bioRxiv Science⌕ Search

bioRxiv · 10.1101/2020.04.01.019299

Structural basis to design multi-epitope vaccines against Novel Coronavirus 19 (COVID19) infection, the ongoing pandemic emergency: an in silico approach

Abstract

The 2019 novel coronavirus (COVID19 / Wuhan coronavirus), officially named as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a positive-sense single-stranded RNA coronavirus. SARS-CoV-2 causes the contagious COVID19 disease also known as 2019-nCoV acute respiratory disease and has led to the ongoing 2019-20 pandemic COVID19 outbreak. The effective counter measures against SARS-CoV-2 infection require the design and development of specific and effective vaccine candidate. In the present study, we have screened and shortlisted 38 CTL, 33 HTL and 12 B cell epitopes from the eleven Protein sequences of SARS-CoV-2 by utilizing different in silico tools. The screened epitopes were further validated for their binding with their respective HLA allele binders and TAP (Transporter associated with antigen processing) molecule by molecular docking. The shortlisted screened epitopes were further utilized to design novel two multi-epitope vaccines (MEVs) composed of CTL, HTL and B cell epitopes overlaps with potential to elicit humoral as well as cellular immune response against SARS-CoV-2. To enhance the immune response for our vaccine design, truncated (residues 10-153) Onchocerca volvulus activation-associated secreted protein-1 (Ov-ASP-1) has been utilized as an adjuvant at N terminal of both the MEVs. Further molecular models for both the MEVs were prepared and validated for their stable molecular interactions with Toll-Like Receptor 3 (TLR 3). The codon-optimized cDNA of both the MEVs were further analyzed for their potential of high level of expression in a human cell line. The present study is very significant in terms of molecular designing of prospective CTL and HTL vaccine against SARS-CoV-2 infection with the potential to elicit cellular as well as humoral immune response. (SARS-CoV-2), Coronavirus, Human Transporter associated with antigen processing (TAP), Toll-Like Receptor (TLR), Epitope, Immunoinformatics, Molecular Docking, Molecular dynamics simulation, Multi-epitope Vaccine Graphical abstractThe designed CTL (Cytotoxic T lymphocyte) and HTL (Helper T lymphocyte) multi-epitope vaccines (MEV) against COVID19 infection. Both the CTL and HTL MEV models show a very stable and well fit conformational complex formation tendency with the Toll like receptor 3. CTL and HTL MEVs: ribbon; Toll like receptor 3: gray cartoon; Adjuvant [truncated (residues 10-153) Onchocerca volvulus activation-associated secreted protein-1]: orange ribbon regions; Epitopes: cyan ribbons regions; 6xHis Tag: magenta ribbon regions. O_FIG O_LINKSMALLFIG WIDTH=87 HEIGHT=200 SRC="FIGDIR/small/019299v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@6cb749org.highwire.dtl.DTLVardef@1752d54org.highwire.dtl.DTLVardef@1f2fc16org.highwire.dtl.DTLVardef@18415a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Srivastava, S., Verma, S., Kamthania, M., Kaur, R., Badyal, R. K., Saxena, A. K., Shin, H.-J., Pandey, K., Kolbe, M.. 2020-04-03. Structural basis to design multi-epitope vaccines against Novel Coronavirus 19 (COVID19) infection, the ongoing pandemic emergency: an in silico approach. https://doi.org/10.1101/2020.04.01.019299

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗