Author:
Mantena Sreekar,Pillai Priya P.,Petros Brittany A.,Welch Nicole L.,Myhrvold Cameron,Sabeti Pardis C.,Metsky Hayden C.
Abstract
AbstractGenerating maximally-fit biological sequences has the potential to transform CRISPR guide RNA design as it has other areas of biomedicine. Here, we introduce model-directed exploration algorithms (MEAs) for designing maximally-fit, artificial CRISPR-Cas13a guides—with multiple mismatches to any natural sequence—that are tailored for desired properties around nucleic acid diagnostics. We find that MEA-designed guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared to guides derived directly from natural sequences, and illuminate interpretable design principles that broaden Cas13a targeting.
Publisher
Cold Spring Harbor Laboratory
Reference59 articles.
1. Sinai, S. & Kelsic, E. D. A primer on model-guided exploration of fitness landscapes for biological sequence design. arXiv (2020). 2010.10614.
2. Sinai, S. et al. Adalead: A simple and robust adaptive greedy search algorithm for sequence design. arXiv preprint (2020).
3. A generative neural network for maxi-mizing fitness and diversity of synthetic DNA and protein sequences;Cell Syst,2020
4. Sequence-to-function deep learning frameworks for engineered riboregulators
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