Computational Discovery of CRISPR-Cas13b Guide RNAs for Broad-Spectrum Dengue Virus Targeting
Dengue (DENV), an RNA virus, remains a significant global health threat, particularly in developing regions, with no widely effective antiviral therapy available. The CRISPRCas13b system, specifically the PspCas13b subtype, has emerged as a promising programmable antiviral tool capable of targeting viral RNA with high specificity. However, the efficacy of Cas13b-based interventions relies heavily on the design of potent and conserved CRISPR RNA (crRNA) spacer sequences, a task complicated by high viral genetic diversity. Unlike CRISPR-Cas9, which targets double-stranded DNA in eukaryotic genomes, Cas13b directly targets single-stranded RNA, making it ideally suited for RNA virus therapeutics; however, existing computational tools predominantly focus on Cas9 DNA targeting or Cas13d for mammalian transcript knockdown, leaving a significant gap for Cas13b-specific viral antiviral design. In this paper, we propose a computational pipeline and machine learning framework for the rational design of high-efficacy Cas13b guide RNAs targeting all four Dengue serotypes. Our approach integrates large-scale genomic data extraction, conservation analysis, and a novel in silico optimization module for guide RNA (gRNA) sequences, based on recently reported Cas13b design rules (e.g., 5 GG motif preference, Cytosine penalties). To predict targeting efficiency, we benchmark classical machine learning models (Random Forest, XGBoost) against foundation model-based predictors (Nucleotide Transformer, RNA-FM) using a dataset of experimentally validated spacers. Our results demonstrate that classical feature-engineered models significantly outperform deep learning approaches when trained on experimentally validated gRNA datasets in low-data regimes. We identify highly conserved, optimized crRNA candidates, including several pan-serotype guides with predicted high potency. This work establishes a baseline for Cas13b efficiency prediction and provides a robust computational resource for accelerating the development of CRISPR-based antivirals against Dengue and other RNA viruses. Code: https://github.com/muhammadali74/CAS13b_pipeline