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Biology subjects

Chen, L.-L.

Publications and source records attributed to Chen, L.-L..

2 recordsLinked to original sources
bioRxiv2020

Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches

Coronavirus disease 2019 (COVID-19) associated pneumonia caused by severe acute respiratory coronavirus 2 (SARS-COV-2) was first reported in Wuhan, China in December 2019. Till date, no vaccine or completely effective drug is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven antigenic proteins were taken as targets and different epitopes (B-cell, T-cell and IFN-{gamma} inducing) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.29% coverage of the worlds population. Finally, 505 amino acids long MEV was designed by connecting sixteen MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. Linkers and adjuvant were added to enhance the immunogenicity response of the MEV. The antigenicity, allergenicity, physiochemical properties and structural details of MEV were analyzed in order to ensure safety and immunogenicity. MEV construct was non-allergenic, antigenic, stable and flexible. Molecular docking followed by molecular dynamics (MD) simulation analysis, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Codon optimization and in silico cloning of MEV ensured increased expression in the Escherichia coli K-12 system. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

immunology↗
bioRxiv2019

A CLEAR pipeline for direct comparison of circular and linear RNA expression

Sequences of circular RNAs (circRNAs) produced from back-splicing of exon(s) completely overlap with sequences from cognate linear RNAs transcribed from the same gene loci with the exception of their back-splicing junction (BSJ) sites. Examination of global circRNA expression from RNA-seq datasets generally relies on the detection of RNA-seq fragments spanning BSJ sites, but a direct comparison of circular and linear RNA expression from the same gene loci in a genome-wide manner has remained challenging. This is because quantification of BSJ fragments differs from that of linear RNA expression that uses normalized RNA-seq fragments mapped to the whole gene bodies. Here, we have developed a computational pipeline for circular and linear RNA expression analysis from ribosomal-RNA depleted RNA-seq (CLEAR, https://github.com/YangLab/CLEAR). A new quantitation parameter, FPB (fragments per billion mapped bases), is applied to evaluate circular and linear RNA expression individually by fragments mapped to circRNA-specific BSJ sites or to linear RNA-specific splicing junction (SJ) sites. Then, circular and linear RNA expression are directly compared by dividing FPBcirc by FPBlinear to generate a CIRCscore, which indicates the relative circRNA expression using linear RNA expression as the background. Highly-expressed circRNAs with low cognate linear RNA expression background can be identified for further investigation.

bioinformatics↗