Molecular Dynamics and Machine Learning for the Design of Peptide Probes for Biosensing

用于生物传感肽探针设计的分子动力学和机器学习

• 批准号: 2313269
• 负责人: John Devin MacKenzie
• 金额: $ 55万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313269&HistoricalAwards=false
• 关键词: Molecular; Dynamics; Machine; Learning; Design

Human breath and skin surface chemistry contain rich mixtures of molecules and biomarkers that can indicate respiratory infections, diabetes, stress, and other conditions. Fast-acting sensors for human breath could enable rapid diagnosis of diseases like future variants of COVID-19, Respiratory Syncytial Virus (RSV), cancer, diabetes, and more, through detection of unique chemical fingerprints for each type of disease. Unfortunately, current sensor technologies are too bulky, expensive, or unable to distinguish between different illnesses or conditions. This project will advance the state-of-the-art in biosensing by designing disease-specific sensor arrays that identify combinations of dozens of molecules that comprise unique “fingerprints” found in human breath. The project will employ physics-based models of molecules interacting with the sensor, state-of-the-art experiments characterizing new sensors, and machine learning (ML) to analyze and design these sensing devices. Data generated from the project will allow the team to explore millions of potential “eNose” designs, creating optimal sensors to detect target diseases. The team will also use the technical research as a platform to support workforce development and broadening participation in STEM. The work will lead to the training of a PhD student with expertise in biosensing, new materials for classroom instruction in emerging technology, and financial support opportunities for undergraduate summer research experiences. Molecular dynamics simulations will explore the physical characteristics of peptide-based binders of volatile organic compounds (VOCs). A high-throughput simulation workflow will calculate structure/function relationship for hundreds to thousands of peptide/VOC binding pairs. This data will then be used to develop a sequence-specific ML model that will permit inverse design of new sequences with ideal binding properties. The most promising molecules will be synthesized and tested using analytical tools and transistor sensor chips for compact eNose systems. This project will synthesize experimental and computational molecular engineering, deep machine learning, and biological sensing mechanisms. By combining experimental data, physics-based simulation, and high throughput ML models, it can, for the first time, assess the true potential sensitivity and specificity of a multi-disease multiplex sensor. If successful, this project will advance the field and overcome barriers to the fast development of bespoke biosensors for various applications with optimized selectivity and sensitivity. Beyond disease detection, this platform can impact the field of sensing and separations, for example, in the separation of molecules or passive detection for security (e.g., chemical/biological warfare agents). This work also addresses critical knowledge gaps in existing ML tools for the sequence-level prediction of peptide binders, and the knowledge and data produced in this study will benefit the scientific community and advance the use of these methods broadly in molecular data science. New compact, affordable, noninvasive, and rapid VOC biosensors would create a novel affordable platform for delivering high-demand tests for blood glucose, pregnancy, infectious diseases, and general wellness. The technology’s future impact could extend to healthcare, public health, agriculture, food storage, environmental monitoring, and defense.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人体呼吸和皮肤表面化学物质含有丰富的分子和生物标记物混合物，可以指示呼吸道感染、糖尿病、压力和其他疾病。人体呼吸的快速反应传感器可以快速诊断未来的新型冠状病毒肺炎（COVID-19）、呼吸道合胞病毒等疾病。不幸的是，当前的传感器技术过于庞大、昂贵，或者无法区分不同的疾病或状况。该项目将采用基于物理的分子与传感器相互作用的模型，通过设计针对特定疾病的传感器阵列来识别包含人类呼吸中独特“指纹”的数十种分子的组合，从而实现生物传感领域的最先进技术。表征新传感器的最先进的实验，以及用于分析和设计这些传感设备的机器学习（ML），该项目生成的数据将使团队能够探索数百万种潜在的“eNose”设计，创建最佳传感器来检测目标。该团队还将使用疾病。技术研究作为支持劳动力发展和扩大 STEM 参与的平台这项工作将培训一名具有生物传感专业知识的博士生、新兴技术课堂教学的新材料以及本科生暑期研究经验的财政支持机会。分子动力学模拟将探索挥发性有机化合物 (VOC) 的肽结合物的物理特性，然后使用高通量模拟工作流程计算数百至数千个肽/VOC 结合对的函数关系。发展序列特异性 ML 模型将允许对具有理想结合特性的新序列进行逆向设计，并将使用用于紧凑型 eNose 系统的分析工具和晶体管传感器芯片进行合成和测试。通过结合实验数据、基于物理的模拟和高通量机器学习模型，它可以首次评估多疾病多重传感器的真实潜在灵敏度和特异性。 ，该项目将推动该领域的发展除了疾病检测之外，该平台还可以影响传感和分离领域，例如分子分离或安全被动检测（例如化学/安全）。这项工作还解决了现有机器学习工具在肽结合物序列水平预测方面的关键知识差距，这项研究中产生的知识和数据将使科学界受益，并促进这些方法在分子领域的广泛使用。数据科学。紧凑、经济实惠、非侵入性和快速的 VOC 生物传感器将创建一个新颖的负担得起的平台，为血糖、怀孕、传染病和一般健康提供高要求的测试，该技术的未来影响可能会扩展到医疗保健、公共卫生、农业、食品。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。