# De novo development of small CRISPR-Cas proteins using artificial intelligence algorithms

> **NIH NIH R21** · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · 2022 · $233,054

## Abstract

Abstract
The large-sized CRISPR Cas proteins have hindered the effective use of CRISPR-mediated gene
editing in human therapeutics due to the lack of delivery strategies for these large-sized proteins to
reach their target locations. To seek solutions to this challenge, multiple efforts have been reported to
search for the smaller-sized Cas proteins in nature. Recent successes in artificial intelligence (AI)
application in the biological field demonstrated the power of AI in solving biological problems and
brought a new perspective to address this challenge. In this application, we propose to develop a proof-
of-concept technology to adapt the AI algorithms used in protein structure prediction and strategic board
games to develop novel protein design tools to minimize the Cas protein size while maintaining its
function. Our objectives are to develop a novel protein design technology to optimize the size of protein
and develop experimentally-validated artificial mini-Cas proteins. We hypothesize that the artificial mini-
Cas proteins with guided double-strand DNA cleavage function can be designed using AI technologies.
To test this hypothesis, we propose to test the feasibility of developing this mini-Cas-design technology
with two approaches. In Aim 1, we will use the sequence-based methods with the generative and
attention-based neural networks to design mini-Cas proteins. In Aim 2, the structure-based semi-
unsupervised reinforcement learning will be used for the technology development. The top candidates
of designed mini-Cas protein will be evaluated by molecular dynamics simulations followed by the
biochemical and cell-based assays. Our proposed technology will have the great potential to advance
the technical field of protein design by providing tools to optimize the size of proteins, shifting the
paradigm of the CRISPR research field from “searching from nature” to “designing in the lab”, and
delivering the first artificially designed Cas proteins validated in biochemical and cell-based assays to
address the CRISPR delivery challenge.

## Key facts

- **NIH application ID:** 10358980
- **Project number:** 1R21GM144860-01
- **Recipient organization:** UNIVERSITY OF NORTH TEXAS HLTH SCI CTR
- **Principal Investigator:** Jin Liu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $233,054
- **Award type:** 1
- **Project period:** 2022-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10358980

## Citation

> US National Institutes of Health, RePORTER application 10358980, De novo development of small CRISPR-Cas proteins using artificial intelligence algorithms (1R21GM144860-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10358980. Licensed CC0.

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