Potent immunogenicity and broad-spectrum protection potential of microneedle array patch-based COVID-19 DNA vaccine candidates encoding dimeric RBD chimera of SARS-CoV and SARS-CoV-2 variants.

Breakthrough infections by SARS-CoV-2 variants pose a global challenge to COVID-19 pandemic control, and the development of more effective vaccines of broad-spectrum protection is needed. In this study, we constructed pVAX1-based plasmids encoding receptor-binding domain (RBD) chimera of SARS-CoV-1 and SARS-CoV-2 variants, including pAD1002 (encoding RBDSARS/BA1), pAD1003 (encoding RBDSARS/Beta) and pAD131 (encoding RBDBA1/Beta). Plasmids pAD1002 and pAD131 were far more immunogenic than pAD1003 in terms of eliciting RBD-specific IgG when intramuscularly administered without electroporation. Furthermore, dissolvable microneedle array patches (MAP) greatly enhanced the immunogenicity of these DNA constructs in mice and rabbits. MAP laden with pAD1002 (MAP-1002) significantly outperformed inactivated SARS-CoV-2 virus vaccine in inducing RBD-specific IFN-γ+ effector and memory T cells, and generated T lymphocytes of different homing patterns compared to that induced by electroporated DNA in mice. In consistence with the high titer neutralization results of MAP-1002 antisera against SARS-CoV-2 pseudoviruses, MAP-1002 protected human ACE2-transgenic mice from Omicron BA.1 challenge. Collectively, MAP-based DNA constructs encoding chimeric RBDs of SARS-CoV-1 and SARS-CoV-2 variants, as represented by MAP-1002, are potential COVID-19 vaccine candidates worthy further translational study.

QSSR Modeling of Bacillus Subtilis Lipase A Peptide Collision Cross-Sections in Ion Mobility Spectrometry: Local Descriptor Versus Global Descriptor.

To investigate the structure-dependent peptide mobility behavior in ion mobility spectrometry (IMS), quantitative structure-spectrum relationship (QSSR) is systematically modeled and predicted for the collision cross section Ω values of totally 162 single-protonated tripeptide fragments extracted from the Bacillus subtilis lipase A. Two different types of structure characterization methods, namely, local and global descriptor as well as three machine learning methods, namely, partial least squares (PLS), support vector machine (SVM) and Gaussian process (GP), are employed to parameterize and correlate the structures and Ω values of these peptide samples. In this procedure, the local descriptor is derived from the principal component analysis (PCA) of 516 physicochemical properties for 20 standard amino acids, which can be used to sequentially characterize the three amino acid residues composing a tripeptide. The global descriptor is calculated using CODESSA method, which can generate > 200 statistically significant variables to characterize the whole molecular structure of a tripeptide. The obtained QSSR models are evaluated rigorously via tenfold cross-validation and Monte Carlo cross-validation (MCCV). A comprehensive comparison is performed on the resulting statistics arising from the systematic combination of different descriptor types and machine learning methods. It is revealed that the local descriptor-based QSSR models have a better fitting ability and predictive power, but worse interpretability, than those based on the global descriptor. In addition, since the QSSR modeling using local descriptor does not consider the three-dimensional conformation of tripeptide samples, the method would be largely efficient as compared to the global descriptor.