Author:
Khalid Kashaf,Andleeb Saadia
Abstract
AbstractGram-negative, opportunist pathogen Acinetobacter baumannii is notorious for causing a plethora of nosocomial infections predominantly respiratory diseases and blood-stream infections. Due to resistance development towards last-resort antibiotics, its treatment is becoming increasingly difficult. Despite numerous therapeutic developments, no vaccine is available against this ubiquitous pathogen. It is therefore apropos to formulate a rational vaccine plan to get rid of the super-bug. Considering the importance of Outer Membrane Porin D (OprD) as a potential vaccine candidate, we methodically combined the most persistent epitopes present in the A. baumannii strains with the help of different immunoinformatic approaches to envisage a systematic multi-epitope vaccine. The proposed vaccine contains highly immunogenic stretches of linear B-cells, cytotoxic T lymphocyte epitopes, and helper T lymphocyte epitopes of outer membrane porin OprD. The finalized epitopes proved to be significant as they are conserved in A. baumannii strains. The final 3D structure of the construct was projected, refined, and verified by employing several in silico approaches. Apt binding of the protein and adjuvant with the TLR4 suggested significantly high immunogenic potential of our designed vaccine. MD simulations showed highly stable composition of the protein. Immune simulations disclosed a prominent increase in the levels of the immune response. The proposed vaccine model is proposed to be thermostable, immunogenic, water-soluble, and non-allergenic. However, this study is purely computational and needs to be validated by follow-up wet laboratory studies to confirm the safety and immunogenicity of our multi-epitope vaccine.
Publisher
Cold Spring Harbor Laboratory
Reference90 articles.
1. Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers;SoftwareX,2015
2. Ahmad, I. , Nawaz, N. , Darwesh, N.M. , ur Rahman, S. , Mustafa, M.Z. , Khan, S.B. , Patching, S.G. , 2018. Overcoming challenges for amplified expression of recombinant proteins using Escherichia coli. Protein Expr. Purif. https://doi.org/10.1016/j.pep.2017.11.005
3. Almagro Armenteros, J.J. , Sønderby, C.K. , Sønderby, S.K. , Nielsen, H. , Winther, O. , 2017. Erratum: DeepLoc: prediction of protein subcellular localization using deep learning (Bioinformatics (Oxford, England) (2017)). Bioinformatics. https://doi.org/10.1093/bioinformatics/btx548
4. SignalP 5.0 improves signal peptide predictions using deep neural networks
5. Ambrosetti, F. , Jandova, Z. , Bonvin, A.M.J.J. , 2020. A protocol for information-driven antibody-antigen modelling with the HADDOCK2.4 webserver.