Computational prediction of potential siRNA and human miRNA sequences to silence orf1ab associated genes for future therapeutics against SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) is an ongoing pandemic caused by an RNA virus termed as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). SARS-CoV-2 possesses an almost 30kbp long genome. The genome contains open-reading frame 1ab (ORF1ab) gene, the largest one of SARS-CoV-2, encoding polyprotein PP1ab and PP1a responsible for viral transcription and replication. Several vaccines have already been approved by the respective authorities over the world to develop herd immunity among the population. In consonance with this effort, RNA interference (RNAi) technology holds the possibility to strengthen the fight against this virus. Here, we have implemented a computational approach to predict potential short interfering RNAs including small interfering RNAs (siRNAs) and microRNAs (miRNAs), which are presumed to be intrinsically active against SARS-CoV-2. In doing so, we have screened miRNA library and siRNA library targeting the ORF1ab gene. We predicted the potential miRNA and siRNA candidate molecules utilizing an array of bioinformatic tools. By extending the analysis, out of 24 potential pre-miRNA hairpins and 131 siRNAs, 12 human miRNA and 10 siRNA molecules were sorted as potential therapeutic agents against SARS-CoV-2 based on their GC content, melting temperature (Tm), heat capacity (Cp), hybridization and minimal free energy (MFE) of hybridization. This computational study is focused on lessening the extensive time and labor needed in conventional trial and error based wet lab methods and it has the potential to act as a decent base for future researchers to develop a successful RNAi therapeutic.

Retention of antibiotic activity against resistant bacteria harbouring aminoglycoside-N-acetyltransferase enzyme by adjuvants: a combination of in-silico and in-vitro study.

Interference with antibiotic activity and its inactivation by bacterial modifying enzymes is a prevailing mode of bacterial resistance to antibiotics. Aminoglycoside antibiotics become inactivated by aminoglycoside-6'-N-acetyltransferase-Ib [AAC(6')-Ib] of gram-negative bacteria which transfers an acetyl group from acetyl-CoA to the antibiotic. The aim of the study was to disrupt the enzymatic activity of AAC(6')-Ib by adjuvants and restore aminoglycoside activity as a result. The binding affinities of several vitamins and chemical compounds with AAC(6')-Ib of Escherichia coli, Klebsiella pneumoniae, and Shigella sonnei were determined by molecular docking method to screen potential adjuvants. Adjuvants having higher binding affinity with target enzymes were further analyzed in-vitro to assess their impact on bacterial growth and bacterial modifying enzyme AAC(6')-Ib activity. Four compounds-zinc pyrithione (ZnPT), vitamin D, vitamin E and vitamin K-exhibited higher binding affinity to AAC(6')-Ib than the enzyme's natural substrate acetyl-CoA. Combination of each of these adjuvants with three aminoglycoside antibiotics-amikacin, gentamicin and kanamycin-were found to significantly increase the antibacterial activity against the selected bacterial species as well as hampering the activity of AAC(6')-Ib. The selection process of adjuvants and the use of those in combination with aminoglycoside antibiotics promises to be a novel area in overcoming bacterial resistance.

An immunoinformatics study on the spike protein of SARS-CoV-2 revealing potential epitopes as vaccine candidates.

BACKGROUND: The pandemic situation of SARS-CoV-2 infection has sparked global concern due to the disease COVID-19 caused by it. Since the first cluster of confirmed cases in China in December 2019, the infection has been reported across the continents and inflicted upon a substantial number of populations. METHOD: This study is focused on immunoinformatics analyses of the SARS-CoV-2 spike glycoprotein (S protein) which is key for the viral attachment to human host cells. Computational analyses were carried out for the prediction of B-cell and T-cell (MHC class I and II) epitopes of S protein and the analyses were extended further for the prediction of their immunogenic properties. The interaction and binding affinity of T-cell epitopes with HLA-B7 were also investigated by molecular docking. RESULT: Three distinct epitopes for vaccine design were predicted from the sequence of S protein. The potential B-cell epitope was KNHTSPDVDLG possessing the highest antigenicity score of 1.4039 among other B-cell epitopes. T-cell epitope for human MHC class I was VVVLSFELL with an antigenicity score of 1.0909 and binding ability to 29 MHC-I alleles. The predicted T-cell epitope for human MHC class II molecule was VVIGIVNNT with a corresponding 1.3063 antigenicity score, less digesting enzymes, and 7 MHC-II alleles binding ability. All these three peptides were predicted to be highly antigenic, non-allergenic, and non-toxic. Analyses of the physiochemical properties of these predicted epitopes indicate their stable nature for plausible vaccine design. Furthermore, molecular docking investigation between the MHC class-I epitopes and human HLA-B7 reflects the stable interaction with high affinity among them. CONCLUSION: The present study posits three potential epitopes of S protein of SARS-CoV-2 predicted by immunoinformatic methods based on their immunogenic properties and interactions with the host counterpart that can facilitate the development of vaccine against SARS-CoV-2. This study can act as the springboard for the future development of the COVID-19 vaccine.