Pan-Genome-Assisted Computational Design of a Multi-Epitopes-Based Vaccine Candidate against Helicobacter cinaedi.

Helicobacter cinaedi is a Gram-negative bacterium from the family Helicobacteraceae and genus Helicobacter. The pathogen is a causative agent of gastroenteritis, cellulitis, and bacteremia. The increasing antibiotic resistance pattern of the pathogen prompts the efforts to develop a vaccine to prevent dissemination of the bacteria and stop the spread of antibiotic resistance (AR) determinants. Herein, a pan-genome analysis of the pathogen strains was performed to shed light on its core genome and its exploration for potential vaccine targets. In total, four vaccine candidates (TonB dependent receptor, flagellar hook protein FlgE, Hcp family type VI secretion system effector, flagellar motor protein MotB) were identified as promising vaccine candidates and subsequently subjected to an epitopes' mapping phase. These vaccine candidates are part of the pathogen core genome: they are essential, localized at the pathogen surface, and are antigenic. Immunoinformatics was further applied on the selected vaccine proteins to predict potential antigenic, non-allergic, non-toxic, virulent, and DRB*0101 epitopes. The selected epitopes were then fused using linkers to structure a multi-epitopes' vaccine construct. Molecular docking simulations were conducted to determine a designed vaccine binding stability with TLR5 innate immune receptor. Further, binding free energy by MMGB/PBSA and WaterSwap was employed to examine atomic level interaction energies. The designed vaccine also stimulated strong humoral and cellular immune responses as well as interferon and cytokines' production. In a nutshell, the designed vaccine is promising in terms of immune responses' stimulation and could be an ideal candidate for experimental analysis due to favorable physicochemical properties.

Molecular docking, simulation and MM-PBSA studies of nigella sativa compounds: a computational quest to identify potential natural antiviral for COVID-19 treatment.

Nigella sativa or black seed is used as a medicinal plant around the globe. Oil and seeds have a long tradition of folklore use in various medicinal and food systems. The conventional therapeutic use of Nigella sativa, in different ways, has been reported in several studies to treat different diseases including influenza, headache, hypertension, diabetes, inflammation, eczema, fever, cough, asthma, bronchitis, and fever. Based on previously reported potential therapeutic uses of N. sativa compounds, and keeping in mind the dire need of time for the development of potent antiviral, a combined docking, ADMET properties calculation, molecular dynamics, and MM-PBSA approaches were applied in the current study to check the therapeutic potentials of N. sativa chief constituents against COVID-19. Among the studied compounds, we found that dithymoquinone (DTQ), with binding affinity of -8.6 kcal/mol compared to a positive control (chloroquine, -7.2 kcal/mol) , has the high potential of binding at SARS-CoV-2:ACE2 interface and thus could be predicted as a plausible inhibitor to disrupt viral-host interactions. Molecular dynamics simulation of 100 ns well complemented binding affinity of the compound and revealed strong stability of DTQ at the docked site. Additionally, MM-PBSA also affirms the docking results. Compound DTQ of the present study, if validated in wet lab experiments, could be used to treat COVID-19 and could serve as a lead in the future for development of more effective natural antivirals against COVID-19. Communicated by Ramaswamy H. Sarma.