Gene editing technology, particularly the CRISPR-Cas9 system, has revolutionized biological research, offering vast therapeutic potential. However, challenges like off-target effects, limited targetable sequences, and DNA strand fractures impede its widespread application. To tackle these issues, a project merges bioinformatics screening and deep learning models, aiming to screen novel CRISPR-Cas9 proteins. This endeavor focuses on screening for new Cas9 proteins from Streptococcus pyogenes and Archaea genomes. The process involves genome searches and predictions using a Variational Autoencoder (VAE) model. Preliminary validation with Nblast and constructing an evolutionary tree of protein ortholog distribution assess specificity. The goal is to discover Cas9 proteins that surpass current gene editing limitations, complementing the existing CRISPR/Cas system. This project advances gene editing therapies by presenting a comprehensive workflow combining bioinformatics and deep learning, serving as a valuable reference for future research.

Yiting Liu. Cas9 protein screening of microbial data based on biological information and VAE methods. Academic Journal of Agriculture & Life Sciences (2024) Vol. 5 Issue 1: 135-144. https://doi.org/10.25236/AJALS.2024.050118.

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