virus biology.

Influenza has a long history of causing morbidity and mortality in the human population through routine seasonal spread and global pandemics. The high mutation rate of the RNA genome of the influenza virus, combined with assortment of its multiple genomic segments, promote antigenic diversity and new subtypes, allowing the virus to evade vaccines and become resistant to antiviral drugs. There is thus a continuing need for new anti-influenza therapy using novel targets and creative strategies. In this review, we summarize prospective future therapeutic regimens based on recent molecular and genomic discoveries.

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New treatments for influenza

A review on the development of urease inhibitors as antimicrobial agents against pathogenic bacteria.

Discovery of novel arylethenesulfonyl fluorides as potential candidates against methicillin-resistant of Staphylococcus aureus (MRSA) for overcoming multidrug resistance of bacterial infections.

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. 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. 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. 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/
 .

/pdf/virsirnapred-a-web-server-for-predicting-inhibition-efficacy-ofyjxswdkk.pdf

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

Influenza virus A (IVA) infection is responsible for recent death worldwide. Hence, there is a need to develop therapeutic agents against the virus. We describe the prediction of short interfering RNA (siRNA) as potential therapeutic molecules for the HA (Haemagglutinin) and NA (Neuraminidase) genes. We screened 90,522 siRNA candidates for HA and 13,576 for NA and selected 1006 and 1307 candidates for HA and NA, respectively based on the proportion of viral sequences that are targeted by the corresponding siRNA, with complete matches. Further short listing to select siRNA with no off-target hits, fulfilling all the guidelines mentioned in approach, provided us 13 siRNAs for haemagglutinin and 13 siRNAs for neuraminidase. The approach of finding siRNA using multiple sequence alignments of amino acid sequences has led to the identification of five conserved amino acid sequences, three in hemagglutinin i.e. RGLFGAIAGFIE, YNAELLV and AIAGFIE and two in neuraminidase i.e. RTQSEC and EECSYP which on reveres translation provided siRNA sequences as potential therapeutic candidates. The approaches used during this study have enabled us to identify potentially therapeutic siRNAs against divergent IVA strains.

/pdf/selection-of-predicted-sirna-as-potential-antiviral-55dm4wh6ve.pdf

Rashid Khushal

Papers

Selection of predicted siRNA as potential antiviral therapeutic agent against influenza virus

Antimicrobial Efficacy of Metal-Barbiturate Conjugates Against Pathogenic Strains of Escherichia coli and Staphylococcus aureus