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.

人类的呼吸和皮肤表面化学含有丰富的分子和生物标志物的混合物，可以表明呼吸道感染，糖尿病，压力和其他疾病。通过发现每种类型疾病的独特化学指纹指纹，可以使人类呼吸的快速发作传感器能够快速诊断疾病，例如COVID-19的未来变体，呼吸道合胞病毒（RSV），癌症，糖尿病等。不幸的是，当前的传感器技术太笨重，昂贵，或者无法区分不同的疾病或状况。该项目将通过设计疾病特异性的传感器阵列来推进生物传感的最新作品，这些传感器阵列识别数十种分子的组合，这些分子完成了人类呼吸中发现的独特“指纹”。该项目将采用与传感器相互作用的基于物理的分子模型，表征新传感器的最新实验以及机器学习（ML）来分析和设计这些传感器。从项目产生的数据将使团队能够探索数百万个潜在的“启发”设计，从而创建最佳传感器来检测目标疾病。该团队还将使用技术研究作为支持劳动力发展并扩大参与STEM的平台。这项工作将导致培训一名具有生物传感专家的博士生，新兴技术课堂教学的新材料以及本科夏季研究经验的财务支持机会。分子动力学模拟将探索挥发性有机化合物（VOC）的基于肽的粘合剂的物理特征。高通量模拟工作流将计算数百至数千个肽/VOC结合对的结构/功能关系。然后，该数据将用于开发一个序列特异性的ML模型，该模型将允许具有理想结合属性的新序列的逆设计。最有希望的分子将使用分析工具和晶体管传感器芯片合成和测试，以进行紧凑的启发系统。该项目将综合实验和计算分子工程，深度机器学习和生物学敏感性机制。通过将实验数据，基于物理的模拟和高吞吐量ML模型相结合，它可以首次评估多疾病多重多重传感器的真正潜在灵敏度和特异性。如果成功的话，该项目将推进该领域，并克服定制生物传感器快速开发的障碍，用于具有优化的选择性和敏感性的各种应用。除了疾病检测之外，该平台还可以影响敏感性和分离领域，例如在分子分离或被动检测安全性（例如化学/生物战剂）中。这项工作还解决了现有的ML工具中的关键知识差距，用于肽粘合剂的序列级别预测，本研究中产生的知识和数据将使科学界受益，并在分子数据科学中广泛地使用这些方法。新的紧凑，负担得起的，无创和快速的VOC生物传感器将创建一个新颖的负担得起的平台，用于为血糖，妊娠，传染病和一般健康提供高需求测试。该技术的未来影响可能会扩展到医疗保健，公共卫生，一致性，食物存储，环境监测和防御。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估而被视为珍贵的支持。