ABI Innovation: Advanced informatics and effective algorithms to enable CryoEM protein structure prediction and density analysis

该奖项旨在表彰一个跨学科项目，该项目旨在开发有效的计算方法和工具，用于在存在错误的情况下从3D图像中确定蛋白质结构。低温电子显微镜（cryo-EM）技术大大提高了对大分子组装机制的理解。这项技术允许可视化几十到一千多个相互作用的分子在一个组装中，因此提供了直接的见解，重要的生物功能和机制，导致异常功能。电子显微镜数据库（EMDB）以2Å到100Å的分辨率保存分子组合的密度图像，快速积累的数据是数据挖掘的宝贵资源，可以提高解释的准确性。该研究将具体证明数据挖掘在EMDB中的适用性，以及使用复杂的图算法方法来更好地预测蛋白质结构。正在开发的计算方法和工具可用于解释具有重要生物功能的大分子组装，如病毒、膜结合通道和细胞机器。了解这些机制对于设计药物和疫苗至关重要。所开发的数据挖掘和算法技术很可能适用于解决其他计算问题，特别是那些处理搜索大空间和必须处理数据错误的问题。通过本科研究人员和暑期高中生的参与，特别是女性和那些将从非裔美国人社区招募的学生，该项目将有助于美国国家科学基金会加强管道和扩大STEM领域参与的目标。该项目的长期目标是开发顺序和并行计算方法和工具，以获得中分辨率范围4-10Å的EMDB密度图像的原子结构。这种方法一般适用于确定没有现有模板可以识别的蛋白质的结构。在这个问题中，新的想法将在两个困难但关键的步骤中实现。第一个解决了长期存在的挑战，即准确识别&amp；#946；-密度图像中的线。第二种方法是利用先进的计算方法，在存在错误的情况下，在密度图中找到二级结构（螺旋和-链）与蛋白质序列上识别的二级结构的正确映射。这个项目是一个多学科的努力，汇集了计算结构生物学、数据挖掘、高效算法设计和并行计算方面的专业知识。将开发的方法将使用实验衍生的低温电镜密度图像进行验证，并将纳入Chimera，一个流行的3D分子观察器，用于低温电镜社区。该项目的结果将在以下网站上公布：http://www.cs.odu.edu/~jhe/

This award is for an interdisciplinary project to develop efficient computational methods and tools for determination of protein structures from 3D images in the presence of errors. Understanding the machinery of large molecular assemblies has been significantly enhanced by the cryo-electron microscopy (cryo-EM) technique. This technique allows the visualization of tens to over a thousand interacting molecules in an assembly and hence provides direct insights into important biological functions and mechanisms that cause abnormal function. The Electron Microscopy Data Bank (EMDB) archives density images of molecular assemblies at resolutions ranging from 2Å to 100Å and the rapidly accumulating data are valuable resources for data mining for enhanced accuracy in interpretation. The research will concretely demonstrate the applicability of data mining the EMDB and the use of sophisticated graph algorithmic methods for better protein structure prediction. The computational methods and tools being developed can be used to interpret large molecular assemblies that have important biological functions such as viruses, membrane-bound channels and cellular machines. Understanding these mechanisms is crucial for designing drugs and vaccines. The data mining and algorithmic techniques developed are very likely to be applicable for solving other computational problems, especially those dealing with searching large spaces and where one has to deal with data errors. Through the involvement of undergraduate researchers and summer high school students in the project, particularly women and those to be recruited from the African-American community, the project will contribute to the NSF goal of strengthening the pipeline and broadening participation in STEM fields.The long term goal of this project is to develop sequential and parallel computational methods and tools to derive atomic structures for EMDB density images at the mid-resolution range of 4-10Å. Such methods will be generally applicable to determine the structure of proteins for which no existing templates can be identified. Novel ideas will be implemented in two difficult but critical steps in this problem. The first one addresses the long-standing challenge of accurately identifying β-strands from density images. The second addresses the problem of finding the correct mapping of secondary structures (helices and β-strands) in a density map to those identified on the protein sequence in presence of errors, making use of advanced computational methods. This project is a multidisciplinary effort bringing together expertise from computational structural biology, data mining, efficient algorithm design, and parallel computing. The methodology to be developed will be validated using experimentally derived cryo-EM density images and will be incorporated into Chimera, a popular 3D molecular viewer, for the cryo-EM community.The outcome of the project will be made available at the following website: http://www.cs.odu.edu/~jhe/