AI-guided data-scarce engineering of RfxCas13d to create a cell selection tool

Aviv Spinner, Ayush Noori, Debora Susan Marks, George Church, Lisa Maria Riedmayr

Track: Machine learning: computational method and/or computational results

Nature Biotechnology: Yes

Keywords: Cas13, CRISPR, machine learning, protein engineering, RNA

TL;DR: Using AI-guided protein engineering, we enhance the collateral activity of RfxCas13d, a programmable RNA-targeting endonuclease, to selectively induce apoptosis in heterogeneous cell populations.

Abstract: CRISPR has revolutionized genetic engineering by providing straightforward tools for many kinds of genetic alterations. CRISPR-Cas13 is a programmable endonuclease that specifically targets and cleaves RNA. After activation by target cis-RNA binding, Cas13 also exhibits non-specific collateral trans-activity against nearby RNAs. In several conditions, this leads to apoptosis. Here, we propose to harness the collateral activity of RfxCas13d for selective induction of apoptosis in heterogeneous cell populations. We design and perform machine-guided engineering of RfxCas13d to increase collateral activity, with applications in highly specific cancer therapeutics.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Ayush_Noori1

Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Submission Number: 90