ReviewFrontiers in artificial intelligence2025
Structural studies of Parvoviridae capsid assembly and evolution: implications for novel AAV vector design.
Review in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Therapeutic landscape of Fabry disease: advances and challenges from classical strategies to emerging therapies.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adeno-associated virus (AAV) vectors have emerged as powerful tools in gene therapy, potentially treating various genetic disorders. Engineering the AAV capsids through computational methods enables the customization of these vectors to enhance their effectiveness and safety. This engineering allows for the development of gene therapies that are not only more efficient but also personalized to unique genetic profiles. When developing, it is essential to understand the structural biology and the vast techniques used to guide vector designs. This review covers the fundamental biology of the Parvoviridae capsids, focusing on modern structural study techniques, including (a) Cryo-electron microscopy and X-ray Crystallography studies and (b) Comparative analysis of capsid structures across different Parvoviridae species. Along with the structure and evolution of the Parvoviridae capsids, computational methods have provided significant insights into the design of novel AAV vector techniques, which include (a) Structure-guided design of AAV capsids with improved properties, (b) Directed Evolution of AAV capsids for specific applications, and (c) Computational prediction of AAV capsid-receptor interactions. Further discussion addressed the ongoing challenges in the AAV vector design and proposed future directions for exploring enhanced computational tools, such as artificial intelligence/machine learning and deep learning.
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Registered trials
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