An optimized deep learning approach for blood-brain barrier permeability prediction with ODE integration

Integrated in-silico design and in vivo validation of multi-epitope vaccines for norovirus

BACKGROUND

Norovirus (NoVs) is a foodborne pathogen that causes acute gastroenteritis. The diversity of its principal antigenic protein poses a significant challenge to vaccine development and the prevention of large-scale outbreaks globally. Currently, no licensed vaccines against norovirus have been approved.

METHODS

We developed a novel pipeline that integrates multiple bioinformatics tools to design broad-spectrum vaccines against NoVs. Specifically, broad-spectrum T-cell epitope vaccines were designed based on consensus sequences and optimized epitope screening, while broad-spectrum B-cell spatial epitope vaccines were constructed using high-throughput antigenicity calculations and epitope mapping.

RESULTS

This pipeline underwent rigorous validation at three levels: firstly, In silico validation: Analysis of properties and structures demonstrated the appropriateness of amino acid composition and the structural integrity of the vaccine sequences. Secondly, theoretical assessment: Evaluation of human leukocyte antigen (HLA) subtype and antigenicity coverage indicated a broad theoretical protective spectrum for the designed vaccine immunogens. Furthermore, in silico simulation confirmed their ability to elicit an immune response. Finally, animal-level validation: Experiments in mice showed that both vaccine immunogens stimulated high levels of IgG and IgA. Notably, Vac-B induced a strong IgG response against GII.2 and a robust IgA response against GII.17, comparable to the immune response elicited by the wild-type NoV non-replicating virus-like particle (VLP) protein group.

CONCLUSIONS

Both in silico and in vivo experimental findings suggest that the proposed pipeline and vaccine immunogens could serve as valuable theoretical guidance for the development of multi-epitope vaccines against NoVs.

Masked graph transformer for blood-brain barrier permeability prediction

The blood-brain barrier (BBB) is a highly protective structure that strictly regulates the passage of molecules, ensuring the central nervous system remains free from harmful chemicals and maintains brain homeostasis. Since most compounds cannot easily cross the BBB, assessing the blood-brain barrier permeability (BBBP) of drug candidates is critical in drug discovery. While several computational methods have been developed to screen BBBP with promising results, these approaches have limitations that affect their predictive power. In this study, we constructed classification models for screening the BBBP of molecules. Our models were trained with chemical data featurized by a Masked Graph Transformer-based Pretrained (MGTP) encoder. The molecular encoder was designed to generate molecular features for various downstream tasks. The training of the MGTP encoder was guided by masked attention-based learning, improving the model's generalization in encoding molecular structures. The results showed that classification models developed using MGTP features had outperformed those using other representations in 6 out of 8 cases, demonstrating the effectiveness of the proposed encoder. Also, chemical diversity analysis confirmed the encoder's ability to effectively distinguish between different classes of molecules.

Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood-Brain Barrier

The discovery and development of new pharmaceutical drugs is a costly, time-consuming, and highly manual process, with significant challenges in ensuring drug bioavailability at target sites. Computational techniques are highly employed in drug design, particularly to predict the pharmacokinetic properties of molecules. One major kinetic challenge in central nervous system drug development is the permeation through the blood-brain barrier (BBB). Several different computational techniques are used to evaluate both BBB permeability and target delivery. Methods such as quantitative structure-activity relationships, machine learning models, molecular dynamics simulations, end-point free energy calculations, or transporter models have pros and cons for drug development, all contributing to a better understanding of a specific characteristic. Additionally, the design (assisted or not by computers) of prodrug and nanoparticle-based drug delivery systems can enhance BBB permeability by leveraging enzymatic activation and transporter-mediated uptake. Neuroactive peptide computational development is also a relevant field in drug design, since biopharmaceuticals are on the edge of drug discovery. By integrating these computational and formulation-based strategies, researchers can enhance the rational design of BBB-permeable drugs while minimizing off-target effects. This review is valuable for understanding BBB selectivity principles and the latest in silico and nanotechnological approaches for improving CNS drug delivery.