Core-Proteomics-Based Annotation of Antigenic Targets and Reverse-Vaccinology-Assisted Design of Ensemble Immunogen against the Emerging Nosocomial Infection-Causing Bacterium Elizabethkingia meningoseptica .
Muhammad IdreesMuhammad Yasir NooraniKalim Ullah AltafEid A AlatawiFaris F Aba AlkhaylKhaled S AllemailemAhmad Abdulaziz A AlmatroudiMurad Ali KhanMuhammad HamayunTaimoor KhanSyed Shujait AliAbbas KhanDong-Qing WeiPublished in: International journal of environmental research and public health (2021)
Elizabethkingia meningoseptica is a ubiquitous Gram-negative emerging pathogen that causes hospital-acquired infection in both immunocompromised and immunocompetent patients. It is a multi-drug-resistant bacterium; therefore, an effective subunit immunogenic candidate is of great interest to encounter the pathogenesis of this pathogen. A protein-wide annotation of immunogenic targets was performed to fast-track the vaccine development against this pathogen, and structural-vaccinology-assisted epitopes were predicted. Among the total proteins, only three, A0A1T3FLU2, A0A1T3INK9, and A0A1V3U124, were shortlisted, which are the essential vaccine targets and were subjected to immune epitope mapping. The linkers EAAK, AAY, and GPGPG were used to link CTL, HTL, and B-cell epitopes and an adjuvant was also added at the N-terminal to design a multi-epitope immunogenic construct (MEIC). The computationally predicted physiochemical properties of the ensemble immunogen reported a highly antigenic nature and produced multiple interactions with immune receptors. In addition, the molecular dynamics simulation confirmed stable binding and good dynamic properties. Furthermore, the computationally modeled immune response proposed that the immunogen triggered a strong immune response after several doses at different intervals. Neutralization of the antigen was observed on the 3rd day of injection. Conclusively, the immunogenic construct produces protection against Elizabethkingia meningoseptica ; however, further immunological testing is needed to unveil its real efficacy.
Keyphrases
- drug resistant
- multidrug resistant
- immune response
- gram negative
- acinetobacter baumannii
- molecular dynamics simulations
- end stage renal disease
- candida albicans
- klebsiella pneumoniae
- newly diagnosed
- ejection fraction
- healthcare
- molecular docking
- rna seq
- early stage
- peritoneal dialysis
- mass spectrometry
- prognostic factors
- emergency department
- convolutional neural network
- machine learning
- escherichia coli
- pseudomonas aeruginosa
- cystic fibrosis
- single cell
- protein protein
- binding protein
- intensive care unit
- extracorporeal membrane oxygenation
- acute respiratory distress syndrome
- dna binding