Whole proteome analysis of MDR Klebsiella pneumoniae to identify mRNA and multiple epitope based vaccine targets against emerging nosocomial and lungs associated infections

Abstract

Klebsiella pneumonia is a Gram negative facultative anaerobic bacterium involved in various community-acquired pneumonia, nosocomial and lungs associated infections. Frequent usage of several antibiotics and acquired resistance mechanisms has made this bacterium multi-drug resistance (MDR), complicating the treatment of patients. To avoid the spread of this bacterium, there is an urgent need to develop a vaccine based on immuno-informatics approaches that is more efficient than conventional method of vaccine prediction or development. Initially, the complete proteomic sequence of K. pneumonia was picked over for specific and prospective vaccine targets. From the annotation of the whole proteome, eight immunogenic proteins were selected, and these shortlisted proteins were interpreted for CTL, B-cells, and HTL epitopes prediction, to construct mRNA and multi-epitope vaccines. The Antigenicity, allergenicity and toxicity analysis validate the vaccine’s design, and its molecular docking was done with immuno-receptor the TLR-3. The docking interaction showed a stronger binding affinity with a minimum energy of −1153.2 kcal/mol and established 23 hydrogen bonds, 3 salt bridges, 1 disulfide bond, and 340 non-binding contacts. Further validation was done using In-silico cloning which shows the highest CAI score of 0.98 with higher GC contents of 72.25% which represents a vaccine construct with a high value of expression in E. coli. Immune Simulation shows that the antibodies (IgM, IgG1, and IgG2) production exceeded 650,000 in 2 to 3 days but the response was completely neutralized in the 5^th day. In conclusion, the study provides the effective, safe and stable vaccine construct against Klebsiella pneumonia, which further needs in vitro and in vivo validations.

Communicated by Ramaswamy H. Sarma

Keywords:

• Klebsiella pneumoniae
• immune-informatics
• mRNA vaccine
• multi-epitope vaccine
• TLR-3

Disclosure statement

The authors declare no conflict of interest.

Authors’ contributions

Conceptualization, M.N. and S.M.; methodology, M.H.A; software, U.A.; validation, K.J, F.N., and T.A.; formal analysis, M.N; investigation, U.A and S.M.; resources, M.N.; data curation, M.S and M.E.A.; writing—original draft preparation, M.A and A.A.S; writing—review and editing, T.A.; visualization, M.A; A.F.A and THA; supervision, T.A.; project administration, M.N.; funding acquisition, THA.

Availability of data and material

All the data used in this research work has been included in this manuscript.

Additional information

Funding

The authors greatly acknowledge and express their gratitude to the Researchers Supporting Project number (RSPD2023R568), King Saud University, Riyadh, Saudi Arabia.