The Development and Experimental Verification of Computational Methods to Design and Predict the Properties of Therapeutic Proteins

设计和预测治疗性蛋白质特性的计算方法的开发和实验验证

• 批准号: 10256808
• 负责人: Robert J Pantazes
• 金额: $ 34.92万
• 依托单位: AUBURN UNIVERSITY AT AUBURN
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10256808
• 关键词: Amino Acids; Antibodies; Antigens; Autoimmune Diseases; Bacterial Antibiotic Resistance; Binding; Binding Proteins; Computer software; Computing Methodologies; Consumption; Development; Enzymes; Epitopes; Foundations; Health; Immune response; Immune system; Individual; Malignant Neoplasms; Medical; Methods; Modern Medicine; Outcome; Patients; Peptides; Personal Computers; Property; Protein Engineering; Proteins; Research; Rest; Savings; Selection Criteria; Technology; Time; Universities; Validation; base; clinical application; design; fighting; flexibility; improved; novel; pandemic disease; personalized cancer therapy; therapeutic protein; tool

Project Summary for The Development and Experimental Verification of Computational Methods to Design Therapeutic Proteins Therapeutic proteins are an important tool in modern medicine, and their use in treating serious illnesses such as cancer and autoimmune diseases continues to grow annually. Antibodies are one of the most important classes of therapeutic proteins. They occur naturally in the immune system, where they bind strongly and specifically to foreign molecules, acting as flags to the rest of the immune system by indicating the presence of materials that should be eliminated from the body. The use of antibodies by medical professionals allows them to guide patients’ immune responses to improve their health outcomes. Although antibodies offer tremendous benefits, they are not without their limitations. They are large, delicate proteins that are relatively expensive to produce, difficult to formulate at high concentrations, and sensitive to the conditions at which they are stored. Additionally, the experimental methods that are currently used to develop new antibodies are time consuming and while they can control the molecule the antibodies bind (i.e. antigens), it is extremely difficult to target specific regions (i.e. epitopes) of those molecules. Finally, there are many experimental and clinical applications where antibodies are currently used despite not being the most appropriate protein for the purpose because there are not convenient alternatives available. Advances in computational protein design over the last decade are poised to revolutionize the development of antibodies and other therapeutic proteins. Recently, the Pantazes Lab at Auburn University has created software capable of designing antibodies or any of 50+ other binding proteins in as little as a few minutes on a personal computer to bind any target epitope of any desired antigen. Preliminary experimental results of this method appear very promising. Over the next five years, the lab plans on building on this foundation to create a therapeutic protein development workflow with unprecedented flexibility. Proposed research includes: 1) Improving the computational design and selection criteria to enhance experimental viability, thereby providing end users confidence that what they design will function as predicted; 2) Expanding the design capabilities to include specific interactions, permitting the design of pH-sensitive binding proteins and enzymes; 3) Extending the design principles from binding proteins to peptides, enabling the design of any amino acid based binding moiety; and 4) Designing a synthetic binding protein with all of the benefits of antibodies and none of the drawbacks. Each project will involve both computational development as well as experimental validation. Altogether, this research will allow for the rapid design of an optimized binding protein for therapeutic applications. Whether it is developing personalized cancer treatments, fighting an antibiotic-resistant bacteria, or countering an emerging pandemic, doctors will be able to develop novel treatments in a timely manner.

计算机辅助设计的开发和实验验证项目总结 设计治疗性蛋白质的方法 治疗性蛋白质是现代医学中的重要工具，它们在治疗严重疾病中的用途 例如癌症和自身免疫性疾病每年持续增长。抗体是最重要的 治疗性蛋白质的种类。它们天然存在于免疫系统中，在那里它们强烈结合， 特异性地针对外源分子，通过指示存在外源分子来作为免疫系统其余部分的标志。 应该从体内清除的物质。医学专业人员使用抗体可以让他们 引导患者的免疫反应，以改善他们的健康状况。 虽然抗体提供了巨大的好处，但它们并非没有局限性。它们很大，很精致 蛋白质生产相对昂贵，难以配制高浓度，并且对 它们被储存的条件。此外，目前用于 开发新的抗体是耗时的，并且虽然它们可以控制抗体结合的分子（即， 抗原），因此极难靶向那些分子的特定区域（即表位）。最后是 目前使用抗体的许多实验和临床应用， 因为没有方便的替代品。 在过去的十年里，计算蛋白质设计的进展有望彻底改变蛋白质的发展。 抗体和其他治疗性蛋白质。最近，奥本大学的潘塔兹实验室创造了 软件能够设计抗体或任何50+其他结合蛋白在短短几分钟内在一个 个人计算机结合任何所需抗原的任何靶表位。初步实验结果表明， 方法似乎很有前途。在接下来的五年里，该实验室计划在此基础上创建一个 具有前所未有的灵活性的治疗性蛋白质开发工作流程。建议的研究包括：1） 改进计算设计和选择标准，以提高实验可行性，从而提供 最终用户相信他们设计的产品将按预期运行; 2）扩展设计能力， 包括特定的相互作用，允许设计pH敏感的结合蛋白和酶; 3）延伸 从结合蛋白质到肽的设计原理，使任何基于氨基酸的结合设计成为可能 4）设计具有抗体的所有益处而没有抗体的任何益处的合成结合蛋白。 缺点.每个项目都将涉及计算开发以及实验验证。 总之，这项研究将允许快速设计一种优化的结合蛋白，用于治疗 应用.无论是开发个性化的癌症治疗，对抗抗药性细菌， 或对抗新出现的流行病，医生将能够及时开发新的治疗方法。