Accelerated Determination of 3D Structures of Proteins and Complexes

加速测定蛋白质和复合物的 3D 结构

• 批准号: 8483934
• 负责人: CHRIS SANDER
• 金额: $ 44.2万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-03 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8483934
• 关键词: Area; Bacteria; Biology; Collaborations; Computational Biology; Crystallography; DNA Sequence; Data Set; Development; Entropy; Genomics; Head; Human; Hybrids; Maps; Memorial Sloan-Kettering Cancer Center; Methods; Molecular; Molecular Computations; Pharmaceutical Preparations; Physics; Planet Mars; Protein Family; Protein Structure Initiative; Proteins; Research; Roentgen Rays; Sequence Alignment; Site; Solutions; Structure; Systems Biology; Technology; Tertiary Protein Structure; Vertebral column; Virus; Work; X-Ray Crystallography; base; biological research; experience; fungus; international center; medical schools; microorganism; new technology; programs; protein complex; protein structure; public health relevance; rapid growth; structural biology; structural genomics; therapeutic target; three dimensional structure

DESCRIPTION (provided by applicant): The project will provide new technology to accelerate the discovery of protein structures and assemblies from humans and other species. Knowing the 3D structures of proteins has important biomedical implications for the development of protein-based therapies and targeted therapeutic drugs, but the 3D structures of proteins of thousands of important protein types remain unsolved. The project aims to close this gap, based on two recent advances: (1) the rapid development of new DNA sequencing technologies and (2) a recent breakthrough in protein 3D structure prediction using statistical physics and bio-molecular computation. The new structure prediction method, developed by the applicant team, extracts evolutionary residue-residue couplings from multiple sequence alignments, using a maximum entropy method. The team will use the evolutionary couplings as distance constraints to predict the structure of many single domains, of multidomain proteins and of protein complexes, and to map functional sites on known and predicted structures, with potentially broad impact on diverse biological research areas. The team will also aim to aid the development of hybrid computational- experimental technologies for structure determination. For X-ray crystallography, the aim is bridge the gap between the predicted 3D structures and the basin of convergence for molecular replacement, allowing structure determination from a single native data set without the need for anomalous or derivative diffraction datasets. For NMR, the aim is to add evolutionary couplings to NMR-derived backbone and residue-residue distance information and thus reduce experimental effort and/or permit the solution of larger structures. The project is a close collaboration between the Computational Biology Program at Memorial Sloan-Kettering Cancer Center and the Department of Systems Biology at Harvard Medical School. Experimental collaborations with PSI:Biology centers and the international structural genomics effort will aim to implement a more efficient technology for the determination of biomedically relevant protein structures.

描述（由申请人提供）：该项目将提供新的技术，以加快从人类和其他物种中发现蛋白质结构和组装的发现。了解蛋白质的3D结构对基于蛋白质的疗法和靶向治疗药物的发展具有重要的生物医学意义，但是数千种重要蛋白质类型的蛋白质的3D结构仍未得到解决。该项目的目的是根据最近的两个进步来缩小这一差距：（1）新的DNA测序技术的快速发展，以及（2）使用统计物理学和生物分子计算的蛋白质3D结构预测的最新突破。由申请人团队开发的新结构预测方法，使用最大熵方法从多个序列比对提取进化残基 - 残基耦合。该团队将使用进化耦合作为距离限制，以预测许多单一域，多域蛋白和蛋白质复合物的结构，并在已知和预测的结构上绘制功能位点，并对多样化的生物学研究领域产生广泛的影响。该团队还将旨在帮助开发结构确定的混合计算实验技术。对于X射线晶体学，其目的是桥接预测的3D结构与分子置换盆地之间的差距，从而可以从单个天然数据集中确定结构，而无需异常或衍生衍射数据集。对于NMR，其目的是将进化耦合添加到NMR衍生的主链和残基 - 残留距离信息中，从而减少实验努力和/或允许解决较大结构的解决方案。该项目是纪念斯隆凯特林癌症中心的计算生物学计划与哈佛医学院系统生物学系之间的密切合作。与PSI的实验合作：生物学中心和国际结构基因组学工作将旨在实施更有效的技术来确定生物医学相关的蛋白质结构。