Delineating the Immuno-Dominant Antigenic Vaccine Peptides Against gacS-Sensor Kinase in Acinetobacter baumannii: An in silico Investigational Approach

A S Smiline Girija

doi:10.3389/fmicb.2020.02078

Abstract

ObjectivesTo predict the novel vaccine peptide candidates against gacS protein involved with the citrate utilization in the two-component system of A. baumannii-associated virulence as an alternative strategy to combat the multi-drug resistant strains using an immuno-informatic approach.MethodsThe study is designed as an observational in silico study design with the application of BepiPred, AlgPred, VaxiJen, AntigenPro, SolPro, Expasy ProtParam server, IEDB database, and MHC cluster analytical tools and servers to predict the immuno-dominant B-cell and T-cell epitopes from gacS FASTA sequences retrieved from UNIPROT database. Further peptide interactions with TLR-4 was assessed based on the number of hydrogen bonds.ResultsNine peptides (20aa) with the highest score of 1 were selected from the 137 epitopes, and five were predicted as antigenic epitopes (E1–E5). E3 was selected as the potent antigen (score: 0.939537) and E1 as the best vaccine candidate (score: 0.9803) under AntigenPro and Vaxijen server, respectively. SolPro predicted all epitopes as soluble peptides. ProtParam predictions showed E3 and E5 as stable proteins with a shelf life of 3.5 and 1.9 h and possessed negative GRAVY values. PsortB server predicted a final localization score of 7.88 for the gacS protein sequence as a cytoplasmic membrane protein. IEDB conservancy analysis showed 100% conserved sequences within the gacS sequence, and class I conservancy yielded positive values for all epitopes. Cluster analysis showed strong interactions, and the protein-peptide interactions with TLR-2 finally detected E5 as the best interacting peptide (H bonds = 14) followed by E3 (H bonds = 12).ConclusionThe study suggests five antigenic peptides as promiscuous vaccine candidates to target the gacS of A. baumannii using immuno-informatic approach toward the peptide synthesis and in vitro analysis. However, the study recommends further experimental validation for immunological response and memory through in vivo studies.

Highlights

Acinetobacter baumannii is a gram-negative non-motile coccobacillus, phenotypically negative for oxidase and positive for catalase (Murray et al, 2016), that belongs to the family of Moraxellaceae (Baumann et al, 1968)
B-Cell Epitope Mapping Retrieved FASTA sequences of gacS were used as the input in BCPred server2, amino-acid paired (AAP) Prediction tool that uses paired amino acid antigenicity scale (Chen et al, 2007), with the BepiPred2.0 server, to sequentially predict the epitopes and non-epitope amino acids from the crystal structures using random forest algorithm software (Larsen et al, 2006) and an ABCPred server to predict the B-cell epitopes based on a recurrent neural network using fixed length patterns (Saha and Raghava, 2006b)
Predictions on Antigenicity The predicted protein sequences from gacS were uploaded in VaxiJen v2.0 server (Doytchinova and Flower, 2007b), which can detect the protective antigens based on alignmentindependent predictions to categorize the antigens according

Summary

Introduction

Acinetobacter baumannii is a gram-negative non-motile coccobacillus, phenotypically negative for oxidase and positive for catalase (Murray et al, 2016), that belongs to the family of Moraxellaceae (Baumann et al, 1968). Earlier infections with Acinetobacter occurred between the 1960s and 1970s, as a low priority pathogen with low virulence (Rice, 2008), but in the twentieth century, multivariate analysis of the clinical isolates has documented A. baumannii as the most virulent pathogen (Maragakis and Perl, 2008). Reports from India, have documented MDR strains of A. baumannii (Smiline Girija et al, 2017, 2018a,b, 2019a,b) along with resistance profiles mediated by plasmids. This major sort of transformation from a low-priority pathogen into a predominant nosocomial pathogen can be related to various virulence factors in addition to antibiotic resistance (Liu et al, 2018)

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