AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation

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Main Authors: Liu, Lijun, Yang, Jiali, Song, Jianfei, Yang, Xinglin, Niu, Lele, Cai, Zeqi, Shi, Hui, Hou, Tingjun, Hsieh, Chang-yu, Shen, Weiran, Deng, Yafeng
Format: Preprint
Published: 2024
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author Liu, Lijun
Yang, Jiali
Song, Jianfei
Yang, Xinglin
Niu, Lele
Cai, Zeqi
Shi, Hui
Hou, Tingjun
Hsieh, Chang-yu
Shen, Weiran
Deng, Yafeng
author_facet Liu, Lijun
Yang, Jiali
Song, Jianfei
Yang, Xinglin
Niu, Lele
Cai, Zeqi
Shi, Hui
Hou, Tingjun
Hsieh, Chang-yu
Shen, Weiran
Deng, Yafeng
contents Recombinant adeno-associated virus (rAAV) vectors have revolutionized gene therapy, but their broad tropism and suboptimal transduction efficiency limit their clinical applications. To overcome these limitations, researchers have focused on designing and screening capsid libraries to identify improved vectors. However, the large sequence space and limited resources present challenges in identifying viable capsid variants. In this study, we propose an end-to-end diffusion model to generate capsid sequences with enhanced viability. Using publicly available AAV2 data, we generated 38,000 diverse AAV2 viral protein (VP) sequences, and evaluated 8,000 for viral selection. The results attested the superiority of our model compared to traditional methods. Additionally, in the absence of AAV9 capsid data, apart from one wild-type sequence, we used the same model to directly generate a number of viable sequences with up to 9 mutations. we transferred the remaining 30,000 samples to the AAV9 domain. Furthermore, we conducted mutagenesis on AAV9 VP hypervariable regions VI and V, contributing to the continuous improvement of the AAV9 VP sequence. This research represents a significant advancement in the design and functional validation of rAAV vectors, offering innovative solutions to enhance specificity and transduction efficiency in gene therapy applications.
format Preprint
id arxiv_https___arxiv_org_abs_2404_10573
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation
Liu, Lijun
Yang, Jiali
Song, Jianfei
Yang, Xinglin
Niu, Lele
Cai, Zeqi
Shi, Hui
Hou, Tingjun
Hsieh, Chang-yu
Shen, Weiran
Deng, Yafeng
Artificial Intelligence
Computational Engineering, Finance, and Science
Biomolecules
Recombinant adeno-associated virus (rAAV) vectors have revolutionized gene therapy, but their broad tropism and suboptimal transduction efficiency limit their clinical applications. To overcome these limitations, researchers have focused on designing and screening capsid libraries to identify improved vectors. However, the large sequence space and limited resources present challenges in identifying viable capsid variants. In this study, we propose an end-to-end diffusion model to generate capsid sequences with enhanced viability. Using publicly available AAV2 data, we generated 38,000 diverse AAV2 viral protein (VP) sequences, and evaluated 8,000 for viral selection. The results attested the superiority of our model compared to traditional methods. Additionally, in the absence of AAV9 capsid data, apart from one wild-type sequence, we used the same model to directly generate a number of viable sequences with up to 9 mutations. we transferred the remaining 30,000 samples to the AAV9 domain. Furthermore, we conducted mutagenesis on AAV9 VP hypervariable regions VI and V, contributing to the continuous improvement of the AAV9 VP sequence. This research represents a significant advancement in the design and functional validation of rAAV vectors, offering innovative solutions to enhance specificity and transduction efficiency in gene therapy applications.
title AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation
topic Artificial Intelligence
Computational Engineering, Finance, and Science
Biomolecules
url https://arxiv.org/abs/2404.10573