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

该奖项是一个跨学科项目，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。了解大分子组装的机械已显着提高了低温电子显微镜（cryo-EM）技术。该技术允许在组装中可视化数十到一千多个相互作用的分子，因此提供了对重要生物功能和导致异常功能的机制的直接见解。电子显微镜数据库（EMDB）的档案密度图像的分子组件的分辨率范围从2微米到100微米和快速积累的数据是宝贵的资源，数据挖掘，提高解释的准确性。该研究将具体展示EMDB数据挖掘的适用性和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体，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。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处理数据错误的问题。通过本科研究人员和暑期高中学生，特别是妇女和从非裔美国人社区招募的学生参与该项目，该项目将有助于实现NSF的目标，即加强管道和扩大STEM领域的参与。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EMDB密度图像的原子结构，4- 10英寸的中分辨率范围。此类方法通常适用于确定无法识别现有模板的蛋白质的结构。在这个问题中，新颖的想法将通过两个困难但关键的步骤来实现。第一个解决了长期存在的挑战，准确地识别#946;-股密度图像。第二个地址的问题，找到正确的映射的二级结构（螺旋和#946;-链）的密度图上的蛋白质序列中存在的错误，利用先进的计算方法。该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的专业知识。将要开发的方法将使用实验得出的cryo-EM密度图像进行验证，并将被纳入Chimera，一个流行的3D分子查看器，用于cryo-EM社区。该项目的结果将在以下网站上提供： 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/