CAREER: Decoding the Code of Glycan-Collectin Interactions: Computational Engineering of Surfactant Proteins for Tailored Glycan Recognition

职业：解码聚糖-收集素相互作用的密码：用于定制聚糖识别的表面活性剂蛋白的计算工程

• 批准号: 2338401
• 负责人: Mona Minkara
• 金额: $ 82.73万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2029-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338401&HistoricalAwards=false
• 关键词: CAREER; Decoding; Code; Glycan; Collectin

Certain viruses and bacteria cause disease (pathogens). Pathogens attach sugars to their exterior to avoid detection by the immune system. The immune system produces proteins (collectins) that recognize and bind to foreign sugars (glycans). The overall objectives of this project are to better understand and improve the binding of collectins to glycans. This could drive the development of improved vaccines and therapies. Artificial intelligence tools will be employed to help develop design rules for the improved collectins. Enhanced binding of the Influenza A virus will be the test case. The results of the project will also provide material for the Blind Scientist Toolkit that the investigator has developed that teaches non-visual approaches to scientific exploration.The primary goals of this project are to elucidate and optimize the binding affinity between collectins, a class of C-type lectins, and glycans, the sugar structures found on the surface of pathogens such as viruses. This research aims to enhance the early detection and response of the mammalian immune system to viral infections. An interdisciplinary approach integrating computational and experimental methodologies will be utilized. The structural variations and amino acid sequences within the Carbohydrate Recognition Domains of Surfactant Proteins A and D (SP-D) will be investigated. Detailing the analysis of the interactions at the molecular level will be used to develop a set of rational design rules. These rules will guide the engineering of SP-D variants with enhanced capabilities for glycan binding, particularly tailored to target Influenza A structures. The methodology includes comprehensive computational analyses, molecular dynamics simulations, docking studies, and in vitro binding assays.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.

某些病毒和细菌引起疾病（病原体）。病原体将糖附着在它们的外部以避免被免疫系统检测到。免疫系统产生的蛋白质（凝集素），识别并结合到外来糖（聚糖）。 该项目的总体目标是更好地了解和改善聚集素与聚糖的结合。这可以推动改进疫苗和疗法的发展。人工智能工具将被用来帮助开发设计规则的改进collectin。甲型流感病毒的增强结合将是测试案例。该项目的成果还将为研究人员开发的盲人科学家工具包提供材料，该工具包教授科学探索的非视觉方法。该项目的主要目标是阐明和优化收集素（一类C型凝集素）与聚糖（病毒等病原体表面的糖结构）之间的结合亲和力。这项研究旨在增强哺乳动物免疫系统对病毒感染的早期检测和反应。将利用整合计算和实验方法的跨学科方法。 本文将研究表面活性蛋白A和D（SP-D）碳水化合物识别结构域的结构变异和氨基酸序列。详细分析分子水平上的相互作用将用于开发一套合理的设计规则。这些规则将指导SP-D变体的工程化，其具有增强的聚糖结合能力，特别是针对靶向甲型流感病毒结构。该奖项反映了NSF的法定使命，并被认为是值得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的支持。