Evolution and optimization of synthetic <READ/WRITE> function from and into cells using genetic programming

使用遗传编程从细胞中进化和优化合成<READ/WRITE>功能

• 批准号: 10668511
• 负责人: Wolfgang Banzhaf
• 金额: $ 56.3万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10668511
• 关键词: Algorithmic Software; Algorithms; Artificial Intelligence; Back; Bacteria; Binding; Binding Proteins; Biochemistry; Biocompatible Materials; Biological; Biomedical Engineering; Cells; Chemicals; Clinical Treatment; Collaborations; Communication; Complex; Computer Models; Data; Data Set; Diagnosis; Diagnostic; Directed Molecular Evolution; Discipline; Disease; Drug Delivery Systems; Engineering; Evolution; Foundations; Gadolinium; Gene Delivery; Genes; Genetic; Genetic Programming; Human; Knowledge; Lanthanoid Series Elements; Machine Learning; Magnetic Resonance Imaging; Mammalian Cell; Measurement; Medicine; Metals; Methods; Modeling; Molecular Genetics; Neck; Needles; Output; Patients; Peptides; Personal Satisfaction; Pharmaceutical Preparations; Pharmacology; Precision therapeutics; Protein Engineering; Proteins; Protons; Public Health; Reading; Relaxation; Reporter; Reporter Genes; Reporting; Signal Transduction; Specificity; System; Technology; Testing; Therapeutic; Therapeutic Agents; Time; Training; Translating; Whole Organism; Writing; clinically relevant; computer science; computerized tools; design; experience; extracellular vesicles; feeding; functional improvement; imaging agent; imaging probe; improved; in vitro testing; in vivo; in vivo Model; innovation; machine learning algorithm; metabolic engineering; new technology; next generation; non-invasive imaging; novel; novel diagnostics; novel therapeutic intervention; peptide drug; protein aminoacid sequence; protein complex; protein function; synthetic biology; synthetic peptide; synthetic protein; tool; treatment strategy; triple-negative invasive breast carcinoma

An immense advancement in machine learning and artificial intelligence has transformed many aspects of our lives. The integration of artificial intelligence into the biomedical field allows us to solve complex biological problems that are the bottle neck of developing progressive diagnostic and therapeutic tools. One example is the need to manipulate the amino acid sequence of peptides to improve their function as bioactive molecules. Embarking on these new technologies, we developed a new machine learning tool that is based on a discipline known as “genetic programing” that can assist in designing new proteins and bioactive peptides. This new technology, termed Protein Optimization Evolving Tool (POET), can generate a model that describes the relationship between a peptide and its respective activity. Moreover, through cycles of protein evolution, we can significantly improve the model and consequently generate peptides with substantially improved function. A major challenge of translating synthetic biology approaches to clinical treatment is the need to improve the communication with biological circuits in vivo. To that end, we will leverage the immense potential of the POET to produce proteins and peptides that can read and write information from and into cells. Here we seek to improve, test and implement this model into three related, yet, independent aims. In the first aim, we will deploy the POET to develop an ultrasensitive peptide-based imaging agent for MRI based on proton exchange. Our preliminary data shows that through only few cycles of peptide evolution we surpassed the state-of-the-art similar peptides. In the second aim, we intend to use a similar approach to develop a novel MRI imaging probe based on T1 relaxation. We will use a metabolic engineering approach to express and load the peptide with Lanthanides, and the POET algorithm to improve the next generations. Lastly, in the third aim, we will use the POET for discovering new peptides for drug and gene delivery. We will utilize a novel platform for gene/drug delivery to test the efficiency of the peptides. All three aims will start with computational design of peptides followed by an in vitro testing and several cycles of peptide evolution until the ultimate peptides are identified. All three aims will be ended by demonstration of the utility of those peptides in a clinically relevant question in an in vivo model followed by non-invasive imaging. We anticipate that this innovative approach will open up a new avenue for developing powerful bioactive peptides and proteins to solve critical biological questions, and for developing new diagnostic and therapeutic approaches that can vastly benefit the well-being of numerous patients.

机器学习和人工智能的巨大进步改变了许多方面 我们的生活。人工智能与生物医学领域的融合使我们能够解决复杂的生物学问题。 这些问题是发展进步的诊断和治疗工具的瓶颈。一个例子是 需要操纵肽的氨基酸序列以改善其作为生物活性分子的功能。 在这些新技术的基础上，我们开发了一种新的机器学习工具， 被称为“遗传编程”，可以帮助设计新的蛋白质和生物活性肽。这个新 技术，称为蛋白质优化进化工具（POET），可以生成一个模型，描述 肽与其各自活性之间的关系。此外，通过蛋白质进化的循环，我们可以 显著改善模型并因此产生具有显著改善的功能的肽。 将合成生物学方法转化为临床治疗的一个主要挑战是需要改进 与体内生物回路的通讯。为此，我们将利用 POET生产蛋白质和肽，可以从细胞读取信息和向细胞写入信息。 在这里，我们试图改进，测试和实施这一模式，成为三个相关的，但独立的目标。在 第一个目标，我们将部署POET开发一种超灵敏的基于肽的MRI成像剂， 质子交换我们的初步数据显示，通过短短几个周期的肽进化， 最先进的类似肽在第二个目标中，我们打算使用类似的方法来开发一种新的 基于T1弛豫的MRI成像探头。我们将使用代谢工程的方法来表达和加载 含镧系元素的多肽，以及POET算法来改进下一代。最后，第三个目标， 我们将使用POET来发现用于药物和基因递送的新肽。我们将利用一个新的平台 用于基因/药物递送以测试肽的效率。 所有这三个目标都将从肽的计算设计开始，然后进行体外测试， 肽进化的循环，直到最终的肽被识别。所有这三个目标都将通过演示来结束 这些肽在体内模型中的临床相关问题中的效用， 显像 我们预计，这种创新的方法将开辟一条新的途径，开发强大的生物活性 肽和蛋白质，以解决关键的生物学问题，并开发新的诊断和治疗 这些方法可以极大地造福于许多患者的健康。