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

人体呼吸和皮肤表面化学物质含有丰富的分子和生物标志物的混合物，可以指示呼吸道感染，糖尿病，压力和其他疾病。用于人类呼吸的快速传感器可以通过检测每种疾病的独特化学指纹来快速诊断疾病，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，目前的传感器技术过于庞大，昂贵，或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传感器阵列来推进生物传感领域的最新技术，这些传感器阵列可以识别数十种分子的组合，这些分子构成了人类呼吸中发现的独特“指纹”。该项目将采用基于物理的分子与传感器相互作用的模型，表征新传感器的最先进实验，以及机器学习（ML）来分析和设计这些传感设备。该项目产生的数据将使该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疾病。该团队还将利用技术研究作为支持劳动力发展和扩大STEM参与的平台。这项工作将导致培养一名具有生物传感专业知识的博士生，用于新兴技术课堂教学的新材料，以及为本科生暑期研究经验提供财政支持的机会。分子动力学模拟将探索挥发性有机化合物（VOCs）的肽基粘合剂的物理特性。高通量模拟工作流程将计算数百至数千个肽/VOC结合对的结构/功能关系。然后，这些数据将用于开发序列特异性ML模型，该模型将允许反向设计具有理想结合特性的新序列。最有前途的分子将使用用于紧凑型eNose系统的分析工具和晶体管传感器芯片进行合成和测试。该项目将综合实验和计算分子工程，深度机器学习和生物传感机制。通过结合实验数据、基于物理的模拟和高通量ML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。 如果成功，该项目将推动该领域的发展，并克服快速开发具有优化选择性和灵敏度的各种应用的定制生物传感器的障碍。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，在分子分离或安全被动检测（例如，化学/生物战剂）。这项工作还解决了现有ML工具中用于肽结合剂序列水平预测的关键知识差距，本研究中产生的知识和数据将使科学界受益，并推动这些方法在分子数据科学中的广泛使用。新的紧凑，负担得起，非侵入性和快速VOC生物传感器将创建一个新的负担得起的平台，用于提供高需求的血糖，妊娠，传染病和一般健康测试。该技术的未来影响可能扩展到医疗保健、公共卫生、农业、食品储存、环境监测和国防。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.