DMREF: Computationally Driven-Genetically Engineered Materials (CD-GEM)

DMREF：计算驱动基因工程材料 (CD-GEM)

• 批准号: 1728858
• 负责人: Jin Montclare
• 金额: $ 158.55万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1728858&HistoricalAwards=false
• 关键词: DMREF; Computationally; Driven; Genetically; Engineered

Non-technical Description: The development of materials capable of targeted therapy and diagnosis or "theranosis" allows for simultaneously interfacing and treating diseased cells while visualizing what is happening through imaging. Powerful computational and experimental tools will be employed to develop magnetic resonance imaging (MRI)-traceable protein fibers capable of delivering drugs. The resulting biomaterials will have broad societal impact in the field of drug delivery and tissue engineering. Lessons learned from these studies can be employed for delivery of other chemical agents with applications extending to non-medical industries including personal care, cosmetics and environmental remediation. This research will train the next generation of scientists and engineers to employ cutting edge technologies in computational biology, protein engineering and materials science to create, rapidly screen and characterize new functional biomaterials. The project will also engage girls (predominantly women of color) from the Urban Assembly Institute for Math and Science for Young Women who are traditionally underrepresented in STEM on biomaterials in alignment with the Next Generation Science Standards. In collaboration with the start-ups, InSchoolApps, and the MakerSpace, the girls will participate in biomaterials chemistry, computer programming, rendering of proteins in three dimensions and visualization.Technical description: The fabrication of multifunctional materials that can self-assemble into defined structures bears tremendous potential for a number of application including drug delivery and tissue engineering. One challenge is to deliver an appropriate chemical agent and non-invasively monitor the surrounding area amidst a complex biological milieu or extracellular matrix (ECM). The ECM is comprised of protein fibers (from the nano to microscale) organized into a mesoscopic three-dimensional network. The development of a material capable of targeted therapy and diagnosis or "theranosis" would enable one to simultaneously interface with specific cells while providing both detailed functional and molecular information through visualization. We intend to drive design of protein fibers through computation and assess through experimental analysis, their physicochemical properties with the objective to predictably: 1) control self-assembly into fibers on the mesoscale with functional capabilities of small molecule recognition; 2) integrate non-canonical amino acids that encode thermostability and traceability using our developed algorithms for fluorinated amino acids, beyond the limits of biological diversity; and 3) biomineralize and order iron oxide nanoparticles on the organized protein fiber substrates leading to image-ready bio-nanocomposites with magnetic properties. The intellectual merit of this proposal relies on developing a new paradigm for coiled-coil protein materials design that cycles between computation and experimentation. This will enable the rapid iteration for controlling fiber assembly and the identification of rules for coiled-coil fiber and nanocomposite design. In addition to employing state-of-the-art open source programs, we will also use synthetic biology to produce the proteins enabling synthesis for high-throughput screening. This work is well aligned with the Materials Genome Initiative, as we will apply the computational tools already built for protein design with the aim of fabricating novel biomaterials, in this case fibers and inorganic-organic hybrid composites. The versatility of the proposed materials will not only prove useful for applications in biomedicine, but also will lead to fundamental insight into computational methods for modeling and designing protein fibers and nanocomposites.

非技术描述：能够靶向治疗和诊断或“治疗”的材料的开发允许同时接口和治疗患病的细胞，同时可视化成像发生的事情。将采用强大的计算和实验工具来开发能够输送药物的磁共振成像（MRI）的可跟踪蛋白质纤维。由此产生的生物材料将对药物输送和组织工程领域产生广泛的社会影响。从这些研究中学到的经验教训可以用于提供其他化学试剂，并将其应用于非医疗行业，包括个人护理，化妆品和环境修复。这项研究将培训下一代科学家和工程师在计算生物学，蛋白质工程和材料科学中采用尖端技术，以创建，迅速筛选和表征新的功能性生物材料。该项目还将吸引城市数学和科学集会学院的女孩（主要是有色妇女），这些年轻女性传统上与下一代科学标准保持一致的生物材料中的人数不足。与初创企业，Inschoolapps和Makerspace合作，这些女孩将参加生物材料化学，计算机编程，在三个维度上的蛋白质渲染和可视化的蛋白质。技术描述：可以自我组装到定义的材料中的多功能材料的制造具有巨大的应用程序，这些材料具有巨大的应用程序，包括应用程序的数量包括药物交付的药物，包括药物递送。一个挑战是在复杂的生物环境或细胞外基质（ECM）中提供适当的化学剂，并非侵入性地监测周围区域。 ECM由蛋白质纤维（从纳米到显微镜）组成，该蛋白质纤维组织到介观三维网络中。能够靶向治疗和诊断或“治疗疗法”的材料的开发将使人们同时与特定细胞接口，同时通过可视化提供详细的功能和分子信息。我们打算通过计算来推动蛋白质纤维的设计，并通过实验分析评估其物理化学特性，其目标是可预测的：1）将自组装控制在中尺度上具有小分子识别功能功能的纤维； 2）整合了非典型的氨基酸，该氨基酸使用我们开发的算法对氟化氨基酸进行编码和可追溯性，超出了生物多样性的限制； 3）在有组织的蛋白质纤维底物上生物矿物质和有序的氧化铁纳米颗粒，导致具有磁性特性的图像准备就绪的生物纳米复合材料。该提案的智力优点依赖于开发一种新的范式，用于在计算和实验之间循环循环的盘绕蛋白质材料设计。这将使能够控制纤维组件的快速迭代以及识别盘绕螺旋纤维和纳米复合材料设计的规则。除了采用最先进的开源程序外，我们还将使用合成生物学来生产可用于高通量筛查的合成的蛋白质。这项工作与材料基因组计划非常一致，因为我们将应用已为蛋白质设计构建的计算工具，目的是制造新型生物材料，在这种情况下为纤维和无机有机混合复合材料。提议的材料的多功能性不仅对生物医学的应用有用，而且还将导致对建模和设计蛋白质纤维和纳米复合材料的计算方法的基本见解。

项目成果

Detection of Cerebrovascular Loss in the Normal Aging C57BL/6 Mouse Brain Using in vivo Contrast-Enhanced Magnetic Resonance Angiography.

• DOI: 10.3389/fnagi.2020.585218
• 发表时间: 2020
• 期刊: Frontiers in aging neuroscience
• 影响因子: 4.8
• 作者: Hill LK;Hoang DM;Chiriboga LA;Wisniewski T;Sadowski MJ;Wadghiri YZ
• 通讯作者: Wadghiri YZ

NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity.

• DOI: 10.1093/bioinformatics/btab098
• 发表时间: 2021-08-25
• 期刊: Bioinformatics (Oxford, England)
• 作者: Barot M;Gligorijević V;Cho K;Bonneau R
• 通讯作者: Bonneau R

Protein‐Engineered Functional Materials

• DOI: 10.1002/adhm.201801374
• 发表时间: 2019-04
• 期刊: Advanced Healthcare Materials
• 影响因子: 10
• 作者: Yao Wang;P. Katyal;J. Montclare

Peptides as key components in the design of non‐viral vectors for gene delivery

• DOI: 10.1002/pep2.24189
• 发表时间: 2020-09
• 期刊: Peptide Science
• 影响因子: 2.4
• 作者: Joseph Thomas;Kamia Punia;J. Montclare

Engineered Coiled-Coil Protein for Delivery of Inverse Agonist for Osteoarthritis.

用于传递骨关节炎反向激动剂的工程卷曲螺旋蛋白。

• DOI: 10.1021/acs.biomac.8b00158
• 发表时间: 2018
• 期刊: Biomacromolecules
• 影响因子: 6.2
• 作者: Yin,Liming;Agustinus,AlbertS;Yuvienco,Carlo;Minashima,Takeshi;Schnabel,NicoleL;Kirsch,Thorsten;Montclare,JinK
• 通讯作者: Montclare,JinK