Applying machine learning to predict viral assembly for adeno-associated virus capsid libraries

Molecular Therapy: Methods & Clinical Development

Michael Kummer¹  Oleksandr Moskalenko²  Arunava Banerjee³  Oleksandr Kondratov⁴  Andrew D. Marques⁵  Sergei Zolotukhin⁵ 

[1] Corresponding author: Andrew D. Marques, Department of Pediatrics, Division of Cellular and Molecular Therapy, University of Florida, Gainesville, FL 32608, USA.;Engineering, University of Florida, Gainesville, FL 32603, USA;;Information Science &;Department of Computer &Department of Pediatrics, Division of Cellular and Molecular Therapy, University of Florida, Gainesville, FL 32608, USA;

关键词: Machine Learning;    AAV;    Capsid Libraries;    Assembly;    Packaging;    ANN;   

DOI  :  

来源: DOAJ

【 摘 要 】

Machine learning (ML) can aid in novel discoveries in the field of viral gene therapy. Specifically, big data gathered through next-generation sequencing (NGS) of complex capsid libraries is an especially prominent source of lost potential in data analysis and prediction. Furthermore, adeno-associated virus (AAV)-based capsid libraries are becoming increasingly popular as a tool to select candidates for gene therapy vectors. These higher complexity AAV capsid libraries have previously been created and selected in vivo; however, in silico analysis using ML computer algorithms may augment smarter and more robust libraries for selection. In this study, data of AAV capsid libraries gathered before and after viral assembly are used to train ML algorithms. We found that two ML computer algorithms, artificial neural networks (ANNs), and support vector machines (SVMs), can be trained to predict whether unknown capsid variants may assemble into viable virus-like structures. Using the most accurate models constructed, hypothetical mutation patterns in library construction were simulated to suggest the importance of N495, G546, and I554 in AAV2-derived capsids. Finally, two comparative libraries were generated using ML-derived data to biologically validate these findings and demonstrate the predictive power of ML in vector design.

【 授权许可】

Unknown