Deciphering the Structural Enigma of HLA Class-II Binding Peptides for Enhanced Immunoinformatics-based Prediction of Vaccine Epitopes

Investigating associations between HLA DQA1 ~ DQB1 haplotypes, H. pylori infection, metaplasia, and anti-CagA IgA seropositivity in a Turkish gastritis cohort

Helicobacter pylori was reported as an important cause of gastritis, and gastric ulcers and CagA oncoprotein-producing H. pylori subgroups were blamed to increase the severity of gastritis. Disparities were reported in that the presence of serum anti-CagA IgA was not parallel with CagA-positive H. pylori cohabitation. We hypothesized that the HLA-DQA1 ~ DQB1 haplotypes in human populations include protective haplotypes that more effectively present immunogenic CagA peptides and susceptible haplotypes with an impaired capacity to present CagA peptides. We recruited patients (n = 201) admitted for gastroendoscopy procedures and performed high-resolution HLA-DQA1 and DQB1 typing. Serum anti-CagA IgA levels were analyzed by ELISA (23.0% positive), and H. pylori was classified as positive or negative in gastric mucosal tissue slides (72.6% positive). The HLA DQA1*05:05 allele (29.1%) and HLA DQB1*03:01 allele (32.8%) were found at the highest frequency among gastritis patients of Turkish descent. In HLA DQA1*05:05 ~ DQB1*03:01 double homozygous (7.3%) and heterozygous (40.7%) haplotype carriers, the presence of anti-CagA IgA decreased dramatically, the presence of H. pylori increased, and the presence of metaplasia followed a decreasing trend. The DQ protein encoded by HLA DQA1*05:05-DQ*03:01 showed a low binding affinity to the CagA peptide when binding capacity was analyzed by the NetMHCIIPan 4.0 prediction method. In conclusion, HLA DQA1 ~ DQB1 polymorphisms are crucial as host defense mechanisms against CagA H. pylori since antigen binding capacity plays a crucial role in anti-CagA IgA production.

Immuno-informatics guided designing of a multi-epitope vaccine against Dengue and Zika

Dengue and zika are amongst the most prevalent mosquito-borne diseases caused by closely related members Dengue virus (DENV) and Zika virus (ZIKV), respectively, of the Flaviviridae family. DENV and ZIKV have been reported to co-infect several people, resulting in fatalities across the world. A vaccine that can safeguard against both these pathogens concurrently, can offer several advantages. This study has employed immuno-informatics for devising a multi-epitope, multi-pathogenic vaccine against both these viruses. Since, the two viruses share a common vector source, whose salivary components are reported to aid viral pathogenesis; antigenic salivary proteins from Aedes aegypti were also incorporated into the design of the vaccine along with conserved structural and non-structural viral proteins. Conserved B- and T-cell epitopes were identified for all the selected antigenic proteins. These epitopes were merged and further supplemented with β-defensin as an adjuvant, to yield an immunogenic vaccine construct. In-silico 3D modeling and structural validation of the vaccine construct was conducted, followed by its molecular docking and molecular dynamics simulation studies with human TLR2. Immune simulation study was also performed, and it further provided support that the designed vaccine can mount an effective immune response and hence provide protection against both DENV and ZIKV. Communicated by Ramaswamy H. Sarma.

Empirical Study of Large-Scale HLA Simulation of Parallel Region-Matching Knowledge Recognition Algorithm Based on Region Matching

Most of the existing region-matching algorithms need to match all regions, resulting in a waste of computing resources, increasing the cost of simulation technology and data redundancy, and resulting in the reduction of network data stream transmission efficiency. This paper presents a parallel region-matching knowledge recognition algorithm. Combined with the shortcomings of existing matching algorithms, a simulation technology is constructed to realize the parallel matching of multiple regions in HLA distributed simulation. The algorithm can realize the parallel matching calculation of multiple changed regions in one simulation. At the same time, the basic idea based on mobile intersection is adopted in the matching calculation, and the historical information before and after the region range is moved is used. The matching is limited to the moving interval, and the moving crossover theory is applied to the matching calculation to realize the relevant historical information before and after the region. Simulation results show that the parallel region-matching knowledge recognition algorithm can support HLA distributed simulation evaluation. In the matching calculation, the basic idea based on moving intersection is adopted, and the matching is limited to the moving interval by using the historical information before and after the region is moved, which reduces a large number of irrelevant calculations. Theoretical analysis and experimental results show that the algorithm is particularly suitable for the application needs of building large-scale distributed simulation based on multi-core computing platform.