Accelerated Determination of 3D Structures of Proteins and Complexes

该项目将提供新技术，以加速发现人类和其他物种的蛋白质结构和组装。了解蛋白质的3D结构对于基于蛋白质的疗法和靶向治疗药物的开发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结构仍然没有解决。该项目旨在缩小这一差距，基于两项最新进展：（1）新DNA测序技术的快速发展和（2）最近利用统计物理学和生物分子计算在蛋白质3D结构预测方面的突破。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中提取进化残基-残基偶联。该团队将使用进化耦合作为距离约束来预测许多单结构域，多结构域蛋白质和蛋白质复合物的结构，并绘制已知和预测结构的功能位点，对不同的生物研究领域产生潜在的广泛影响。该团队还将致力于帮助开发用于结构确定的混合计算-实验技术。对于X射线晶体学，目标是弥合预测的3D结构和分子置换的收敛盆地之间的差距，允许从单个原生数据集确定结构，而不需要异常或衍生衍射数据集。对于NMR，目的是将进化耦合添加到NMR衍生的主链和残基-残基距离信息，从而减少实验工作和/或允许解决更大的结构。该项目是纪念斯隆-凯特琳癌症中心计算生物学项目和哈佛医学院系统生物学系之间的密切合作。与PSI：生物学中心和国际结构基因组学工作的实验合作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。

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.