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

使用人工智能算法从头开发小型 CRISPR-Cas 蛋白

• 批准号: 10544772
• 负责人: Jin Liu
• 金额: $ 18.14万
• 依托单位: UNIVERSITY OF NORTH TEXAS HLTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10544772
• 关键词: 3-Dimensional; Address; Amino Acids; Artificial Intelligence; Attention; BCAR1 gene; Bacteriophages; Base Sequence; Biochemical; Biological; Biological Assay; CRISPR/Cas technology; Cells; Classification; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Computer software; DNA; Development; Evaluation; Genes; Graph; Human; Learning; Location; Machine Learning; Mediating; Methods; Modeling; Molecular Conformation; Nature; Performance; Play; Positioning Attribute; Protein Engineering; Proteins; Psychological reinforcement; Reporting; Research; Sequence Alignment; Site; Structure; Technology; Testing; Therapeutic; Training; Validation; artificial intelligence algorithm; design; ds-DNA; feasibility testing; gene therapy; generative adversarial network; high dimensionality; improved; learning algorithm; molecular dynamics; nanoparticle; neural network; novel; programs; protein data bank; protein structure; protein structure prediction; success; technology development; three dimensional structure; tool; unsupervised learning

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.

抽象的 大型CRISPR CAS蛋白阻碍了CRISPR介导的基因的有效使用 由于缺乏这些大型蛋白质的交付策略，在人类治疗中进行了编辑 到达目标位置。为了解决这一挑战的解决方案，已经报告了多项努力 在自然界中搜索较小的CAS蛋白。人工智能（AI）的最新成功 在生物学领域的应用证明了AI在解决生物学问题和 购买了一种新的观点来应对这一挑战。在此应用程序中，我们建议制定证明 - 概念技术适应蛋白质结构预测和战略委员会中使用的AI算法 开发新型蛋白质设计工具的游戏，以最大程度地减少CAS蛋白质的大小 功能。我们的目标是开发一种新型的蛋白质设计技术来优化蛋白质的大小 并开发实验验证的人工迷你-CAS蛋白。我们假设人造迷你 具有带导双链DNA裂解功能的CAS蛋白可以使用AI技术设计。 为了检验这一假设，我们建议测试开发这种迷你CAS设计技术的可行性 有两种方法。在AIM 1中，我们将使用基于序列的方法与通用和 基于注意的神经网络设计迷你CAS蛋白。在AIM 2中，基于结构的半 - 无监督的强化学习将用于技术开发。顶级候选人 设计的迷你CAS蛋白将通过分子动力学模拟评估 生化和基于细胞的测定。我们提出的技术将有巨大的潜力 蛋白质设计的技术领域通过提供优化蛋白质大小的工具，转移 CRISPR研究领域的范式从“从大自然搜索”到“实验室设计”， 在生化和基于细胞的测定中验证的第一个人为设计的CAS蛋白 应对CRISPR交付​​挑战。