Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii

Abstract

The emergence of drug-resistant Acinetobacter baumannii is the global health problem associated with high mortality and morbidity. Therefore it is high time to find a suitable therapeutics for this pathogen. In the present study, subtractive proteomics along with reverse vaccinology approaches were used to predict suitable therapeutics against A. baumannii. Using subtractive proteomics, we have identified promiscuous antigenic membrane proteins that contain the virulence factors, resistance factors and essentiality factor for this pathogenic bacteria. Selected promiscuous targeted membrane proteins were used for the design of chimeric-subunit vaccine with the help of reverse vaccinology. Available best tools and servers were used for the identification of MHC class I, II and B cell epitopes. All selected epitopes were further shortlisted computationally to know their immunogenicity, antigenicity, allergenicity, conservancy and toxicity potentials. Immunogenic predicted promiscuous peptides used for the development of chimeric subunit vaccine with immune-modulating adjuvants, linkers, and PADRE (Pan HLA-DR epitopes) amino acid sequence. Designed vaccine construct V4 also interact with the MHC, and TLR4/MD2 complex as confirm by docking and molecular dynamics simulation studies. Therefore designed vaccine construct V4 can be developed to control the host-pathogen interaction or infection caused by A. baumannii.

Figure 1

Illustration of predefined comparative and subtractive proteomics systemic workflow.

Figure 2

Illustration of reverse vaccine devolvement workflow.

Figure 3

Protein-protein interaction diagram of (A) Channel-tunnel spanning the outer membrane and periplasm segregation of daughter chromosomes (B) penicillin binding protein predicted by STRING tool.

Figure 4

Cluster analysis of the HLA alleles for both MHC molecules through heat map representation. (A) Representing the cluster of the MHC-I. (B) Representing the cluster of MHC-II molecules. Epitopes are clustered on the basis of interaction with HLA and shown as red colour indicating strong interaction with appropriate annotation. Yellow zone indicates the weaker interaction. Here, all the available alleles are shown.

Figure 5

Protein ID B0VMD0 B cell epitope (A) Bepipred Linear Epitope, (B) Chou & Fasman Beta-Turn Prediction, (C) Emini Surface Accessibility Prediction, (D) Karplus & Schulz Flexibility Prediction, (E) Kolaskar & Tongaonkar Antigenicity, (F) Parker Hydrophilicity Prediction.

Figure 6

Protein ID B0VUZ6 B cell epitope (A) Bepipred Linear Epitope, (B) Chou & Fasman Beta-Turn Prediction, (C) Emini Surface Accessibility Prediction, (D) Karplus & Schulz Flexibility Prediction, (E) Kolaskar & Tongaonkar Antigenicity, (F) Parker Hydrophilicity Prediction.

Figure 7

Secondary structure prediction of vaccine constructs (V1 to V4) using PESIPRED server.

Figure 8

Tertiary Structure prediction and validation of vaccine construct V4. (A) Tertiary structure of model construct V4. (B) Ramachandran plot of the modelled V4 showing 92.0 residues in the allowed region.

Figure 9

Docked complex of vaccine construct V4 with human TLR4-MD2 complex. The vaccine construct docked within the TLR-4 receptor.

Figure 10

Molecular dynamics simulation of V4- TLR4-MD2 complex. The result shows the RMSD obtained for the complex which showed that complex is stable after 2 ns at 0.8 nm.

Figure 11

In-silico restriction cloning of gene sequence of final vaccine construct V4 into pET28a expression vector showing V4 sequence red colour surrounded between BglII (401) and AscI (1543).