Coevolution based immunoinformatics approach considering variability of epitopes to combat different strains: A case study using spike protein of SARS-CoV-2.

In the recent past several vaccines were developed to combat the COVID-19 disease. Unfortunately, the protective efficacy of the current vaccines has been reduced due to the high mutation rate in SARS-CoV-2. Here, we successfully implemented a coevolution based immunoinformatics approach to design an epitope-based peptide vaccine considering variability in spike protein of SARS-CoV-2. The spike glycoprotein was investigated for B- and T-cell epitope prediction. Identified T-cell epitopes were mapped on previously reported coevolving amino acids in the spike protein to introduce mutation. The non-mutated and mutated vaccine components were constructed by selecting epitopes showing overlapping with the predicted B-cell epitopes and highest antigenicity. Selected epitopes were linked with the help of a linker to construct a single vaccine component. Non-mutated and mutated vaccine component sequences were modelled and validated. The in-silico expression level of the vaccine constructs (non-mutated and mutated) in E. coli K12 shows promising results. The molecular docking analysis of vaccine components with toll-like receptor 5 (TLR5) demonstrated strong binding affinity. The time series calculations including root mean square deviation (RMSD), radius of gyration (RGYR), and energy of the system over 100 ns trajectory obtained from all atom molecular dynamics simulation showed stability of the system. The combined coevolutionary and immunoinformatics approach used in this study will certainly help to design an effective peptide vaccine that may work against different strains of SARS-CoV-2. Moreover, the strategy used in this study can be implemented on other pathogens.

Development of QSAR model using machine learning and molecular docking study of polyphenol derivatives against obesity as pancreatic lipase inhibitor.

In developed countries and developing countries, obesity/overweight is considered a major problem, in fact, it is now recognized as a major metabolic disorder. Additionally, obesity is connected with other metabolic diseases, including cardiovascular disorders, type 2 diabetes, some types of cancer, etc. Therefore, the development of novel drugs/medications for obesity is essential. The best target for treating obesity is Pancreatic Lipase (PL), it breaks 50-70% triglycerides into monoglycerol and free fatty acids.The major aim of this in silico study is to generate a QSAR model by using Multiple Linear Regression (MLR) and to inhibit pancreatic lipase by polyphenol derivatives mainly flavonoids, plant secondary metabolites shows good inhibitory activity against PL, maybe with less unpleasant side effects.In this in silico study, a potent inhibitor was found through calculating drug likness, QSAR (Quantitative structure-activity relationship) and molecular docking. The docking was performed in Maestro 12.0 and the ADME (absorption, distribution, metabolism, and excretion) properties (drug-likeness) of compounds/ligands were predicted by the Qikprop module of Maestro 12.0. The QSAR model was developed to show the relationship between the chemical/structural properties and the compound's biological activity. We have found the best interaction between pancreatic lipase and flavonoids. The best docked compound is Epigallocatechin 3,5,-di-O-gallate with docking score -10.935 kcal/mol .All compounds also show drug-likeness activity.The developed model has satisfied all internal and external validation criteria and has square correlation coefficient (r2) 0.8649, which shows its predictive ability and has good acceptability, predictive ability, and statistical robustness.Communicated by Ramaswamy H. Sarma.