A Computational Tool for Modeling of Protein Macromolecules Guided by Cryo-Electron Microscopy Data
冷冻电子显微镜数据引导的蛋白质大分子建模计算工具
基本信息
- 批准号:9442191
- 负责人:
- 金额:$ 39.92万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-09-30 至 2021-08-31
- 项目状态:已结题
- 来源:
- 关键词:AcademiaAddressAlgorithmsAmino Acid SequenceAmino AcidsAreaBehaviorBenchmarkingBioinformaticsBiologyBlood CellsBlood VesselsBrain imagingCollaborationsCommunitiesComplexComputational GeometryComputer-Assisted Image AnalysisComputing MethodologiesCryoelectron MicroscopyDataDesigner DrugsDetectionDimensionsDisciplineDrug DesignEducationElementsGraphGroup ProcessesImageIndustryMapsMedicalMembrane ProteinsMethodsModelingMolecular BiologyMolecular ConformationMontserratMotionPerformancePlanning TechniquesPrincipal InvestigatorProcessProteinsResearchResearch PersonnelResolutionScienceSecondary Protein StructureSeedsServicesSkeletonSourceSpecimenStructureStudentsSystemTechniquesTennesseeUniversitiesVertebral columnVirginiaVirusWorkcareercomparativecomputer frameworkcomputer sciencecomputerized toolsdesignexperiencefield studyforestgraduate studentgraph theoryimage processingkinematicsmacromoleculeminority studentnanometernovelpeerprotein structureskillsthree dimensional structuretoolundergraduate studentvirologyweb site
项目摘要
Abstract
Cryo-Electron Microscopy (cryo-EM) is a powerful technique produces volumetric images of large molecules. The images produced at near-atomic (<5Å) resolution can be used to determine the structure of those molecules. Due to experimental difficulties, only small portion of the images are produced at near-atomic resolution while the dominant number of available images is produced at sub/nanometer resolution. At subnanometer (5-10Å) resolution, the backbone of the structure cannot be constructed directly from those images. Nevertheless, de novo modeling can be used to derive the atomic structure of the molecules. The detection of secondary structure elements (helices and sheets) from the volumetric images is crucial for de novo tools. Moreover, the observation of the structural connections between these elements is extremely helpful in order to answer the topology determination problem. Topology determination problem can be defined as the correspondence between the secondary structure elements found on the sequence of the protein molecule and those found on its cryo-EM volumetric image. This problem is proven to be NP-hard. In this project, a complete de novo system that is capable of efficiently deriving the structure of large molecules from the authentic cryo-EM volumetric images will be developed.
The proposed de novo system is divided into a number of components each of which will address an important sub-problem. De novo modeling will be accomplished by three main sub-systems (1) extracting a fine-quality skeleton of the molecule from its noisy cryo-EM image (2) addressing the topology determination problem and (3) building the atomic structure of the target molecule. The extracted skeleton of the protein molecule will be used to confront the problem of topology determination so that the search space size can be drastically reduced. In addition, the skeleton will be used to construct the structure of the molecule. The system will be evaluated using a benchmark of authentic and synthesized images.
The novel algorithms that will be developed in the proposed de novo system are significant to various fields. For example, the proposed algorithm of skeletonization can be used by biomedical sciences that use the skeletons. Moreover, the dynamic matching algorithm of this research can be generalized to be used for similar matching problems in any field of study. Further, the proposed system will help understanding the fundamental functions of some protein types such as membrane proteins and large protein molecules that are hard to study using the traditional experimental techniques.
This project will bring the effort and skills of the collaborators, Dr. Kamal Al Nasr, Dr. Wei Chen, and Dr. Matthew Hayes from the Department of Computer Science at Tennessee State University (TSU), and the consultant, Dr. Montserrat Samso from Virginia Commonwealth University (VCU), with the undergraduate and graduate students. The principal investigators will be responsible to develop the proposed algorithms and carry out the design and analysis part of the project. The students will be exposed to bioinformatics research and they will gain hands-on experience with some important bioinformatics problems. The students, with the guidance of the principle investigators, will collect data, implement the proposed algorithms, and analyze the results. Further, this project will help TSU prepare a strong workforce of minority students who can compete with their peers in industry or academia in various areas of bioinformatics. The proposed activities will enhance integration of research and education in biology and computer science and it is expected that undergraduate students will be more motivated to pursue career in medical fields.
摘要
低温电子显微镜(CRYO-EM)是一种产生大分子体积图像的强大技术。在近原子分辨率下产生的图像可以用来确定这些分子的结构。由于实验上的困难,只有一小部分图像是以近原子分辨率产生的,而大部分可用图像是以亚/纳米分辨率产生的。在亚纳米(5-10ä)分辨率下,结构的主干不能直接从这些图像中构建。然而,从头模型可以用来推导分子的原子结构。从体积图像中检测二级结构元素(螺旋和薄片)是从头开始工具的关键。此外,观察这些单元之间的结构连接对于回答拓扑确定问题非常有帮助。拓扑确定问题可以定义为蛋白质分子序列上的二级结构元素与其低温电子显微镜体积图像上的二级结构元素之间的对应关系。这个问题被证明是NP-难的。在这个项目中,将开发一个完整的从头开始系统,它能够有效地从真实的低温电磁体积图像中得出大分子结构。
拟议的从头系统分为若干组成部分,每个组成部分都将解决一个重要的分问题。从头建模将通过三个主要子系统来完成:(1)从噪声较高的低温EM图像中提取分子的优质骨架;(2)解决拓扑确定问题;(3)建立目标分子的原子结构。将提取的蛋白质分子骨架用于拓扑确定问题,从而大大减小搜索空间的大小。此外,骨架将被用来构建分子结构。该系统将使用真实和合成图像的基准进行评估。
在所提出的从头开始系统中将开发的新算法对各个领域都具有重要意义。例如,提出的骨架化算法可以被使用骨骼的生物医学科学所使用。此外,本研究的动态匹配算法可以推广应用于任何研究领域的类似匹配问题。此外,所提出的系统将有助于理解一些蛋白质类型的基本功能,如膜蛋白质和大蛋白质分子,这些蛋白质很难用传统的实验技术来研究。
该项目将带来来自田纳西州立大学(TSU)计算机科学系的Kamal Al Nasr博士、WeChen博士和Matthew Hayes博士以及来自弗吉尼亚联邦大学(VCU)的顾问Montserrat Samso博士与本科生和研究生的努力和技能。主要研究人员将负责开发拟议的算法,并进行项目的设计和分析部分。学生将接触到生物信息学研究,他们将获得一些重要的生物信息学问题的实践经验。学生将在主要调查人员的指导下收集数据,实施所提出的算法,并分析结果。此外,这个项目将帮助台州大学培养一支强大的少数族裔学生队伍,他们可以在生物信息学的各个领域与工业界或学术界的同龄人竞争。拟议的活动将加强生物学和计算机科学方面的研究和教育的结合,预计本科生将更有动力在医学领域追求职业生涯。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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