Therapeutics Insight with Inclusive Immunopharmacology Explication of Human Rotavirus A for the Treatment of Diarrhea

Abstract

Rotavirus is the most common cause of severe infant and childhood diarrhea worldwide, and the morbidity and mortality rate is going to be outnumbered in developing countries like Bangladesh. To mitigate this substantial burden of disease, new therapeutics such as vaccine and drug are swiftly required against rotavirus. The present therapeutics insight study was performed with comprehensive immunoinformatics and pharmacoinformatics approach. T and B-cell epitopes were assessed in the conserved region of outer capsid protein VP4 among the highly reviewed strains from different countries including Bangladesh. The results suggest that epitope SU1 (TLKNLNDNY) could be an ideal candidate among the predicted five epitopes for both T and B-cell epitopes for the development of universal vaccine against rotavirus. This research also suggests five novel drug compounds from medicinal plant Rhizophora mucronata Lamk. for better therapeutics strategies against rotavirus diarrhea based on 3D structure building, pharmacophore, ADMET, and QSAR properties. The exact mode of action between drug compounds and target protein VP4 were revealed by molecular docking analysis. Drug likeness and oral bioavailability further confirmed the effectiveness of the proposed drugs against rotavirus diarrhea. This study might be implemented for experimental validation to facilitate the novel vaccine and drug design.

Keywords: diarrhea; drug development; human rotavirus; immunoinformatics; pharmacoinformatics; vaccine design.

Figure 1

Schematic presentation of vaccine and drug designed against human rotavirus.

Figure 2

Divergence analysis of 24 VP4 proteins from 14 different countries. The neighbor joining method was used for phylogenetic tree construction for iteratively joining clusters which are close to each other but at the same time far from all other clusters. In this method, “Kimura” protein distance measurement was selected for the equal amino acid frequency and equal substitution rates. Bootstrap value was also shown by producing 1000 replicates for measuring the accurate distance among the 24 strains.

Figure 3

Conserved region in 24 different strains of Human Rotavirus A. Four conserved regions of more than 10 amino acids were found on multiple sequence alignment. The conserved region “SAIIDFKTLKNLNDNYGI” showed higher antigenic score which allowed to predict some probable candidates for peptide based vaccine design.

Figure 4

Protein disorder regions of VP4 protein including the most antigenic conserved region. (A) The disorder regions of the whole protein sequence. (B) The disorder region in the conserved region “SAIIDFKTLKNLNDNYGI.” Here, blue color indicates the disorder region while green color indicates the globular domain.

Figure 5

Population coverage of different strains of VP4 protein. The peptides with different HLA binding specificities will increase the population coverage as the various HLA types are expressed at radically different frequencies in various ethnicities. The populations were grouped upon 14 countries for our study.

Figure 6

Docking simulation study. Structure of the predicted epitope SU1‘TLKNLNDNY’ (Red stick) (A) and visualize of docking results of SU1 with MHC-I (HLAB^*15:01); Chain A-yellow, Chain B-Bluish (B) as well as with MHC-II (HLA-DR) (C) Chain A-Yellow, Chain B-Bluish, Chain C-Blue.

Figure 7

Prediction of B-cell antigenic properties for most antigenic conserved region. SU1 showed all the antigenic criteria to be predicted as B-cell epitope. (A) Kolaskar and Tongaonkar antigenicity prediction. (B) Emini surface accessibility prediction. (C) Chou and Fasman beta-turn prediction. (D) Karplus and Schulz flexibility prediction. (E) Bepipred linear epitope prediction. (F) Parker hydrophilicity prediction. The x-axis and y-axis represent the sequence position and corresponding antigenic properties score, respectively. The threshold level is 1.0 for most of the properties except for (A) (0.90) and (E) (0.19). The regions having antigenic properties are shown in yellow color above the threshold value.

Figure 8

Selected region for T-cell and B-cell epitope from built model. Black region indicates the SU1 epitope region of 3-D structure of VP4 protein (A). Position of SU1 epitope in linear amino acid sequence of VP4 protein (B).

Figure 9

Two dimensional (2D) structure of selected compounds from medicinal plant Rhizophora mucronata Lamk. (A) Caffeic acid, (B) Quercetin, (C) Rhizophorine, (D) Stigmast-7-en-3β-ol, (E) 1-Hydroxy-5 oxobicyclo[6.4.0] dodecane.

Figure 10

Drug compounds binding amino acid residues of VP4 3D model. (A) Caffeic acid, (B) Quercetin, (C) Rhizophorine E, (D) Stigmast-7-en-3β-ol, (E) 1-Hydroxy-5-oxobicyclo [6.4.0] dodecane.

Figure 11

Interactions of drug compounds with the VP4 protein. (A) Binding site of drug compounds in VP4 protein. Circled area depicts the binding site of VP4 protein. (B) Active site residues (Blue) of VP4 protein model.

Figure 12

Quantitative estimation of drug likeness. Rhizophorine showed the highest score of drug likeness (0.804) compared to other compounds.

Figure 13

Oral bioavailability rate of compounds. Rhizophorine and 1-Hydroxy-5-oxobicyclo [6.4.0] dodecane indicated higher oral bioavailability rate (70–100%) than the other plant medicinal drug compounds.