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: 10488161
• 负责人: Wolfgang Banzhaf
• 金额: $ 56.44万
• 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/10488161
• 关键词: Algorithmic Software; Algorithms; Amino Acid Sequence; Artificial Intelligence; Back; Bacteria; Binding; Binding Proteins; Biochemistry; Biocompatible Materials; Biological; Biomedical Engineering; Cells; Chemicals; Clinical Treatment; Collaborations; Communication; Complex; Data; Data Set; Diagnosis; Diagnostic; 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; base; clinically relevant; computer science; computerized tools; design; experience; extracellular vesicles; feeding; imaging agent; imaging probe; improved; improved functioning; 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; programs; 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。人工智能融入生物医学领域使我们能够解决复杂的生物学 问题是开发渐进诊断和治疗工具的瓶颈。一个例子是 需要操纵辣椒的氨基酸序列以提高其作为生物活性分子的功能。 启动这些新技术，我们开发了一种基于学科的新机器学习工具 被称为“遗传编程”，可以帮助设计新的蛋白质和生物活性胡椒粉。这个新 技术，称为蛋白质优化工具（诗人），可以生成一个描述该模型的模型 肽与其相对活性之间的关系。此外，通过蛋白质进化的循环，我们可以 显着改善了模型，因此产生了具有大大改善功能的胡椒粉。 将合成生物学方法转化为临床治疗的主要挑战是需要改进 体内与生物电路的通信。为此，我们将利用 诗人产生可以从细胞中读取和写入信息的蛋白质和胡椒粉。 在这里，我们寻求改进，测试和实施此模型，以三个相关的独立目标。在 首先，我们将部署诗人以基于MRI的超敏感性成像剂开发基于 质子交换。我们的初步数据表明，只有少量的肽演化循环超越 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发展小说 基于T1松弛的MRI成像探针。我们将使用代谢工程方法表达和加载 带有灯笼的胡椒和诗人算法，以改善下一代。最后，在第三个目标中， 我们将使用诗人发现用于药物和基因输送的新肽。我们将利用一个新颖的平台 用于基因/药物输送以测试肽的效率。 这三个目标将从Petides的计算设计开始，然后进行体外测试和几个 肽进化的循环直到确定最终的宠物。这三个目标将以示威结束 在体内模型中，在临床上相关问题中，这些私人的实用性随后是无创的 成像。 我们预计这种创新的方法将为发展强大的生物活性开辟新的途径 肽和蛋白质解决关键的生物学问题，并开发新的诊断和治疗 可以极大地使众多患者福祉受益的方法。